11th Esri Eastern Africa User Conference

Conference Proceedings

11th Esri Eastern Africa User Conference2 - 4 November, 2016

Acacia Premier Hotel, Kisumu, Kenya

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Table of Contents Foreword ....................................................................................................................................................... 7

Natural Resources ......................................................................................................................................... 8

GIS Utilization in Petroleum Data Management, A Case Study of National Data Centre (NDC) in

National Oil Corporation of Kenya ............................................................................................................... 9

Abstract ..................................................................................................................................................... 9

Introduction ............................................................................................................................................ 10

Examples of structured and unstructured .......................................................................................... 10

National Data Center (NDC) .................................................................................................................... 10

NDC System ............................................................................................................................................. 11

NDC Architecture .................................................................................................................................... 12

NDC Functionality ............................................................................................................................... 12

ArcGIS System. ........................................................................................................................................ 12

Conclusion ............................................................................................................................................... 17

Refrences ................................................................................................................................................ 17

Improved Analysis of Cheetah - (Acinonyx jubatus) Occupancy and Gene Flow in Kenya Using GIS

Tools ........................................................................................................................................................... 18

Abstracts: ................................................................................................................................................ 18

Problem Definition/ Background ............................................................................................................ 19

Methods/Methodology........................................................................................................................... 19

Expected outcomes (Results and major findings) ................................................................................... 21

Biological references ............................................................................................................................... 21

Biographical Notes .................................................................................................................................. 22

Contacts .................................................................................................................................................. 22

Impact of Climate Change on Desertification in Arid Areas of Kenya ...................................................... 23

Abstract ................................................................................................................................................... 23

Introduction ............................................................................................................................................ 24

Aridity .................................................................................................................................................. 24

Climate change. ................................................................................................................................... 24

Desertification. .................................................................................................................................... 25

Droughts causes and desert spread. ................................................................................................... 25

Magnitude of Desertification in Africa ................................................................................................ 26

Causes of Desertification. ................................................................................................................... 26

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Objectives of Study ............................................................................................................................. 26

Methodology ........................................................................................................................................... 27

Description of study site ..................................................................................................................... 27

Results and Findings ................................................................................................................................ 32

Findings ............................................................................................................................................... 33

Interventions to Avert Future Occurrences ........................................................................................ 33

Acknowledgement .................................................................................................................................. 34

References .............................................................................................................................................. 34

Contacts .................................................................................................................................................. 36

Local Government ....................................................................................................................................... 37

A Village Level GIS-Based County Government and Environmental Risk Management Decision Support

System ......................................................................................................................................................... 38

Abstract ................................................................................................................................................... 38

Introduction ............................................................................................................................................ 39

Methodology ........................................................................................................................................... 40

Area of study ....................................................................................................................................... 40

Geodatabase Construction ................................................................................................................. 40

Chlorine gas plume footprint modeling and analysis ......................................................................... 42

Results and Discussion ............................................................................................................................ 44

Household Proximity to infrastructure ............................................................................................... 44

Environmental Risk Analysis and Management .................................................................................. 45

Conclusion and Recommendations ........................................................................................................ 47

Reference ................................................................................................................................................ 47

GIS Based Modeling to Assess Pollution Vulnerability to Groundwater in the Vicinity of Solid Waste

Disposal Site: A Case Study of Pugu Kinyamwezi Dumpsite in Dar Es Salaam, Tanzania....................... 48

Abstract ................................................................................................................................................... 48

Introduction ............................................................................................................................................ 49

Mapping and Site Characterizations of Existing Situation ...................................................................... 49

Location .............................................................................................................................................. 49

Description of Pugu dumpsite ............................................................................................................. 49

Water supply ....................................................................................................................................... 50

Social services ..................................................................................................................................... 50

Existing land use patterns ................................................................................................................... 50

Economic Activities ............................................................................................................................ 50

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Hydrological conditions ...................................................................................................................... 50

Solid waste existing practice ............................................................................................................... 51

Material and Method .............................................................................................................................. 52

Result and Discussions ............................................................................................................................ 52

Pugu Dump Site Pollution Modeling, Risk Analysis and Water Demand ................................................ 53

Pollution modeling .............................................................................................................................. 53

Groundwater flow predictions ............................................................................................................ 53

Modeling analysis and assumptions .................................................................................................... 54

Modeling development, assumption and Output ................................................................................ 54

Output of the model ............................................................................................................................ 55

Sensitivity of the model ...................................................................................................................... 58

Conclusion and Recommendations ........................................................................................................ 59

Conclusion .......................................................................................................................................... 59

Recommendations ............................................................................................................................... 59

Reference ................................................................................................................................................ 59

Comparing two geospatial approaches for delineating crop ecologies in Tanzania ................................... 62

Abstract ................................................................................................................................................... 62

Introduction ............................................................................................................................................ 63

Methods .................................................................................................................................................. 63

Study area ............................................................................................................................................ 63

Statistical analysis ............................................................................................................................... 64

Study area and the data ...................................................................................................................... 64

Data exploration and analysis ............................................................................................................. 66

Results ..................................................................................................................................................... 67

Selecting the best performing integrated technology .......................................................................... 67

Top-down approach ............................................................................................................................ 68

Bottom-up approach ............................................................................................................................ 69

Discussion................................................................................................................................................ 72

Differences in approaches ................................................................................................................... 72

Relevance ............................................................................................................................................ 73

Limitations .......................................................................................................................................... 73

References .............................................................................................................................................. 74

Contacts .................................................................................................................................................. 74

iv

Analysis of Urban Growth and Agricultural Land Use of Kuta in Shiroro L.G.A, Niger State, North-

Central Nigeria ............................................................................................................................................ 75

Abstract ................................................................................................................................................... 75

Introduction ............................................................................................................................................ 76

Study Area .......................................................................................................................................... 76

Research Methodology ........................................................................................................................... 79

Data Acquisition ................................................................................................................................. 79

Image Geometric Correction ............................................................................................................... 80

Data Analysis. ..................................................................................................................................... 80

Conclusion ............................................................................................................................................... 84

References .............................................................................................................................................. 85

Contacts .................................................................................................................................................. 85

National Government ................................................................................................................................. 86

A Geographic information System driven integrated land management System ....................................... 87

Abstract ................................................................................................................................................... 87

Introduction ............................................................................................................................................ 88

Problem Statement ................................................................................................................................. 89

Integrated Conceptual Framework ......................................................................................................... 90

Implementation Strategy ........................................................................................................................ 90

Implementation progress ..................................................................................................................... 91

GIS Integration .................................................................................................................................... 93

Conclusion ............................................................................................................................................... 93

Reference ................................................................................................................................................ 94

Challenges of Developing Land Information Management Systems (LIMS) for County Governments in

Kenya .......................................................................................................................................................... 95

Abstract ................................................................................................................................................... 95

Introduction ............................................................................................................................................ 96

Methodology ........................................................................................................................................... 96

Results and Findings ................................................................................................................................ 97

Legal challenge ................................................................................................................................... 97

Social challenges ................................................................................................................................. 98

Political challenges ............................................................................................................................. 98

Technical challenges ........................................................................................................................... 98

Economic challenges........................................................................................................................... 99

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Conclusion ............................................................................................................................................. 100

References ............................................................................................................................................ 100

Biographical Details .............................................................................................................................. 102

Contacts ................................................................................................................................................ 102

Utilities & Transportation ......................................................................................................................... 103

Ves Sites Selection Model for Ground Water Analysis and Mapping ...................................................... 104

Abstract ................................................................................................................................................. 104

Introduction .......................................................................................................................................... 105

Study Area ........................................................................................................................................ 105

Datasets and Methodology ................................................................................................................... 107

Datasets ............................................................................................................................................. 107

Methods............................................................................................................................................. 107

Results and Discussions ........................................................................................................................ 111

Conclusions. .......................................................................................................................................... 115

Acknowledgment. ................................................................................................................................. 116

References ............................................................................................................................................ 116

Biographical Notes ................................................................................................................................ 117

Contacts ................................................................................................................................................ 117

Cross-Cutting Issues .................................................................................................................................. 118

GI-diversity – Taking the activities in the Kakamega-Nandi forests area as example ............................. 119

Abstract ................................................................................................................................................. 119

Introduction .......................................................................................................................................... 120

More Recent Activities .......................................................................................................................... 121

A Web GIS-based viewing tool on forest use history ....................................................................... 121

Developing environmental education tools ....................................................................................... 123

Streamlining GIS teaching across universities .................................................................................. 124

Conclusions ........................................................................................................................................... 126

References ............................................................................................................................................ 126

Acknowledgements ............................................................................................................................... 128

Biographical Notes ................................................................................................................................ 128

Contacts ................................................................................................................................................ 128

Use of GIS and SDI in Promoting Coffee Quality in Maraba Sector, South Province of Rwanda .......... 129

Abstract ................................................................................................................................................. 129

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Introduction .......................................................................................................................................... 130

Methodologies ...................................................................................................................................... 130

Methodology Using Site Selection (Environment) and GIS ............................................................. 130

Methodology Using SD .................................................................................................................... 138

Results: Geoportal Creation .................................................................................................................. 139

Conclusion ............................................................................................................................................. 140

References: ........................................................................................................................................... 140

Biographical notes: ............................................................................................................................... 141

Contacts: ............................................................................................................................................... 141

Use smart maps and spatial analysis to better manage the earth's natural resources. Increase production, optimize workflows, and mitigate risk.

Natural Resources

Natural Resources Track

Diana Matee

GIS Utilization in Petroleum Data Management, A Case Study of National Data Centre (NDC) in National Oil

Corporation of Kenya

11th Esri Eastern Africa User Conference (EAUC)

2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya

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GIS Utilization in Petroleum Data Management, A Case Study of National

Data Centre (NDC) in National Oil Corporation of Kenya

Diana Matee, Kenya

Abstract The petroleum industry is an information led business in which the market capitalization of any oil

companies is mainly dependent on an expectation of the value of future production. This number depends

entirely on interpretation and understanding of datasets about resources that are both hidden far below

earth’s surface and are often also in remote inaccessible locations. Oil companies are not unique on how

much they rely on information, but E&P is one of the activities where the financial impact of data is

highest. Therefore, data and information management is crucial for the success of any oil company.

National Oil Corporation of Kenya as a custodian of all the oil and gas data, it embarked on a process of

implementing a National Data Center (NDC). NDC is a centralized dynamic system that manages and

preserves a country’s petroleum data assets with diverse set of data management tools such as automated,

quality assured workflows and services that help to encourages external investment. Most of the data in

NDC has spatial component to them such as well coordinates, seismic line location or a regional span survey

the design of NDC utilizes GIS as the defector spatial tool.

Natural Resources Track Diana Matee GIS Utilization in Petroleum Data Management, A Case Study of National Data Centre (NDC) in National Oil Corporation of Kenya 11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya

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Introduction Petroleum industry just like most of research based industries is an information led business .In order to capitalize on the market, companies mainly depend on an expectation of the value of future production. This value depends entirely on the interpretation of data about resources that are location based. The data can be categorized into-structured and unstructured data. Structured data which is organized geoscientific data e.g. seismic, well logs, gravity and magnetic data just to mention a few, whereas unstructured data entails mostly support reports for the geoscientific data. Examples of structured and unstructured

To manage this National Oil Corporation of Kenya with the Ministry of Energy deployed a National Data Center. National Data Center (NDC) A National Data Center (NDC) is a platform that enables archival and retrieval of regional, national, or governmental exploration and production data. It manages a country’s petroleum resources by preserving data resources through centralizing the data. Moreover it assists in promoting more investments to and from the data by having a safe and secure platform where investors can view and buy data. In addition to increasing collaboration among government bodies, international oil companies (IOC) together with research centers. The country’s NDC works flow is dynamic such that is caters for the different data formats ranging from

physical to softcopy. The below diagram shows the work flow of the NDC

• Geological - surface & sub-surface geology maps

• Geophysical - Seismic, gravity, magnetic, navigation data

• Petro physical -logs, cores, cuttings

• Geochemical -fluid samples

Structured data

• Reports e.g. geological reports, seismic sections images,core images e.t.c

Unstructured data


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Data is received whether physical or digital, it is taken through data management process where the digital data is store in a database and the physical in cabinets. NDC System The NDC system comprises of two systems that run concurrently:

ProSource Enterprise System, entails; ProSource Data Service (PDS) application which is an online interface to access the data. ProSource server which manages the data. Seabed database which stores non spatial data.

ArcGIS Enterprise System entails; ArcGIS portal which is an online application where all the published content can be accessed. ArcGIS server where data in published. ArcSDE database where spatial data is stored.

NDC Structure

Digital database

Data management

environment

Offsite backup

Data access

environment

Physical data

assets


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NDC Architecture

NDC Functionality All the data types are stored in the database, which comprises of seabed and ArcSDE oracle based databases. The unstructured data is stored in the Seabed databases (seabed db) while the structured data is stored on ArcGIS database (ArcSDE) which have a spatial entity to it. For the ProSource System data is fetched from the database and managed by the ProSource sever and can be accessed by the user through ProSource Data Service Client (PDS). ArcGIS System. Spatial data is stored in the ArcSDE database, it is the fetched by the ArcGIS Desktop application from the connection, as shown by the image below;

Pro

So

urc

e S

yste

m

Arc

GIS

Syste

m

ProSource Data Service Client

ArcGIS Server

ArcGIS Desktop

ProSource Server

Seabed DB ArcSDE

DB

ArcGIS Online

User


Diana Matee





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This data is used to populate a map, as shown below;


Diana Matee





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The map is then published to the ArcGIS server;


Diana Matee





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Then the link is redirected to the ProSource Data Service client for view and also the ArcGIS Portal;


Diana Matee





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And the portal for data mining purposes;


Diana Matee





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Conclusion ArcGIS in general is a powerful tool for not only mapping but also visualization. In National Oil

Corporation of Kenya it’s being utilized to not only map our data resources but due to its versatility we are

able to build upon it other systems that can be used to better manage our resources.

In addition to this we are able to centralize all our tools and resources helping to easily data management

and resource utilization.

Refrences The Management of Oil Industry Exploration & Production Data by Steve Hawtin


Noreen Mutoro, Gertrud Schaab, Mary Wykstra

Improved Analysis of Cheetah - (Acinonyx jubatus) Occupancy and Gene Flow in Kenya Using GIS Tools



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Improved Analysis of Cheetah - (Acinonyx jubatus) Occupancy and Gene Flow

in Kenya Using GIS Tools

Noreen MUTORO1, Kenya; Gertrud SCHAAB2, Germany; Mary WYKSTRA1, Kenya 1 Carnivores, Livelihoods and Landscapes; Acton for Cheetahs in Kenya (ACK), 2 Karlsruhe University of Applied Sciences, Karlsruhe

Keywords: Gene flow, Range wide Planning, Remote Sensing, Species Occupancy, Species

Distribution

Abstracts:

Action for Cheetahs in Kenya (ACK) is the only range-wide cheetah conservation organization in

Kenya. ACK conducted the first Kenya national cheetah survey in collaboration with the Kenya

Wildlife Service, Cheetah Conservation Fund and East African Wildlife Society between 2004 and

2007. We were the first to create a range-wide map of cheetahs based on actual site visitation

across the entire country. Results of the survey formed the baseline for national and regional

strategic planning. Methodology for the second survey will include land cover and anthropogenic

influences mapped in ArcGIS with cheetah occupancy from field surveys, detection dog scat

collection and gene flow analysis. This presentation will highlight the changes being made from

the first survey in order to assure improved knowledge to influence cheetah conservation

strategies. Occupancy modelling and genetic mapping will be used to map and analyse trends in

cheetah status and genetic flow between populations. Detection dogs will locate scat to evaluate

prey selection, cheetah health and genetic variability. Remote sensing technology will test

assumptions on land use change affecting cheetah habitat. Results of pilot studies conducted

between December 2015 and August 2016 form the framework for completing the range-wide

GIS-based evaluation.






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Problem Definition/ Background

Of all the large carnivores, the cheetah (Acinonyx jubatus) is one of the most vulnerable to

environmental degradation(Bashir et al. 2004). Kenya supports globally important cheetah

populations which are currently experiencing dramatic declines in their ranges and size due to

habitat loss and fragmentation (KWS 2010). Informed conservation management of the cheetah

requires reliable status assessments and inferences on their ability to utilize human-influenced

landscapes. Cheetah conservation activity in the country is hindered by the species’ massive area

requirements and insufficient knowledge on the effects of anthropogenic activities on their

population and habitat (KWS 2010).There are few quantitative studies on cheetah population status

or distribution for use in conservation planning.

Previous nationwide surveys in Kenya on cheetahs were conducted to determine cheetah status

and distribution after speculations that their populations were dramatically declining in the country

(Isaboke et al. 2005). These surveys which provided insightful information on the species’

distribution, status population estimates and threats relied on sighting reports, limited researcher

sightings and interview based surveys (Graham and Parker 1965 ,Gros 1998,Hamilton and Miller

1986). Although nationwide (large-scale) surveys that are based on questionnaires, static range

maps and other forms of expert opinion as basic data provide foundational information about a

species’ ecology and adaptation, they are usually inaccurate and biased because such surveys are

commonly affected by the problem of species being present at some locations but going

unreported. In addition, this data does not highlight the relationship between animal presence and

habitat covariates.

Action for Cheetahs in Kenya (ACK) is the only range-wide cheetah conservation organization in

Kenya. ACK conducted the first Kenya national cheetah survey in collaboration with Kenya

Wildlife Service, Cheetah Conservation Fund and East African Wildlife Society between 2004 and

2007. Results from this survey informed national and regional strategic planning for cheetah

conservation and also created the first range-wide map of cheetahs based on actual site visitation

across the entire country.

In the next national survey, ArcGIS tools will be used to analyze how land cover and anthropogenic

activity influence cheetah occupancy and gene flow based on field survey and genetic analysis.

Information on the relationship between ecological and social determinants that influence cheetah

distribution and survival will be determined on a landscape level. In addition, landscape

connectivity that facilitates cheetah dispersal, especially those living in small isolated population

and the genetic viability of isolated populations inside and outside protected areas across their

ranges, will also be assessed.

Methods/Methodology

Sampling approach for a national-wide survey

A grid sampling approach will be used to sample cheetah geographical ranges in Kenya. The

country will be divided into 20-km x 20-km sampling units using ArcGIS to identify the survey

route. Baseline data from previous cheetah surveys in Kenya on status and distribution will be






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included to identify areas where cheetah occurrence is biogeographically and ecologically possible

and exclude areas where the species was historically absent.

Monitoring Cheetah distribution in Kenya

Occupancy surveys will be used to determine cheetah distribution and quantify their status using

presence-only data. A stratification process will be used to eliminate areas and vegetation types

where the probability of cheetahs is very low (Hebblewhite et al. 2011). Fixed transect surveys

will be used to detect cheetah presence/ occupancy in each grid cell. Driving transects will be

conducted along the roads but in areas where there is limited road network, transects will be

conducted along game trails on foot. ArcGIS Online mobile applications will be used to collect

data digitally in the field and store data online to reduce the need to carry paper forms and the time

in entering data.

Scat detection dogs will also be used to augment occupancy surveys by determining the proportion

of landscape occupied by cheetahs based on detection of cheetah scats. Survey routes will be

positioned to maximize the probability of encountering cheetah tracks both on roads, trails and at

random through the study area. Detections will be represented by unambiguously identified

cheetah tracks, scat or sighting reports.

A questionnaire will also be developed to gather detection/non-detection data on cheetah presence

from area residents. Each respondent will be asked to report all cheetah sightings they can clearly

remember and the location name and the name of surrounding landmarks. Locations where the

respondent knows of the presence of the cheetah without being able to remember the precise

sightings will also be recorded. For each reported sighting, the respondent will be asked to specify

the date of observation or approximate dates by referring to important events in the life of the

community; total number of cheetah observed and age, sex of cheetahs that were sighted.

Interviewees will be ranked for confidence and results will compliment field survey and scat data.

Predicting heterogeneity in cheetah occupancy

Occupancy and species distribution modeling is are an innovative methods for assessing species

status, mapping species distribution and investigating determinants of species occurrence

(Andresen et al. 2014). The formula developed in pilot studies based on detection and non-

detection of species over several sampling occasions provide a model that can be used rapidly on

larger scale .We will use aspects of distribution and occupancy methods where weighting the

detection, density and search effort will provide us with the best possible formula for predicting

trends in cheetah population over time. Landscape-scale occupancy surveys can also be used to

identify meta-populations, which if combined with ecological (prey occurrence models) and

anthropogenic information can allow the delineation of important corridors and suitable locations

for reintroductions (Andresen et al. 2014).

Heterogeneity in cheetah occupancy will be determined by hierarchical ranking of predictor

variables (covariates) and how they influence cheetah distribution and resource selection. A

combination of environmental (climate, elevation, landscape structure and land cover/ habitat),






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anthropogenic (land use, human population densities and proximity to agro-pastoralist settlement,

proximity to protected areas) and biotic (occurrence probability of main prey species) spatial

covariates will be used to understand cheetah occupancy across their geographical ranges (Karanth

et al. 2009). A spatial vegetation community land cover model that describes vegetation

community associations available in Kenya will be developed using Landsat-8/Sentinel-2 images

in combination with already existing, often outdated geo-data sets. Remotely sensed data will be

complemented with field based assessments on a range-wide scale to minimize errors resulting

from misclassification and resolution issues, and also to put into consideration changes that may

have taken place at the ground level since GIS data was collected. Geo-data on settlement, human

population densities and proximity to settlements, water and protected areas will determine levels

of resistance or augmentation of cheetah distribution in the area. Only covariates considered

having a significant influence on cheetah distribution and habitat use in a sampling unit will be

used in cheetah models.

Determining landscape connectivity

GIS-based landscape layers will be combined to produce a movement cost surface quantifying the

matrix between cheetah populations in terms of difficulty of movement. Least cost corridors will

then be modeled across this cost surface between known cheetah populations identified in previous

range wide surveys. A range-wide least cost connectivity analysis between known cheetah

populations will be used to determine where potential and/or actual cheetah corridors exist.

Genetic analysis from fecal samples will be used to measure the degree of maternal connections

across populations and to further measure the population flow across their range. These corridors

are probable connections between cheetah populations that maintain genetic viability and health

in the population (Zeller et al. 2011). Field based assessments will confirm the use of the corridor

by the cheetah and contribute to the remote data in ArcGIS to examine corridor boundaries for

conservation planning.

Expected outcomes (Results and major findings)

- Spatial distribution maps and predictive maps on cheetahs’ geographic range and status in

Kenya.

- Identification of habitat covariates/ resource selection functions that influence cheetah

habitat occupancy in Kenya.

- Identification of cheetah population patches across current geographic ranges and their

genetic viability.

- Identification of existing and potential corridors which will help promote corridor

conservation and inform management decisions based on scientific data.

- Classification of site specific threats to cheetah populations, habitats and corridors which

can be used to inform management and conservation decisions.

Biological references

Andresen L, K Everatt, MJ Somers. 2014. Use of site occupancy models for targeted monitoring

of the cheetah. Journal of Zoology 292: 212-220.






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Bashir S, B Daly, SM Durant, H Förster, J Grisham, L Marker et al. 2004. Global cheetah

(acinonyx jubatus) monitoring workshop report. . Conservation Breeding Specialist Group

(SSC/ IUCN), Pretoria.

Graham AD, ISC Parker. 1965 East african wildlife society cheetah survey Nairobi.

Gros PM. 1998. The status if the cheetah acinonyx jubatus in kenya: A field-interview assessment.

Biological Conservation 85: 137-149.

Hamilton P, SD Miller. 1986. Status of cheetah in kenya, with reference to sub-saharan africa.

In: D. Everett (ed.) Cats of the world: Biology, conservation and management. Natl

Wildlife Federation, Washington, D.C.

Hebblewhite M, D Miquelle, A Murzin, V Aramilev, D Pikunov. 2011. Predicting potential

habitat and population size for reintroduction of the far eastern leopards in the russian far

east historic range of far eastern leopards. Biological Conservation, 10: 2403-2413

Isaboke W, M Kahiu, CM Wambua, M Wykstra. 2005. Cheetah census in kenya, priority 1: South

western kenya (2004-2005) - report submitted to east african wildlife society, stichting-

netherlands., Action for Cheetahs in Kenya.

Karanth KK, JD Nichols, JE Hines, KU Karanth, NL Christensen. 2009. Patterns and

determinants of mammal species occurrence in india. Journal of Applied Ecology 46:

1189-1200.

KWS. 2010. Kenya national strategy for the conservation of cheetahs and wild dogs. In: Research

(ed.). Kenya Wildlife Service, Nairobi.

Zeller AK, S Nijihawan, R Salmon-Peréz, HS Postome, EJ Hines. 2011. Integrating occupancy

modelling and interview data for corridor identification: A case study of jaguars in

nicaragua. Biology Conservation: 892-901.

Biographical Notes

Noreen Mutoro completed her Master’s through the University of Nairobi and Action for Cheetahs

in Kenya in affiliation with the Kenya Wildlife Service and the Cheetah Conservation Fund. She

is now a PhD candidate with Technische Universität München under supervision with Jany

Christian Habel and Gertrud Schaab. She is a research assistant with Action for Cheetahs and

works closely with Mary Wykstra, MEM, on development of the second national cheetah survey

conducted with this institution

Contacts 1 Carnivores, Livelihoods and Landscapes; Acton for Cheetahs in Kenya (ACK), Nairobi, Kenya,

[email protected]

2 Karlsruhe University of Applied Sciences, Karlsruhe, Germany, [email protected]


Solomon M. Mwenda, Alex Mugambi, James Nyaga

Impact of Climate Change on Desertification in Arid Areas of Kenya



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Mr. Solomon M. Mwenda, Mr. Alex Mugambi, Mr. James Nyaga

Kenyatta University, Regional Centre for Mapping of Resources for Development.

Key words: Desertification, Climate Change, Arid Areas, GIS.

Abstract Climate change and a deteriorating environment is a key challenge to sustainability, bio-diversity,

food security and stability across Africa. Pollution, deteriorating soil quality, desertification and

poor air quality are threatening the lives and future of all the continent's people.According to

previous studies, the impacts of climate change in Africa will be severe, and are already ongoing

in many places. Desertification, along with climate change and the loss of biodiversity are the

greatest challenges to sustainable development identified during the 1992 Rio Earth Summit.

UNCCD links environment and development to sustainable land management. The Convention

addresses specifically the arid, semi-arid and dry sub-humid areas, known as the dry lands, where

some of the most vulnerable ecosystems and peoples can be found. UNCCD strategy (2008-2018)

adopted in 2007 aims: "to forge a global partnership to reverse and prevent desertification/land

degradation and to mitigate the effects of drought in affected areas in order to support poverty

reduction and environmental sustainability". In pursuit of this goal, GIS modeling was used for

global-aridity using the data available from the World Clim Global Climate Data as input

parameters. Monthly average PET was spatially characterized and then tested using four different

temperature-based methods applied to the WorldClim Global Climate Data to determine their

prediction accuracy. Desertification is intensifying and spreading in Kenya, threatening millions

of inhabitants’ and severely reducing productivity of the land due to a growing imbalance between

population, resources, development and environment. This paper analyzed the impact of these

factors on the arid areas of Kenya and mitigating measures and interventions to avert future

occurrences using Geographic Information Systems.






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Introduction

According to a United Nations report (UNCCD, 2004) more than one billion people worldwide,

most of them among the poorest in the world are affected by drought and desertification. These

people, occupying approximately one quarter of the planet, are facing major problems which

include soil degradation and vegetation loss, leading todeterioration of arable land and eventually

to chronic food insecurity. Desertification of the arid lands of the world has been proceeding

rapidly for more than a thousand years. The Arid and Semi-Arid lands (ASAL) constitute about

80% (467,200 sq. km) of Kenya’s total landmass and is grouped into geographical zones including

the Savannah covering most of the North eastern and South-eastern parts, the Coastal region, the

North Rift Valley, the Highlands and the Lake Victoria Basin. The ASAL hosts about 35% of

Kenya’s population (13 million people) and over 60% of its inhabitants live below the poverty

line, subsisting on less than one US dollar per day. (UNEP 2013).

Aridity Thompson (1975) explained that aridity and lack of moisture could be caused by climatic

processes: off-shore cold currents, topography and dynamic anti-cyclonic subsidence, and high

pressure systems. Desserts are found where one or more of these processes operate over a

significant area for sufficient time. The arid lands are characterized by high ambient temperatures

with a wide diurnal range. In most areas, evapotranspiration rates are more than twice the annual

rainfall. These areas receive low anderratic bimodal rainfall that is highly variable both inspace

and time. In most cases, rain falls as short highintensity storms that produce considerable runoff

and soil erosion. Average annual rainfall in the arid lands ranges from 150-450mm. The soils are

shallow, highly variable, and of light to medium texture. The soils are also of low fertility and are

subject to compaction, capping and erosion. A few areas have volcanic soils and alluvial deposits

which are suitable for crop production. Heavy clays are found in these areas also, but cultivation

is difficult on them due to their poor workability as well as salinity problems. Water availability

and accessibility is highly variable and is a considerable constraint to agricultural production.Arid

lands are mainly inhabited by pastoralists and agro- pastoralists. Large areas are suitable only for

nomadic livestock production.

Climate change. Climate change is a change in the statistical distribution of weather patterns when that change lasts

for an extended period of time. Climate change may also refer to a change in average weather

conditions, or in the time variation of weather around longer-term average conditions. Climate

change is caused by factors such as biotic processes, variations in solar radiation received by Earth,

plate tectonics, and volcanic eruptions. Certain human activities have also been identified as

significant causes of recent climate change, often referred to as "global warming". Some of the

general adverse effects of climate change experienced in Kenya include; Variations in weather

patterns (reduced rainfall and failed seasons), frequent and prolonged droughts and diminishing

water resources, Floods/flash floods and landslides, environmental degradation and habitat

destruction, resurgence of pests and diseases, loss of biodiversity, severe famine and hunger

causing food insecurity and resource use conflicts.






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Desertification. Desertification is the impoverishment of terrestrial ecosystems under the impact of man. It is the

process of deterioration in these ecosystems that can be measured by reduced productivity of

desirable plants, undesirable alterations in the biomass and the diversity of the micro and macro

fauna and flora, accelerated soil deterioration, and increased hazards for human occupancy.

In the International Agreement on Combating Desertification that was held in Paris in 1994,

desertification was defined as the reduction or loss of biological or economic productivity resulting

from land use or from human activities and habitation patterns. A number of factors have increased

land degradation and the vulnerability of the African arid regions to desertification. Most of them

have had similar effects in Asia and Latin America. They can be grouped in three categories:

Increased human and animal population, improved health services and injudicious use of

technology.

Due to the increased sedentary population, pressures on cultivated land led to a shortening of the

fallow period in the shifting cultivation cycle and the extension of cropping into the more

precarious drier regions. Crop harvests became less reliable and more variable as the desert edge

was approached. Concurrently, nomadic pastoralists were deprived of some of their best grazing

lands as the cultivators moved in (Delwaulle, 1977). At the same time the rangeland area was

contracting, populations of pastoralists and their livestock were increasing and the provision of

improved veterinary services and the lack of a viable marketing system helped assure that animal

numbers would grow rapidly (Widstrand, 1975). The result was inevitable: overgrazing and

accelerated desertification. Overgrazing inadvertently was made worse, particularly in the Sahel,

by the drilling of additional wells that provided drinking water for livestock throughout the year.

Without the rest period that intermittent water supplies previously assured, forage conditions

deteriorated around the wells where water was no longer a limiting factor in livestock survival.

Local authorities did not or could not impose a control system that would allow forage plants to

recover from heavy grazing. Destruction of woody vegetation has been hastened by the ever-

increasing need for firewood to meet the demands of the larger population. The destruction is

especially noticeable around the rapidly growing urban centers, where the circle of deforested

lands gets larger every year (Delwaulle, 1973). While desertification was a long-standing problem

even in the absence of droughts, the gradually increasing vulnerability of the land made the impact

of the inevitable droughts worse than ever (Dahl and Hjort, 1979). The factors responsible for that

vulnerability are still operating, desertification continues, and future droughts will have ever-

greater damaging effects.

Droughts causes and desert spread. A common misapprehension about desertification is that it spreads from a desert core, like a ripple

on a pond. Land degradation can and does occur far from any climatic desert; the presence or

absence of a nearby desert has no direct relation to desertification. Desertification usually begins

as a spot on the land-scape where land abuse has become excessive. From that spot, which might

be around a watering point or in a cultivated field, land degradation spreads outward if the abuse

continues. A second misconception is that droughts are responsible for desertification. Droughts

do increase the likelihood that the rate of degradation will increase on non-irrigated land if the






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carrying capacity is exceeded. However, well-managed land will recover from droughts with

minimal adverse effects when the rains return. The deadly combination is land abuse during good

periods and its continuation during periods of deficient rainfall.

Magnitude of Desertification in Africa About 18 percent of the arid region of Africa is severely desertified, and most of that represented

by grazing lands and rain-fed cropping lands on the south side of the Sahara, the mountain slopes

and the plains of North Africa. Moderate to high salinity affects about 30 percent of the irrigated

land in Egypt (Aboukhaled et al., 1975).

Wind erosion is dominant in the drier regions and water erosion on the wetter sloping lands.

Ethiopia, Kenya, and the Maghreb countries of Algeria, Morocco, and Tunisia have been subjected

to especially serious water erosion, whereas wind erosion has been most damaging in sub-Saharan

West Africa. While good data on the effect of land degradation on crop and livestock yields are

not available, it seems likely that soil fertility losses, alone, have reduced dry land crop yields by

25 to 50 percent in the severely desertified areas. Animal productivity may well have declined by

at least 50 percent nearly everywhere that domestic livestock are raised. In many areas south of

the Sahara, rangeland forage production probably is less than 25 percent of the potential.

Causes of Desertification. Factors leading to desertification can in general be divided into two categories: climatic variability

and human activities.

Climatic variability: Dry lands have limited water supplies (annual rainfall is less than 100mm).

Rainfall can vary greatly during the year, while wider fluctuations occur over years and decades.

This leads directly to drought, which is often associated with land degradation and hence a vital

factor behind desertification.

Human activities: The human activities that lead to desertification can be outlined as

overgrazing: This is described as the major cause of desertification worldwide and Overexploiting

land: This can happen due to various reasons. It can happen due to expand in human population

and hence the need for more crops, international economic forces that can lead to short-term

exploitation of local resources for export.

Objectives of Study 1. To access the magnitude of desertification in Kenya’s arid areas

2. To access impact of climate change in Kenya’s arid areas

3. To analyze the impact of growing imbalance between population, resources, development and

environment on the arid areas of Kenya

4. To propose mitigating measures and interventions to avert future occurrences.






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Methodology

Description of study site The study area in Kenya falls partly in the arid zones covering of the Rift Valley. It covers Turkana,

Wajir, Mandera, Marsabit, Isiolo, Garissa, Samburu, Baringo, Tana River counties which consist

of a population of 4,620,199 which is 12% of the national population. (Vision 2030 Development

Strategy for Northern Kenya and other Arid Lands (2011).






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Figure 1.1: Map of Arid Areas in Kenya






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Global Potential Evapo-Transpiration (Global-PET) and Global-Aridity Geospatial Datasets were

used. Potential Evapo-Transpiration (PET) is a measure of the ability of the atmosphere to remove

water through Evapo-Transpiration (ET) processes. The FAO introduced the definition of PET as

the ET of a reference crop under optimal conditions, having the characteristics of well watered

grass with an assumed height of 12 centimeters, a fixed surface resistance of 70 seconds per meter

and an albedo of 0.23 (Allen et al. 1998).

The Global-PET and Global-Aridity were both modeled using the data available from the World

Clim Global Climate Data (Hijmans et al. 2005) as input parameters. The World Clim, based on a

high number of climate observations and SRTM topographical data, is a high-resolution global

geo-database (30 arc seconds or ~ 1km at equator) of monthly average data (1950-2000) for the

following climatic parameters: precipitation, mean, minimum and maximum temperature. This set

of parameters is insufficient to fully parameterize physical radiation-based PET equations (i.e. the

FAO-PM), though can parameterize simpler temperature-based PET equations.

Monthly average PET was spatially characterized and then tested using four different temperature-

based methods applied to the WorldClim Global Climate Data to determine their prediction

accuracy. The modes that were used and tested are Thornthwaite (1948), Thornthwaite modified

by Holland (1978), Hargreaves et al. (1985), Hargreaves modified by Droogers and Allen (2002).

The monthly average values using these high resolution temperature PET layers, together with

existing medium resolution (10’) FAO-PM monthly average (1950-2000) PET layers (FAO 2004),

were compared to Penman-Monteith PET values estimated at climate stations in South America

and Africa (n = 2288). The PET measurements used in the validation are calculated using the more

complex Penman-Monteith model applied on direct observations of the various climatic

parameters, and were obtained from the FAOCLIM 2 climate station dataset (Allen et al., 1998),

available online from FAO. Based on the results of the comparative validation, the Hargreaves

model was chosen as the most suitable to model PET globally. (Hargreaves and Allen 2003).

Natural Resources Track Solomon M. Mwenda, Alex Mugambi, James Nyaga Impact of Climate Change on Desertification in Arid Areas of Kenya 11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya

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Hargreaves (1985) uses mean monthly temperature (Tmean), mean monthly temperature range (TD) and mean monthly extra-terrestrial radiation (RA, radiation on top of atmosphere) to calculate mean PET, as shown below: PET = 0.0023 * RA * (Tmean + 17.8) * TD0.5 (mm / day).

Tmean

TD

RA

PET

Mean Annual

Potential

Evapotranspiration

MAP

Mean Annual

Precipitation

Aridity Index

(AI)






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Average monthly and annual PET (mm) layers at spatial resolution of 30 arc-seconds (~ 1km at

tropics) for the 1950-2000 period are calculated using the Hargreaves method with available layers

of monthly average temperature parameters, available from WorldClim database, and extra-

terrestrial radiation, calculated for specific months using a methodology presented by Allen et al.,

(1998). Temperature range (TD) is an effective proxy to describe the effect of cloud cover on the

quantity of extra-terrestrial radiation reaching the land surface and, as such, it describes more

complex physical processes with easily available climate data at high resolution.

Aridity is usually expressed as a generalized function of precipitation, temperature, and potential

evapotranspiration (PET). An Aridity Index (UNEP, 1997) can be used to quantify precipitation

availability over atmospheric water demand.

Global mapping of mean Aridity Index from the 1950-2000 period at 30 arc second spatial

resolution is calculated as:

Aridity Index (AI) = MAP / MAE where:

MAP = Mean Annual Precipitation

MAE = Mean Annual Potential Evapotranspiration

In the Global-Aridity dataset, which uses this formulation, Aridity Index values increase for more

humid conditions, and decrease with more arid conditions. Mean annual precipitation (MAP)

values were obtained from the WorldClim Global Climate Data (Hijmans et al. 2005), for years

1950-2000, while PET layers estimated on a monthly average basis by the GPET (i.e. modeled

using the Hargreaves method, as described above) were aggregated to mean annual values (MAE).

The Global-Aridity surface shows moisture availability for potential growth of reference

vegetation excluding the impact of soil mediating water runoff events. UNEP (UNEP 1997) breaks

up Aridity Index, in the traditional classification scheme presented in Table 2.

Value Climate Class

< 0.03 Hyper Arid

0.03 – 0.2 Arid

0.2 – 0.5 Semi-Arid

0.5 – 0.65 Dry sub-humid

> 0.65 Humid






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Results and Findings

Annual Aridity Index 1950-2009 Annual PET 1950-2009

Source: CGIAR-CSI GeoPortal (http://www.csi.cgiar.org).

Annual Average PET 1990 Annual Average PET 2000 Annual Average PET 2010

http://www.csi.cgiar.org/

http://www.csi.cgiar.org/






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Aridity Index 1990 Aridity Index 2000 Aridity Index 2010

Findings The magnitude of desertification in Kenya’s arid areas caused mainly by impact of climate change

in Kenya’s arid areas. This has caused the growing imbalance between population, resources,

development and environment on the arid areas of Kenya. There are several mitigating measures

and interventions to avert future occurrences.

Interventions to Avert Future Occurrences World Day to Combat Desertification

The World Day to Combat Desertification (WDCD) is observed every year since 1995 but more

needs be done in order to promote public awareness on the dangers of desertification. The day

could also be used as an opportunity to inform the local and international community about the

implementation of the United Nations Convention to Combat Desertification in those countries

experiencing serious drought and/or desertification, particularly in Africa.

United Nations Decade for Deserts and the Fight against Desertification

The United Nations Decade for Deserts and the Fight against Desertification (UNDDD) is another

international framework that recognizes the need to conserve and rehabilitate degraded land for

enhancement of socio-economic wellbeing of the more than 2 billion dry land inhabitants

worldwide. The launch of the framework on 16 August 2010 marked the beginning of a decade

(2010-2020) long strategy seeking to raise awareness and action to improve the protection and

management of the world’s dry land ecosystems.






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Kenya National Action Programme – A framework for combating desertification

Kenya ratified the United Nations Convention to Combat Desertification (UNCCD) on 24 June

1997. UNCCD, adopted on 17 June 1994, is an international legal agreement for action to combat

desertification and mitigate the effect of drought in arid, semi-arid and dry sub-humid zones. One

of the main commitments of the affected and developing country Parties to the Convention is to

develop national action programmes (NAPs), security, and environmental conservation. Therefore

Kenya national action program should:

- Strengthen the knowledge base and developing information and monitoring systems

for regions prone to desertification and drought, including the economic and social aspects of

these ecosystems;

- Combating land degradation through, inter alia, intensified soil conservation, a forestation and

reforestation activities;

- Developing and strengthening integrated development programmes for the eradication of

poverty and promotion of alternative livelihood systems in areas prone to desertification;

- Developing comprehensive anti-desertification programmes and integrating them into

national development plans and national environmental planning;

- Developing comprehensive drought preparedness and drought-relief schemes, such as

early warning systems, for drought-prone areas and designing programmes to cope with

environmental refugees;

- Encouraging and promoting popular participation and environmental education, focusing on

desertification control and management of the effects of climate change.

The WDCD initiative in ASALs of Kenya should support the local communities to adapt and build

resilience by seeking to:

- Increase food security through enhancing the drought resilience of local agricultural practices

- Reduce poverty through diversification of enterprises to improve livelihoods

- Facilitate the integration of adaptation to drought into Kenya’s sustainable development plans

and policies

- Undertake measures to reduce the vulnerability of inhabitants of ASALs to vagaries of drought

- Illustrate how national policies through NAP may be influenced and modified based on lessons

from the field.

Acknowledgement

The authors acknowledge the support from Antonio Trabucco, The Consortium for Spatial

Information (CGIAR-CSI), CEDA and Mr. John Kapoi for their immense contribution towards

achieving this in providing data methodology and input of ideas and support.

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Contacts

[email protected],

[email protected], James Nyaga [email protected] or [email protected].

mailto:[email protected]




ArcGIS for Local Government includes a set of free maps, apps, and best practices developed especially for your local government. As an ArcGIS user, you can deploy this ready-to-use solution to improve government operations and enhance citizen services.

ArcGIS forLocal Government

Local Government Track

Charles Kigen, Philip Kisoyan, James Chelang’a

A Village Level GIS-Based County Government and Environmental Risk Management Decision Support System



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A Village Level GIS-Based County Government and Environmental Risk

Management Decision Support System

1*Charles Kigen and 2Philip Kisoyan and 3James Chelang’a 1,3Moi University, Department of Natural Resources, P. O. Box 3900-30100, Eldoret

Email: [email protected]; [email protected] 2Egerton University, P. O. Box 536-20115, Egerton

Email: [email protected] *Corresponding author

Key words: Village-level, geodatabase, decision-making, ALOHA, ArcGIS

Abstract

Kenya’s devolved system of governance lead to devolution of resources and prudent decision

making is key in promoting sustainable development. Resource allocation, risk and environmental

management are some decisions made at the county level with limited information. This paper

seeks to support the decision making process by development of a village level geodatabase

containing infrastructure, and potential hazards within a given area. An area in Western Kenya

was used in the study, data was sourced from google earth and threat levels from chlorine gas

pollution modeled using ALOHA software. The geodatabase was constructed and spatial analyses

run in ArcGIS. These processes were digitization, buffering, distance generation, distance

extraction, intersection and land size estimation. The results generated the number of households

and their proximity to roads, schools, piped water networks, rivers and electricity grid. Further,

the number of households and size of land under different pollution threat levels were generated.

The integration of this information in the decision making process is invaluable in guiding

infrastructure development, classifying the population and land under different levels of threats by

chlorine gas pollution and eases the process of identification of affected individuals and land.









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Introduction

Decision making is a process of making assessments and conclusions based on certain factors

under consideration among them being limited resources and hazards. The achievement of

sustainable development goals (SDGs) and Kenya’s Vision 2030 is dependent on prudent decision

making. Decisions have been made in many cases using political support leading to uneven

development. Judicious decisions are made if data and the factors to be taken into consideration

are given attention. The advent of Kenya’s decentralized system of governance presented many

uncertainties in carrying out development projects. The county governments have implemented

many projects and established risk management plans for certain emergencies. However, the

projects and prioritization can be improved by collecting baseline information which constitutes

population distribution, infrastructure and potential hazards. This paper examines the potentials of

application of GIS technology to aid decision making at village-level about project prioritization

in terms of available resources but also about the siting and also for emergency planning and

management.

The GIS spatial analysis requires establishment of a geodatabase containing the entire

infrastructure, the potential hazard and their locations. An area in western Kenya, part of Webuye

town was used in the study. The area was picked due to the presence of a pulp paper factor and the

study assumed a scenario where chlorine gas escaped accidentally. The data of interest in the area

included road networks, piped water network, power lines, public schools and the potential risks

of accidental release of chlorine gas modeled using Areal Locations of Hazardous Atmospheres

(ALOHA). ALOHA was used in the Chlorine gas emission modeling because of its compatibility

with ArGIS (Chakraborty and Armstrong, 1994). ALOHA is a free software developed by United

States Environment Protection Agency (EPA) and National Oceanic and Atmospheric

Administration (NOAA). It contains about 1000 database of chemicals that can be released into

the atmosphere through tank explosion, leaking pipes and open containers (EPA and NOAA,

2007). It predicts the direction of these chemicals based on the prevailing weather conditions which

are required to generate the direction and concentrations of any given chemical at a given point in

the area of interest. The ALOHA’s use in the modeling is because it enables chemical plumes

footprints to be exported into ArcGIS platform ((EPA and NOAA, 2007); Chakraborty and

Armstrong, 1994) for integration with other data and further analysis.

Spatial analysis of the data was done against the population distribution of the area. The results

showed the population distribution of the area, the proximity of each household to the

infrastructure and river network, the location and number of households, the size of land under the

different chlorine gas acute exposure guideline levels (AEGLs). Moreover, the population to be

compensated and the size of land to be rehabilitated can be quickly and easily estimated. With

such information and powerful spatial analysis tools, a decision support system can be established

at a village-level to foster even sustainable development for a given area using the open source

software.






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Methodology

Area of study The area of study lies between 692000m and 69900m longitude and 60000 and 65000m latitude

in zone 36N (figure 1). The area population is high with an average density of 515 people per

square kilometer. The regions poverty index is one of the highest in the country at 57%. The major

economic activities are small-scale subsistence farming, cash crop farming (sugarcane), businesses

and employment. The major water sources in the village of study comprise springs/rivers and piped

network mostly in the urban areas.

Geodatabase Construction The household, public schools, river network and road network data were extracted from google

earth maps while the power lines and water networks were hypothetical. ArcGIS software was

used in personal goedatabase construction and spatial analysis. The google earth maps were

downloaded, assigned WGS 1984 Projection and registered using the latitude and longitude grids

in decimal degrees before digitization of the required data. For all the data, distance raster were

generated using ‘euclidean distance’ tool (figure 2) and then distance of each household extracted

using ‘extract multi value to points’ tool following guidelines from Darbra, et al. (2008). The

generated distance table was categorized at intervals of 500m and the number of households in

each group.






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Figure 1: The villages of study close to the Pan paper pulp factory






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Figure 2: The distance raster and distribution of households (a), schools (b), power lines (c), roads (d), piped water

(e) and rivers (f)

Chlorine gas plume footprint modeling and analysis Modeling chlorine gas footprint in ALOHA required data (table 1) which include site data

(location, building air exchanges rate and time), chemical data (chemical name, molecular weight,

AEGL for 60mins, (Immediately Dangerous to Life and Health Limits) IDLH, ambient boiling

point, vapor pressure at ambient temperature and ambient saturation concentration), atmospheric

data (wind velocity and direction, ground roughness, cloud cover, air temperature, inversion height

and relative humidity) (EPA and NOAA, 2007). Other useful information required to run ALOHA

model is source of chlorine gas strength that comprise source, flammability, tank diameter, tank

Length, tank volume, other materials in the tank, internal Temperature, chemical mass in tank,

internal press, opening length, opening width, release duration, max average sustained release rate,

(averaged over a minute or more) and the total amount released.

Table 1: The data required to run the ALOHA model

SITE DATA:

Location: WEBUYE, KENYA

Building Air Exchanges Per Hour: 0.28 (unsheltered single storied)

Time: July 17, 2016 0253 hours ST (user specified)

CHEMICAL DATA:

Chemical Name: CHLORINE






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CAS Number: 7782-50-5 Molecular Weight: 70.91 g/mol

AEGL-1 (60 min): 0.5 ppm AEGL-2 (60 min): 2 ppm AEGL-3 (60

min): 20 ppm

IDLH: 10 ppm

Ambient Boiling Point: -39.3° C

Vapor Pressure at Ambient Temperature: greater than 1 atm

Ambient Saturation Concentration: 1,000,000 ppm or 100.0%

ATMOSPHERIC DATA: (MANUAL INPUT OF DATA)

Wind: 2.24 miles/hour from 45° true at 10 meters

Ground Roughness: open country Cloud Cover: 3 tenths

Air Temperature: 20° C Stability Class: F

No Inversion Height Relative Humidity: 90%

SOURCE STRENGTH:

Leak from hole in horizontal cylindrical tank

Non-flammable chemical is escaping from tank

Tank Diameter: 2.5 meters Tank Length: 7 meters

Tank Volume: 34.4 cubic meters

Tank contains gas only Internal Temperature: 20° C

Chemical Mass in Tank: 513 kilograms

Internal Press: 70 psia

Opening Length: 90 centimeters Opening Width: 6 centimeters

Release Duration: 1 minute

Max Average Sustained Release Rate: 6.95 kilograms/sec (averaged

over a minute or more)

Total Amount Released: 417 kilograms

The output of chlorine gas plume model are a text summary table (table 1) containing all the input

data used chlorine concentration threat zones of Red (1.6 kilometers with a concentration of 20

ppm), Orange (4.1 kilometers with a concentration of 2 ppm) and Yellow (7.2 kilometers with a

concentration of 0.5 ppm) (figure 3). Generated also are the threat levels wind direction confidence

lines. The chlorine gas plume was the exported to .kml format and converted to shapefiles in

ArcGIS while maintaining the threat levels and the wind direction confidence lines (figure 4). In

the ArcGIS, the number of households, schools and land under different levels of threats of

Chlorine gas were extracted and tabulated as modified from Jakala (2007).






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Figure 3: Chlorine gas plume ALOHA footprint

Figure 4: Exported ALOHA chlorine gas plume to ArcGIS

Results and Discussion

Household Proximity to infrastructure A total of 5355 households were in the study village distributed as in figure 2 (a). The household

are concentrated more in the northwest section a peri-urban area and decrease away low

concentration in the other areas. The village infrastructure distribution and coverage is not uniform






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(figure 2 (b), (c), (d) and (e)) while the river network is as figure 2 (f). The distances of households

away from the infrastructure and rivers were measure in meters at intervals of 500m table 2. The

schools distribution is fairly even with 45.2% of households within a distance of 500-100m and

0.5% at 2500-3000m away. While the power lines cover more of the urban areas with over 80%

of the households within 500m, 1.1% is 1500-2000m away. The all-weather roads though not

tarmac showed the same pattern as power lines with 80.9% of the households within 500m. The

piped water network covers a small section mainly in the urban and western areas of the village.

Almost 50% of the households are within 500m, with 2.9% at 2500-3000m away from the water

network. The natural rivers and the stream networks cover almost the entire village where over

90% of the population is at 0-1000m away. The furthest households from the river were 0.3% at

a distance of less than 2000m.

The distance raster of the infrastructure away from the household give indication of not only what

infrastructure to prioritize but also where to locate it. Figure 2 (b, c, d, e and f) show infrastructure

coverage and in case the government wants to expand the infrastructure, consulting such spatial

data add value in the decision making process.

Table 2: Percent distribution of infrastructure and rivers

Distance (km) Schools Power line Roads network Piped water River

500 24.5 81.0 80.9 48.7 55.4

1000 45.2 17.9 17.5 22.4 35.9

1500 17.0 1.1 1.6 11.6 8.3

2000 10.0 - - 6.6 0.3

2500 2.8 - - 5.4 -

3000 0.5 - - 2.9 -

Environmental Risk Analysis and Management Further, the spatial data analysis has invaluable benefits in the environmental risk analysis and

management. The study hypothesized an accidental release of chlorine gas from the nearby pulp

paper industry. The chlorine gas plume is a function of weather elements and it spread in the village

is from northeast to southwest direction (figure 4). The plume concentration was categorized into

three threat zones each with a wind direction confidence (table 3) (EPA and NOAA, 2007).

Table 3: Chlorine gas plume threat levels

Threat zone Percent

households

Population

Schools

Area (km2) Major crops

Red (over 20ppm) 1.6 1336 1 0.78 Sugarcane

Red wind direction confidence 5.5 2347 0 1.37 Sugarcane






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Orange (over 2ppm) 6.8 1825 1 2.18 Sugarcane

Orange wind direction confidence 21.4 4683 2 8.23 Sugarcane

Yellow (over 0.5ppm) 8.9 1258 3 3.82 Sugarcane

Yellow wind direction confidence 46.8 8403 7 22.65 Sugarcane

The results of chlorine gas plume spatial analysis were in relation to the number of households,

population, schools, area of land and the major crops at risk and the levels of threats. The threat

levels were three namely red (over 20 ppm), orange (over 2 ppm) and yellow (over 0.5 ppm) each

with wind direction confidence lines. The red threat zone affects 1.6% of the households with a

population of 1336 individuals, 1 (one) school and covers an area of 0.78 km2 with major crop

being sugarcane. This threat level requires more attention as the population, land and crops will be

exposed to the highest levels of chlorine where maximum negative impacts will be experienced.

The red wind direction confidence line takes care of wind uncertainties that can change direction

locally due to barriers such as hills and tall trees. The household percent population in this zone

was 5.5 with a population of 2347 covering an area of 1.37 km2. The yellow threat zone with the

least concentration of chlorine gas at less than 0.5 ppm covered the biggest area of 3.82 km2 with

a population of 1258 individuals. The area being in Kenya’s sugar belt zone, sugarcane is the major

crop grown and will be negatively impacted.

The availability of such information is valuable in the environmental risk analysis and management

processes. The identification and estimation of the affected households and population as shown

is key in planning for emergencies. The hospitals within the vicinity should be equipped to deal

identified risks and the capacity of affected populations by each threat level. Those in the red and

orange threat level zones should be given priority when it comes to medical attention. In case of

the need for evacuation, the authorities should also be in the know of the schools affected and

populations to be able to put up an effective reaction plan. Compensation for victims can be

estimated using the same spatial information. Since the population is under the different threat

levels is known, the total compensation for the victims can be easily calculated.

The generated data of this nature (table 3) can also be used to identify and quantify the size of land

affected and the type of crops damaged by the chlorine gas precipitation from the atmosphere. In

the study village the soils will require remedial actions such as application of base elements to

counter soil acidity. Once the change in soil characteristics are established, it is possible to quantify

the required amount of base elements using the size of land affect in each threat zone. This

information which can be generated within a short time is valuable to risk and environmental

management managers as they aid response plan to the emergency at hand as also concluded by

Tseng (2012).

The GIS potentials in Kenya have not been realized and establishment of village level godatabase

and its objective application has the capacity to overhaul decision making processes more so in

the County governments. The requirements for the decision making support system are a GIS






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platform, air pollution modeling software which are freely available for use and skilled person in

GIS.

Conclusion and Recommendations

Spatial planners and environmental risk and management planners are yet to realize the benefits

of using Geographic Information Systems for visualization of information and as an important

planning tool. The establishment and use of a geodatabase in combination air pollution modeling

software will be an invaluable resource for planning and responding air pollution scenarios. The

tools can also be used in impact analysis stage in the environmental impact assessment process

and guide on the expected scope of the impacts. However, there are issues with model validation

that needs to be addressed especially in hilly areas and when the wind velocity is very low. The

study has demonstrated usefulness of a village level goedatabse and recommends its establishment

by the county governments.

Reference

Chakraborty, J. and Armstrong, M. 1994. Estimating the Population Characteristics of Areas

Affected by Hazardous Materials Accidents. GIS- LIS. Pp. 154-163. Retrieved February

10, 2007 from EBSCO database.

Darbra, R.M., Demichela, M. and Murè, S., (2008). Preliminary risk assessment of ecotoxic

substances accidental releases in major risk installations through fuzzy logic. Process Saf.

Environ. Prot., 86, pp. 103-111.

Environmental Protection Agency and National Oceanic and Atmospheric Administration (2007)

ALOHA User’s Manual. Washington D.C. Seattle, WA.

Jakala, S. D. (2007). A GIS Enabled Air Dispersion Modeling Tool for Emergency Management.

Volume 9, Papers in Resource Analysis. 20pp. Saint Mary’s University of Minnesota

Central Services Press. Winona, MN. Retrieved on 4th September 2016 from

http:/www.gis.smumn.edu

Tseng, J.M., T.S. Su, C.Y. Kuo (2012) Consequence Evaluation of Toxic Chemical Releases by

ALOHA. International Symposium on Safety Science and Technology Procedia

Engineering 2012, Vol.45:384–389, doi:10.1016/j.proeng.2012.08.175,


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GIS Based Modeling to Assess Pollution Vulnerability to Groundwater in the Vicinity of Solid Waste Disposal Site:

A Case Study of Pugu Kinyamwezi Dumpsite in Dar Es Salaam, Tanzania



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GIS Based Modeling to Assess Pollution Vulnerability to Groundwater in the

Vicinity of Solid Waste Disposal Site: A Case Study of Pugu Kinyamwezi

Dumpsite in Dar Es Salaam, Tanzania

Mike Yedgo, Tanzania

Abstract The study uses GIS based modeling in assessing pollution vulnerability of groundwater in the vicinity of

solid waste disposal sites. Heavy Metal contamination of the groundwater inside and around the Pugu

Kinyamwezi dumpsite was assessed; pollution modeling using shallow water wells, and populations at risk

were assessed, as was future water demand. Water from about 12 shallow wells was taken for testing.

According to the WHO, the Maximum Contaminant Levels (MCL) for Copper, Lead, chromium Zinc and

Cadmium were 1.5, 0.01, 0.08, 3 and 0.003mg/L respectively.

Results obtained from the laboratory were used as a benchmark in finalizing the model. Pollution modeling

was carried out through the construction of a water table contour map. In this case, a shallow well was

measured in height using ropes during morning hours, instead of digging a shallow well to reach the point

of water seep. Therefore measurements were taken before any disturbance of the shallow well. In modeling

of spatial distribution of pollutants in the case study, a water table contour, which is useful in predicting

groundwater flow, was used ARCGIS 9.3 in developing water table contour. The output flow direction

indicated groundwater flows were east and south east of the case study area. Leachate

distribution/movement underground was successfully presented using ArchiCAD 15.

The model developed paves the way for effective action before conditions worsen. Continuous consumption

of shallow well water for drinking purposes results in waterborne related health problems and other

problems due to heavy metal contamination.

Heavy saturation of the soil in the disposal site would also increase transportation of groundwater

pollutants. Bacterial contribution, present in the moisture in the disposal site, would increase leachate

production that would be transported by the groundwater in the modeled site.


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Introduction Dar es Salaam city consists of three municipal authorities: Temeke, Ilala and Kinondoni. Each disposes of

waste at the Pugu Kinyamwezi dumping site. Observation shows that the equipment used includes truck

loaders, tractors and others.

In Dar es Salaam today, groundwater has become one source of water supply because the government is

unable to meet the ever increasing water demand. Thus, inhabitants have had to look for alternative

groundwater sources such as shallow wells and boreholes. The quality of these groundwater sources is

affected by the characteristics of the media through which the water passes on its way to the groundwater

zone of saturation; thus, the heavy metals discharged by industries, traffic, municipal wastes, hazardous

waste sites, as well as from fertilizers for agricultural purposes and accidental oil spillages from tankers,

can result in a steady rise in contamination of ground water (Igwilo et al., 2006).

Open dumping and non-engineered disposal sites in Pugu ward portray negative attitude for the population

in the area. Leachate development and seepage to groundwater is inevitable in the site, which experiences

average annual rainfall of 1300 mm. Moreover, groundwater movement is inevitable, thus actual

transportation of pollutants is also inevitable in the soil and groundwater. Emphasis should be placed on

raising awareness to prevent people from contracting illnesses associated with contaminated water.

The quality of water is affected by the quality of groundwater entering the system of water supply in the

borehole (Shwille, 2000). This is because the water table elevation is approximately the same as the gaining

borehole surface elevation; both elevations may be used to construct water table maps (contour) and to

predict groundwater flow direction.

Mapping and Site Characterizations of Existing Situation

Location Pugu Kinyamwezi dumpsite, located in Pugu ward, Ilala Municipality (see Figure 1), is the current main

dumping site for most solid wastes in Dar es Salaam city. The rapid population increase is influenced by

both natural causes and immigration (birth rates and net immigration rates respectively). Pugu ward has an

estimated population of 49,422, of which 24,159 are males and 25,263 are females. The Average Household

Size is 4.2, according to the national census of 2012.

Description of Pugu dumpsite Pugu ward experiences a modified type of equatorial climate. It is generally hot and humid throughout the

year, with an average temperature of 28oC. The hottest season is from October to March, while it is

relatively cool between May and August.

Currently, the Pugu dumpsite operates as an open dumpsite, receiving waste from different areas of Dar es

Salaam city, namely, Temeke, Ilala and Kinondoni municipalities, with extreme lack of: designed cells; full

leachate management; full landfill gas management; daily soil cover; a final soil cover and a compaction

process; a fence with a gate; daily record of volume, type, and source of waste; and a waste scavenging

plan.

The dumpsite is approximately 20 km from the city centre and lies at latitude 6° 51' 41" S and longitude

39° 07' 02" E. It covers an area of approximately 75 hectares. The site was previously used as a sand quarry

and then afterward turned into waste disposal site, which has been in operation since 2007 after the closure

of another dumpsite, namely Mtoni.( ERC, 2004).


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The site receives solid wastes containing both industrial, agricultural, domestic, commercial, institutional,

medical and other special wastes (yard wastes, batteries and electronic). Since the site indicates no control

of waste moisture content, waste decomposition in the site is inevitable (Valencia et al. 2009).

The temperature is around 23ºC. There are two rain seasons: the short rains from October to December and

the long rain season between March and May. The average annual rainfall is 1300 mm.

Water supply The source of water supply in Pugu ward is groundwater – shallow wells, deep wells (boreholes), and both

individual and government owned water wells. Fresh water from shallow wells observed in the case study

is used for drinking purposes and for other domestic use.

Social services Social services include education, safe and clean water, health (health centers both private and public owned

by individuals) and energy distribution (different sources of energy, such as electricity, kerosene, charcoal,

firewood, solar, etc.), transportation infrastructure such as main roads, railways, etc.

Existing land use patterns Land units in Pugu ward hamlets are characterized by weathered slopes and are well drained with

unconsolidated clay-bond sands. An occasional outcrop of raised coral limestone also occurs inthe area.

Furthermore the dumpsite is characterized by formation of leachate that seeps in the soil. This also plays a

significant role in altering the geographical condition of the surrounding area for residents of Pugu.

Economic Activities Trade and agriculture are the most important economic activities in the area. Trade is mainly limited to

small scale petty traders in the informal sectors of the ward’s population. Small shops and market stands

are a common sight and ensure the distribution of consumer goods to all the sub-villages in the area.

Hydrological conditions The uppermost water-bearing unit in the study area is the unconfined aquifer, which consists primarily of

unconsolidated materials. The unconfined aquifer is shallow in the south-western part with an average

thickness of 10 m and deep in the eastern part of the study area, with a thickness of up to 50 m. The lower

aquifer system is under semi-confined conditions in unconsolidated sediments. It is considered as a

Pleistocene to Recent deposit and has an average thickness of 100 m. The semi-confined aquifer overlays

the base of the groundwater reservoir, formed by an aquifer of a thickness of about 1000 m in the Mio-

Pliocene clay-bound sands. The figure below presents the features of the hydrological condition of Pugu

areas (Mjemah, 2012b).

The compacted layers of aquitards (clay, silt or rock) in the case study play significant roles in retarding

water flow underground; that is, they act as a barrier for groundwater - they form a body of material with

very low permeability. Aquitards separate aquifers and partially disconnect the flow of water underground.

The space is mainly dominated by a seasonal water table. During the dry season, some of the existing

shallow wells become dry, between 10 and 15 m deep. Deep water in the Pugu ward exists at not less than

80 m. The water at this depth is mainly dominated by saline and is used for domestic purposes such as


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drinking during seasons of water scarcity. Pugu ward satisfies its water-supply demands almost entirely

from the freshwater contained within the unconsolidated sediments of Pleistocene.

Solid waste existing practice Solid waste entrance in the site It has been estimated by the authorities that approximately 4,200 tons per day of solid waste were generated

in Dar es Salaam as per 2011. This represents a generation rate of 0.93 kg/cap/day based on a population

of 4.5 million (Robert, 2012). The authorities have also estimated that less than 40% of the total wastes

generated in the city are collected and disposed of in the Pugu dump site or otherwise recovered. The

remaining wastes (approximately 60%) are either dumped by the road side or into drainage canals,

contributing to health problems for local residents, annual flooding events and methane generation (Robert,

2012).

Waste category compositions Waste composition entering the dump site contains food waste, paper, textiles, plastic, grass/wood, metal,

glass and other materials. This indicates accumulation of solid waste in the site that ensures high generation

of various parameters (heavy metal, physical-chemical and biological).

Water usage The residents near the disposal site use groundwater, both shallow and deep well. They also use seasonal

water for their domestic use during periods of rainfall. With extreme development and the increase in

population, the need for water is growing, and in the future the heavy deterioration of groundwater quality

may be high. Excessive groundwater withdrawal has the severe effect of lowering the water table in some

well fields. As a result, contamination via water pumping, especially on this site, is inevitable, as well

difficult to detect. This can be done only through monitoring of excessive pumping of the nearby well.

Dumping sites for municipal waste often produce leachate that migrates to adjacent areas, resulting in gross

pollution of soil, surface water and groundwater. The leachate may contain matter that is resistant to

biological or chemical changes, and that therefore remains in the soil for many years. This study aims at

mapping the site characterization, assessing available water quality, both in shallow wells and boreholes,

developing and modeling spatial pollution distribution at the site, and an assessment of risk exposure due

to use of the water well in the modeled site, as well as general water demand.


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Figure: 1 Pugu dumpsite location

Material and Method This section provides information on materials, tools and methods used, including a literature review,

laboratory analysis, physical observation of the case study area, a questionnaire, consultation, and process

based computer GIS software version 9.3 and ArchiCAD 15 based software, in developing a model of

underground leachate movement. Materials used include: plastic bottle (1 litre), nitric acid ropes, AAS tape

measure.

Samples were taken from selected water wells, samples for cations analysis were acidified with nitric acid

to around pH=1.5. Prior to the sampling process and transportation the sample was preserved using Nitric

acid in a sample digestion process. 1 ml of Nitric acid was added to each 1-litre bottle of sample water, and

the bottle was finally stored in a freezing container at 4°C. The samples were analyzed within 4 hours of

sampling. No filtration was done because the samples were assumed not to interfere with the AAS i.e. any

presence of organic impurities was disregarded.

A shallow well near the dumpsite was chosen in the development of the model, based on the nature of

geological and hydrological conditions of the area.

Result and Discussions


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Table 1 below depicted the results.

Sampling location Parameter concentration (mg/L) n=3 (AAS)

Cu Pb Cr Zn Cd

WW-1 0.00817 0.98067 0.16033 0.238 0.0115

WW-2 0.01333 0.201 0.11533 0.06867 0.02333

WW-3 0.021 1.10433 bal 0.05767 0.03767

WW-4 0.01067 0.92567 0.022 0.08267 0.02733

WW-5 0.02667 Bdl bdl 0.15667 0.02667

WW-6 0.0105 0.24933 bdl 0.52333 0.02717

WW-7 0.011267 0.162 1.131333 0.151333 0.0346

WW-8 0.0024 0.572 0.3523 0.0583 0.534

WW-9 0.0079 Bdl 0.0641 0.0701 bdl

WW -10 bdl 0.084 0.256 0.13 0.068

WW-11 bdl 0.021 bdl 0.004 0.002

WW-12 0.02 0.255 0.393 bdl 0.051

TBS 2 0.2 1 5 0.013

WHO 1.5 0.05 0.08 3 0.05

Pugu Dump Site Pollution Modeling, Risk Analysis and Water Demand

Pollution modeling Modeling is the process of producing a model; a model is a representation of the construction and working

of some system of interest. A model is similar to but simpler than the system it represents, and is useful in

prediction of the effect of changes to the system.

The aim of this model is to investigate the effectiveness of the groundwater in carrying pollutants from the

disposal site to inhabitant areas. Modeling the spatial distribution of the pollutant in the regional area of the

case study has significant impact on decision making for present and future generations. Leachate that

contains different toxic parameters such as heavy metal, volatile organic compounds and others has

significant impact on deterioration of groundwater. These parameters are potentially generated at the

disposal site.

Groundwater flow predictions Water contours, which predict the flow of groundwater, have been used in this research to show the

groundwater flow pattern; using water well data from a survey of 50 water wells within the case study areas,

a water table contour map was constructed. The water table level was assumed to be the same as that of the

water well top level. The water table elevation in the case study area is well described in the map, and this

marks the point that plume dispersion of the pollutant may be back and forth in movement. Thus, it can be

concluded that a water table range of 8.36 – 9.45 ft and 7.2 – 8.36 ft may have higher vulnerability to

pollutants from the disposal site.


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Modeling analysis and assumptions A large portion of the areas surrounding the disposal site was found to be dominated by sand soil. Different

studies indicate that the average groundwater flow in sand soil is 0.24384 to 0.70104 m/day. Based on

analysis performed on heavy metal in the case study, with reference to the operational phase of the disposal

site (2007- 2014), maximum concentration of the water well in the sampling point was a result of 7 years.

Modeling of pollutants for the next seven years (2014-2021) was important to knowing the safeness of the

water for the future generation. Future distance dispersion was computed using an average flow velocity of

0.24384 m/day for groundwater in sand soil.

The estimated rate of ground-water flow through the sand and gravel is 0.8 to 2.3 ft/d. This estimate was

obtained from Darcy’s law (LeBlanc and Guswa, 1977)

V= K(dh/dl) / n

Where

v = average velocity, K = hydraulic conductivity, dh/dl = hydraulic gradient (change in water-table altitude

with distance), and n = effective porosity

Using the values of hydraulic conductivity and the water-table slope given above, and assuming an effective

porosity of 0.20 to 0.40 for sand and gravel,

V= (200 to 300 ft/d)(8 ft/5280 ft) = 0.8 to 2 .3 ft/d .

0.20 to 0.40

The average velocity of groundwater in the fine to very fine sand and silt, and the sandy till is lower

than the velocity in the sand and gravel because the hydraulic conductivity of the fine-grained sediments

is much lower than the hydraulic conductivity of the sand and gravel.

Modeling development, assumption and Output Under certain environmental conditions, disposal sites are susceptible to pollutant dispersion in many ways,

as the result of extreme continuous decomposition of solid waste. At the Pugu Kinyamwezi disposal site,

which is an open and uncontrolled dumping system, different receptors such as air, surface water, soil and

groundwater are exoposed to various output products. Interaction with both surface water and ground water

results in deterioration of groundwater quality, in hand with water exploitation through water pumping.

Modeling made different factors constant in order to successfully display pollutant migration in the

groundwater in the sand soil, which was found to be the most significant in posing environmental problems.

An estimation of pollutant migration from Pugu dumpsite towards residential areas was developed under

the following assumptions

Initial concentration of pollutants in the case study before disposal of solid waste disposal site was

assumed to be below detection limit

Only the advection process will take place in this model (groundwater will be moving relative with the

pollutant from the disposal site), and other factors are minor

Laboratory analysis performed from various sampling points in the case study was used as the initial

concentration for developing model table 4.1

Water and pollutant movement in the subsurface behave with the same properties of flow, with an

average velocity of 0.24384 m/day for sand soil, which is the predominant type in the case study.

The existence of a very low steep slope, with relative uniform structure in the region, comprised of sand

soil, while the layer beneath the sand soil was assumed to contain material of aquitard which does not

easily allow water flow. Thus down seepage of leachate in the area was disregarded.


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The assumed control point in the disposal site was used as the reference control point. Flow velocity

was used to compute the distance from the disposal site, to identify the affected residential areas.

The shallow well was given more weight in developing the model.

Darcy's law on the average flow velocity of groundwater is 0.8 -2.3 ft/day for sand and gravel soil.

Output of the model Without any engineered and environmental scientific application in the Pugu disposal site, contamination

will increase to a very large extent. A large portion of the population will be exposed to heavy metal

contaminants from the disposal site.

Seven years of Pugu dumpsite operation could contain a maximum pollution concentration that can move

underground, polluting other wells.

Heavy metal, which is invisible, was assumed to move with the water and be transferred to various areas

outside the disposal site. Using ArchCAD version 15 as a powerful tool, the modeled output was designed..

Analysis of the shallow well, with its distance from the assumed disposal site, has been shown clearly in

figure 2 and 3.


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Figure: 2 Contour and water flow descriptions in the case study


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Figure 3: Water well used as benchmark for model development

Figures 4 and 5 below harness the existing situation of figure 3 above, presented with ArchiCAD graphic

software. Figure 4 is an indication or output of seven years of dumpsite operation. Using initial output in

the first model, 7 years of projection, using average flow distance, indicated that the flow of groundwater

contaminated with the pollutant from disposal site in sand soil will move a distance of 695.5 m from all

initial sampled points used as initial values in developing this model. Figure 6.5 indicates the extension of

the initial model forecasted to 7 years using Darcy's flow formula. The distance deduced has been used in


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a map to verify the most affected areas in figure 3, such as Majohe ward and Kinyamwezi suburb.

Figure: 4 Lead projection distance of contaminated well for seven years of dumpsite operations

Figure: 5 Modeled lead concentration projection for 7years

Sensitivity of the model Continuous consumption of shallow well water for drinking purposes will result in health problems such as

waterborne diseases, and others, where there is no boiling or other form of pre-treatment for heavy metals.

Heavy saturation of the soil in the disposal site would also yield higher values of groundwater pollutant

transportation. Bacterial contribution as the presence of moisture in the disposal site would increase leachate

production that will eventually be transported by groundwater in the modeled site.


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Growth rates in the development of new pollutants will affect the groundwater quality. The model did not

take this into account, but the distribution of pollutants in the future will resemble the heavy metal

concentration obtained with 7 years of operation.

It assumed that the modeled site will be affected by the same pollution towards the end of a projected point

of10 years.

Conclusion and Recommendations

Conclusion A GIS based simulation model was successfully designed through characterization and mapping of the

existing situation, analysis of groundwater samples. GIS simulation modeling through water table contour

mapping played a greater role in prediction of groundwater flow, hence successfully showing the flow

direction of pollutants from the disposal site to different existing residential habitats. Mapping and

characterization of the existing situation, through field surveys, observation and other methods, showed that

the dug wells are contained in unconfined aquifers, which are mainly sand soil. During the rain period the

water table rises. On the other hand, the wells are more vulnerable to groundwater contamination since the

nature of the soil is sand soil, which is easily saturated, hence allowing for easy movement of water

contaminants. The water table contour map revealed that the direction of groundwater flow was toward the

eastern and south eastern part of the dump site. Our analysis indicated that the people of Majohe,

Kinyamwezi, in the eastern part, are susceptible and vulnerable to heavy metal pollutants from the disposal

site.

Recommendations Disposal practice in Pugu Kinyamwezi does not reflect a scientific well planned sanitary landfill. It is

therefore recommended that dumpsites should be modified in order to accommodate the desirable standard

for better management of solid waste. Based on the flow pattern of the aquifer system in the case study,

groundwater contamination in resident water wells is inevitable, hence leading to human exposure to heavy

metal as the result of continuous use of groundwater for domestic purposes.

Based on the findings and analysis of the results presented in this paper, and the model developed, it is

concluded that water from water sources around the eastern side of the dumpsite is not safe for drinking.

Thus it is recommended that dumpsites should be modified in order to accommodate the desirable standard

for better management of solid waste.

Transforming disposal i.e. well-designed landfills with provision of capping, lining; effective

arrangement of the site for evaporation and leachate treatment could reduce pollutant

migration/movement from the disposal site via groundwater.

Capping of the site and gas capturing from the disposal site could also reduce volatile organic and

inorganic components, some of which are suspected to cause cancer in case of inhalation. Dermal

contact of solid waste and distribution of heavy metal would be minimized at large scale.

Reference Adelekan B.A.; (2010), International Journal of Water Resources and Environmental Engineering, 2 (6):

137-147.

Adepoju-Bello, A.A. and O.M. Alabi, 2005. Heavy metals: A review. The Nig. J. Pharm., 37: 41-45.


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Bakare-Odunola M. T., (2005) Determination of some metallic impurities present in soft drinks marketed

in Nigeria. Nig. J. Of Pharm. Res.4 (1) 51-54.

Berti, W. R., and D. Jacobs, 1998. Distribution of trace elements in soil from repeated sewage sludge

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Francis Kamau Muthoni

Comparing two geospatial approaches for delineating crop ecologies in Tanzania



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Comparing two geospatial approaches for delineating crop ecologies in

Tanzania

Francis Kamau, MUTHONI, Tanzania

International Institute of Tropical Agriculture (IITA)

Key words: Arcgisbinding package, Extrapolation detection tool, Improved varieties, Fertilizers,

Spatial targeting

Abstract

Mapping distribution of suitable environmental conditions for agronomic technologies guides

spatial targeting to enhance adoption. This paper compares a bottom-up and a top-down geospatial

approach for delineating suitable ecologies for a technology package comprising of improved

maize varieties and fertilizers. Bioclimatic variables for the Feed the Future zone in Tanzania were

utilised. Maize yields data from trial sites were used to identify the variety and fertilizer treatment

with the best performance. For top-down approach, GIS overlay operations were conducted to

delineate suitable zone for best performing technology. For bottom-up approach, the extrapolation

detection tool was used to generate maps on two types of dissimilarities between the bioclimatic

conditions at reference site and outlying projection domain and a map of the most limiting variable.

SC719 maize variety grown with YaramilaCereal and Sulfan fertilizers was the best performing

treatment in trials. The top-down approach delineated 15% of FtF zone as suitable. The bottom-up

approach revealed the magnitude of deviation from univariate range and novel combinations of

environmental covariates between the reference site and the projection domain. Precipitation was

most limiting factor. Although the top-down approach is more insightful, its applicability in Africa

is limited by sparse crop trial data.






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Introduction Food insecurity is a prevalent problem in sub-Sahara Africa and the situation is worsened by

increasing population. Adoption of improved crop varieties that are high yielding and tolerant to

drought, pests and diseases is one panacea to increase food production. These varieties are

disseminated together with related good agronomic practices (GAPs) as technology packages to

increase yields and conservation of natural resource capital [1]. The potential impact and the rates

of adoption of these agronomic practices can be accentuated if they are disseminated in their

suitable biophysical environments [2]. To a large extent the (dis)similarity in environmental

conditions is a proxy for differences in crop suitability [3]. Therefore delineation of zones with

similar environments will guide spatial targeting of agronomic technologies to areas with the

highest potential. Geographical Information Systems (GIS) and remote sensing tools are used to

delineate suitability indices at landscape, regional and global scales [4].

This paper compares the utility of a top-down and bottom-up geospatial approaches for mapping

suitability of integrated agronomic technologies in the Feed the Future (FtF) zone in Tanzania. The

paper evaluates the suitability of improved maize varieties treated with different fertilizers. Input

variables include selected gridded biophysical layers that are known to limit crop growth and

efficiency of inorganic fertilizers. Results demonstrate the differences between the two approaches

followed by a discussion on advantages and the context in which each method is most suitable.

Methods

Study area

The study area covers the FtF zone in Tanzania (Figure 1). Figure 2 summarizes the implemented

workflow for comparing a top-down and bottom-up approaches for delineating suitable zones for

scaling integrated agronomic practices comprising of improved maize varieties and inorganic

fertilizers. The environmental layers used in both methods are shown in table 2.






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Figure 1. The location of demo sites where improved maize varieties and application of

inorganic fertilizers was evaluated in Mbozi District, Mbeya region in Tanzania. The inset

map to the right show the extent of FtF zone that used as the projection domain.

Statistical analysis

Study area and the data The study was conducted in the Feed the Future (FtF) zone that covers 594,282 Km2 area (31.9oE,

-3.4oS; 38.5oE, -10.6oS). The study area is within five administrative regions in Tanzania (Figure

1). The FtF zone is used as the projection domain since the aim is to extrapolate the successful

agronomic technology packages to suitable biophysical environments within this zone.

Demonstration plots for improved maize varieties and application of fertilizers were implemented

during the 2016 growing season in 16 farms located in four administrative wards in Mbozi Distirct,

Mbeya region (Figure 1). Although the sites were primarily intended to demonstrate the best-bet

technologies to farmers, they are hereafter referred to as trial sites since different treatments

combining maize varieties and inorganic fertilizers were evaluated. A polygon of four wards (783

Km2) where trials were conducted was used as the reference site.

Three bioclimatic grid layers (Table 2) with 1 Km resolution were obtained from the Worldclim

database [5]. The elevation layer in metres above sea level was obtained from 30m resolution

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital






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Elevation Model Version 2 [GDEM-V2; 6]. The elevation layer was resampled to 1 Km resolution.

The four layers (Table 2) were selected based on variety release reports and expert knowledge of

maize breeders that pointed to their relevance in determining the suitability of maize varieties.

Table 1. Characteristics of maize varieties and fertilizers treatments evaluated in plot trials

in Mbozi District.

Variety

Attainable

Grain

yield (t/ha)

Optimal biophysical range

Fertilizer type

Treatment

ID

Altitud

e (a.s.l.)

Rainfal

l (mm)

Maturit

y (days)

HB 614 7 >1500

800-

1500 180-190

DAP+ Urea Ver1Fer1

YaramilaCereal +

Sulfan Ver1Fer2

MERU

513 11

800-

1200

700-

1500 100-110

DAP+ Urea Ver2Fer1

YaramilaCereal +

Sulfan Ver2Fer2

PAN

691 7 >1500

800-

1500 103

DAP+ Urea Ver3Fer1

YaramilaCereal +

Sulfan Ver3Fer2

SC 719 4.5-5.0

800-

1500

800-

1200 145-153

DAP+ Urea Ver4Fer1

YaramilaCereal +

Sulfan Ver4Fer2

UH 615 8.0-9.0

1200-

1800

800-

1200 85-92

DAP+ Urea Ver5Fer1

YaramilaCereal +

Sulfan Ver5Fer2

UH

6303 9.0-10.0

1200-

1800

800-

1500 92

DAP+ Urea Ver6Fer1

YaramilaCereal +

Sulfan Ver6Fer2

Table 2. The geospatial layers used in analysis. All grid layers were resampled to 1Km

resolution. The letter z in bracket was used to distinguish the layers for the reference area in

Mbozi District from that of entire FtF zone.

Code Variable name Resolution Source

DEM(z)ftf Elevation 1 Km ASTER DEM [6]

pptcv(z)ftf Precipitation seasonality

(coefficient of variation)

1 Km Bioclim [5]

ppt(z)ftf Annual precipitation 1 Km “

Tempcv(z)ftf Temperature seasonality 1 Km “






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Data exploration and analysis Analysis was conducted using Arcmap 10.4.1 and R for statistical computing [7]. Processing and

analysis interchanged between Arcmap 10.4.1 and R particularly using the newly released

‘arcgisbinding’ package [8]. Other R packages utilized intensively includes the ‘raster’ [9] and

‘BiodiversityR’ packages [10]. Boxplot plots for maize yields recorded in different treatments

were generated using ‘BiodiversityR’ and utilised to select the best performing treatment.

For top-bottom approach, the DEM and precipitation layers were used to run GIS overlay

operations to delineate suitable zones based on expert knowledge on optimal limits of the candidate

technologies (Figure 2). The two layers for seasonality could not be used since it was not possible

to obtain information on thresholds for the candidate maize variety. The suitable area for the best

performing maize variety was delineated from each grid layer based on optimal thresholds for

growth of candidate maize varieties (Table 1). The resulting suitability maps based on individual

layers were intersected to generate the final binary map on suitability map for candindate variety.

For bottom-up approach, the extrapolation detection (ExeDet) tool [11] was used to calculate an

environmental dissimilarity surface between the reference site and the projection domain (FtF

zone). ExeDet is multivariate statistical tool that use Mahalanobis distance to measure the

dissimilarity between a reference site and a projection domain by accounting for both the deviation

from the mean and the correlation between variables. The projection domain represents the search

region that is targeted for extrapolation of candidate technology package. The method return maps

on two sources of dissimilarity (novelty); the novel univariate range (NT1) and the novel

combinations of covariates (NT2). NT1 map shows the magnitude at which the environmental

conditions at any particular location in the projection domains fall outside the range of values

observed in the reference sites [11]. NT1 ranges from zero to an infinite negative value with zero

indicating no extrapolation beyond the univariate coverage of reference data. The lower the value

is from zero the more the environmental dissimilarity of a location compared to the conditions in

the reference site.

The NT2 map identifies locations where individual covariates are within the ranges observed in

the reference site but the combination of observed values of covariates are different (differing

correlation). The value of NT2 range from zero up-to an infinite positive value. Values ranging

from 0 to 1 indicate similarity in terms of both univariate range and multivariate combination, with

values closer to zero being more similar [11]. Locations within this range (0 to 1) is very similar

to reference sites and therefore is the most suitable for scaling the target technology.Values larger

than one are indicative of novel combinations of environmental variables. Moreover the Exedet

tool [11] generates a map of the most influential covariate (MIC), showing the environmental

variable that is most limiting the suitability of a technology in every pixel the projection domain.

The MIC is determined by considering both NT1 and NT2. The MIC map identifies where any

particular covariate has the most extreme univariate ranges (NT1) or its highest contribution to the

largest correlation distortion (NT2).

Although there is a standalone ExDet tool [11], for this analysis we recoded the ExeDet algorithm

in R and plotted the NT1 and NT2 maps using BiodiversityR package. Two sets of input grid layers






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(Table 2) were clipped to the extent of the reference site and the projection domain. The ExeDet

calculated the dissimilarity between environmental conditions in the reference site (area of 4 wards

in Mbozi District) and the projection domain that encompass the entire FtF zone (Figure 1). The

returned NT1 map showed a gradient of increasing dissimilarity from range of values of individual

covariates that were observed in the reference sites. Sites with the lowest dissimilarity to the

reference sites (less negative values) are designated as potentially suitable for scaling the maize

variety and fertilizer technology package that performed well in the reference site. The MIC maps

identify the biophysical variables that induce the highest limit to suitability of particular

technology package in different locations of the projection domain.

Figure 2. Flow chart summarizing the workflow for comparing the top-down and bottom-up

geospatial approaches for identifying extrapolation domains for scaling maize varieties and

fertilizers.

Results

Selecting the best performing integrated technology

Results from trial experiment revealed that treatment Ver4Fer1 that comprise of SC719 improved

maize variety and application of Yaramila-Cereal and Sulfan fertilizers had the highest grain yields

(6.2 t/ha; Figure 3). Therefore this was selected as the best-bet technology package in the trials

conducted in Mbozi District. The bottom-up and top-down geospatial approaches were explored

for identification of suitable locations for extrapolating this best-bet technology package in the

entire FtF zone in Tanzania (sections 3.2-3.3).






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Figure 3. The grain yields recorded from the experimental treatments with 6 different improved

maize varieties and 2 fertilizers in Mbozi district. Varieties treated with fertilizer 2 had higher

grain yields except for variety 6 (UH 6303). Treatment Ver4Fer1 recorded the highest yield (6.2

t/ha).

Top-down approach

Results obtained after GIS overlay operations revealed that an area covering 156,817 Km2 in the

Ftf zone had suitable altitudinal range for growth of SC719 maize variety (Figure 4). However

after intersecting with the area with suitable precipitation range, only 89,782 Km2 (15% of FtF

zone) was finally earmarked as suitable (Figure 4).






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Figure 4. Biophysical suitability of technology package comprising SC719 maize variety and

Yaramila-Cereal and Sulfan fertilizers in the FtF zone in Tanzania derived by overlaying

the optimal elevation and precipitation range. The thresholds for optimal range are indicated

in table 1.

Bottom-up approach

A comparison of grid layers representing the reference site and projection domain revealed that

the later had higher variance compared to the former (Figure 5). The NT1 Map revealed the

gradients of dissimilarity in univatiate values falling outside the range observed in reference data

(Figure 6). The dissimilarity ranges from 0 to -12 (increasing dissimilarity gradient from purple to

red tone). Values close to zero are relatively similar to the reference site and therefore interpreted

as suitable to scaling-out the technology package comprising of SC719 maize variety with

application of Yaramila-Cereal and Sulfan fertilizers. The section with value -12 is extremely

dissimilar and therefore highly unsuitable for the candidate technology.






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The NT2 map ((Figure 6) reveals the magnitude of occurence of novel combinations of covariates.

Locations with values ranging 0-1 are the most similar to the reference sites and should be the first

priority in scaling-out the best-bet technology. The MIC map reveals that annual precipitation was

the most limiting factor in the largest area (Western) followed by precititation seasonality (Eastern)

and temperature seasonality (South-East) (Figure 7).

Figure 5. Boxplots reflecting the variation of environmental conditions for (a) the reference

site in Mbozi District and (b) the projection domains covering the entire FtF zone. The range

of values of biophysical variables observed in the reference site are a narrower subset of

values in projection domain. The elevation (demftf) of the reference site ranged from 1000 -

1700 but in the projection domain it ranged between 100-3000 m A.S.L.






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Figure 6. Map for NT1 and NT2 novelties generated by the ExeDet tool from the 4 gridded

biophysical layers. NT1 represents the magnitude by which values of individual covariates

fall outside the range observed in the reference site. The NT1 values range from zero to -12.

The reference site is the white polygon at the extreme South West in the NT1 map. Region

immediately surrounding the reference site have NT1 values less than 1, therefore they are

more similar to the reference site. Negative NT1 values reflect the degree of environmental

dissimilarity between the reference site and the projection domain, hence the decreasing

suitability of candidate technology. The NT2 map shows the degree at which the

environmental conditions in the projection domain exhibit novel combinations of covariates.

NT2 ranges from zero up to infinite positive values. Values ranging from 0 to 1 indicate






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similarity in terms of both univariate range and multivariate combination and therefore this

is the most suitable locations for scaling candidate technology. Values larger than one are

indicative of novel combinations.

Figure 7. Map of the most important covariate (MIC) in determining the suitability of SC-

719 maize variety and fertilizer treatment in the FtF zone in Tanzania. MIC is the covariate

that is most limiting the suitability of candidate technologies. The suitability of SC719 maize

variety with application of Yaramila-Cereal and Sulfan fertilizers in in Eastern, Western

and Southern section of the FtF zone was largely influenced by precipitation seasonality

(pptcvzftf), annual precipitation (pptzftf) and temperature seasonality (tempcvzftf)

respectively. The area shaded blue (labelled 'none' in the legend), is the reference site and

therefore does not have any covariate with values outside the range nor any non-analogous

combination of covariates. The codes for variables are listed in Table 2.

Discussion

Differences in approaches

This paper compares two geospatial approaches for delianating suitability maps for integrated

agronomic technology package comprising of improved maize varieties and inorganic fertilizers.

Results revealed that the utility of the two approaches is dependent on the context in which they

are applied. The top-down method utilizes gridded geospatial layers on biophysical environment

to delineate suitability maps based on known biophysical range of optimal performance of

particular technology package. This approach is most suited for locations where crop trial data is

sparse but with considerably high expert knowledge on biophysical requirements of candidate

technologies. Although the top-down approach is widely used [3, 12, 13], it is overly simplistic

since it evaluates a single variable at a time without factoring the possibility of unique






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combinations of environmental variables across sites that may significantly affect suitability of a

particular crop variety [11].

The bottom-up approach is suitable for localities with long-term crop trials data. Information

obtained from long-term trials are utilized to identify the combinations of agronomic technologies

with the best results based on selected attributes such as crop yields or stover biomass. The long-

term trials increase confidence in identification of agronomic technologies with the best

perfomance at particular locations. These locations are consequently used as reference sites when

deriving the extrapolation domains. Results obtained from the bottom-up approach is more

intuitive beacause the ExeDet tool utilise all the the input environmental layers to generate the

dissimilaity gradients compared to the GIS overlays that use each layer at a time. The NT2

dissimilarity is generated by considering unique combinations of values rather than a binary

approach in the top-down approach that only check if the values are within the optimal range or

not. Some locations delianated as suitable by the top-down method solely because values of

individual covariates layers were within the suitable range for particular technology can be

earmarked to be unsuitable if the combinations of the environmental variables are different.

Relevance

The two approaches facilitate identification of suitability gradients of integrated agronomic

technology packages that guide extension agencies and development programs when selecting

sites for scaling-out. The higher variance of environmental conditions in the projection domain

compared to the search domain highlights the fact that scaling of agronomic technologies has

significant uncertainty. This emanated from extrapolation beyond the environmental space

observed in trial sites since performance of varieties in the novel conditions in unknown. The

results generated by ExeDet tool showing the magnitude of deviation from the univariate range of

environment in the trial sites would assist extension agents in reducing risks associated with the

failure of a technology when introduced in unsuitable environments. Moreover the map showing

the spatial distribution of the most limiting factor for particular technology across the projection

domain would be useful for extension agencies when recommending remedial solutions for

increasing yields at different locations. For example, irrigation schemes could be recommended in

locations where low precipitation is the most limiting factor. The suitability maps are also

important to crop breeder’s interested in establishing multi-locational trials to develop cultivars

with specific environmental adaptation [4].

Limitations

In this study, the trials to identify the best performing technology package were conducted in one

growing season. The confidence in the performance of particular technology could be enhanced

by using long-term experimental trials, expert knowledge on performance of candidate agronomic

technologies in different agro-ecologies and simulation models. Moreover only biophysical

variables are included in the analysis although suitability of crop varieties is influenced by variety

of factors such access to market and consumer preferences. Incorporating these socio-economic

variables in the analysis is limited by availability of reliable spatial data at appropriate scale.






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253-267.

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Sustainability, 2015: p. 1-17.

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environments. Frontiers in Physiology, 2013. 4.

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Journal of Climatology, 2005. 25(15): p. 1965-1978.

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29/10]; Available from: http://www.jspacesystems.or.jp/ersdac/GDEM/E/4.html.

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10/10]; Available from: https://CRAN.R-project.org/package=raster.

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ecological and biodiversity studies. Vol. ISBN 92-9059-179-X. 2005, Nairobi: World Agroforestry

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Distributions, 2014. 20(10): p. 1147-1159.

Notenbaert, A., et al., Identifying recommendation domains for targeting dual-purpose maize-based

interventions in crop-livestock systems in East Africa. Land Use Policy, 2013. 30(1): p. 834-846.

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Contacts

Dr. Francis Kamau Muthoni

Postdoctoral Fellow – GIS Specialist - Africa RISING Project

International Institute of Tropical Agriculture (IITA)

c/o, The World Vegetable Center, P.O. Box 10, Duluti,

Arusha, TANZANIA

Tel: +255 272 553051 | Mobile no: +255 785252 986

Email: [email protected]

www.iita.org or http://africa-rising.wikispaces.com/

http://www.jspacesystems.or.jp/ersdac/GDEM/E/4.html

https://www.r-project.org/

https://cran.r-project.org/package=raster


https://owa.iita.org/OWA/redir.aspx?C=1YmIVYWAfEmhfOPFOJDyZpg8CJmbeNMIoOlGhhrPu7a2azQQy76xu2ZMbY8dnbozV5DtQ8jJzBY.&URL=http%3a%2f%2fwww.iita.org

http://africa-rising.wikispaces.com/


Mohammed Abdulkadir and Amina Yusuf

Analysis of Urban Growth and Agricultural Land Use of Kuta in Shiroro L.G.A, Niger State, North-Central Nigeria



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Analysis of Urban Growth and Agricultural Land Use of Kuta in Shiroro

L.G.A, Niger State, North-Central Nigeria


Department of Geography and Faculty of Natural Sciences,Ibrahim Badamasi Babangida

University, Lapai, Niger State, Nigeria.

KEY WORDS: Urban Growth, Land use/ change and Agricultural

Abstract The study explores the urban growth analysis on agricultural landuse in Kuta and its environs. The

objectives of the research is to examine the implication of urban growth encroachment on

agricultural land, between 1990, 2001 and 2013 respectively. Land-sat imageries of Kuta and its

environs for 1990, 2001 and 2013 were acquired, processed and classified using GIS techniques.

The methods adopted in this research were maximum likelihood classification and area calculation

in hectares for the various land use/land cover for each study year. The result shows that, in the

year 1990 the built-up area was 1481.662 hectares and farmland was 36165.98 hectares, this

indicates that urban area was very small and agricultural activity was at the peak in 1990. In the

year 2001, the built-up area increased to 2584.641 hectares and agricultural land decreased to

20323.35 hectares. This indicates that urban growth was gradually taking place at the expense of

agricultural land. In the year 2013, built-up area increased to 10074.373 hectares and agricultural

land decreased to 16530.98 hectares. This shows that there was a significant change from 2001 to

2013 as urban area grows four times of its size, diminishing agricultural land. Result also shows

that between 1990 and 2013, the rate of urban growth encroachment on agricultural land was

9.61%. It was found out that urban growth has more negative effects than positive effects on

agricultural land. It was recommended that Government by way of policy should be strict in

preserving farmland from illegal occupation, in order to reduce the monitor reduction of farmlands

by human activities in Kuta and its environs, Shiroro Local Government of Niger State, Nigeria.






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Introduction

Urban growth had become a global phenomenon affecting all countries of the world, rich or poor.

This phenomenon became a challenge to most countries of the world, especially developing ones.

The high rates of population increases and consequently, the depletion of farmlands around rural,

and urban areas. In recent years, understanding the dynamics of urban growth, quantifying them

and subsequently predicting the same for a future period has attracted significant interest of

research. Growth of urban centers consumes farmland. This has resulted in low food productivity

(Groot et al. 2002, Curran and de Sherbinin 2004)

Nigeria being a developing nation has most of it population dwelling in rural areas, this is because

they mainly engage in primary activities, and their dependences much on the natural environment

for their livelihood. Among these primary activities, is agriculture, which has being the most vital

and predominant activity. The rapid increase in population has placed great demands on the

available living space. As the population increases, there is need to provide residence and

infrastructural facilities (i.e. road, water supply, electricity, sewerage and drainage) for the entire

populace, leading to the conversion of agricultural land to build-up areas at an incredible rate and

decreasing the size of lands for agriculture (Nuissl et al. (2008).

Urban growth has been criticized for eliminating agricultural lands, spoiling water quality, and

causing air pollution (Allen and Lu, 2003). As population increases, so does the need for new

housing, schools, transportation and other civic amenities increases at the expense of agricultural

land (Wilson et al 2003).

The technologies of Geographical Information Systems (GIS) and Remote Sensing have been

combined to detect changes in urban growth and project the rate of urban encroachment in a way

which is easier and faster than the traditional methods of surveying the urban environment. In this

study, 1990, 2001and 2013years period land-use changes in Kuta and its environs were examined.

To access the effects of urban growth on agricultural land in Kuta town and its environs and

analyze change detection on Farmland using multi-temporal Remote Sensing data and GIS based

techniques, to identify Land use land cover (LULC. The main LULC types identified were the bare

ground, built-up, water body, vegetation and farmland.

Study Area

Kuta is the Headquarter of Shiroro Local Government of Niger State. The study area lies between

latitude 9°50̍25̎and 10°04̍18̎ North and longitude 6°51̍16̎ and 6°82̍82̎ East, on a geological base of

undifferentiated basement complex of mainly gneiss and magnetite. It is bounded by Gurmana and

Erena to the north, Zumba (Shiroro hydro-electric power station) to the northeast, Shata to the

southwest and Gijuwa to the west respectively.

It is located in a tropical climatic zone, experiencing distinct wet and dry season with annual

rainfall varying from between 110mm and 1600mm. The rainfall characteristics of the study area

are from March to Octobe. In April, rainfall is at 70mm or more, covering the central part of the






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local government, and the peak value is about 280-300mm from July to October. It determines the

weather condition of the area, encourages agriculture due to its influence on vegetation.

The study area has an average temperature of 13.88°c (67°f), while it is about 34.44°c (94°f)

between March and June.

The topography is highly undulating and varied in height. Isolated hills of over 600m above sea

level are common, while the valley in-between can get as lower as 500m above sea level. The

natural vegetation is guinea savanna type, characterized with tall grasses and scattered trees. The

grasses are between 1.5 to 3.5m height, the trees are short, bold broad leaves of up to 16.5m in

height, riparian and gallery forest are predominantly along the river valley. The soils are derived

from the Precambrian basement complex rocks, comprising of granite, gneiss and amphiboles.

Figure1.1: Nigeria showing Niger State Source: Researcher’s work






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Figure 1.2: Niger State showing Shiroro LGA Source: Niger state Ministry of Land and Survey






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Figure 1.3: Land Use Map of Kuta and its environs Source: Niger State Urban and Town Planning

Research Methodology

Data Acquisition Table 1.1: Land-sat characteristic

Landsat Data used

for the study

Acquisition Date Dimensions

(in Pixels)

Actual Spatial

Resolution

Acquisition

Source

ETM+ 17/12/1990 7327 x 7757 30 m x 30m GLCF

ETM+ 2001 8525 x 7512 30m x 30m GLCF

NigSat X 2013 7586 x8707 22m x 22m NARSDA






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Image Geometric Correction

This is to correct for geometric errors associated with the satellite images due a variety of reasons.

These include instrumental errors, attitude of the sensors with respect to the rotation of the Earth

and swath width of the sensor etc. Also image registration is executed to assign coordinates

systems and projections to images. Image registration ensures that the features and process found

on the satellite image are allocated to their correct dimensions and positions on the ground location.

This is very important for change detection since we only compare the same geographic location

at different times. The image registration in this study was executed with the Arc-GIS software

from ESRI. The images were registered to the 1984 World Geodetic System Universal Transverse

Mercator (WGS ’84 UTM) in the Geographic Coordinate System.

Although the images were already geo-referenced to the Geographic Coordinate System, they were

re-projected to UTM ‘84 Zone 32 N so as to ensure that they are allocated their correct ground

coordinates. This is normally referred to as geometric correction or raster projection. The satellite

image was imported into Arc-GIS 10.1 for data conversion from tiff format to .img format to be

used in Erdas Imagine 9.2 software for analysis.

Data Analysis.

Table 1.0 below: Shows that, in 1990, urban area was 1481.662 hectares, (1.09%). While

agricultural land was 36165.98 hectares, (26.62%). This implies that agricultural activity was at

very high rate and urban activity was very low in 1990. However, by 2001 comparative analysis

of 1990 and 2001 images, show little changes in the land use, as urban area increased to 2584.641

hectares, (1.903%). While agricultural land diminished to 20323.35 hectare, (14.95%). This

implies that urbanization was gradually coming up as of 2001

Table 1.0: Magnitude and Annual Change rate of land use between 1990 and 2001 Land use

type

A B C D E

1990

Area in

hectares

2001

Area in

hectares

Magnitude of

(B-A)

Annual rate

change

C/12

% change

C/A *100

Farmland

36165.98

203233.5

-15842.63

-1320.23

-43.81%

Bare surface

41628.54

69798.11

28169.57

2347.46

67.67%

Built up Area

1481.662

2584.641

1102.979

91.91

74.44%

Vegetation

54561.86

40876.22

-13685.64

-1140.47

-25.08%






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Figure 1.0: Land use classified image of 1990 and 2001

Table 2.0 below: Shows that in 2001, urban area increased from 2584.641 hectares, (1.903%) in

2013 to 10074.373 hectare, (7.415%) and Farm land decreases to 16530.98 hectares, (12.17%).

This indicates that Kuta and its environs experienced rapid urban growth within the period of 2001

and 2013, as it increased to four times of what it was in 2001. Consequently, farm land and

vegetation were decreased to two times of their sizes

Table 2: Magnitude and Annual Change rate of land use between 2001 and 2013

Land

use/cover

types

A B C D E

Area in

hectares

Area in

hectares

Magnitude of

(B/A)

Annual rate of

chances

% change

C/A * 100

Water bodies

2013.55

2269.263

255.713

21.309

12.699%






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2001 2013

Farm land

20323.35

16530.98

-3792.37

-316.03

-18.66%

Bare surface

69298.11

77436.26

7638.1

636.51

10.94%

Built up Area

2584.641

10074.373

7489.732

624.14

289.78%

Vegetation

40876.22

29496.6

-11379.62

-948.30

-27.84%

Water bodies

2269.263

2313.42

44.157

3.68

1.95%






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Figure 2.0.: Extent of Land use image classification between 2001 and 2013

Table 3 below: Shows that in the year 1990, water body covered an area of 2013.55 hectares,

(1.482%); built-up area covered an area of 1481.662 hectares, (1.090%); farmland covered an area

of 36165.98 hectares, (26.62%); bare ground covered an area of 41628.54 hectares, (30.46%) and

vegetation covered an area of 54561.86 hectares, (40.16%).In the year 2001, water body increased

to 2269.263 hectares, (1.67%); built-up area increased to 2584.641 hectares, (1.903%); farmland

decreased to 20323.35 hectares, (14.95%); bare ground increased to 69798.11 hectares, (51.37%)

and vegetation also decreased to 40876.22 hectares, (30.08%). While in the year 2013, water body

increased from what it was in 2001 to 2313.42 hectares, (1.702%); built-up area drastically

increased to 10074.37 hectares, (7.415%); farmland decreased to 16530.98 hectares, (12.17%);

bare ground increased to 77436.21 hectares, (57%) and finally the vegetation decreased to 29496.6

hectares, (21.71%) as illustrated in 3. Table 3: Magnitude and Annual Change rate of land use between 1990 and 2013

Land use/cover

Types

Area

(Hectare)

(%)

Area

( Hectare )

(%)

Area

( Hectare )

(%)

1990 2001 2013

Water body 2013.55 1.482 2269.263 1.670 2313.42 1.702

Built up area 1481.662 1.090 2584.641 1.903 10074.373 7.415

Farmland 36165.98 26.62 20323.35 14.95 16530.98 12.17

Bare ground 41628.54 30.64 69798.11 51.37 77436.21 57.00

Vegetation 54561.86 40.16 40876.22 30.08 29496.6 21.71

TOTAL AREA 135851.582 100 135851.582 100 135851.582 100






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Table 3.0: Land use analysis between 1990 and 2013

Conclusion

Urban growth on agricultural land in Kuta town and its environs and change detection analysis on

Farmland using multi-temporal Remote Sensing data and GIS based techniques, indicates that

farm land has been widely consumed, or encroached.

The trend of farmland, forest and vegetation cover loss within the study area could be explained

by the LULC conversions to residential purposes and the lumbering activities in the area. This loss

is attributed to the built-up areas, between 1990 and 2013.

Using Satellite image data, GIS and RS technique can be a valuable tool in locating and predicting

Farm land and forest cover change. Thematic maps of forest cover types and various LULC classes

can be distinguished by the satellite image interpretations and to evaluate their conversions as well

as analyzing their trends. These aids in farmland and forest cover change detection and

identification of areas under risk of invasions.

Finally, the study area is experiencing a lot of socio-economic and political changes that is

impacting negatively on the ecological landscape. In the area of farmland sector as assessed in this

research, the following observations are noteworthy:

i. Urbanization is taking its toll on Kuta and its environs faster than envisaged






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ii. The urbanization growth rate of the area is increasing significantly from 1.090 to 7.415

hectare between l990 to 2013.

iii. A total of 19635 Ha of farmlands had been lost to urban growth within 23 years period

under study, which has led to food insecurity.

References Allen, J., Lu K. 2003. Modeling and Prediction of Future Urban Growth in the Charleston Region of South

Carolina: GIS-based Integrated Approach. Conservation Ecology 8 (2): 2.

Batty M. (2008), The size, scale and shape of cities, Science 319-771.

Bhatta, Basudeb, 2010, Analysis of urban growth and sprawl from sensing data. Springer

Curran S. R. and De Sherbinin A. (2004), Completing the Picture: The Challenges of Bringing

“Consumption” into Population, Environment Equation, Population and Environment, 26 (2): 107-131.

De Groot R. S., Wilson M. A. and Boumans R. M. J. (2002), A typology for the classification, description

and valuation of ecosystem functions, goods and services, Ecological Economics, 41 (3): 393-408.

Nelson AC (1999). Analysis based on indicators with policy implications. Land Use Policy, 16: 121-127.

Wilson, E.H., Hurd, J.D., Civco, D.L., Prisloe, M.P., Arnold, C. 2003. Development of a geospatial model

to quantify describe and map urban growth. Remote Sensing of Environment 86: 275-285.

Contacts

Email:[email protected]


National Government

Enhancing quality of life is the goal. Proven location-based technology is the key to achieving it.

National Government Track

Mary Wandia

A Geographic information System driven integrated land management System



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A Geographic information System driven integrated land management

System

Mary Wandia, Kenya

National Lands Commission

Key words: GIS, NLIMS, workflows, integrated system

Abstract The National Land Commission is currently developing a National Land Information Management System

(NLIMS). This is a parcel-centric based Geographic Information System (GIS) solution geared towards

automating land processes and procedures. The system is to be implemented using a five-phase strategy.

So far, phase 1 has been implemented with the setting up of infrastructure, system design and development,

integration of GIS with other systems and automation of land processes which include land administration,

valuation, settlement and adjudication. Once finalized, the system will support all components in land

administration using an integrated approach. The integrated system is based on Microsoft SQL server,

Dynamics NAV, SharePoint and ArcGIS solutions. In retrospect, it will enhance provision of land

management services and provide a platform for citizen interaction through an online portal featuring free

services and for pay services.

National Government Track Mary Wandia A Geographic information System driven integrated land management System 11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya

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Introduction The National Land Commission has been mandated to manage public land on behalf of the national & county governments as outlined in the National Land Commission Act, 2012 Section 5 (2) (d) (GoK, 2009: Gunk, 2012). This implementation shall be at both national and county level. Currently, the Commission is in the process of implementing a National Land Information Management System (NLIMS) to streamline, harmonize and improve service delivery to stakeholders in land matters and the general public. Theoretically, NLIMS is a system that comprises of sub-systems to support processes of the components of land administration1 in Kenya (See Error! Reference source not found. below). In this context, system efinition comprises (i) software, (ii) data, (iii) workflows (procedures & processes), (iv) network infrastructure and (v) staff. The system is designed to create, process, analyze and publish parcel-based data such as parcel information, location, zoning, land-use, ownership and any other general property information.

Figure 2 Components of land administration and management in Kenya Prior to the establishment of the Commission, the line Ministry in charge of lands had in place an NLIMS strategy. NLIMS was conceptualized in Project for Improving Land Administration (PILAK) whose mandate was: safeguarding land paper records, developing business and IT architecture, modernizing the geodetic framework, parcel identification reform, develop land rent collection system, systematic conversion to RLA titles, develop other land administration systems and create public awareness. The basic building block in any land information system is the land parcel as identified in the cadaster. According to the International Federation of Surveyors (FIG) (1995), a cadaster is a parcel based, and up-to-date land information system containing a record of interests in land (e.g. rights, restrictions, responsibilities and risks). It usually includes a geometric description of land parcels (cadastral maps) linked to other records describing the nature of the interests, the ownership or control of those interests, and often the value of the parcel and its improvements. It may be established for fiscal purposes (e.g. valuation and equitable taxation), legal purposes (conveyancing), to assist in the management of land and land use (e.g. for planning and other administrative purposes), and enables sustainable development and environmental

1 The term ‘land administration’ as used here is based on a definition adopted by the United Nations Economic Commission for Europe (UN ECE). It refers to the processes of recording and disseminating information about the ownership, value, and use of land and its associated resources. This includes the determination of rights and other attributes of the land, the survey and description of land, its detailed documentation, and any other relevant information in support of land


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protection. From this viewpoint it is clear that while NLC is charged with implementing and maintaining

this system, there needs to be synergies and harmony in operations between the Commission, the land

registries, national mapping agency, county governments, and treasury as they all need portions of the

parcel based NLIMS.

From this view, NLIMS is an all-encompassing system. Some of the initial offerings that can be packaged

through the system include the ability by Kenyans, on the payment of applicable fees (using online or

mobile solutions) to perform searches of land parcels, ability to make applications online for various

services offered by the Commission and the Ministry, access to respective land data by professionals

registered in the system and ability for persons to visualize spatial locations of various parcels and some of

the information (attributes) held in the registry about the parcels. Additional functions to be implemented

should include support for various workflows allowing the update of registry information, survey data and

other transactions. Any system is as good as the data held in it and especially for a spatially supported one,

as the NLIMS will be, the quality of data is paramount. A lot of this data is in analog (hard copy) format

and it takes time to convert data. The conversion process should have in place various quality control and

quality assurance measures. The most costly part will be this conversion process, but it requires to be hosted

on capable and secure infrastructure.

Problem Statement Over the years, land administration in Kenya has been marred with a myriad of problems in what has been

largely associated with a lack of an efficient, computer-based Land Information Management System

(LIMS) (Kuria et al., 2016). The manual land administration system has led to missing land records (files),

inadequate space for records, multiple allocation of plots, forgeries and altering of land allocation,

encroachment, overlapping surveys, inefficient revenue generation and loss, rampant sub-division,

amendments and falsification survey information on land titles.

The country continues to experience growth in all its sectors of development including land which is

considered to be an important factor of production. For a long time land administration in Kenya has been

a relatively stable paper- based system which has accumulated millions of records and is now deemed

incompatible and ambiguous with the increased complex and high demand use of land. The paper-based

system is cumbersome with complete human effort required. Updating of records for land transactions, sub-

divisions, mortgages and other transfers depend on the tedious manual system which is painfully slow.

Land records are sometimes not easy to find because there are hardly back-up copies created in the manual

system. Updating of land records particularly spatial data is quite a challenge. Technical experts are forced

to edit manually on the map that have rendered them illegible, hard to derive specific information, torn and

dilapidated. Incidentally, there are no back up data of the sad maps.

Similarly, the manual system is time consuming where land records have to be shared from one department

to the other to facilitate whatever request required by a client. The paper-based files move slowly to and fro

the red tape structure. This decreases the productivity of land information handlers that has a lasting

negative in the public eye. It can also take a long period of time to search for a particular record. Security

is also not guaranteed to a certain level as the system allows ease in either copying, mishandling crucial

information or loosing data. Therefore, it is about time that the paper-based system be replaced by an

automated system which has proven to be efficient in land administration operations worldwide. This will

also facilitate timeliness and ease in accessing land information in a transparent and reliable manner that

will lead to securing more investments to meet the needs of the growing population.


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Integrated Conceptual Framework

Figure 2: NLIMS Conceptual Framework (Adapted from NLIMS Proposal: NLIMS – from strategy to implementation, 2014)

The integrated conceptual framework of NLIMS is captured in 2 above. Land related data generated by the

Commission, Ministry of Lands and Physical Planning (MoLPP) and County Governments through but not

limited to, workflows for the different applications of the components of land administration (See Error!

eference source not found.1) will be hosted in a data center, accessed by both database and GIS servers.

It will be accessed through an online portal and mobile applications that feature both free and for-pay

services. Payments made will be remitted to the bank or via the mobile money platform to avoid cash

handling at the agency. For purposes of maintaining the data, administrative capabilities will be provided

but which will be restricted to routine system maintenance and not tinkering with data held on the system.

Implementation Strategy The implementation strategy for NLIMS through the NLIMS Directorate mandated to spearhead the

development of an NLIMS system has already undertaken a number of activities using a phased approach.

The components of the integrated system are a Citizen Relationship Management (CRM) to handle all

citizen related interactions with the Commission, Enterprise Resource Planning (ERP) to handle all resource

management interactions and the National Land Information Management System (NLIMS) to handle and

manage all land related data and information.


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Due to the sensitive nature of the land subject in Kenya, high availability and secure system guaranteeing

an all-round system availability is mandatory. This means that capable servers and network infrastructure,

secure data repositories and backup mechanisms, which have to be accessed from the headquarters and also

from the counties, have to be procured and commissioned. Given that most of the survey data and land

records data are still in paper form, there needs to be concerted efforts in conversion to forms ready for

digital consumption and dissemination.

This conversion goes beyond the mere scanning of documents and archiving of the same, but includes

extraction of information held in these documents and their storage in a well-designed database (Nkote,

2015). The extraction of the information varies depending on the type of documents: for non-spatial records,

these can be extracted by either manually reading off and entering the data in the database or by using

Optical Character Recognition (OCR) technology to read, decode and store the information; for spatial

records, these will be geo-referenced first and the spatial information captured by onscreen digitization or

tablet digitization thereafter. In both of the cases, the elaborate quality control and quality assurance

mechanisms alluded earlier will be enforced. Each parcel in existence has a history from when it was created

to its current state and each stage has documentation supporting the various transactions.

From this it can be seen that to have the system fully implemented, procurement of the system is the first

step (which is itself costly) acquisition and conversion of the data forms the main body of the system. It is

anticipated that the initial procurement of the system, development of some custom applications, some data

acquisition and conversion could well get into the region of 4 Billion Kenya Shillings spread over a three

– four year period, with the overall costs easily going to upwards of 20 Billion Kenya Shillings by 2030

when all records and graphical (survey and planning) data will have been converted and entered into the

system.

This project has come a long way, encompassing automation of all the land administration and management

business processes into digital workflows that have been diligently built into the Integrated National Land

Information Management System implementation featuring Land registration systems, Cadastral

Framework, and CRM. This system is a noble effort to make access to land administration service more

feasible and easy for Kenyan citizen and corruption free (however, this is debatable as to whether the

technology will bet the vice). Key components of the system are web based and has a greater beauty of tight

integration between the various components from Electronic Document Management System (eDMS),

workflow engine to cadastral framework and the CRM side. No more pools of technologies at different

departmental or organizational level. The systems leverage powerful and modern technologies to deliver

the end solution such as Microsoft (MS) SharePoint, ArcGIS System (JavaScript API for Web and the Land

Records Solutions), Custom built Document Management System (DMS), MS Dynamics CRM among

other technologies. Also, it’s important to note that every enterprise solution must ride on a well modeled

Information and for this, NLIMS is built on top of an adaptation of the Land Administration Domain Model

(LADM) (Lemmen, 2012), the ISO standard for Land administration.

Implementation progress A number of activities have already been put in place to propel the realization of the NLIMS strategy. These

are:

i) Enhancement of the capacity to implement NLIMS

The NLC was constituted slightly 3 year ago and it is thus, still a nascent organization with a huge mandate.

It embarked on a fairly ambitious goal of establishing its various units through a vigorous human resource


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recruitment exercise. Among the biggest beneficiaries of this effort was the NLIMS directorate, which now

has a core team of 13 specialists in Spatial Information Management as GIS developers and Spatial

Database experts. The model being considered by the Commission is where these core staff serve as Quality

Control officers in assuring quality of outsourced works and helping secure the data and systems given the

sensitivity of land information.

ii) Development of integrated systems

The NLIMS directorate developed system requirements (for phase one implementation) in conjunction with

the ICT directorate. Recognizing the integrated way in which the NLIMS and the other systems of the

Commission and the wider Government work, a tender for the supply of integrated systems solution was

published.

The successful bidder was awarded the contract at the beginning of the year 2015 and has already developed

and rolled out the ERP component of the integrated solution. The CRM part is steadily nearing completion

as all the Functional Requirement Documents (FRDs) have been agreed upon and customization of the

electronic Document Management Systems and CRM is well defined. The NLIMS component being

specific to Kenya requires more work as the various workflows driving land administration and

management processes in Kenya are largely unique, and the new legal dispensation in addition has modified

some processes with respect to the actors, meaning that some of the processes have changed radically. Most

of these workflows have been identified, the data model developed in line with the Land Administration

Domain Model (picking relevant pieces that apply to the Kenyan context) and bearing in mind the principles

of Cadastre 2014 (Kaufmann & Steudler, 1998), and programming and customization work is now in

progress.

iii) Establishment of a Spatial Data Conversion Laboratory

The engine of NLIMS is a data collection, reparation, collation and conversion unit, from which maps,

plans and other documents will be converted from. A tender for the supply of the equipment and software

to run in the unit, christened the NLIMS Laboratory, was awarded and established. This Laboratory is now

fully operational, with scanning and digitization work steadily going on. Some of the equipment in this

laboratory includes; scanners, plotters, GIS workstations and barcode scanners.

Given the nature of spatial referencing in Kenya, where we have 3 main types of spatial reference systems,

namely Universal Transverse Mercator (UTM), Cassini-Solder system and local coordinate systems, the

Directorate in conjunction with the National Mapping Agency (Survey of Kenya) is developing a

transformation scheme that will allow wall-to-wall seamless coverage of parcels countrywide.

iv) Development of NLIMS Standards and Guidelines

NLC is cooperating with counties in the development of specific components of LIMS at the county level

and other agencies dealing with parcel data. The NLC realizing the need to allow various agencies to get

along with implementing their own specific LIMS solutions, initiated the development of NLIMS standards

and guidelines with input and contributions from stakeholders in the land sector. These standards and

guidelines are to be used by data producers and stakeholders as they implement their versions of LIMS.

This approach allows the various agencies to focus on delivering on their mandates that may include

developing specific aspects of LIMS and allows the LIMS so developed to integrate with the NLIMS. The

standards and guidelines have already been gazetted.


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GIS Integration The GIS Module in the Integrated NLIMS has been built using the ArcGIS System and the cadastral

framework leverages the Parcel Fabric solution. The Parcel Information Management system is based on

the Parcel Fabric data model enabled with NLIMS Data Model. The NLIMS Data Model is a local

adaptation of the Land Administration Domain Model (LADM) to fit the Kenyan land administration

system. The Parcel Fabric specific attribute and properties have also been customized to fit in the local

cadastral framework. In the developed data model for NLIMS three primary classes have been created that

is the Parcel Feature Class (Managed exclusively through Parcel Fabric feature class to control quality as

far as accuracy and topological correctness is concerned, RRR (Rights, Restrictions and Responsibilities)

table as the possible interests that can be registered on a Land Parcel and Party table to represent the records

of the various entities that can have recorded interests of a particular land parcel These core classes are

related to other tables that hold critical secondary information that pertains to various element in the core

classes. The cadastral and parcel Information System for NLIMS are built to be managed primarily in the

ArcGIS Workspace featuring an Enterprise Geodatabase running on SQL Database Server hosted at the

NLC Data Center, ArcGIS Server for managing the Services consumed by other applications and the Map

Viewers, and ArcGIS Desktop leveraging the Parcel Fabric Solution from Esri Land Records Solutions.

Of key necessity to the Commission is the ability to record and maintain the chain of evidence of the

transitions on Parcel boundaries and attributes. Using Parcel Fabric solution for ArcGIS, the Integrated

NLIMS has been built to keep the chain of evidence of the transitions on parcels of land in the database as

Historic Parcels for the purpose of tracking the historic changes that occur in the parcel objects. Kenya as

a country that guarantees Titles as the legal document for land ownership, the validity of the titles or rights

on a parcel of land are based on the chain of evidence of previous survey and documents. The maintenance

of the chain of boundary delineation and record changes over time is essential. New survey on land parcels

cannot be conducted without surveyors accessing the evidence of the previous boundary delineation and

the records.

The Cadaster and LIS have been integrated with other applications to allow consumption of the Parcel

Information within these other applications used by other departments and institutions. Land

Administration business processes have been digitized into workflows that have been implemented on

SharePoint Foundation Server to deliver a public facing portal for accessing the processes as online services

and an internal portal for administrative operation on the business processes. On the other hand customer

service portal built using the Dynamics CRM for exposing query management services to the public has

been delivered as a component of the integrated system. These key components of NLIMS have been

integrated with the LIS/Cadastral component via Web Services to allow for consumption of the GIS Server

services through them. With the integration Parcel Information can be accessed from any of the three

components.

Conclusion NLIMS is an integrating solution, allowing various data providers to manage their data generation and

maintenance on the platform, and allowing linking of all land related data, and the subsequent merging and

sharing of these information from a unified platform. This allows data producers to manage their

components according to the uniqueness and complexity of their workflows and structuring of their data.

Adherence to the Standards and Guidelines will help in strengthening the integration of system components.

Cooperation, collaboration and consultation are key to successful integration of all the units. Without

genuine partnership driven by a common interest in reforming land information management, it will be

difficult to integrate the individual elements. Mutual mistrust and unnecessary competition is detrimental


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to realizing an integrated solution. This development has to have all the players speaking the same language

and working to leverage the strengths that each of the unit possesses.

Digitization of all paper records is very important, but automation of all workflows is crucial to sustainable

system. Automation of workflows is critical to ensuring sustainability of the system. In this way, data

remains current as data is updated and maintained through the automated workflows.

Decentralizing of the implementation allows breaking of the huge system into smaller more manageable

components. This allows for the disaggregation of financing, efficient allocation of project management

efforts and ease of monitoring since smaller components would be in consideration.

Change management is critical to the successful usage of the system across the country. Rolling out of the

system will in some cases require a transition from a paper based system to a digital system. This calls for

the reassurance of officers that they will not be affected negatively, but rather that, these systems will

enhance service delivery and increase their productivity. In addition, fitting training programs will help in

easing absorption and usage of the system developed.

Reference FIG (1995). FIG Commission 7 Statement on the Cadastre. Retrieved from

www.fig.net/commission7/reports/cadastre/statement_on_cadastre.html

GoK (2012). The National Land Commission Act. Government Press, Nairobi

GoK (2009). The National Land Policy. Government Press, Nairobi

Kaufmann, J. & Steudler, D. (1998). Cadastre 2014: A vision for a future Cadastral System. Paper

presented at the 1st Congress on cadaster in the European Union. Granada, Spain

Kuria, D., Ngigi, M., Gikwa, C., Mundia C., Kibui S., Omondi, S., Thaa, B., & Macharia M. (2016).

Improving the State of Land Administration on developing countries: a Case of LADM for Kenya

– Opportunities & Challenges. Paper presented at the World Bank Conference of Land and Poverty

Washington DC, 2016.

Kuria, D. (2014). NLIMS Proposal: NLIMS – from strategy to implementation. NLC Press, Nairobi

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http://cdn.intechopen.com/pdfs-wm/37998.pdf


Robert Wayumba, Ntonjira Lizahmy

Challenges of Developing Land Information Management Systems (LIMS) for County Governments in Kenya



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Challenges of Developing Land Information Management Systems (LIMS) for

County Governments in Kenya

ROBERT Wayumba and Ntonjira LIZAHMY

Technical University of Kenya

Key Words: Land Information Systems, Counties

Abstract

This paper describes the challenges of developing a Land Information Management Systems

(LIMS) for County Governments in Kenya. In most developing countries land information is still

held in paper format, which can be destroyed through wear and tear and can also be difficult to

retrieve. In order to improve land administration services, there is a need to develop digital LIMS.

In addition, according to the County Government Act, 2012 in Kenya, all County Governments

are supposed to develop digital Geographic Information Systems (GIS) based Spatial Plans that

should include land information. Despite the need for LIMS, there are multiple challenges that

face implementation of the systems. In this regard, there is a need to study the challenges, as a

means of averting them in future LIMS initiatives. This paper uses case study methodology to

document challenges that were encountered in developing part of a LIMS for Kerugoya County.

The results show the legal, social, political, technical and economic challenges that were

encountered in the project. The conclusion is that there is a need to establish means of resolving

the challenges if effective LIMS are to be implemented not only in County Governments in Kenya,

but also in other developing countries.






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Introduction

Implementation of Land Information Management Systems (LIMS) can contribute towards

economic growth and development in a country. In this case, a LIMS can be described as a

computerized system for managing land records, which usually consist of property boundaries and

related attribute data (Dale & McLaughlin, 2000). The LIMS can contribute towards development

by: enhancing land ownership security, increasing access to credit, enabling development of land

markets, reducing land disputes, enabling land taxation and land reforms among other possible

benefits (Williamson, Enemark, Wallace, & Rajabifard, 2010).

Despite the possible benefits of LIMS, many developing countries continue to hold paper based

land records. In essence, land records usually consist of maps and records related to the land. The

maps are usually developed by cadastral surveyors and include the spatial extent of the land and

its area (Larsson, 1991). The attributes related to the land normally include the proprietor’s name,

any encumbrances on the land, size of the land and legal system in which the land is registered

among other aspects (Simpson, 1976). These records are usually held in paper files which are

susceptible to wear and tear and which can also get lost (Kuria, Ngigi, Gikwa, Mundia, &

Macharia, 2016). If the paper records get lost or destroyed, very valuable information on land

ownership and any encumbrances on the land may be lost forever.

Implementation of LIMS is also hindered by the low extent of formal land registration in

developing countries. In general, land registration describes the method through which matters

concerning ownership or other rights to land are recorded (Zevenbergen, 2002). The type of

registration can be based on a deeds system, a title system or an improved deeds system (Simpson,

1976). According to a World Bank report that was released in 2003, in Sub-Saharan Africa

countries, only about 10 percent of the land has been formally registered (Deininger & others,

2003). In 2013, a more optimistic outlook was provided for all developing countries in the World,

in which only about 30 percent of the land has been registered (Zevenbergen, Augustinus, Antonio,

& Bennett, 2013). Nonetheless, the figure is still very low. Hence, one of the reasons why most

countries in Sub-Sahara Africa continue to be poor has been attributed to the lack of extensive

coverage of land registration and lack of LIMS (De Soto, 2000). Therefore, there is a need to

explain challenges that hinder implementation of LIMS, as a means of enabling solutions to be

established, to allow for economic growth and development.

Methodology

In order to explain the challenges associated with implementation of LIMS, case study

methodology was selected as the main form of inquiry for this paper. The methodology was

selected because it is suitable when a phenomenon under investigation is not easily distinguished

from its context and it allows the use of qualitative analysis (Eisenhardt & Graebner, 2007). The

methodology was also selected because it is a recommended form of inquiry for research on land

administration issues (Çağdaş & Stubkjaer, 2009).

The case study that was selected is part of a project on implementation of LIMS for Kerugoya

County in Kenya. In Kenya, according to the County Government Act, 2012, all County






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Governments are supposed to have Geographic Information Systems (GIS) based spatial plans. As

a result, The Technical University of Kenya, carried out a pilot project on how part of the GIS,

which deals with land records i.e. the LIMS, can be implemented. Thus, this paper provides some

of the challenges that were encountered in the project. The main aim is to enable better

implementation of LIMS, not only in the County, but also in other parts of Kenya and other

developing countries. The following section shows the legal, political, social, technical and

economic challenges that were faced in the project.

Results and Findings

Legal challenge There were some legal challenges during the initial stages of the project. The first legal challenge

was associated with contractual agreements between the university and the county. At the

inception stage, there was a need for the two institutions to sign a Memorandum of Understanding

(MoU). There was a need to agree on dispute resolution mechanisms, extension of time if the

consultant did not deliver the project in time, and a need to resolve perceived loss of power by

some institutions in the county. A major part of these challenges were resolved, and the two

institutions were able to sign a MoU.

A key legal challenge was lack of a clear legal roadmap on how to implement the system. At the

moment, there are no formal guidelines on how LIMS should be implemented in Kenya. As a

result, there is no Act of Parliament that describes how the computerized land records would be

managed as opposed to the existing manual records. In the manual system, the mandate of various

actors in producing formal land records is clear. As an example, the Director of Surveys is

responsible for authenticating cadastral maps, while the land registry is responsible for producing

title deeds (Njuki, 2001). In the project, it was not clear how the digital records would be handled

be handled by the various institutions, which created some hesitation on the project. At the

moment, the National Land Commission (NLC) is in the process of developing Standards and

Guidelines that will most likely be used to implement LIMS at both the National and County levels

in Kenya (Kuria, Kasaine, Khalif, & Kinoti, 2016).

Another legal challenge is that there were many unresolved land disputes, which made it difficult

to identify legitimate land owners. In the process of developing a LIMS, there is a need to capture

land ownership records (Dale & McLaughlin, 2000). In the study area, identification of land

owners was first carried out by the colonial and post-independence Governments of Kenya. At

around 1960, the British Colonial government initiated a process of land adjudication in parts of

Kenya (Sorrenson & others, 1967). The aim of the adjudication exercise was to identify the people

who held rights to land and confer upon them ownership of land through formal registration. In

1963, The Republic of Kenya obtained independence from the British, and the independent

government continued with the process of land registration in the country (Sorrenson & others,

1967). A major challenge is that after the first registration was conducted, succession has not

occurred in most areas, hence current owners are not known. To this extent, there is a need for

succession to be completed in the counties as a means of enabling LIMS that will be implemented

to have accurate ownership information.






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Social challenges

In the implementation of information systems, social challenges are related to human factors that

can hinder planning, development and utilization of the systems (Laudon & Laudon, 2004). As a

result, implementation of LIMS requires an adequate assessment of user needs requirements

(Goodchild, 2009). In Kerugoya, the views of the users and stakeholders were many and

diversified. Thus, the consultant could not incorporate all the user needs within the limited time

frame of the project.

A few of the users were uneasy with the changes that would be introduced by the digital LIMS.

As stated earlier, in Kenya, various actors know their role in the process of developing and using

paper based land records. A proportion of the employees in the government were uneasy that the

digital system would make them redundant or require them to go through lengthy and expensive

training, which they could not afford. Thus, in order for LIMS to be properly implemented in any

jurisdiction, there is a need to allay the fears of existing staff members on the effects of

computerization.

Political challenges

Political challenges were also observed in the project. As much as these challenges could be

categorized as social, they are isolated because politicians play a major role in determining

development in their jurisdiction. According to Rakai, (1995), land information is a resource that

can be used for political gain. On the one hand, politicians can use the ignorance of people on land

information for political gain, to the detriment of the people. On the other hand, the information

can be used positively for the benefit of the people (Rakai & Willlamson, 1995).

In the study area, some politicians were against implementation of the system because it was too

expensive for the county to finance. Indeed, one of the major challenges that face introduction of

LIMS in most developing countries is that they are expensive to implement (Williamson et al.,

2010). However, on the other hand, the systems can contribute significantly to collection of

revenue, hence, justifying implementation costs (Williamson et al., 2010).

A number of the politicians were concerned that the system would have detrimental effects on

their political control. In this regard, the consultant had to carry out extensive sensitization of the

potential benefits of the system, as opposed to the fears of the people. Eventually, the political

challenges were overcome, and the project commenced. Nonetheless, the continued success of

implementation still requires goodwill from the politicians.

Technical challenges

There were also several technical challenges encountered in the project. The first technical

challenge was associated with very low extents of fixed boundary surveys in the county. In Kenya,

land registration is either based on Registry Index Maps (RIMS) or Deed Plans (Siriba, Voss, &

Mulaku, 2011). In the areas where registration has been introduced, approximately 80 percent is

covered by RIMS, which do not have any mathematical boundaries. The development of most of






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the RIMS was based on unrectified aerial photographs, which were not mathematically defined

(Siriba et al., 2011). In contrast, a few parts of the country are surveyed and boundaries depicted

on deed plans which have mathematical coordinates. A very large portion of Kerugoya County,

like many parts of Kenya, is covered with RIMS, which are not mathematically defined. In this

regard, it was very difficult to include the boundaries in an LIS, which requires mathematical

coordinates.

Another technical challenge is that RIMS are not updated. According to the law in Kenya, every

cadastral survey on land should be legally documented by updating survey plans or RIMS. In most

parts of Kenya, the original registered land owners have subsequently passed on the land to their

offspring or sold it off to other people. During the process of inheritance the land is usually divided

to the offspring and during a sale, a portion of the land can be divided to the seller. The inheritance

and sales are secured through informal agreements, or custom, as opposed to the formal system

(Cotula & Chauveau, 2007). As a result, the spatial changes on the ground are not reflected in the

RIMS. To this extent, when the consultant tried to input existing RIM data into the LIMS, there

were many discrepancies with existing boundaries on the ground, which have not been mapped.

There was also a challenge in the numbering system of the parcels on the map. At the initial stages

of the project, an assumption was made that the parcels would be sequentially numbered on the

RIMs. However, when the work started, it was realized that there was no systematic numbering

system in some parts of the county. In addition, some parcels on the ground did not have a known

number. In this regard, there is a need to develop a unique and legally recognized numbering

system for the parcels of land. Indeed, as stated by De Soto (2000), one of the reasons why

“Capitalism” fails in developing countries, is because most business are operated without a legal

description on where they can be found, hence cannot be taxed adequately (De Soto, 2000). To

remedy the situation, the National and County Governments need to develop a unique geocoding

system that can be used to identify each land parcel.

Another technical challenge was that at the time of implementation, no staff member in the county

had been trained on how to operate the LIMS. A major source of failure of information systems is

lack of adequately trained personnel (Laudon & Laudon, 2004). Thus, if the LIMS is to work, the

County government must invest in training staff members on how to use a LIMS. The training can

be in the form of short term or long term courses. In this regard, county officials who already have

a background in spatial sciences can be taken for short courses on the use of Geographic

Information Systems for land records management. If the county is able, it can also sponsor some

people on long term courses on LIMS.

Economic challenges

There were also economic challenges encountered in the project. In the process of capacity

building on land administration, there is a requirement that people should invest in what they can

manage (Williamson et al., 2010). Lack of adequate finances can hinder implementation of

necessary projects such as LIMS. Implementation of an effective LIMS is a big financial

undertaking for any country (Larsson, 1991). In the County, the finances were not enough to






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implement a fully functional LIMS. As a result, the project was a pilot of what could be done if

finances were fully available.

During the project, there were project overheads that had not been budgeted for. The project on

LIMS was one of the first attempts at introducing LIMS in the counties and was very innovative.

As stated earlier, it is also a legal requirement for the counties to implement GIS based spatial

plans. However, because these projects are being implemented for the first time, and without

standards and guidelines, there were bound to be some oversights. Thus, in the initial budget, the

number of personnel and equipment for implementing the project was underestimated. As an

example, during the user needs requirements, the finances budgeted for, were not adequate to

administer the questionnaire to all users that were supposed to be sampled.

The process of releasing project funds was also very slow. A major recommendation on how to re-

engineer land administration systems is related to reducing the number of steps required to obtain

legal documents (Williamson et al., 2010). In the project, there were many officials required to

sign documents before the release of funds. On the one hand, this was positive, because the number

of people would discourage misuse of funds. On the other hand, too many bureaucratic processes

slowed down the project.

Conclusion

The main conclusion from this paper is that the challenges facing implementation of LIMS should

be resolved if effective systems are to be implemented. The legal challenges should be resolved

by developing adequate standards and guidelines on how to implement and manage LIMS at both

National and County levels. In addition, the standards and guidelines should be translated into a

law that can be legally enforced. The social and legal challenges can be resolved by adequate

sensitization on the possible benefits of computerization. The technical challenges can be reduced

by improving on the existing cadastral maps. In essence, there is a need to update all cadastral

maps not only in the County but also in Kenya as a whole. Finally, the economic challenges can

be resolved through proper project planning and management. In this regard, if the finances are

limited, a systematic approach can be used for computerization, in which small portions of the

County are digitized, as opposed to tackling the whole county as a whole.

The authors hope that this paper will contribute towards implementation of effective Land

Information Management Systems (LIMS) that will contribute towards economic growth and

development in Kenya. The authors also recognize that further quantitative and qualitative studies

should be carried out to find out more details on the challenges that hinder implementation of

LIMS and how the challenges can be resolved.

References

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26(4), 869–889.

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challenges. Academy of Management Journal, 50(1), 25–32.

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Conference on Land And Poverty, Washington: World Bank.

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Implementation of the Africanized Land Administration Domain Model for Kenya—A

Case Study of Nyeri County. Journal of Geographic Information System, 8(2), 171.

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management. Addison-Wesley Longman Publishing Co., Inc. Retrieved from

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(Kenya) (pp. 2–5).

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into a land information system. Australian Surveyor, 40(4), 29–38.

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604.

Biographical Details

Dr. Robert Wayumba is a lecturer at the Technical University of Kenya. He holds a Doctor of

Philosophy (PhD) from the University of Otago in New Zealand. He also holds a Master of Science

in Land Management, from the Royal Institute of Technology, Stockholm, Sweden and a Bachelor

of Science in Surveying, University of Nairobi.

Ms. Ntonjira Lizahmy is a final year student at the Technical University of Kenya, Department

of Land Administration and Information.

Contacts

Dr. Robert Wayumba

Department of Land Administration and Information



Ms. Ntonjira Lizahmy

Department of Land Administration and Information





Utilities &

Transportation

Utilities & Transportation Track

John Esther, Njenga Wainaina, Maxwell Barasa

VES Sites Selection Model for Ground Water Analysis and Mapping


2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya

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Ves Sites Selection Model for Ground Water Analysis and Mapping

John ESTHER and Njenga WAINAINA, Maxwell BARASA, Kenya

Rural Focus Ltd.

Keywords: Groundwater, VES, Aquifer, Geospatial analysis, Suitability analysis.

Abstract

Turkana is an arid area experiencing severe water shortages. The government of Kenya in

collaboration with international donors speculated that there was an abundant supply of ground

water that can sustain the whole country for the next 70 years and contracted a company to do

ground water analysis and mapping. The process involved analyzing digital geological data,

remote sensing imagery analysis, and creation of 3D basement formation model from structural

contours and selection of most suitable sites for Vertical Electric Sounding (VES) survey to

determine the availability and depth of aquifers.

A customized geospatial tool combining expertise from key hydrogeologists and geospatial

analysts was designed in ArcGIS to perform site suitability analysis for the VES survey process.

The tool was able to create analytical maps indicating areas with high ground-water potential.

On performing the VES survey, over 90% of the sites analyzed in Turkana County within the

zones indicated by the tool, shows availability of ground water within a depth of 500 meters

from the surface.






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Introduction

The problem of bulk water in arid and semi-arid areas has plagued most parts of Kenya for over

half a decade. Some of the areas such as Turkana which is in the arid areas has had some of the

worst effects of lack of water. In the years it has experienced severe droughts as river beds dry,

this has presented the need to look for water underground. According to UNESCO the water

problem is so severe that Turkana is one of the hottest, driest and poorest parts of Kenya and

has been hit by devastating water shortage. In recent times various research groups including

the government have done a number of studies in this county to try and alleviate the water

deficit problem in Turkana County. The most notable work was the work done by RTI under

UNESCO funding. RTI identified an aquifer in the Lotikipi where previous drilling had come

up dry until the study showed drilling needed to be a bit deeper to get to the aquifer. The study

showed that the aquifer could sustain the country in the next 70 years.

Study Area

Turkana County is one of Kenya’s 47 counties with a population of 855,399 (KNBS 2009)

covering an area of 77000 sqkm (approximately 13% Kenya’s surface area). The county is

bordered by Marsabit County to the east, West Pokot and Baringo counties to the south-west,

Samburu County to the south-east, Uganda to the west, South Sudan to the north-west and

Ethiopia to the north-east. (Turkana County Government, 2013-2017).

Turkana County being a part of the rift valley has varied complex physiographic properties

which comprise escarpments, mountain ranges, plains and swamps, and the lakes. Most of the

mountain ranges are volcanic in nature and include Muruanachock hills, Lobur hills,

Lokwanamoru hills, Sogot hills, Moggila hills and the Muruasigar hills mostly capped with

rhyolites and olivine basalts. The plains are usually dry during the dry season but once the rain

falls they become swampy. The biggest swamp is the Lotikipi swamp which stays soggy for

the better part of the year. The plains are composed mainly of recent superficial deposits and

silts. Turkana has the two lakes; Lake Turkana (formerly Lake Rudolf) is the largest permanent

desert lake and the largest alkaline lake. It gets water from the Omo River flowing from the

Ethiopian highlands, Turkwel River and Kerio River from Kenyan highlands. It is also fed by

various seasonal rivers from Marsabit county of Kenya. The other lake is Lake Logipi which is

a very shallow tiny lake to the south west of Lake Turkana (Turkana County Government, 2013-

2017). It is separated from Lake Turkana by a barrier volcanic complex which is composed of

young volcanic e.g. ashes and phonolites. The expansive Uganda escarpment to the west of the

county is a fault scrap composed of a basement system gneisses and pockets of tertiary volcanic

(Walsh, Dodson, 1969).

Turkana County is classified as an arid and semi-arid area experiencing a maximum

temperature of 37oC and a minimum of 21oC. It experiences a bimodal rainfall regime that

doesn’t amount to much since the average annual rainfall ranges between 300 mm to 400 mm

with rainfall averages hitting as low as 150mm. Even if the regime is classified as bimodal the

pattern is at best unpredictable with some areas staying for a whole year without rainfall. The

County is very windy (creating a modified hot and dry climate to be somewhat cooler)

especially along the lake leading to even the development of Turkana Wind power plant. As an

arid and semi-arid area, the county experiences clear skies most of the time all year round with

scorching sun.






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The main form of livelihood is nomadic pastoralism (keeping cattle, donkeys, camels and

goats). Other forms of livelihoods include fishing along the Lake Turkana and basket weaving.

The economy of the county is driven by tourism, cattle sales and on the recent trend petroleum

and wind power revenues.

The land cover of Turkana County is composed of vast expanses of bare hard rocks, shrubs and

scattered trees.

Geology of Turkana is characterized by depositional superficial deposits of sandstone, grits,

alluvial sands, conglomerates, limestone and siltstone ranging in age from Pleistocene to recent.

Basalts, rhyolites, andesites, tuffs, ashes, nepehelinites and phonolites of tertiary period overly

Archean basement (Walsh 1969, Dodson 1971, Joubert 1966, Fairburn., et al 1970)

Fig 1: Map of Turkana (courtesy of Turkana County Government)






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Datasets and Methodology

Datasets

The data used in the analysis includes digital (pdf) geological maps at various scales from the

Ministry of Mining created from 1960 to 1988, Geological Map of Kenya with Structural

Contours, topographical maps of Kenya at a scale of 1:250000 and Aster 30m resolution Digital

elevation model data. The data was prepared, digitized and analyzed via ArcGIS software

algorithms and scripts to perform overlay analysis.

Methods

There are various approaches (both scientific and non-scientific) to determine the availability

of ground water. This include; water dowsing, preliminary surveys, topography analysis and

hydro geophysics.

(A) Preliminary survey involves visiting the area of study and doing a through desk study of

the area. In this survey aspects of the area like where the springs have been or are located, where

boreholes have been drilled and whether they were dry or successful and their yields, where the

vegetation has always been green even on dry season and consulting local dowsing specialists

if available. In this period, various datasets are collected including satellite imagery, aerial

photos, seismic profiles, geological maps, elevation data, vegetation data and climate data.

(B) Analyzing topography. Various physical features are analyzed in this step to give an

indication of where the aquifers maybe located. Vegetation is analyzed using satellite imagery

and/or aerial images in remote sensing software environment to determine; where vegetation is

permanent and green during driest seasons, the dipping, outcrops and outlines of geological

formations, lineation and faulting of the area and the elevation profiles of the area. (Kumar,

2014).

(C) Hydro-geophysics. This method involves measuring the electrical resistivity (capacity of a

material to resist flow of electric current). It encompasses various methods which are selected

depending on the geological context of the area, the depth of the aquifers needed to be identified

and budgets available (Loke, 2001). The geophysical methods include:

Measuring resistivity using a direct current. In this method a direct electric current is sent into

the geological structure being investigated using two electrodes and measuring the resistivity

of the structure as the current penetrates to a specified depth (McNeill, 1992). This method is

preferable for determining moderately deep aquifers in areas which are relatively flat and free

of buildings. (Christensen et al, 1998)

Measuring reactivity by magnetic means. These methods measure the reactivity of soils to

electromagnetic excitation by measuring electromagnetic signals due to magnetic induction

phenomena. These methods cannot be used for all types of grounds or on aquifers that’s are

more than 20m below the surface. These methods include isotope tracing method and proton

magnetic resonance method.

After the examination of the task at hand, most of the methods concepts and ideas were used

with additional geospatial analysis and techniques to determine ground water availability and

the best places to perform geophysical investigations without excessive failed test points.






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The method adopted in this analysis was for identifying areas with potential for ground water

availability on the basis of proximity of the basement system that creates pockets for water to

be trapped and favorable geologies to store the water infiltrating.

Data Preprocessing First, all the maps were converted from .pdf to .tiff. This .tiff files were without any spatial

reference information on them.

Second, the geological maps were georeferenced to their specific projection they were initially

created in which Arc 1960 Datum is based on Clarke 1880 reference ellipsoid, Universal

Transverse Mercator projection. Map geo-referencing as accurate as possible is needed to

accurately trace the needed data and create digital vector data (Ellwood et al., 2016). For each

map the points used as control points were all the cross points of the various gridlines. Due to

inaccuracy of hand drawn maps, further control points were added by comparing the maps with

current accurate data of various physical features like rivers and base of mountains to improve

the overall accuracy.

Third, all the maps were projected to a reference system which was Geographic Coordinate

system WGS 84 using the transformation of Arc_1960_To_WGS_1984_2 which is the

recommended transformation for Kenya. The datasets were then projected to World Mercator

Projected Coordinate system so as to conform to the raster datasets projections used to create

outputs like hill shades.

Fourth, a Mosaic was created combining the various geological maps into one extended image.

This mosaic aided in identifying the gaps in geology that were needed to be filled. These gaps

include the upper part of the county (Kibish) which was updated via a geological map of The

Sudan. The other gaps were left empty.

Fifth, a geodatabase schema was created to hold the anticipated geological formations from the

maps. After the schema heads-up digitization of the mosaic commenced creating a wide range

of geological formations in vector format. In the same way, the structural contours from the

map were digitized. The structural contours in this map shows smoothened depth to the

basement in kilometers.

Sixth, after the digitization was finished, all the gaps in the data were identified and filled up

with the geological map of Kenya lithological dataset and the output clipped to fit to Turkana

County borders. The geological data obtained was left with the naming of the original maps

first and then after wards the datasets were merged where the geologists felt they were one and

the same geological formation. For structural contours the digitization also included the

addition of the depth field to act as an indicator of how deep the contour was below the surface.

The structural contours were extended to reach the adjacent counties.

Surface Elevation Model Processing The 30m AsterGDEM V2 data was used in this project. The choice for this was based on the

fact that it is the one of the high resolution open source elevation model existing for the whole

world today. The elevation model was then taken through a low pass filter analysis so that it






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becomes more accurate for morphometric analyses and physiography analysis (Tachikawa et

al, 2011). Existing AsterGDEM data was downloaded from USGS website, mosaicked and

clipped to fit to Turkana County. The raster was projected to World Mercator Projected

Coordinate System. County hillshade was then created to depict hills and plains. The hillshade

is used to limit the areas which are too steep to use the electrical sounding tool.

Creation of a Basement Depth Raster Conventionally elevation models are mostly used for depicting only surface topography. This

elevation models can be created from point elevation data or contours using various

interpolation techniques e.g. Inverse Distance Weighing and Triangulated Irregular Network.

These methods use near neighbor idea where it is assumed points that are closer are more related

than those far away (Fisher et al, 1987). In order to create a higher accuracy elevation model,

some interpolation techniques that uses both local and global interpolation methods is needed.

One of the best interpolation methods used today is the ANUDEM program that calculates

regular grid elevation models with sensible shape and drainage structure from arbitrary large

topographic data sets (Hutchinson 2008, Hutchinson and Gallant 1999, 2000). The inputs for

the ANUDEM algorithms can be point elevations, elevation contours, streamlines, sink data

points, cliff lines, boundary polygons, lake boundaries and data mask polygons which have a

value field indicating the elevation of a surface from an arbitrary surface e.g. the sea level.

The ANUDEM uses algorithms that apply drainage enforcements and elevation tolerance

constrains to adjust the accuracy of the elevation model in relation to the accuracy and density

of the input elevation data (Hutchinson 1989).

Since the ANUDEM algorithms create very high accuracy DEM, it was adopted as the main

method to use in this analysis as opposed to other interpolation methods. This method was used

in an ArcGIS platform as a tool named Topo to Raster. The Topo to Raster tool is an

interpolation method specifically designed for the creation of hydrologically correct digital

elevation models (DEMs) adopted from the ANUDEM program. It takes all the data formats

compatible with the ANUDEM algorithms and performs environmental modelling as discussed

by Hutchinson (2000, 2008, 2009, 2011). As a tool the Topo to Raster interpolates elevation

values from a raster while imposing constrains to ensure a correct representation of ridges,

streams and sinks from contour data. The tool uses an iterative finite difference technique by

having the computational efficiency of local interpolation methods e.g. IDW but without losing

the surface continuity of global interpolation methods e.g. Kriging. Currently the method was

adopted as the best to depict the structure of the basement from contours.

After each of the structural contours was digitized, a value was added that shows the depth of

each contour below the surface. The Geological Map of Kenya with structural contours has data

that depicts a smoothened depth to the basement for Kenya in kilometers (Government of

Kenya, 1987). The kilometer value was converted to meters and used in the topo to raster tool.

Since the tool enforces drainage on the resultant elevation model and removes inconsistent

sinks, the tool was made to avoid enforcing since there are breaks in the basement caused by

volcanism that should not be filled up. The final result of the process was clipped to fit to

Turkana County from the bigger interpolated surface which was extending to other couties and

countries.






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Raster Difference Raster difference is used to calculate the difference of the surface contour to the basement

contour to form the basement elevation model based on the height above sea level. The

basement elevation model represents an elevation above or below sea level. In this model to be

able to determine the depth to the basement from the surface we need a way of creating that

difference data. Since all the raster datasets are in the same resolution and alignment, the raster

map algebra algorithms were used to create a raster showing the elevation of the basement

above or below sea level. Map Algebra is a simple and powerful algebra with which you can

execute all Spatial Analyst tools, operators, and functions to perform geographic analysis (Esri,

2015). In this case the formula used was:

ASTER DEM –DEPTH RASTER = BASEMENT DEM.

Geological Reclassification Groundwater flow in aquifer is dependent on permeability and porosity of the water bearing

material that utilizes Darcy’s law, permeability is a measure of ability of a material to transmit

water. For instance, sandstones may vary in permeability from less than one to over 50,000

millidarcys (md), permeability is more commonly in the range of tens to hundreds of

millidarcies. A rock with 25% porosity and a permeability of 1 md will not yield a significant

flow of water. Such “tight” rocks are usually basement and other granitic rocks (Bear.,et al

1972). In Turkana, aquifers are found in alluvial, sandstone, grits and basaltic formation.

Turkana geological formations were classified according to their permeability and porosity.

This generated a raster showing suitability of various geological formations to contain ground

water.

Previous groundwater mapping done in this area (RTI 2013) classified alluvial aquifers as the

best yielding aquifers in the region. This study also prioritizes alluvial and sandstone aquifers

as best yielding aquifers due to their porosity and permeability, however sandstone porosity

decreases with increasing clay content. Turkana Grits which consists of sandstones, limestone,

conglomerates, Kalapata beds is rated high in terms of groundwater occurrence and movement

(Walsh 1969)

Weighted Overlay Analysis. Overlay analysis is a technique for applying a common scale of values to diverse and dissimilar

inputs to create an integrated analysis (Esri, 2015). It combines various spatial analytics

algorithms, models, datasets and techniques to create favorable outputs of a given phenomenon

on the earth surface. The analysis follows a general order of process which is; define a problem,

break the problem into sub models, determine the significant datasets, transform the datasets to

fit into the analysis process, weight the input datasets depending on their influence to the

phenomena, combine the datasets and finally perform analysis. (Mayfield C.J, 2015). Weighted

overlay analysis assigns high values to favorable conditions for the phenomena being analyzed.

The method employed in the study to identify the suitability of aquifer potential was employed

based on the available data and time. The model builds on the fact that for an aquifer to be

present it is largely a factor of geology and recharge. The geology plays a very major role since

it is the one that retains water where it is favorable. This method employs geology as the major

contributor with some geologies retain water while others retaining none. The other factor






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employed is structural contours that describe the profile of the basements which are

impermeable which thus retains water in pockets when the overlaying geology is permeable

and has high water retention capacity.

The method adopts a raster analysis process which overlays and adds weight to different

geologies and depth of the basements. The method builds up on key knowledge of

hydrogeologists who described how they deduct the potential for aquifers and geospatial

analysis expertise to combine the geological explanations of the hydrogeologists to create a

model for determining the aquifer availability on a larger scale. In essence the method has been

verified and is based on technical hydrogeological knowledge.

The process starts with calculating the influence of the various geological formations to ground

water occurrence in the area (Jones, 1985). From the preliminary survey period weights were

assigned to the geologies based on the available springs and boreholes. These weights were on

a range from 1 to 10 which shows the order with which water has been known to occur in this

areas.

10 shows the areas with the highest potential for storing ground water while 1 shows the least

favorable geologies for retaining water. E.g. Turkana grits and sandstones take the highest

priority since majority of the boreholes with high yield in the area are drilled into them.

The geologies are converted to a raster using the priority field as the value. This gives a raster

with 10 values which are then reclassified to nine classes to fit into the weighing model.

After the reclassification process then the two datasets are ready to be weighted together. From

consensus of the hydrogeologists, the basement was given a weight of 15% and the geology a

weight of 85%. This is because the geological formations are the aquifer bearing materials with

the basement as the benchmark where all overburden materials are categorized as aquifers.

(Hussein.,et al 2016).

Results and Discussions

The main objective of the process is to identify areas with high potential for underground water

availability at a depth of not more than 600m from the surface. The suitability output is then to

be used as a guide to perform Vertical Electrical Sounding to determine the availability of the

aquifers. Hydro-geologically, groundwater occurrence is controlled by geological conditions

including lithology and structures (Ismael. et al 2016). From the analysis a scale of 1 to 9 was

generated to represent areas with high potential depending on the proximity of the geologies to

the impermeable basement rock. Nine represents the most probable area to perform electrical

sounding while 1 represent areas where potential for underground water is very low.

The basement elevation model is shown in fig. 2 from the model we can be able to conceptualize

and view the profile of the basement system in Turkana County. The profile is a good indicator

of the basins with water.






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Fig 2: Basement elevation map

The reclassified geological map (Fig 3) shows the suitability of the geological formations to

store underground water. It can be noted that the geological formations on top of hills are least

favorable as compared to those on the foot of the hills. This owes to the fact that most of this

hills are capped with rock outcrops thus water flows rapidly without percolating to collect at

the foot of the hill where debris and sands have collected over time and been buried.






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Fig 3: Classified geology map

The output of the suitability model was then compared to geology before commencing to select

the areas to perform Electrical sounding analysis. Further analysis meant that a buffer of 1.5

kilometers was created around all the sandstones, conglomerates and Grits since they have a

tendency of dipping and expanding below the other recent geologies for more than two

kilometers from where they are observed on the surface. (Dodson, 1971). Furthermore, the hilly

areas were noted by creating a hill shade to depict the hills (Fig 4).






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Fig 4: Hillshade map depicting hilly areas

The approach of verifying results of the GIS model generated results was through observing

location of previously drilled high yielding boreholes and analysis of targeted geophysical

electrical resistivity sounding data for the area (RTI 2013-2014). Electrical resistivity

measurement is considered the most suitable for groundwater prospecting since it determines

the sub-surface material properties, VES data analysis was done using resistivity software

Interpex I1D.

Electrical sounding was performed on 102 selected sites based on analysis output. The priority

areas were picked from a suitability level of 5 to 9. In areas where sandstones and grits are

present they were extended to a 1.5 kilometer buffer even if the suitability model was less than

5. Of the 102 tested sites. Only 8 returned dry results representing 7.84% of the tested sites. On

further analysis of the results 5, representing 4.9 % of the tested sites were discovered that the

geology on the geological maps was different from what was observed in the field.

At the final step the GIS analysis confirmed presence of aquifer in the mapped areas where

confirmatory drilling was done and found to be high yielding. Results of analysis shows aquifer

presence in Lotikipi, Lodwar and Napuu which are part of the areas where suitability study






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indicated presence of groundwater (Fig 5). The analysis showed unconsolidated sands and

gravel which are basin filed aquifers provided best areas for aquifer occurrence where the semi

permeable basement acted as the ‘basin’.

Fig 5: Suitability map depicting suitable areas to perform VES analysis

Conclusions.

In conclusion, the suitability model presents a blueprint for future groundwater exploration

using GIS analysis that yield best targeted geophysical survey areas as compared to carrying

out blanket geophysical investigations. Limitations of this study was the other factors that

control groundwater occurrence and movement such as porosity, fracturing, fault zones grain






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size and sorting of particles that were not included. Further modification can thus improve its

overall accuracy if inclusion of this factors is adopted.

Acknowledgment.

Authors are grateful to Ministry of Mining, Govt. of Kenya, County Govt. of Turkana and

WRMA for providing necessary support during the study. Authors also acknowledge ESRI

Eastern Africa for support on the use of their software and literatures.

References

Christensen, B.N. and Sorensen, K., 1998. Surface and borehole electric and electromagnetic

methods for hydrological investigation. Eur. J. Environ. Eng. Geophysics. 3, 75-90.

Dodson R.G, 1971, Geology of Northern Turkana Degree sheet 18, Geological Survey of

Kenya, Report No.87, Ministry of Natural Resources, Republic of Kenya

Ellwood, E.R. et al., (2016). Mapping Life – Quality Assessment of Novice vs. Expert

Georeferencers. Citizen Science: Theory and Practice. 1(1), p.4. DOI:

http://doi.org/10.5334/cstp.30

ESRI 2015. ArcGIS Desktop: Release 10.3. Redlands, CA: Environmental Systems Research

Institute.

Fisher, N. I., T. Lewis, and B. J. J. Embleton, 1987, Statistical Analysis of Spherical Data,

Cambridge University Press.

Hussien M. Hussien , Alan E. Kehew a, Tarek Aggour, Ezat Korany , Abotalib Z. Abotalib ,

Abdelmohsen Hassanein , Samah Morsy (2016) An integrated approach for

identification of potential aquifer zones in structurally controlled terrain: Wadi Qena

basin, Egypt, published in Catena Journal 2016

Jones M.J. , 1985, The weathered zone aquifers of the basement complex areas of Africa,

Quarterly Journal of Engineering Geology and Hydrogeology, v. 18:35-46, Geological

Society of London.

Kumar C. P., 2014, Groundwater Data Requirement and Analysis, Munich, GRIN Verlag,

http://www.grin.com/en/e-book/281602/groundwater-data-requirement-and-analysis

Loke, M.H., 2001. Tutorial: 2-D and 3-D electrical imaging surveys. Course Notes for USGS

Workshop "2-D and 3-D Inversion and Modeling of Surface and Borehole Resistivity

Data", Torrs, CT.

Malczewski, J (2006). GIS-based Multicriteria decision analysis: a survey of the literature.

International Journal of Geographical Information Science, 20, 703 – 726.

Mayfield, C. J. (2015). Automating the Classification of Thematic Rasters for Weighted

Overlay Analysis in GeoPlanner for ArcGIS (Master's thesis, University of Redlands).

Opeyemi J. Akinrinade , Rasheed B. Adesina,(2016) Hydro geophysical investigation of

groundwater potential and aquifer vulnerability prediction in basement complex terrain

– A case study from Akure, Southwestern Nigeria.

RTI. 2014, Taking stock of groundwater Discovery In Turkana (Kenya): The socio-economic

impact of WATEX exploration one year later (September 2013-2014).

T. Tachikawa, M. Hato, M. Kaku and A. Iwasaki, 2011, "Characteristics of ASTER GDEM

version 2," Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE

International, Vancouver, BC, 2011, pp. 3657-3660. Doi:

10.1109/IGARSS.2011.6050017






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Walsh J., Dodson R.G, 1969, Geology of Northern Turkana Degree sheet 1, 2, 9 and 10,

Geological Survey of Kenya, Report No.82, Ministry of Natural Resources, Republic

of Kenya

Biographical Notes

John Esther received his BSc Degree in Geography from Egerton University in 2014. He did

short Courses in GIS at ESRI Eastern Africa late 2014 and has been working since 2015 in

Rural Focus Ltd as GIS Analyst.

Njenga Wainaina has a BSc. Degree in Geomatic Engineering and Geospatial Information

Systems from Jomo Kenyatta University in 2013. He is currently pursuing MSc. Geospatial

Information Systems and Remote Sensing. He is currently working at Rural Focus Ltd as a

Surveyor/GIS officer.

Maxwell Barasa received a BSC Degree in Geology from the University of Nairobi in 2008, he

did a certificate course in GIS and Remote sensing at Regional Centre for Mapping of

Resources for Development. He also has a postgraduate Diploma in Hydrology from the

University of Nairobi. Maxwell has 7 years’ experience in hydrogeology and has worked with

Rural Focus for 5 years as an assistant hydrogeologist. He is currently pursuing Msc. Applied

Geophysics.

Contacts

John Esther

Rural Focus Ltd

P.O.BOX 1011

Nanyuki, Kenya

[email protected]

www.ruralfocus.com


Apply Geography to Your Work

and Make Better Decisions

Adding a location aspect to your

projects gives you more insight into your

data, improves planning, and helps you

work more efficiently.

Cross-Cutting Issues Track

GI-diversity – Taking the activities in the Kakamega-Nandi forests area as example

Gertrud Schaab



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GI-diversity – Taking the activities in the Kakamega-Nandi forests area as

example

Gertrud Schaab, Germany

Karlsruhe University of Applied Sciences (HsKA),

Faculty of Information Management and Media (IMM)

Key words: forest conservation, interactive visualization, environmental education, capacity

building

Abstract Fifteen years of engagement for and in Eastern Africa has resulted in a myriad of activities,

either research driven or related to capacity building. Geodata processing offers here versatile

opportunities for cooperation, as the spatial reference enables inter- and transdisciplinary

approaches. The paper provides an overview on the past achievements resulting from close

collaboration with Kenyan and Ugandan counterparts. However, the focus is on more recent

examples from the Kakamega-Nandi forests area in Western Kenya. While the processing of

data from disparate sources was achieved by means of ArcGIS and remote sensing approaches,

an open source software-based Web GIS tool now combines the spatially related scientific

findings on the forest use history for visualization and exploration by anyone. The participatory

development of environmental education tools, engaging many stakeholders from the local

communities, resulted in various playful means. Some help to make a start in passing on

abilities in regard to map reading, some to sensitize and steer discussions on the need of forest

conservation. Finally, a university cooperation targeted a streamlined GIS teaching across the

various university departments by jointly developing teaching material which includes geodata

of regional relevance.



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Introduction

Fifteen years of engagement for and in Eastern Africa has fostered many activities, either

research driven or related to capacity building. The more than 50 written publications which

have resulted so far are proof of this engagement. Geodata processing has offered here versatile

opportunities for cooperation, as the spatial reference enables inter- and transdisciplinary

approaches. The paper provides an overview on the past achievements resulting from close

collaboration with Kenyan and Ugandan counterparts. However, the focus is on more recent

examples from the Kakamega-Nandi forests area in Western Kenya.

It all started with the BIOTA East Africa project, funded by BMBF (German Federal Ministry

of Education and Research) from 2001 to 2010. About 10 project groups investigated the

impact of fragmentation and human use on the biodiversity of Eastafrican rainforests. The

overall goal had been to come up with recommendations for a sustainable use of biodiversity.

By considering three upland rain forest areas in Kenya and Uganda, comparisons were possible

(see www.biota-africa.de/reg_east_intro_ba.php?Page_ID=L800

_04_01). Kakamega Forest in western Kenya served as focus study site. Kakamega Forest is

known for its high biodiversity. Together with the Nandi Forests it once formed one continuous

forest block. But 60% of natural forest cover got lost over the past 100 years. Being placed in

one of the most densely populated rural areas of Kenya, the forests experience severe pressure.

There is thus need to create awareness for forest conservation, also as many people depend on

the forest’s resources (Mitchell et al., 2009).

Within the research framework, subproject E02 provided support by means of GIS and remote

sensing. Our research questions were linked to investigating longterm forest cover change (e.g.

Lung & Schaab, 2010) and forest use history (Mitchell, 2011). The findings helped to study

their impact on biodiversity via spatial extrapolation of biological field data (e.g. Lung et al.,

2012) and scenario simulation demonstrating if e.g. tree plantations could compensate forest

loss (Farwig et al., 2014). As today’s forest is heavily shaped by man, research moved also into

the surrounding agricultural landscape. Here, we derived information on farmland use and run

livelihood scenarios (Lübker, 2014). In regard to activities related to capacity building (Schaab

et al., 2009b), we got actively involved in forest management planning assuring that the

scientific findings of the BIOTA East Africa project would be of use for implementations on

the ground. A couple of GIS and geodata use courses had been offered to the counterparts or

local stakeholders. BIOTA-East became especially known for its training of PhD and Masters

Students. As a major measure to sustain the started activities, the Biodiversity Information

Centre (BIC, www.iaf.hs-karlsruhe.de/gvisr/bic/) was set up in Kakamega Town. It

encompasses a GIS unit with all the many geodatasets processed during BIOTA times.

The processing and application of geodata enabled the integrated analysis of diverse field data,

their extrapolation in space and over time, as well as recommendations for a sustainable use

and conservation of Kakamega Forest. About 500 geodatasets are available at the BIC, of which

about 300 are related to the Kakamega Forest or Kakamega-Nandi forests area. The information

centre is meant to support and empower local stakeholders. We are aware that handling of

geodata requires expertise. Therefore, with the progressing of the BIOTA project visualizations

for the communication of the often also interdisciplinary results gained increasing importance

(Schaab et al., 2009a). A major impact had the publication and dissemination of The BIOTA



Gertrud Schaab



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East Africa Atlas. Rainforest Change over Time (Schaab et al., 2010). The atlas is special as it

addresses three distinct user groups (Schaab et al., 2011). However, map reading literacy does

not seem to be a common skill in Eastern Africa. This observation has guided our efforts in the

more recent years.

More Recent Activities

A Web GIS-based viewing tool on forest use history For tracing use histories of East African forests, data from disparate sources was processed by

means of ArcGIS and remote sensing approaches. Extending time series based on satellite

imagery with historical aerial photography and old topographic maps enabled for a reaching

back beyond the start of any commercial-scale exploitation of these forests. Further information

was gained from forestry records as well as archive materials. The deduction of land cover

from village names, interviews with the oldest people living adjacent to the forests as well as

soil pollen analysis added further valuable information. Via the spatial reference all data could

be integrated and jointly interpreted (Mitchell, 2011).

A visualization tool now allows for a combined study of the resulting scientific text and the

processed geodata (Weist et al., 2013). Realized as a Web GIS-based viewing tool, it combines

the spatially related scientific findings on the forest use history for visualization and exploration

by anyone. The tool consists of three components (Fig. 1): a text window for reading the

scientific text, a map window displaying the geodatasets as well as the geodata quality diagram.

Implemented functionality allows for navigation within the text, from where hyperlinks open

the map window to display the relevant geodatasets. The user can change between the text and

the map windows via tabs. For navigation within the map view the commonly expected

functionalities are available. In the table of content (TOC) the layers to be displayed can be

selected. Further geodatasets can be added from a predefined list. Where needed, the user can

open a legend. For each geodataset, meta information is provided. And finally, one geodataset

can be selected at a time for visualizing its six quality parameters in the quality diagram (Huth

et al., 2008). If the user is not yet accustomed with how the visualization works, an explanation

of the quality diagram can be accessed.

The application was implemented based on free OpenSource GIS technology and is database-

driven: In a PostgreSQL database all required details on the geodatasets (information on quality

parameters and for display mainly), map extents and geodata layers per text link are stored.



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Fig. 1: Web GIS-based viewing tool for a combined text and geodata study. The tool integrates

a geodata quality diagram.

The PostGIS spatial extension also enables the storing of vector datasets in the

database. UMN Mapserver is used for rendering the raster datasets into PNG graphics. Realized

as a client-server architecture, a get-request for the map, text and values is evoked when a

hyperlink in the text window is clicked. As response SVG, XTML/JavaScript and PNG data is

sent. Vector data is transferred in SVG geometries on demand, while JavaScript enables

interactivity.

The scientific text builds on geodata-based forest cover narratives for twelve case studies

pointing to the underlying causes and drivers. By further including spatially-explicit indices

for the three investigated forest areas, local versus commercial disturbance can be compared

with forest cover change, thus adding to understanding of the various impacts (Mitchell. 2011).

The tool, therefore, serves the purpose of documenting and presenting the particular research

results. It enables the working with the gathered data/information in an interactive environment

with features/functions exceeding those commonly supported by a Web mapping system.

Especially the dynamic geodata quality diagram has to be named here. With about 300 spatio-

temporal geodatasets having been judged for their quality covering here six distinct quality

parameters, this unique collection can serve as a concept in particular for those geodata

collections which include a historical dimension. Overall, the tool facilitates the direct tracing

of scientific statements/conclusions made in the text and offers thus the opportunity for

scientists to gain new insights. As such, a comprehensive visualization and information tool on

five Eastern African rainforests in Kenya and Uganda has become available.



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Developing environmental education tools

Increased knowledge leads to awareness, and both together stimulate action (Hungerford &

Volk, 1990). In order to create awareness for the Kakamega-Nandi forests ecosystem,

environmental education tools based on geodata were considered useful. By following a

participatory development which engaged many stakeholders from the local institutions and

communities, various tools were jointly developed. Here, playful means allowing for informing

in an entertaining way had been favoured. For a more detailed description see Paul & Schaab

(2015). Some of the tools help to make a start in passing on abilities in regard to map reading,

some to sensitize and steer discussions on the need of forest conservation. The combination of

playful approaches with mapped information appears promising for in particular creating the

needed map reading literacy. In any, ideally participatory natural resource management and

planning, geodata is considered a prerequisite. Implementation aims at analogue as well as

digital versions. The analogue versions were to be produced first to be locally applicable also

in environments without electricity and technical devices. In order to counteract the so-called

digital divide, currently also digital versions are produced acknowledging their high attraction.

For successful environmental education programs the participation of stakeholders in the

development process is a requirement (Athman & Monroe, 2001). We had opted for

‘participation by consultation’ as most suitable strategy. Four iterations served the purpose of

enhancing the anticipated tools. Representatives of almost 40 stakeholder institutions tested

four tools. During the first stakeholder workshops the tools were introduced, tested and

assessed within focus groups by means of demonstration prototypes. In the second stakeholder

workshop, evaluation of the further enhanced tools was based on focus group discussions and

questionnaires. The goal was to learn from insights of those representing the multipliers of the

tools when in use later. Finally, end-user testing addressed various potential user groups and

were based on observations and semi-structured interviews by means of the previously tested

questionnaires. This way, final prototypes could be achieved serving final production.

The following four analogue tools could be produced: Flipbooks using cartoon drawings relate

to the four major forest threats. A story each on the harmful forest use and, after turning the

flipbook over and using it in the opposite direction, on a sustainable use instead, exemplify the

potential of behavior change. Here, however, no geodata is involved. Leaflets on nature trails

make use of geodata and are meant to support ecotourism. Their link to environmental

education is within the development process itself. Besides, two games were developed. The

Local Forest Use Card Game works similar to the Memory card game which is widely enjoyed

in Europe. The players have to pair cards on 27 forest uses identified for Kakamega Forest.

When a user finds the matching second card, it presents the rules in regard to the particular

forest use including reasoning and alternatives. A small map allows for locating the restricted

areas. This environmental education tool proved to be very much liked as it informs in a playful

manner. The Forest Cover Change Jigsaw Puzzli is more difficult and requires an instructor. It

is accompanied by a narrative on forest change in the Kakamega-Nandi forest area. Here, users

of an advanced learning level can visually experience the landscape’s changes while gaining

knowledge on reasons for the tremendous change. This game too, showed high potential in

steering discussions among the participants. Of all three tools based on geodata (Fig. 2), the

jigsaw puzzle demanded the most sophisticated cartographic skill, this for coming up with

puzzle pieces. ArcGIS was used here for the manually performed cartographic generalization.



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Fig. 2: Final prototypes of three environmental education tools which build on geodata.

The environmental education tools are meant for non-formal education, where learners choose

to learn. Here, the chance is to involve also adults and less-educated people who often are the

important decision makers in rural areas (Pandey, 2006). The aims 1) of creating awareness for

forest conservation (keyword ‘sustainable forest use’) , and 2) of building and enhancing map

reading literacy (keyword ‘spatial citizenship’) are likely to benefit from the playful

approaches. But the success depends on disseminating and making the tools widely available.

Here, further workshops are required for training multipliers and instructors.

Streamlining GIS teaching across universities

The experiences made during the BIOTA East Africa project had revealed that there is still a

long way to go in Eastern African society for tapping the full potential of geodata and thus

gaining its full benefits (cp. Schaab, 2007). Weaknesses exist in the application of geodata

which reaches well beyond the mere creation of maps. Therefore, in addition to having set-up

a GIS unit of use of all stakeholders and equipping it with the many geodata having arisen from

the project, and the dissemination of diverse map-based information sources, we aimed at a

more sustainable capacity building. A university cooperation served the purpose of passing on

skills in training the next generations of decision makers. This way also institutions like the

Biodiversity Information Centre will be strengthened and the many geodata available will be

used in a sustainable way (cp. Schaab, 2015).

Funded by DAAD (German Academic Exchange Service) with finances from BMZ (German

Federal Ministry for Economic Cooperation and Development) a partnership between

Karlsruhe University of Applied Sciences (HsKA), Masinde Muliro University of Science and



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Technology (MMUST) and Makerere University (MAK) was established. Taking advantage

of knowledge transfer from Germany to Kenya and Uganda in regard to educating students in

geomatics technology, the harmonization of GIS teaching would allow also for mobility

between Eastern African universities. Under the project name ‘UnivGisKoop’ (see www.iaf.hs-

karlsruhe.de/gvisr/project/univgiskoop.html) progress has been made in streamlining up-to-

date GIS teaching into existing curricula structures across various departments of the two East

African universities over the past four years. Emphasizing a regional focus, geodata use

applications of regional relevance were considered during the joint development of adequate

teaching material. At MMUST a GIS lab was established via additional funding by DAAD

which allows the teaching of a higher number of students in the practical usage of GIS

functionality.

Fig. 3: Impressions from workshops and final conference within the UnivGisKoop project

which streamlined and promoted enhanced GIS teaching.

Most importantly, teaching materials for three modules were elaborated which include lecture

notes and exercises, the latter applying ArcGIS for Desktop. Besides ‘GIS basics’ and

‘Advanced GIS’, a module on ‘Geodata use’ was deemed beneficial to provide the ground for

an effective teaching of GIS theory and techniques. The joint development of harmonized GIS

content for use in undergraduate and graduate programmes across faculties and universities has

led to enhanced capacities related to resource materials and delivery, the latter covering

knowledge as well as skills. Concepts of integrating the modules into the curricula have already

benefited curricula reviews at the two target universities. For reaching out and visibility an



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international conference on GIS was organized in 2015. Although funding ended, the project

partners agreed that the established partnership can further serve as springboard for more

cooperation. Fig. 3 provides impressions from the project workshops and the conference.

The project allowed supporting those responsible for educating a large pool of young people.

Integrating GIS teaching into various university curricula offers the chance for enhanced,

modern study programs fostering integrated interdisciplinary approaches based on state-of-the-

art techniques. However, computer labs dedicated to and suitably equipped for professional

geodata processing require the full institutional backing. Applying geodata of regional

relevance provide a closer link to the regions the universities are placed in and their specific

environmental challenges. Thus, geomatics can serve as empowerment tool for a sustainable

development of the region.

Conclusions

By providing an overview plus referring to three efforts in more detail, the intention has been

to demonstrate that geographic information (GI) allows for far more than creating pretty maps

which make reports look more beautiful. GI-diversity encompasses a versatility of different

data sources, offers a myriad of application possibilities, and thus benefits plenty of real-life

situations or questions. However, of major importance is to adequately address the potential

users. Here we have provided one example of addressing scientists by providing easy access

and a working environment for exploration of geodata which is available but less known and

had resulted from laborious data gathering and compilation. The Web GIS application is meant

to stimulate the use of this data. In the second example, the ordinary people are the addressees,

but in their role as stakeholders of the protected forest resources or as representatives of local

institutions/organizations. As map reading literacy cannot be taken for granted, playful

environmental education tools building on geodata have been developed, which are meant to

create ‘spatial citizenship’ for participating in informed decision making for natural resource

planning. The third effort described is aiming at a much broader impact in regard to both people

reached in the longterm as well as coverage of methods for geodata processing and

interpretation. Hands-on how to train the next generations in benefitting from making use of

the spatial reference and GI-technology will contribute to empowering many more people. As

such, since the BIOTA project ended, a start has been made in reaching out for a more effective

capacity building on geodata usage to enable the tapping of the full potential arising from

geodata, which is increasingly becoming available but is under-used. More specifically, our

work aims to contribute to the conservation and sustainable use of forests in Eastern Africa.

The example of the Kakamgea-Nandi forests can serve here as an example for demonstrating

how the spatial reference enables inter- and transdisciplinary approaches and facilitates science

to feed into implementations. But as pointed out, geodata processing per se offers far more

versatile opportunities for cooperation.

References

Athman, J.A. & M.C. Monroe (2001): Elements of effective environmental education

programs. In: A. Fedler (Ed.), Defining Best Practices in Boating, Fishing, and Stewardship

Education, Recreational Boating and Fishing Foundation, Washington D.C., 37– 48,

http://www.d.umn.edu/~kgilbert/educ5165-

731/Readings/Elements%20of%20Effective%20EE.pdf (25 October 2014).



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Farwig, N., T. Lung, G. Schaab & K. Böhning-Gaese (2014): Linking land use scenarios,

remote sensing and monitoring to project impact of management decisions. In: Biotropica,

46(3), 357–366 (doi: 10.1111/btp.12105).

Hungerford, H.R. & T.L. Volk (1990): Changing learner behavior through environmental

education. In: The Journal of Environmental Education, 21(3), 8 –22.

Huth, K., N. Mitchell & G. Schaab (2008): Judging and visualising the quality of spatio-

temporal data on the Kakamega-Nandi forest area in west Kenya. In: A. Stein, J. Shi & W.

Bijker (Eds.), Quality Aspects in Spatial Data Mining, London, Boca-Raton, 297-314.

Lübker, T. (2014): Object-based remote sensing for modelling scenarios of rural livelihoods in

the highly structured farmland surrounding Kakamega Forest, western Kenya. PhD thesis,

Technical University Dresden, Institute for Cartography, http://nbn-

resolving.de/urn:nbn:de:bsz:14-qucosa-150628.

Lung, T., M.K. Peters, N. Farwig, K. Böhning-Gaese & G. Schaab (2012): Combining long-

term land cover time series and field observations for spatially explicit predictions on

changes in tropical forest biodiversity. In: International Journal of Remote Sensing, 33(1),

13-40 (doi: 10.1080/01431161.2010.527867).

Lung, T. & G. Schaab (2010): A comparative assessment of land cover dynamics of three

protected forest areas in tropical eastern Africa. In: Environmental Monitoring and

Assessment, 161(1), 531-548 (doi: 10.1007/s10661-009-0766-3).

Mitchell, N. (2011): Rainforest change analysis in Eastern Africa: A new multi-sourced, semi-

quantitative approach to investigating more than 100 years of forest cover disturbance. PhD

thesis, University Bonn, Department of Geography, http://nbn-resolving.de/urn:nbn:de:

hbz: 5N-26793.

Mitchell, N., G. Schaab & J.W. Wägele (Eds.) (2009): Kakamega Forest ecosystem: An

introduction to the natural history and the human context. In: Karlsruher

Geowissenschaftliche Schriften (KGS), Reihe A, Bd. 17, ed. by G. Schaab.

Pandey, V.C. (2006): Environmental Education. Delhi.

Paul, L. & G. Schaab (2015): Developing environmental education tools based on geodata to

create awareness for the Kakamega-Nandi Forests ecosystem. In: Kartographische

Nachrichten. Journal of Cartography and Geographic Information, 5/2015, 296-303

(http://www.dgfk.net/index.php?do=pub&do2=kna&do3=oa).

Schaab, G. (2015): Die Bedeutung von DAAD-Hochschulkooperation für die angewandte

Forschung in Entwicklungsländern: Geodatennutzung in Ostafrika. In: Hochschule

Karlsruhe – Technik und Wirtschaft, Forschung aktuell 2015, 23-25.

Schaab, G. (2007): Capacity development within the BIOTA East Africa project - Promoting

the use of spatial information in biodiversity research and management. In: P. Zeil & S.

Kienberger (Eds.), Geoinformation for Development. Bridging the Divide through

Partnerships, Heidelberg, 44-49.

Schaab, G., B. Asser, K. Busch, P. Dammann, N. Ojha & H. Zimmer (2009a): Interaktive

Visualisierungen zur Unterstützung von Biodiversitätsforschung und -management in

einem Entwicklungsland – Erfahrungen und Herausforderungen. In: Kartographische

Nachrichten. Fachzeitschrift für Geoinformation und Visualisierung, 5/2009, 264-272.

Schaab, G., B. Khayota, G. Eilu & J.W. Wägele (Eds.) (2010): The BIOTA East Africa atlas.

Rainforest change over time. Karlsruhe.

Schaab, G., T. Lübker, T. Lung & N. Mitchell (2009b): Remotely sensed data for sustainable

biodiversity management. The case model of Kakamega Forest in western Kenya. In:



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Proceedings (digital) of the 33nd International Symposium on Remote Sensing of

Environment “Sustaining the Millennium Development Goals”, 4-8 May 2009, Stresa,

Lago Maggiore (Italy), ref. 479.

Schaab, G., T. Lübker & S. Schwarz (2011): The challenge of targeting different user groups

with The BIOTA East Africa Atlas. In: Proceedings (digital) of the 25th International

Cartographic Conference (ICC 2011) ‘Enlightened View on Cartography and GIS’, 3-8

July 2011, Paris (France), ID CO-296.

Weist, C., K. Busch, N. Mitchell & G. Schaab (2013): Investigating East African Forest Use

Histories: a Visualisation Tool for a Combined Text and Geodata Study. http://www.iaf.hs-

karlsruhe.de/ gvisr/projects/tools/vis_tool/text/title.htm.

Acknowledgements

Special thanks goes to former coworkers (by name Nick Mitchell, Cornelia Weist, Dorothea

Heim, Nirmal Ojha), the former student Lisa Paul, project colleagues (including Alex Khaemba

and Gerald Eilu) and many more cooperation partners in Kenya (e.g. at MMUST, Nature

Kenya, KWS, KFS, NMK) and Uganda (e.g. at Makerere University).

Biographical Notes

Gertrud Schaab teaches at Karlsruhe University of Applied Sciences within the Bachelor study

programmes on Geo-Information Management and Geodesy & Navigation as well as within

the International Geomatics Master programme. Her lectures cover cartography, GIS and

remote sensing. These fields she is also following with her research, currently focusing on

Eastern Africa.

Contacts

Prof. Dr. Gertrud Schaab

Karlsruhe University of Applied Sciences

Faculty of Information Management and Media

Moltkestr. 30

D-76133 Karlsruhe

GERMANY

Tel.: +49 /(0)721/925-2923

Fax: +49 /(0)721/925-2597


Web site: www.iaf.hs-karlsruhe.de/gvisr/


Use of GIS and SDI in Promoting Coffee Quality in Maraba Sector, South Province of Rwanda

Hitimana Jean Pierre



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Use of GIS and SDI in Promoting Coffee Quality in Maraba Sector, South

Province of Rwanda

Hitimana Jean Pierre, Rwanda

Key words: GPS, GIS and SDI, environmental restoration, metadata, Geodata, Webmapping,

Geo-portal

Abstract In these last recent years farmers in the sector of Maraba in South Province of Rwanda had face

challenges to keep producing good quality coffee and to be the 1st place in competition of cup of

Excellence. We conducted this research in order to show how the use of Geographic Information

Systems (GIS) and Spatial Data Infrastructure (SDI) models as the research method growing and

producing good quality coffee in taking into consideration environmental factors like: Elevation and

temperature, Rainfall and water supply, Soil, Aspect and slopes.

The findings in this research about the selection of zones of coffee plantation and relation relationship

to coffee quality will be published on Geo-Portal where maps and metadata created or collected will be

available to the public and particularly to Maraba sector community. The results of this research will

be presented to Maraba sector community in a workshop so that they can gain knowledge of the land

and the good quality of Maraba coffee.






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Introduction In these last recent years farmers in the sector of Maraba in South Province of Rwanda had face

challenges to keep producing good quality coffee and to be the 1st place in competition of cup of

Excellence. We conducted this research in order to show how the use of Geographic Information

Systems (GIS) and Spatial Data Infrastructure (SDI) models as the research method growing and

producing good quality coffee in taking into consideration environmental factors like: Elevation and

temperature, Rainfall and water supply, Soil, Aspect and slopes.

This research paper focuses on the impact of environment or site selection on coffee quality using GIS

and SDI. According to Nebert (2004) … business development, flood mitigation, environmental

restoration, community land use assessments and disaster recovery are just a few examples of areas in

which decision-makers are benefiting from geographic information, together with the associated

infrastructures (i.e. Spatial Data Infrastructure or SDI) that support information discovery, access, and

use of this information in the decision-making process.

Methodologies

Methodology Using Site Selection (Environment) and GIS Fieldwork According to FAO (2011) growing and producing good quality coffee, several important environmental

factors should be taken into account for instance Elevation and Temperature, Rainfall and water supply,

Soil, Aspect and slopes and a Fieldwork has been conducted in maraba sector, south province in Rwanda

in order to determine those factors (see the picture1below).

Picture1: Discussion with the local Community in localization of the area of research






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The Maraba sector, south province in Rwanda has got 3 Zones of coffee fields and each Zones has got

its particular flavor of coffee.

Map1: Maraba sector, South Rwanda

Elevation The influence of geography on the flavor of a coffee bean is profound. All coffee grows in the tropics,

but the altitude at which it is grown contributes significantly to a coffee’s taste profile. Mountainous

regions of the Coffee Belt, a tropical band extending approximately 30º north and south of the equator,

produce the world’s truly great arabica coffees and Rwanda is included in that zone.






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Map3: World Map

Central and South America, southern Asia and some Pacific islands, and mid to southern Africa

represent the world’s foremost coffee growing regions. High elevations above 3,000 to 6,000 feet and

beyond provide ideal growing conditions for the coffee tree: a frost-free climate averaging 70º F year-

round, moderate rainfall, and abundant sunshine. These conditions prolong bean development to

enhance flavor, brightness, and aroma—the primary factors by which coffee quality is typically

evaluated.






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Figure1: effect of altitude on coffee flavor

High-grown beans are hard, dense, and possess the potential for concentrated coffee flavor. Central

America grades the quality of its coffees on the basis of the altitude at which they are grown. A strictly

hard bean (SHB) designation in Guatemala, for example, signifies coffee grown at or above 4,500 feet.

Mexico applies the term altura, meaning “high” in Spanish, to identify its high-altitude coffees.

Generally, as growing elevation increases, a coffee’s flavor profile becomes more pronounced and

distinctive. From the mild and sweet taste qualities of a low-grown Brazilian bean at 2,500 to 4,000 feet

to the soaring floral notes of an Ethiopian grown at elevations approaching 6,000 feet, altitude heightens

a coffee’s ability to deliver bigger and brighter varietal nuance and complexity.

Low-elevation coffee regions, on the other hand, impose harsher growing conditions on the coffee tree.

Higher temperatures and less rainfall cause coffee to ripen more quickly resulting in beans with taste

qualities that range from simple and bland to earthy or murky. The bean structure of coffee grown

downslope tends to be softer than the hard-bean coffees grown above 4,500 feet. Consequently, these

more delicate coffees do not tolerate darker roasts well and suffer from increased flavor loss when

stored.






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Map4: Map of Altitudinal regions of Rwanda

High elevation improves the quality of the bean and potential cupping quality. Due to a delay in ripening

brought about by cooler weather associated with higher altitudes, the inherent characteristics of acidity,

aroma and bold bean can develop fully. (Bold bean is classified as being the size between a large and a

medium sized bean, with its width/ length ratio bigger than that of a large bean).

Temperature Arabica coffee prefers a cool temperature with an optimum daily temperature of between 20° to 24°C.

Temperatures greater than 30°C cause plant stress leading to a cessation of photosynthesis. Mean

temperatures of less than 15°C limit plant growth and are considered sub-optimal. Arabica coffee is

frost susceptible. Use of shade trees will reduce the incidence of frost.

The average mean temperatures of selected area of Maraba are 18 – 20 degree (see the map below):






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Map5: Temperature map of Rwanda

Rainfall and water supply Ideal rainfall for Arabica coffee is greater than 1200 to 1500 mm per year. Both the total amount and

the distribution pattern are important.

The maraba Annual rainfall or precipitation is situated between 1200 to 1400 mm (see the map)






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Map6: Precipitation map of Rwanda

Rain should to be distributed over seven to nine months of the year, as is the case especially at higher

elevations. At lower elevations, the dry season is often too pronounced. Lack of rainfall in either amount

or timing can be compensated for by using irrigation.

Coffee needs a dry, stress period with little or no rain to induce a uniform flowering. Without a stress

period, flowering many extend over many months making harvesting more difficult. Maraba has

normally has such a stress period of three to four months of dry weather at elevations of 3000 m.a.s.l.

or more.

Coffee requires adequate water during the growing and cropping period, however it also requires a dry

stress period followed by sufficient rain or irrigation to promote uniform flowering and a good fruit set.

Local community in Maraba has inaf water to irrigate their coffee plantation because of the river Butamu

which passes through Maraba. Moreover the local community has huge quantity of ground water that

they use to drink and coffee Industry management.

Many plantings suffer from moisture stress at the time of year when they need adequate water for growth

and cropping. The local rainfall pattern indicates that supplemental irrigation, especially to induce

uniform flowering and good fruit set, would be beneficial. Unless regular rain is received, young trees

should be irrigated (or hand watered at least twice a week if irrigation is not available) to ensure

establishment of the newly planted trees. Locating coffee plantings near a water supply for possible

irrigation as well as for processing of cherry is desirable. Water requirements can be reduced by use of

proper, well-established, shade trees, mulch and cover crops.

Soil type For successful production, a free draining soil with a minimum depth of one metre is required. Coffee

will not tolerate water logging or 'wet feet'.






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Coffee can be grown on many different soil types, but the ideal is a fertile, volcanic red earth or a deep,

sandy loam. Yellow-brown, high silt soils are less preferred. Avoid heavy clay or poor draining soils.

Most soils in maraba loam earths suitable for coffee.

Map7: Rwanda map of texture class

Coffee prefers a soil with pH of 5 to 6. Many cultivated soils of Maraba are acid (less than pH 5) and

need lime or dolomite. Few soil test results exist, but indicator plants point to a pH less than 5 with low

available phosphorus and thus shortages of many other nutrients. Low pH will limit crop performance

by upsetting the availability of key nutrients to coffee plants (see Figure 2). Good management and

applications of dolomite or lime can alter and improve soil pH and fertility.






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Figure 2. Effect of soil pH on nutrient availability

Slope and aspect (slope % and direction) An easterly or southern facing aspect with a slope less than 15% is preferable. Most locations on the

Central Plateau where Maraba is, have a gentle slope and no extra measures are required. Steeper slopes

present a major erosion risk and require terracing or special management such as contour furrows or

preferably grass strips.

A slight slope will improve air drainage and reduce damage from frost. Do not plant coffee at the bottom

of a slope or in shallow dips where cold air can pool, as frost damage is more likely here. Usually it is

best not to plant the bottom third of a slope as it will be colder and sometimes waterlogged. Exposed

aspects subject to strong winds, should either be avoided

Methodology Using SD The increasing dissemination of geospatial data has the ability to support more informed decisions in a

large number of sectors of today’s society including to improve the quality of coffee in Rwanda. The

National Spatial Data Infrastructure (NSDI) is defined in the US Presidential Executive Order as ‘the

technology, policies, standards and human resources necessary to acquire, process, store, distribute, and

improve utilisation of geospatial data’.

We created a model for GeoPortal and this Model helped us to plan and to test the GeoPortal before it

creation (see figure3). The Model below has got 3 parts: The input, where remote sensing data,

fieldwork and GIS data have inserted into the model. Tools: at this part of the model, data coming from

the input are processed by tools like ArcGIS (to create maps), Apache (to publish data), Mysql (to store

data) and GeoNetwork for maps and metadata visualization for giving the Output on the GeoPortal.






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A Model for SDI Creation

Figure3: GeoPortal Model

Results: Geoportal Creation To grow and produce good quality coffee and several other plants, the use of spatial data for site

selection it is very important. The Maps of Rwanda on rainfall and water supply, Soil type, elevation

and temperature, aspect and slope that are shown in this paper, and their metadata they can retrieve on

this GePortal: http://gis.ur.ac.rw/






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Figure4: Metadata Portal

Conclusion High-altitude coffees generally command a far better market price due to their exceptional flavor and

vibrancy, lower yield per coffee tree, and the challenge they pose to coffee farmers in remote

mountainous areas who must produce and market the crops. This is not to say that the higher a coffee

is grown, the better it is. After all, the quality of any coffee is ultimately determined by an individual’s

taste preference. Altitude is but one factor that shapes a coffee’s overall flavor profile.

Elevation influences a number of these factors and must be considered along with temperature, rainfall

and water supply, soil, slope and aspect when determining where to plant coffee. An elevation greater

than 1000 m above sea level (m.a.s.l.) is required for Arabica coffee. Low elevation Arabica coffee does

not possess the quality required by the world markets. The areas with 3000 metres are preferred for

production of superior quality coffee and Maraba has ample areas of land 3000m m.a.s.l. and above.

References: FAO: http://www.fao.org/docrep/008/ae939e/ae939e03.htm

MINAGRI SPATIAL DATABASE http://www.minagri.gov.rw/IMG/jpg/

COFFEE QUALITY ASSESSMENT

http://www.fao.org/docrep/008/ae939e/ae939e09.htm#TopOfPage

THE EFFECT OF ALTITUDE ON COFFEE FLAVOR

http://scribblerscoffee.wordpress.com/2009/12/02/the-influence-of-altitude-on-coffee-flavor/

RESPONSIBLE GEOSPATIAL DATA SHARING: A CANADIAN VIEWPOINT

http://www.sdimag.com/20120319657/Responsible-Geospatial-Data-Sharing-A-Canadian-

Viewpoint.html






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SPATIAL DATA INFRASTRUCTURE: INTERNATIONAL SCENARIO

http://geospatialworld.net/index.php?option=com_content&view=article&id=19898&Itemid=96

Biographical notes: Hitimana Jean Pierre is a GIS Officer at University Rwanda in the Center of GIS. He holds a Bachelor’s

Degree in Computer Engineering and Information Technology from Kigali Institute of Science and

Technology in Rwanda. He holds a Master in ICT Policy and Regulation.

He has got 4 Certificates in Cisco Systems (CCNA), one Certificate in Spatial Data Infrastructure (SDI)

and He has got skills in Geographic Information Systems. The research interest of Jean Pierre lies on

the application of Geographic Information Science and Remote Sensing to development related issues

and Spatial Data Infrastructure (SDI), Web Mapping, poverty reduction, land use/land cover mapping

and Climate Change analysis, Natural resource management and Sustainable Development. Database

Systems, Analysis of Algorithms, Web Technologies, Software Engineering, Networking, Specification

and design of Graphical User Interface, Spatial Data Infrastructure (SDI), I gained all those skills and

knowledge in the field of Computer Science.

Contacts: Institution: University of Rwanda

Address: P.O.Box: 56 Huye-Rwanda

Tel: +250781139420

E-mail: [email protected]

Website: www.ur.ac.rw

www.cgis.ur.ac.rw

http://geospatialworld.net/index.php?option=com_content&view=article&id=19898&Itemid=96

Esri Eastern Africa

3rd Floor KUSCCO Center, Upper Hill P.O.Box 57783-00200, Nairobi, Kenya

Tel: 254-20) 2713630/1/2, (0) 722 521341Email: [email protected]

11th Esri Eastern Africa User Conference

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