Change Detection and Estimation of Change Analysis using Satellite Images

Proceedings of 2nd International Conference on Innovations in Electronics and Communication Engineering (ICIECE-2013)

9-10th August 2013, Guru Nanak Institutions, Hyderabad P a g e | 252

Change Detection and Estimation of Change Analysis using Satellite

Images Merugu Suresh1, Anuj Tiwari2 and R.P.Naraiah3 1Research Scholar, Department of Civil Engineering 2Research Scholar, Department of Civil Engineering

3Associate Professor, Department of ECE, GURUNANAK ITC,IBP.

Indian Institute of Technology - Roorkee, Roorkee, Uttarakhand, India-247667. Email

Id: [email protected]

ABSTRACT

Urban development and migration of population from

rural to urban areas are global phenomena. Many small and

isolated population centres are rapidly changing into large

metropolitan cities, and hence the conversion of natural land to

urban use is quite obvious. The expansion of urban areas is a

great problem for many countries, especially in the developing

countries, where the rate of urban growth is much higher than

in developed countries. Therefore, it is necessary for the urban

planners to study the growth pattern about various land cover

types. GIS and remote sensing provide a broad range of tools for urban area mapping, monitoring and management.

The results of this work will show the change

detection in land encroachment of built-up land/lakes/ponds

between 1995 and 2011. The business requirements for the

system include land encroachment data propagation, field

database creation, data visualization, feature editing, and

quality assurance/quality control. Subsequently, an attempt to

be made at projecting the observed land use land covers in the next fifteen years.

Key words: Change Detection, Image Processing, Remote Sensing & GIS.

1. BACKGROUND TO THE STUDY

Various studies have shown that there remains only

few landscapes on the Earth that are still in their natural state.

Due to anthropogenic activities, the Earth surface is being

significantly altered in some manner and human being’s

presence on the Earth and their use of land has had a profound

effect upon the natural environment thus resulting into an observable pattern in the land use/land cover over time.

The land use/land cover pattern of a region is an

outcome of natural and socio – economic factors and their

utilization by man in time and space. Land is becoming a

scarce resource due to immense agricultural and demographic

pressure. Hence, information on land use / land cover and

possibilities for their optimal use is essential for the selection,

planning and implementation of land use schemes to meet the

increasing demands for basic human needs and welfare. This

information also assists in monitoring the dynamics of land use resulting out of changing demands of increasing population.

Land use and land cover change has become a central

component in current strategies for managing natural

resources and monitoring environmental changes. The

advancement in the concept of vegetation mapping has greatly

increased research on land use land cover change thus

providing an accurate evaluation of the spread and health of

the world’s forest, grassland, and agricultural resources has become an important priority.

The collection of remotely sensed data facilitates the

synoptic analyses of Earth - system function, patterning, and

change at local, regional and global scales over time; such data

also provide an important link between intensive, localized

ecological research and regional, national and international

conservation and management of biological diversity (Wilkie and Finn, 1996).

Therefore, attempt will be made in this study to map

out the status of land encroachment of Dehradun between 1975

and 2012 with a view to detecting the land consumption rate

and the changes that has taken place in this status particularly

in the built-up land so as to predict possible changes that might

take place in this status in the next 15 years using both Geographic Information System and Remote Sensing data.

2. LITERATURE REVIEW

P. K. Yadav, Mohnish Kapoor, Kiranmay Sarma

(2012) carried out a study on change detection and Conflict

Analysis of Nagzira-Navegaon Corridor, Central India Using

Geospatial Technology, an attempt has been made to find out

the status of ecological corridors between Nagzira Wildlife

Sanctuary and Navegaon National Park using temporal remote

sensing data. It is found that 6.22 percent dense forest is

converted to open forest and 6.66 percent open forest to non

forest between 1990 to 1999. After observation of change

analysis, it is found that maximum deforestation occurred in

the corridors. In the following decade (1999 to 2009), 1.81

percent dense forest is converted to open forest and 2.21

percent of the open forest to non forest. Water bodies have been decreasing continuously in both decades.

Forest loss and degradation occur due to human

interference, urbanization, cattle grazing, noise pollution, air

pollution and so on. As per the details obtained from field

survey regarding the conflict analysis in the corridor, it can be



inferred that most of the sites along the national

highway, state highways and railway tracks show presence of human

1 encroachment in terms of agriculture land and build-up area.

Due to high frequency of traffic on roads/railway, wild animals

often divert from their original dispersal route and enter these hamlets leading to conflict situations.

Yudi Setiawan and Kunihiko Yoshino (2012) carried

out a study on MODIS EVI to detect land use land cover

change due to the change in temporal vegetation patterns. The

wavelet transform was applied to filter out some noises in 161

time series MODIS EVI. Consequently, the MODIS EVI

wavelet-filtered could determine the vegetation phenology in

many vegetated lands. Consistent land use has a typical,

distinct and repeated temporal vegetation dynamics inter-

annually; accordingly, a change in land use type could be

recognized through a change in the pattern of the long-term

vegetation dynamics. Analysed the dynamics pattern of long-

term image data of wavelet-filtered MODIS EVI from 2001 to

2007. The change of temporal vegetation dynamics was

detected by differentiating distance between two successive

annual EVI patterns. Moreover, they were defined the type of

changes using the clustering method, which were then validated by ground check points and secondary data sets.

Arvind C. Pandy and M. S. Nathawat (2006) carried

out a study on land use land cover mapping of Panchkula,

Ambala and Yamunanger districts, Haryana State in India.

They observed that the heterogeneous climate and

physiographic conditions in these districts has resulted in the

development of different land use land cover in these districts,

an evaluation by digital analysis of satellite data indicates that

majority of areas in these districts are used for agricultural

purpose. The hilly regions exhibit fair development of reserved

forests. It is inferred that land use land cover pattern in the area

are generally controlled by agro – climatic conditions, ground water potential and a host of other factors.

It has been noted over time through series of studies

that Landsat Thematic Mapper is adequate for general

extensive synoptic coverage of large areas. As a result, this

reduces the need for expensive and time consuming ground

surveys conducted for validation of data. Generally, satellite

imagery is able to provide more frequent data collection on a

regular basis unlike aerial photographs which although may

provide more geometrically accurate maps, is limited in

respect to its extent of coverage and expensive; which means, it is not often used.

Daniel et al, 2002 in their comparison of land use land

cover change detection methods, made use of 5 methods viz;

traditional post – classification cross tabulation, cross

correlation analysis, neural networks, knowledge – based

expert systems, and image segmentation and object – oriented

classification. A combination of direct T1 and T2 change

detection as well as post classification analysis was employed.

Nine land use land cover classes were selected for analysis. They

observed that there are merits to each of the five methods examined,

and that, at the point of their research, no single approach can solve the land use change detection problem.

Adeniyi and Omojola, (1999) in their land use land cover

change evaluation in Sokoto – Rima Basin of North – Western

Nigeria based on Archival Remote Sensing and GIS techniques, used

aerial photographs, Landsat MSS, SPOT XS/Panchromatic image

Transparency and Topographic map sheets to study changes in the

two dams (Sokoto and Guronyo) between 1962 and 1986. The work

revealed that land use land cover of both areas was unchanged before

the construction while settlement alone covered most part of the area.

However, during the post - dam era, land use /land cover classes changed but with settlement still remaining the largest.

Change detection is the process of identifying differences in

the state of an object or phenomenon by observing it at different times

(Singh, 1989). Change detection is an important process in

monitoring and managing natural resources and urban development

because it provides quantitative analysis of the spatial distribution of the population of interest.

Macleod and Congation (1998) list four aspects of change detection which are important when monitoring natural resources:

i. Detecting the changes that have occurred

ii. Identifying the nature of the change

iii. Measuring the area extent of the change iv. Assessing the

spatial pattern of the change

The basis of using remote sensing data for change detection is that

changes in land cover result in changes in radiance values which can

be remotely sensed. Techniques to perform change detection with

satellite imagery have become numerous as a result of increasing

versatility in manipulating digital data and increasing computer power.

A wide variety of digital change detection techniques have

been developed over the last two decades. Singh (1989) and Coppin

& Bauer (1996) summarize eleven different change detection

algorithms that were found to be documented in the literature by

1995. These include: 1. Mono-temporal change delineation.

2. Delta or post classification comparisons.

3. Multidimensional temporal feature space analysis.

4. Composite analysis.

5. Image differencing.

6. Multitemporal linear data transformation.

7. Change vector analysis.

8. Image regression.

9. Multitemporal biomass index

10. Background subtraction.

11. Image ratioing

In some instances, land use land cover change may result in

environmental, social and economic impacts of greater damage than

benefit to the area (Moshen A, 1999). Therefore data on land use

change are of great importance to planners in monitoring the

consequences of land use change on the area. Such data are of value

to resources management and agencies that plan and assess land use patterns and in modelling and predicting future changes.



Land cover can be altered by forces other than

anthropogenic. Natural events such as weather, flooding, fire,

climate fluctuations, and ecosystem dynamics may also initiate

modifications upon land cover. Globally, land cover today is

altered principally by direct human use: by agriculture and

livestock raising, forest harvesting and management and urban

and suburban construction and development. There are also

incidental impacts on land cover from other human activities

such as forest and lakes damaged by acid rain from fossil fuel

combustion and crops near cities damaged by tropospheric ozone resulting from automobile exhaust (Meyer, 1995).

Hence, in order to use land optimally, it is not only

necessary to have the information on existing land use land

cover but also the capability to monitor the dynamics of land

use resulting out of both changing demands of increasing population and forces of nature acting to shape the landscape.

A remote sensing device records response which is

based on many characteristics of the land surface, including

natural and artificial cover. An interpreter uses the element of

tone, texture, pattern, shape, size, shadow, site and

association to derive information about land cover.

The generation of remotely sensed data/images by

various types of sensor flown aboard different platforms at

varying heights above the terrain and at different times of the

day and the year does not lead to a simple classification system.

It is often believed that no single classification could be used

with all types of imagery and all scales. To date, the most

successful attempt in developing a general purpose

classification scheme compatible with remote sensing data has

been by Anderson et al which is also referred to as USGS

classification scheme. Other classification schemes available

for use with remotely sensed data are basically modification of the above classification scheme.

Land use affects land cover and changes in land cover

affect land use. A change in either however is not necessarily

the product of the other. Changes in land cover by land use do

not necessarily imply degradation of the land. However, many

shifting land use patterns driven by a variety of social causes,

result in land cover changes that affects biodiversity, water and

radiation budgets, trace gas emissions and other processes that

come together to affect climate and biosphere (Riebsame, Meyer, and Turner, 1994).

Also, An analysis of land use and land cover changes

using the combination of MSS Landsat and land use map of

Indonesia (Dimyati, 1995) reveals that land use land cover

change were evaluated by using remote sensing to calculate the

index of changes which was done by the superimposition of

land use land cover images of 1972, 1984 and land use maps

of 1990. This was done to analyze the pattern of change in the

area, which was rather difficult with the traditional method of

surveying as noted by Olorunfemi in 1983 when he was using

aerial photographic approach to monitor urban land use in developing countries with Ilorin in Nigeria as the case study.

4. JUSTIFICATION AND PROPOSED

METHODOLOGY

Various studies have shown that there remain only few

landscapes on the Earth that is still in their natural state. Due to

anthropogenic activities, the Earth surface is being significantly

altered in some manner and human being’s presence on the Earth and

their use of land has had a profound effect upon the natural

environment thus resulting into an observable pattern in the land use/land cover over time.

5. CHANGE DETECTION WITH STEREO

IMAGERY

5.1 RESULTS AND EXPERIMENTS:

Once we have analysed the change detection on IKONOS

images then we can estimate the volume of change by using Cartosat

images. Cartosat stereo images at 2.5 meter resolution can provide

DEM with accuracy in elevation of about 5 or 6 meters. The temporal

stereo pair by Cartosat would help to know the change in elevation

which is equal to the elevation introduced by mining activity. There

have been many studies which have employed temporal DEM from

satellites to detect urbanisation, landslides and earthquake after

effects. The use of this technique in estimation of illegal mining

would be innovative and effective. The process discussed above

would be to check illegal mining and the flow diagram below is complete for checking and estimation.

The flow chart below shows how we would proceed:

Figure 1: Flow chart showing the proposed methodology

Figure 4: Land use change in urban area



Figure 2 : Land use map of year 19 9 5

Figure 3 : Land use map of year 2011

Figure 5 : Land use change in Agriculture area

Figure 6 : Land use change in Forest area

Figure 7 : Land use change in Drainage area



Figure 8: Land use change in Road length.

Table 1. Showing the land use change analysis of between

1995 and 2011

Category Change

(Km2)

Percentage

Change (% )

Remark

Urban Area 36.42 34.61 Increase

Agriculture Area 11.17 9.42 Increase

Forest Area 5.31 3.17 Decrease

Drainage area 4.47 3.57 Decrease

Road length 9.92 8.54 Decrease

From the change analysis of different maps between 1995 and

2011, the following results are obtained about various land cover types.

Table 2. Showing the areas for different years with of

different layers.

Land Use/Land Cover Areas (Km2)

1995 2011

Urban Area 12.31 48.05

Agriculture Area 41.23 32.01

Forest Area 15.61 8.01

Drainage area 37.85 21.06

Road length 4.36 2.23

Total 111.36 111.36

6. CONCLUSIONS

This work demonstrates the ability of GIS and Remote Sensing

in capturing spatial-temporal data. Attempt was made to

capture as accurate as possible five land use land cover classes

as they change through time. It is necessary for the urban

planners to study change detection for further development, so

as to plan the city scientifically. It is evident from the above

results that the city has experienced a large rate of growth in

last sixteen years. The urbanization has mostly extended

towards the south east of the city during 1995-2011. This is

because most of the vegetation lands, gardens and forests have been converted into urban planning areas.

7. REFERENCES

[1] P. K. Yadav, Mohnish Kapoor, Kiranmay Sarma (2012).

Land Use Land Cover Mapping, Change Detection and

Conflict Analysis of Nagzira-Navegaon Corridor, Central

India Using Geospatial Technology, International Journal

of Remote Sensing and GIS, Volume 1, Iss 2, 2012, 90-98.

[2] Yudi Setiawan and Kunihiko Yoshino (2012), Change

detection in land-use and land-cover dynamics At a

regional scale from MODIS time-series imagery, ISPRS

Annals of the Photogrammetry, Remote Sensing and

Spatial Info. Sciences, Volume I-7, 2012 XXII ISPRS Congress, 25 Aug–01Sep 2012, Melbourne, Australia.

[3] Arvind C. Pandy and M. S. Nathawat 2006. Land Use

Land Cover Mapping Through Digital Image Processing of

Satellite Data – A case study from Panchkula, Ambala and

Yamunanagar Districts, Haryana State, India.

[4] Olaniran, J.O (2002). Rainfall Anomalies in Nigeria: The

contemporary Understanding. 55th inaugural lecture,

University press Ilorin. [5] Daniel, et al, 2002 A comparison of Landuse and

Landcover Change Detection Methods. ASPRS-ACSM Annual Conference and FIG XXII Congress pg.2.

[6] Adeniyi P.O and Omojola A. (1999) Landuse landcover

change evaluation in Sokoto – Rima Basin of North

Western Nigeria based on Archival of the Environment

(AARSE) on Geoinformation Technology Applications for

Resource and Environmental Management in Africa. Pp

143-172.

[7] Xiaomei Y and Ronqing L.Q. Y, (1999). Change

Detection Based on Remote Sensing Information Model

and its Application to Coastal Line of Yellow River Delta

– Earth Observation Center, NASDA, China.

[8] Coppin, P. & Bauer, M. 1996. Digital Change Detection in Forest Ecosystems with Remote Sensing Imagery.

Remote Sensing Reviews. Vol. 13. p. 207-234.

[9] Dimyati, et al.(1995). An Analysis of Land Use/Land

Cover Change Using the Combination of MSS Landsat and

Land Use Map- A case study of Yogyakarta, Indonesia,

International Journal of Remote Sensing 17(5): 931 – 944. [10] Macleod & Congalton. 1998. A Quantitative

Comparison of Change Detection Algorithms for

Monitoring Eelgrass from Remotely Sensed Data.

Photogrammetric Engineering & Remote Sensing. Vol. 64.

No. 3. p. 207 - 216.

[11] Meyer, W.B. 1995. Past and Present Land-use and Land-cover in the U.S.A. Consequences. p.24-33.

[12] Moshen A, (1999). Environmental Land Use Change

Detection and Assessment Using with Multi – temporal

Satellite Imagery. Zanjan University.


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Change Detection and Estimation of Change Analysis using Satellite Images

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