Conservation Prioritization using Multi-criteria Decision Model: A Case Study in Part of Western...

JoRSG (2014) 16-26 © STM Journals 2014. All Rights Reserved 6

Journal of Remote Sensing & GIS ISSN: 2230-7990

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Conservation Prioritization using Multi-criteria

Decision Model: A Case Study in Part of Western

Himalayan Ecoregion

N. K. Sharma1*, Richa Tripathi Sharma

2

1Jharkhand Space Applications Centre, Second Floor, Engineer’s Hostel-1, Dhurwa,

Ranchi-834004, India 2Saraswati Kunj, Magistrate Colony, Doranda, Ranchi-834002, India

Abstract Vegetation and land use map was prepared using the satellite image of IRS-1D LISS III sensor. Four vegetation types were mapped, viz., temperate broadleaf forest, temperate

conifer forest, pine forest and degraded forest. Different forest types were characterized

for conservation prioritization based on parameters obtained through field sampling and landscape analysis. These parameters were (i) species diversity, (ii) mosaic diversity, (iii)

total plant value, and (iv) contagion. The areas of low, medium and high priorities for

conservation were mapped by integration of weighted input computed through analytic hierarchy process of these parameters. Present approach of area prioritization

considered biodiversity as well as ecosystem attributes to provide better information to natural resource managers.

Keywords: Area prioritization, conservation, contagion, western Himalaya

*Author for Correspondence E-mail: [email protected]

INTRODUCTION Any reliable assessment of large-scale species

richness is bound to be lengthy and expensive

[1, 2]. Therefore, we need models based on the

structure and composition of a landscape that

predict the significance of a landscape element

for large-scale species richness [3]. The

biodiversity centered approach focusing on the

conservation of hot spots using the criteria of

endemism, habitat loss and species richness

seems insufficient and is criticized due to

several reasons by many researchers [4–8].

Therefore, there is a need for a balanced

approach for conservation considering

biodiversity as well as ecosystem services [5].

Reyers et al. [9] discussed the relationship

between biodiversity and ecosystem services

and by citing Nelson et al. [10] and Reyers et

al. [11] showed that in a win-win scenario,

ecosystem-centric management practices are

good for biodiversity conservation. Integrated

approach including biodiversity and ecosystem

services is more meaningful from

anthropogenic point of view; also it plays an

important driver in the context of conservation

[12]. Maintenance of sacred natural sites by

native communities across the globe is a good

example of protected-area management linked

to religion [13–16]. In the present scenario of

human-dominated planet [17], there is need to

link the biodiversity conservation with the

ecosystem services for effective biodiversity

management [18–20].

In the present study, the authors developed a

method of area prioritization for conservation

in a watershed of Western Himalaya

Ecoregion utilizing a biodiversity and

ecosystem-oriented approach. Present study

utilizes the spatial information (contagion) and

ground parameters (Shannon–Wiener index,

mosaic diversity and total plant value) for area

prioritization.

Study Area

Kalsa watershed located in the lower

temperate region of Western Himalayas was

selected for case study (Figure 1) considering

watershed as a geographical unit to prioritize

the area at local scale for management of

mailto:[email protected]

Multi-criteria Decision Model: A Case Study Sharma and Sharma

__________________________________________________________________________________________

JoRSG (2014) 16-26 © STM Journals 2014. All Rights Reserved Page 17

natural resources [21]. Kalsa watershed

located between latitudes 29° 16' N to 29° 27

'

N and longitudes 79° 31' E to 79° 42

' E

comprising an area of 140.46 km2 under

administrative boundary of Nainital district of

Uttarakhand State, India. The watershed is

constituted by a number of small streams and

ultimately drains water into the Kalsa river.

The river Kalsa joins river Gola at the

southernmost part of the study area. The

elevation varies from 774 to 2523 m above sea

level with varying topographic and

microclimatic conditions. On the basis of

altitudinal variation, the study area can be

divided into subtropical (up to 1500 m) and

temperate (1500–2300 m) zones.

Geographically, very diverse climatic

conditions prevail within the area due to large

variations in altitudes. The microclimatic

conditions vary mainly due to slope, slope-

aspect, vegetation cover and soil conditions.

Along southern part of the watershed (having

lower elevations), summer and rainy seasons

are quite warm, whereas higher elevations are

characterized by chilling and severe winter

and moderate to cool summers. The soil in all

the forest types is derived from the parent

material comprising mainly of quartzite,

quartz porphyry and schists.

Fig. 1: Location Map of Kalsa Watershed.

Journal of Remote Sensing & GIS

Volume 5, Issue 1, ISSN: 2230-7990


MATERIALS AND METHODS

Satellite data (path 98, row 50) of IRS-1D

LISS III (linear image self-scanning) sensor of

February 2001 was used for mapping of

different vegetation types and land use of

Kalsa watershed at 1:50,000 scale. Satellite

data has four spectral bands: green (0.52–

0.59 m), red (0.62–0.68 m), near infrared

(0.77–0.86 m) and shortwave infrared (1.55–

1.70 m). Spatial resolution is 23.5 m with

exceptional shortwave infrared band, which

has a spatial resolution of 70 m. False color

composite (FCC) was generated using the

green, red and infrared bands. Hybrid

approach of classification was utilized, which

includes supervised classification,

unsupervised classification and contextual

refinements [22, 23]. Accuracy estimation was

worked out using error matrix [24]. Vegetation

and land use map was taken as an input for

sampling of different forest types and

computation of contagion. Methodology has

been depicted in Figure 2.

Fig. 2: Diagram Showing Methodology for Area Prioritization Mapping.

Species Diversity Different forest ecosystems classified using

satellite remote sensing data were sampled

using stratified random sampling at 33 sites

using contiguous quadrat approach to ascertain

spatial attributes of vegetation [25, 26]. The

ideal aim of sampling was to study different

communities in each stratum at different

locations in different microclimatic conditions.

Ten quadrats of 10 × 10 m have been laid out

at each site in order to cover variations in

microclimatic conditions. Seedlings and shrubs

were measured in four quadrats of 2 × 2 m and

herbs in four quadrats of 1 × 1 m within each

quadrat of 10 × 10 m. As a result, the area-

weighted sampling design for various

vegetation types utilized a total of 0.04% of

total forested area. Species diversity for each

forest type was computed following Shannon

and Wiener [27] information function which

measures both components of diversity, i.e.,


__________________________________________________________________________________________


species richness and evenness

where, s is the number of species, pi is the

proportion of individuals or the abundance of

the ith species expressed as a proportion of the

total cover. In case of a sample the true value

of pi is unknown; therefore, it is estimated as

ni/N, the maximum likelihood estimator

[28, 29].

Mosaic Diversity

Mosaic diversity providing estimate of

ecosystem complexity was computed by

affinity analysis [30] for different forest types,

using qualitative (presence/absence) data. The

computation method involved three steps,

(i) computation of pairwise similarities among

all subunits using Jaccard index and the mean

similarities for each subunit measuring

distance and dispersion of distance;

(ii) pairwise affinity among all subunits by use

of a standardized rank-sum statistic measuring

the relative distances of two subunits from all

other subunits; and (iii) measuring the slope of

line by plotting the mean affinity of each site

against the mean similarity of each site. The

slope measured the mosaic diversity (M).

Value of m greater than 3 represents a

complex landscape, which indicates presence

of many ecological gradients or not any strong

gradient in particular controlling the

vegetation.

Total Plant Value Total plant value (TPV) for each forest

ecosystem was derived considering the

ecosystem services provided by forests in the

form of forest products. Method for the

computation of TPV was derived by the

modification of formula as follows given by

Belal and Springuel [30]:

TPV = ∑(ni/N) × (Wtree, shrub, herb)

where, ni is the number of uses and N

represents total number of uses. W represents

the weightage assigned to herb, shrub and trees

based on the criteria of plant life cycle strategy

and biomass.

Plant uses were categorized into 10 categories,

viz., medicinal, food, fiber, timber, fodder,

fuel, oil, spices, gum and miscellaneous for

each vegetation type. Value of 1 and 0 in each

category was assigned to each plant species for

a particular use and non-use, respectively,

based on literature [31–33] and personal

communication with locals. Thus, each plant

species got a value from 1 to 10 based on the

number of uses. The weightages of 1, 2 and 3

were assigned to herb, shrub and trees,

respectively, and multiplied to the computed

plant value of each species. Herbs having short

lifespan and less biomass got less weightage in

comparison to shrubs and trees. Summation of

plant values for each plant species provided

the TPV for a vegetation type.

Contagion

Contagion reflects the landscape texture by

quantifying the patch adjacencies and spatial

intermixing. It has advantage over

interspersion, which reflects the spatial

intermixing only [34]. Contagion measures

overall clumpiness on thematic maps and is

used widely in landscape ecology [35–37],

proposed by O’Neill et al. [38]. Large patches

having large proportion of like adjacencies

produce high contagion.

However, contagion decreases with the

increasing fragmentation [39]. Contagion

representing the dispersion and interspersion

of patches was computed using FRAGSTATS

[40]. Forest-type map was taken as primary

input in which forest classes were masked out

separately. In moving-window analysis, a

window of 3 × 3 pixel was moved on the entire

landscape to produce a new output grid

representing contagion. The 4-cell patch

neighbor rule was followed which considers

only the 4 adjacent cells (orthogonal

neighbors) that share a side with the focal cell

for determining the patch membership. Mean

value of contagion for different forest types

was computed. Contagion map was regrouped

in three classes showing areas of low, medium

and high contagion.

Weightage Assignment and GIS modeling

Four parameters computed for each forest type

were subject to weightage assignment using

pairwise comparison matrix [41]. This method

involved pairwise comparisons to create a ratio

matrix of relative weights based on the

underlying scale with values from 1 to 9 to rate

the relative preference of two parameters. The




relative preference values for different forest

ecosystems were derived based on the

parameter values. Each value in the pairwise

comparison matrix is divided with the sum of

column resulting in normalized pairwise

comparison matrix. In normalized matrix, the

average of each row provided an estimate of

the relative weight of the parameter being

compared. Computed weight for each forest

type was assigned to respective forest in

spatial domain. Area prioritization map was

produced by combining the weighted input of

all the forest types to show the priority areas

on the scale of low, medium and high priority

for conservation.

RESULTS AND DISCUSSION

Landscape Composition and Patch Analysis

A total of 10 forest and land-use classes were

mapped (Figure 3) and spatial extent was

quantified in terms of area in hectares (ha).

Forest covers 7724.56 ha area (55% of total

geographical area) while a total of 45%

(6321.44 ha) of the total geographical area is

covered with land-use types including scrub,

agriculture, fallow/barren land, wasteland,

river bed and water body.

Fig. 3: Areal Statistics Showing Landscape Composition in Kalsa Watershed.

(TB = Temperate Broadleaf, P = Pine, TC = Temperate Conifer, DG = Degraded Forest,

SC = Scrub, AG = Agriculture, F/B = Fallow and Barren land, WS = Wasteland,

RB = River Bed, WB = Water Body)

Temperate broadleaf forest (TBL) is spread

over 2551.94 ha area (18.17% of geographical

area). This includes Quercus leucotrichophora

(Banj) mixed and Quercus floribunda (Tilonj)

mixed communities in addition to few patches

of Quercus lanata (Rianj) community. The

moist places along the streams are occupied by

a mixed community of Daphniphyllum

himalense, Fraxinus micrantha, Alnus

nepalensis and Betula alnoides. Altitude of

banj oak forest varied from 1800 to 2300 m.

Banj formed a community with Rhododendron

arboreum, Lyonia ovalifolia, Litsea umbrosa,

Myrica esculenta and Ilex dipyrena. Tilonj-

mixed forest dominated above the 2200 m

elevation with its associates, viz.,

Rhododendron arboreum, Lyonia ovalifolia

and Symplocos chinensis; most of them were

common with Banj mixed forest.

Temperate conifer forest (TC) showed

dominance of Abies pindrow (Silver fir).

Exceptionally growing in a low altitudinal

range, Silver fir shares the dominance with

Quercus floribunda, Rhododendron arboreum

and Lyonia ovalifolia.


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Pine forest (P) is dominated by a single

species, i.e., Pinus roxburghii. A total of

3152.69 ha area (22.45% of geographical and

40.81% of total forest area) was under this

category, showing dominance in Kalsa

watershed.

Degraded forest (DG) occupied nearly 14% of

the total geographical area. These forests are

usually located near settlements and occupy

1972.69 ha area between 750 to 2200 m asl.

This forest includes degraded dry deciduous

forest and degraded temperate broadleaf forest

on the lower altitudes and high altitudes,

respectively. Degraded dry deciduous forest is

composed of Acaica catechu, Dalbergia sissoo

and Holoptelia integrefolia as the dominant

tree species with scattered Euphorbia

royleana. Degraded temperate broadleaf forest

associated with higher altitudes includes the

Rhododendron arboreum as the dominant

species with the varying density of Pinus

roxburghii, Quercus leucotrichophora as chief

associates.

Species Diversity (S), Mosaic Diversity (M),

Total Plant Value (T) and Contagion (C)

Total numbers of species recorded were 135,

57, 60 and 89 in temperate broadleaf,

temperate conifer, pine and degraded forest,

respectively. In forest-type-wise analysis,

diversity was high (H' = 1.44) for temperate

broadleaf forest followed by degraded forest

(H' = 1.40), temperate conifer forest (H

' = 1.02)

and pine forest (H' = 0.96).

The results of affinity analysis showed

maximum value of mosaic diversity (m) for

temperate conifer forest (6.53) followed by

temperate broadleaf forest (6.49), degraded

forest (5.72) and pine forest (5.58). All the

forest types showed the value of m more than

3, which indicates presence of many

ecological gradients controlling the vegetation,

maybe due to varying microclimatic

conditions. Highest values recorded for

temperate conifer forest showing high

ecosystem complexity may explain the

presence of this forest in exceptionally low

altitude area. The most complex landscapes

(high mosaic diversity) were reported showing

high correlation with large areas, high

productivity and warm winters by Scheiner

and Benayas [41] in their study on global

patterns of plant diversity.

Total plant value calculated was 29.2, 12.3,

18.9, 32.7 for temperate broadleaf forest,

temperate conifer forest, pine forest and

degraded forest, respectively.

Contagion value varied from 0 (for maximally

disaggregated patches) to 100% (for

maximally aggregated patches) (Figure 4). The

mean values of contagion for different

vegetation types were 69.94% (temperate

broadleaf forest), 65.09% (temperate conifer

forest), 60.11% (pine forest) and 44.30%

(degraded forest). Three classes of contagion,

viz., low (1.52–34.35%), medium (34.36–

67.17%) and high (67.18–100%) occupied

19.49, 41.97 and 38.54% area, respectively.

The values of all these parameters have been

arranged in Table 1. These values were taken

as input for the computation of weight factor

for each forest type.

Table 1: Value of Species Diversity, Mosaic

Diversity, Total Plant Value and Contagion

for Different Forest Types.

Parameters TB TC P DG

S 1.44 1.02 0.96 1.40

M 6.49 6.53 5.58 5.72

T 29.2 12.3 18.9 32.7

C 69.94 65.09 60.11 44.30

S = Species Diversity, M = Mosaic diversity,

T = Total Plant Value, C = Contagion

TB = Temperate Broadleaf forest,

TC = Temperate Conifer forest,

P = Pine forest, DG = Degraded forest

Weightage Assignment and Mapping of

Priority Areas

A pairwise comparison matrix was generated

for all the four parameters by assigning the

values varying from 1 to 8 and these values

were normalized (Table 2). The average of the

parameter value for each forest type exhibited

the relative weight for that forest type.




Fig. 4: Contagion Index Map of Kalsa Watershed Showing Distribution of Low, Medium and High

Contagion.


__________________________________________________________________________________________


Table 2: Pairwise Comparison Matrix of Species Diversity, Mosaic Diversity, Total Plant Value and

Contagion for Different Forest Types.

TB TC P DG

S, M, T, C S, M, T, C S, M, T, C S, M, T, C S, M, T, C

TB 1, 1, 1, 1 (0.51, 0.28,

0.44, 0.58)

4, 0.5, 8, 3

(0.55, 0.26, 0.47,

0.66)

5, 4, 7, 4

(0.42, 0.33, 0.52,

0.49)

2, 3, 1, 7

(0.52, 0.35, 0.42,

0.41)

0.50, 0.31, 0.46,

0.53

TC

0.25, 2, 0.13, 0.33

(0.13, 0.56, 0.06,

0.19)

1, 1, 1, 1

(0.14, 0.51, 0.06,

0.22)

3, 5, 0.33, 3

(0.25, 0.42, 0.03,

0.36)

0.50, 4, 0.20, 5

(0.52, 0.35, 0.42,

0.41)

0.16, 0.49, 0.06,

0.27

P

0.20, 0.25, 0.14,

0.25

(0.10, 0.07, 0.06,

0.15)

0.33, 0.2, 3, 0.33

(0.05, 0.10, 0.18,

0.07)

1, 1, 1, 1

(0.08, 0.08, 0.08,

0.12)

0.33, 0.5, 0.20, 4

(0.09, 0.09, 0.08,

0.24)

0.08, 0.08, 0.14,

0.10

DG

0.50, 0.33, 1, 0.14

(0.26, 0.09, 0.44,

0.08)

2, 0.25, 5, 0.2

(0.27, 0.13, 0.29,

0.04)

3, 2, 5, 0.25

(0.25, 0.17, 0.38,

0.03)

1, 1, 1, 1

(0.26, 0.12, 0.42,

0.06)

0.26, 0.13, 0.05,

0.38

1.95, 3.58, 2.27,

1.73 7.33, 1.95, 17, 4.53 12, 12, 13.33, 8.25

3.83, 8.5, 2.4, 17

Values in parenthesis show the normalized value, average of which provides the weight

factor of parameters for different forest types.

Similarly, pairwise and normalized pairwise

index was prepared for different parameters.

Species diversity was given high priority over

other parameters. Mosaic diversity and

contagion were considered showing equal

importance and strong importance over total

plant value (Table 3).

Table 3: Pairwise and Normalized Comparison Matrix for Species Diversity, Mosaic Diversity,

Contagion and Total Plant Value.

Parameters S M C T Weight

S 1 (0.588) 5 (0.694) 3 (0.577) 6 (0.353) 0.553

M 0.2 (0.118) 1 (0.139) 1 (0.192) 5 (0.294) 0.186

T 0.1667 (0.098) 0.2 (0.028) 0.2 (0.038) 1 (0.059) 0.056

C 0.333 (0.196) 1 (0.139) 1 (0.192) 5 (0.294) 0.205

1.7 7.2 5.2 17

Values in parenthesis show the normalized value.

Table 4: Computation of Overall Criterion Weights for Different Forest Types.

Parameters S M T C Weight

TB 0.276 0.057 0.110 0.026 0.468

TC 0.089 0.091 0.055 0.003 0.238

P 0.044 0.015 0.030 0.006 0.094

DG 0.144 0.023 0.011 0.021 0.200

Thus, the relative weights produced were

0.468, 0.238, 0.094 and 0.2 for temperate

broadleaf, temperate conifer, pine and

degraded forest, respectively (Table 4).

These values were assigned to respective

forest types in GIS domain and all the forests

were combined to produce an area

prioritization map. This map was grouped

showing areas of low, medium and high

priority for conservation. A total of 25.54%

area has been recorded under high

prioritization. Low- and medium-priority areas

showed dominance in 40.81 and 33.65% area,

respectively (Figure 5).




Fig. 5: Area Prioritization Map for Conservation of Different Forest Types

In spatial analysis for area prioritization,

temperate broadleaf forest and temperate

conifer forest (Table 5) showed more than

95% of their area under high prioritization

category. Pine forest showed 97.4% area under

medium category. Degraded forest exhibited

94% area under low-prioritization class and

2% area under high-prioritization class.

Table 5: Area of Different Forest Types under Low, Medium and High-Prioritization Classes.

Area

prioritization

Vegetation types

Temperate

broadleaf forest

Temperate conifer

forest

Pine Forest Degraded Forest

Low 1.11 0.00 2.14 94.16

Medium 1.04 0.53 97.48 3.84

High 97.85 99.47 0.38 2.00

CONCLUSIONS Temperate broadleaf forest was found most

diverse; it exhibited the high values for total

plant value, mosaic diversity and contagion.

High-priority areas were found composed of

temperate broadleaf and temperate conifer


__________________________________________________________________________________________


forest mainly. Majority of pine forest and

degraded forest contributed towards medium-

priority and low-priority areas for

conservation, respectively.

Priority areas of adjacent watersheds may be

linked and scaled up to a regional scale. Large

patches of temperate broadleaf forest mainly

in the northern part of watershed are important

for ecosystem services. Characterization of

patches in terms of species diversity, total

plant value, mosaic diversity and contagion in

a spatial domain will be useful for natural

resource managers to provide them a scientific

basis for making management decision.

ACKNOWLEDGMENTS The present study was funded by the

Department of Biotechnology (DBT),

Department of Space (DOS) and ISRO

Geosphere Biosphere Programme (IGBP). Dr.

G. S. Rawat, Wildlife Institute of India and

Late Dr. Anil Kumar Tiwari, RRSSC, Indian

Space Research Organization are

acknowledged for guidance and constant

support throughout the study. The help

provided by the forest officials of Nainital

Forest Division during field work is duly

acknowledged.

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