modeling and visualization of forest cover transition in...

Citation: Ramachandra T.V, Setturu Bharath, Nimish Guptha, Bhargavi R Sharma 2017, Modeling and

Visualization of Forest Cover Transition in Protected Areas, Sahyadri E-news, 2017 (58) : 3-45,

http://wgbis.ces.iisc.ernet.in/biodiversity/sahyadri_enews/newsletter/Issue58/index.htm.

Modeling and Visualization of forest cover transition in protected areas

Forests are complex ecosystems, requires efficient forestry management. This involves

consideration of current and future management issues including the richness of living organisms

in forests, the uniqueness of forest dependent people. Monitoring and mediating the negative

consequences LULC (land use land cover) change will help in sustainable planning, decreasing

environmental consequences such as climate change, biodiversity loss, land degradation, soil

erosion and pollution of water, air. Modeling and visualization techniques have been widely

applied to account and forecast changes in forests across the world. Modeling and visualization

allows to quantify spatial and temporal variability in landscape at a finer scale, where similar

studies on the ground would be logistically impossible (Amato et al., 2011). LULC modeling for

forested landscape is often used for predicting trajectories of future, analyzing the fundamental

socio-political, economic, cultural and biophysical forces acting. Modeling approach focuses on

two disparate parts acting at a landscape i.e. processes, and spatial patterns. The two parts have

included in dynamic models by presuppose homogeneity and immediate information transfer at

various spatial extensions (Ramachandra et al., 2017). In general models are developed based on

defensible empirical data, which bring findings back from abstraction to some real world

understanding. The two basic modeling approaches are theoretical and simulation modeling.

Theoretical models are built on mathematical principles and applying them to natural systems

(Clark, 1991), which adds complexity in understanding pattern and process. Simulation models

incorporate simple complexity, but reflect actual ecological accurately with better readability.

Due to continuous improvement in technology, many models are developed for accounting

patterns and processes effectively which connect biophysical and human related factors, explicit

inclusion of human nature interaction mechanisms. The development of various models has

considered biophysical and human interactions on land use at varied scales rather than land

systems as a whole (Rounsevell et al., 2012). Numerous models have been advanced such as

Markov cellular automata models (MCA), AHP (Analytical Hierarchical Processes), logistic

regression models, ANN system (Artificial Neural Network), SWAM optimization (Ballestores et

al., 2012; Bharath et al., 2016; Hyandye and Martz, 2017; Satty, 2008; Bharath et al., 2017;




Tayyebi et al., 2014; Yuan et al., 2009; Liu et al., 2013) for better understanding land use pattern

and processes. Markov chain approach is a stochastic widely implemented to study the dynamics

of land use change at different scales under the assumptions of probability of a system being in a

certain state at certain time can be determined if its state at an earlier time is known (Halmy et al.,

2015). Markov chain analysis does not assume statistical independence of data as required by

logistic regression, which makes it suitable for the spatially dependent land use data to predict all

multi directional land use changes (Overmars et al., 2003; Pontius and Malanson, 2005).

The stationary transitions are assumed by the Markov models is one of the issues and not spatially

explicit which makes it more suitable for short-term projections (Sinha and Kimar, 2013). This

shortcoming of the technique can be overcome through the integration with other different

dynamic and empirical models (Bharath et al., 2014). Integrated modeling approaches are

considered more suitable for modeling land use change processes (Guan et al., 2011). Markov

models can be integrated with cellular automata models (MCA) to model and predict land use

change at different scales. Forest land use changes are most non-linear process, MCA considered

as effective compare to other techniques. MCA model is simple and provides better understanding

of how nonlinear process can aptly simulated by less complexity. MCA has the ability to predict

any transition among any number of categories, as compared to many techniques. Many models

transitions can be only a one way as from one category to one alternative category (Pontius and

Malanson, 2005). Due to MCA’s advantages of multiple LU classes transition, that integrate the

aspatial Markov with the spatial CA model outperformed regression based models in prediction,

accuracy at multiple resolutions stands out as an effective modeling tool.

The modeling of spatiotemporal changes across the protected areas are evaluated using MCA

analysis. A cellular automaton has capable of modeling surprisingly complex behavior, defined as

a large lattice of identical finite state automata (cells) (Von Neumann, 1966). Each state or move

is defined as a triplet:

< 𝐼, 𝑆, 𝑊 >

where I is the set of inputs, S is a set of states (both sets being finite), and W is the next-state

function, defined on input-state pairs. The set of inputs is defined as ordered (or non-ordered) n-

tuples of the states of a finite set of “neighboring” cells. In a two dimensional CA typically




neighborhood is considered as four or eight the adjacent cells in a square lattice based on typical

rules as the Moore and von Neumann neighborhoods respectively. The Markov model is a theory

based on the process of the formation of Markov random process systems for the prediction and

optimal control theory method. Based on the Bayes conditional probability formula, the prediction

of land use changes is calculated by the following equation:

𝑃(𝑁) = 𝑃(𝑁 − 1) ∗ 𝑃𝑖𝑗 (1)

where, P(N) is state probability of any times, and P(N−1) is preliminary state probability.

Transition area matrix can be obtained by,

𝑇𝑟𝑎𝑛𝑠𝑖𝑡𝑖𝑜𝑛 𝑎𝑟𝑒𝑎 𝑀𝑎𝑡𝑟𝑖𝑥 𝑃 = [𝑃11 𝑃12 𝑃13

⋮ ⋮ ⋮𝑃𝑁1 𝑃𝑁2 𝑃𝑁𝑁

] (2)

where, Pij is the sum of areas from the ith land use category to the jth category during the years from

start point to target simulation periods; and n is the number of land use types. The transition area

matrix must meet the following conditions

i. 0 ≤ Pij ≤1

ii. ∑ 𝑃𝑖𝑗 = 1𝑛𝑖,𝑗=0

Predictions of future LULC change of each protected areas (PA) using a MCA model emerged in

three steps: 1) applying the Markov chain analysis to the 1973, 1991, and 2016 LULC maps for

calculating transition matrices; 2) calculating transition potential maps of LULC; and 3)

application of the CA model to the transition matrices and the transition potential maps to predict

the spatial distribution of LULC. Markov process results in a transition probability matrix of land

use change between two different dates, which is derived from observations. The transition

probability matrix provides an estimate of the probability that each pixel of certain LULC class

will be transformed to another class or remains in its class. The accuracy of each prediction is

evaluated using current land use maps based on Kappa statistic. The Kappa-standard index of

optimum point of the diagram as well as Kappa-location index were computed proves a significant




correlation between the simulated and the actual maps Table 1 shows the transition matrix of

ADTR region based on land use maps of 1973-1992; 1992-2016. The transition probability and

transition areas were computed for all protected areas to account category wise changes.

Table 1: Transition probability matrix of ADTR based on 1973-1992.

Sno Category 1 2 3 4 5 6 7 8 9

1 Ever green to

semi evergreen 0.823 0.076 0.020 0.004 0.043 0.008 0.000 0.000 0.026

2 Deciduous

forest 0.221 0.561 0.023 0.113 0.004 0.006 0.008 0.035 0.026

3 Scrub_Grass 0.045 0.101 0.436 0.077 0.006 0.064 0.022 0.048 0.196

4 Forest

Plantations 0.168 0.118 0.033 0.511 0.003 0.019 0.016 0.033 0.100

5 Horticulture 0.058 0.139 0.055 0.045 0.562 0.048 0.018 0.045 0.025

6 Agriculture 0.024 0.040 0.050 0.090 0.196 0.37 0.018 0.097 0.116

7 Water 0.027 0.064 0.031 0.078 0.013 0.020 0.53 0.017 0.221

8 Built-up area 0.011 0.031 0.045 0.041 0.004 0.087 0.026 0.563 0.188

9 Open area 0.000 0.004 0.132 0.019 0.244 0.061 0.003 0.004 0.534

Table 2: Transition probability matrix of ADTR based on 1992-2016.

Sno Category 1 2 3 4 5 6 7 8 9

1 Ever green to

semi evergreen 0.867 0.008 0.013 0.000 0.096 0.008 0.000 0.000 0.009

2 Deciduous

forest 0.007 0.714 0.087 0.000 0.073 0.014 0.000 0.004 0.102

3 Scrub_Grass 0.000 0.163 0.540 0.096 0.110 0.003 0.000 0.001 0.088

4 Forest

Plantations 0.089 0.019 0.099 0.400 0.180 0.033 0.001 0.013 0.166




5 Horticulture 0.091 0.000 0.080 0.000 0.692 0.112 0.000 0.001 0.025

6 Agriculture 0.049 0.001 0.066 0.000 0.195 0.523 0.000 0.028 0.137

7 Water 0.079 0.000 0.049 0.001 0.067 0.022 0.575 0.081 0.126

8 Built-up area 0.004 0.000 0.022 0.000 0.077 0.085 0.001 0.619 0.192

9 Open area 0.014 0.070 0.036 0.000 0.117 0.119 0.000 0.005 0.639

1. Anshi Dandeli tiger reserve (ADTR):

ADTR region is known for its rich flora and fauna in Uttara Kannada district of Central Western

Ghats. The CA_MARKOV analysis has helped in visualization of land use for year 2026 based on

previous land use analysis. Simulated and predicted Land use analysis has depicted in Figure 2 (A,

B) the trend as increase in non-vegetative areas. The land use changes as compared to buffer region

ADTR core region has least changes of forest cover but buffer region has lost major forest cover

near to existing developments and increase in forest plantations. The region lost evergreen forests

from 50.51 to 47.17% (2016-2026) with an increase in plantations and agriculture areas (Table 3).

The monoculture plantations will spread (16.83 %) further and disturb the local ecology and the

built-up area will also show increase in Dandeli town (Figure 1). The population pressure in the

periphery of PA is one more driver of change in land use. Realizing the adverse effect due to

monoculture plantations, forest department should not take initiative of spreading. Reforestation

activities are prompted with native vegetation involving local community.

Table 3: Land use of simulated, projected ADTR with 10 km buffer and accuracy of simulation.

Category ACTUAL LU_2016 SIMULATED_2016 PROJECTED_2026

Ha % Ha % Ha %

Ever green to semi

evergreen forest 155,749.89 50.51 156550 50.77 145440.58 47.17

Deciduous forest 49,942.13 16.20 51044.74 16.55 53999.74 17.51

Scrub_Grass 10,827.89 3.51 9073.22 2.94 10469.42 3.40

Forest Plantations 42,998.74 13.95 44462.45 14.42 51890.53 16.83

Horticulture 3,174.08 1.03 2102.52 0.68 3049.56 0.99

Agriculture 14,354.82 4.66 15173.07 4.92 13584.48 4.41




Water 17,247.87 5.59 17253.48 5.60 17815.47 5.78

Built-up area 3710.29 1.20 2013.74 0.65 3495.36 1.13

Open area 10,328.67 3.35 10661.16 3.46 8589.54 2.79

Total Area 308334.38

Accuracy of

simulation

Kno Klocation Kstandard

0.79 0.82 0.83




Figure 1: Simulated and Predicted LU of ADTR

2. Sharavathy Wild life Sanctuary:

Sharavathy Wildlife Sanctuary (SWLS) is located on the Sharavathy river basin dominated by

dense evergreen to semi evergreen forests in the hills and moist deciduous in foot hills. It was

declared as protected wild life sanctuary under section 18 of WPA, 1972 as (amended upto 2003).

It is a rich habitat for many exotic species of animals including the lion-tail Macaque, varied

mammals, birds and tigers with regular river flow. The evergreen forest cover has lost from 22.87

to 20.99 % due to increase in plantations and built-up area (Table 4). The population pressure also

another factor for forest cover change in SWLS region as the increase in deciduous forest cover

depicted in buffer region at the cost of evergreen cover due to various alterations. The plantations

will constitute 6.67 % by 2026 to reduce the population pressure on forest cover (Figure 2). The

exotic plantations in edges and islands of Linganmakki reservoir and other regions shown an

increment, which will affect the native vegetation and ecological processes. The growth in exotic

cover has affected water yield in the region, which is also evident from earlier field estimations.

The increase in built-up area in the buffer region highlights the growth of various towns and other

villages in the buffer region. The shift from agriculture to horticulture is noticed at very high rates

in buffer areas as constituted 9% of entire region.

Table 4: Land use of simulated, projected SWLS with 10 km buffer and accuracy of simulation.


Ha % Ha % Ha %




Ever green to semi

evergreen forest 49578.93 22.87 52362.04 24.15 45504.67 20.99

Deciduous forest 61303.15 28.28 62976.78 29.05 61430.9 28.33

Scrub_Grass 11356.91 5.24 12022.72 5.55 13137.79 6.06

Forest Plantations 9229.62 4.26 12352.54 5.70 14457.47 6.67

Horticulture 25238.75 11.64 19536.7 9.01 18180.07 8.39

Agriculture 20118.75 9.28 19507.09 9.00 21798.95 10.05

Water 15076.67 6.95 16453.75 7.59 16453.75 7.59

Built-up area 6008.60 2.77 7208.7 3.32 9582 4.42

Open area 18897.31 8.72 14388.37 6.64 16263.09 7.50

Total Area 216,808.69

Accuracy of

simulation


0.81 0.86 0.82




Figure 2: Simulated and Predicted LU of SWLS

3. Mookambika Wild Life Sanctuary:

Mookambika Wildlife Sanctuary is located in Udupi district of Karnataka, established with

AFD.48.FWL.74, dated: 22-05-1978 due to flag species such as lion-tailed macaque and cane

turtle, dense evergreen to semi evergreen forests. The rivers Chakra and Sowparnika drains

through the sanctuary. Projected land use of MWLS highlights loss of vegetation cover during

2016 to 2025 due to anthropogenic activities (Figure 3). The population pressure construction of

series of dams are major factors for forest cover change in the buffer region and all agriculture




regions are getting transformed for coffee plantations post 1980’s. Loss of evergreen cover from

22.38 to 19.22 % and increase in deciduous cover (Table 5). The increase in horticulture (15.32%)

is depicted in buffer region at the cost of forest cover due to various alterations. The increase in

built-up area in the buffer region highlights the growth and transformation of horticulture regions.

Table 5: Land use of simulated, projected MWLS with 10 km buffer and accuracy of simulation.


Ha % Ha % Ha %

Ever green to semi



Scrub_Grass 11721.44 5.14 12319.00 5.40 14804.05 6.49


Horticulture 41861.83 18.35 40916.89 17.94 34956.10 15.32

Agriculture 27375.47 12.00 27403.21 12.01 31356.17 13.75

Water 10667.88 4.68 10107.82 4.43 9520.40 4.17

Built-up area 8904.04 3.90 8451.81 3.71 9792.72 4.29

Open area 15564.11 6.82 16815.66 7.37 15466.84 6.78


Accuracy of

simulation


0.79 0.88 0.87




Figure 3: Simulated and Predicted LU of MWLS

4. Someshwara Wild Life Sanctuary

Someshwara Wildlife Sanctuary is located the presiding deity of the famous Someshwara temple.

The sanctuary has semi-evergreen evergreen and moist deciduous forests. Sanctuary is drained by

Seethanadi river and nalas like Narasipura hole, Golihole etc. The projected land use of 2026

depicts loss of evergreen forests in the buffer region towards eastern side. The loss of evergreen

forest from 31.43 % to 28.84 % due to intensive horticulture cover 21.46 to 28.08 % by 2026

(Figure 4). The deciduous forest cover in west side of PA will be major loss (Table 6). The upsurge

of horticulture cover will lead to higher water requirement causes ground water depletion.

Table 6: LU of simulated, projected SOMWLS with 10 km buffer and accuracy.


Ha % Ha % Ha %

Ever green to semi

evergreen forest 57720.42 31.43 55,078.15 29.99 52,968.09 28.84

Deciduous forest 51310.10 27.94 54,316.27 29.57 45,576.62 24.82

Scrub_Grass 3529.16 1.92 3623.95 1.97 5450.86 2.97


Horticulture 39411.14 21.46 39,432.31 21.47 51,564.96 28.08

Agriculture 13563.64 7.39 13,609.64 7.41 13,452.49 7.32

Water 4760.64 2.59 5285.17 2.88 5409.28 2.95

Built-up area 938.26 0.51 518.04 0.28 1040.89 0.57

Open area 12255.69 6.67 11,630.99 6.33 8,050.48 4.38





Accuracy of

simulation


0.81 0.86 0.84




Figure 4: Simulated and Predicted LU of SOMWLS.

5. Kudremukh National Park (KNP)

KNP is located in Chikmagalur, Dakshina Kannada, Udupi districts of Karnataka, with an area of

736.28 sq. km. The Tunga River, Bhadra River and Nethravathi River originate in the park and

harbors and rich, diverse flora, fauna. The projected land use of 2026 depicts loss of evergreen

cover from 27.22 to 23.04 % (Table 6). The core portion has showed no changes in its cover, but

buffer region has greater loss in its cover due to pressure from non-forest cover land use activities

(Figure 5). The core portion and buffer region has some abandoned mine excavation sites,

reclamation is prompted as a primary forest management activity to reduce impact on wild flora

and fauna. The soil erosion is high from abandoned mine sites during monsoon, which affecting

rivers Tunga, Bhadra. The silt accumulation in Bhadra river is high and some regions tiny channels

are buried due to it. If current forest loss is continued, then by 2026 major native forests will

undergo transition. The horticulture activities will dominate entire landscape by 2026 with 36.16

% (Tabale 7). The government should allot abandoned mining company assets to forest department

for usage than commercial purposes. The agriculture areas in eastern side will shows some increase

in its extent as 5.62 % by 2026.

Table 7: Land use of simulated, projected KNP with 10 km buffer and accuracy of simulation.


Ha % Ha % Ha %

Ever green to semi

evergreen forest 79188.98 27.22 83009.94 28.53 67,041.39 23.04




Deciduous forest 72181.26 24.81 58343.14 20.05 55,126.27 18.95

Scrub_Grass 18747.29 6.44 14715.8 5.06 16,566.00 5.69


Horticulture 73517.56 25.27 86789.86 29.83 105,200.36 36.16

Agriculture 10130.68 3.48 21137.62 7.27 16,356.00 5.62

Water 337.25 0.12 392.13 0.13 392.22 0.13

Built-up area 2447.58 0.84 1928.45 0.66 2887.35 0.99

Open area 32368 11.13 23440.36 8.06 25,988.36 8.93

Total Area 290946.2

Accuracy of

simulation


0.79 0.85 0.82




Figure 5: Simulated and Predicted LU of KNP.




6. Bhadra Wildlife Sanctuary (BWLS)

BWLS is having high tropical biological richness, declared as a 25th tiger reserve under Project

Tiger area by the Government of India on 19th November 1999. The monoculture plantations

transformed composition of forest from past three decades. The large tracts of forests were clear

felled apparently for regeneration, simultaneously selection felling to feed the open market

continued till 1980. The projected land use of 2026 depicts no change in evergreen forest cover,

whereas deciduous forest will reduce from 37.43 to 34.71 % (Table 8). The horticulture in the

buffer region shown some positive changes followed by forest plantations. Horticulture will

constitute 18.5 % and agriculture to horticulture transformation also observed in western side of

study area (Figure 6). The changes will impact the wild fauna as the region is supporting greater

tiger population. The earlier coffee estates in buffer region with native vegetation supporting

diverse herbivorous populations are getting transformed to commercial rubber plantations, causing

serious impact on wild life. The conversion from tea estate to rubber plantations is another major

threat on ecosystem is noticed in recent times.

Table 8: Land use of simulated, projected BWLS with 10 km buffer and accuracy of simulation.


Ha % Ha % Ha %

Ever green to semi



Scrub_Grass 19921.96 9.07 15331.44 6.98 18607.15 8.47


Horticulture 32050.55 14.59 33425.57 15.21 40655.75 18.50

Agriculture 10570.89 4.81 10428.56 4.75 10540.91 4.80

Water 9955.07 4.53 10033.4 4.57 10014.32 4.56

Built-up area 1430.9 0.65 1200.25 0.55 1409.05 0.64

Open area 26524.3 12.07 23710.8 10.79 23473.64 10.68


Accuracy of

simulation


0.88 0.89 0.88




Figure 6: Simulated and Predicted LU of BWLS.

7. Pushpagiri Wildlife Sanctuary (PWLS)

PWLS adjoins Bisle reserve forest to the north and Kukke Subramanya forest range to the west

dominated by steep terrains (160-1712 m) with waterfalls, swampy areas and streams. Projected

land use of 2026 depicts no changes inside PWLS, whereas buffer region adjoining horticulte and

agriculture plots depicts some changes. The loss of evergreen forests would be from 64.29 to 57.9

% by 2026 (Figure 7), with increase in deciduous forest cover from 13.79 to 17.26 % (Table 9). It

is one of the largest tropical evergreen forests in Karnataka with nearly 70% of the sanctuary

covered with evergreen forests. The changes in PWLS will have immense impact on ecology and

hydrology. The scrub forests and deciduous forests are increasing in regions adjacent to national

highways, existing agriculture areas due to conversion of evergreen forests. The introduction of

commercial crops, uncontrolled harvesting of zinger is causing serious impact on water, forest

cover.

Table 9: Land use of simulated, projected with 10 km buffer and accuracy of simulation.


Ha % Ha % Ha %

Ever green to semi


Deciduous forest 13697.26 13.79 13,997.91 14.10 17,140.52 17.26

Scrub_Grass 5417.84 5.46 7,740.72 7.80 9730.097 9.80


Horticulture 6551.82 6.60 6,573.16 6.62 7155.342 7.21




Agriculture 2857.55 2.88 2862.725 2.88 2774.114 2.79

Water 320.75 0.32 0.81 0.00 6.84 0.01

Built-up area 133.88 0.13 0.36 0.00 18.81 0.02

Open area 5952.51 5.99 5474.481 5.51 4977.999 5.01

Total Area 99296.91

Accuracy of

simulation


0.89 0.82 0.81




Figure 7: Simulated and Predicted LU of KNP.

8. Bramhagiri Wildlife Sanctuary (BWLS)

Brahmagiri Wildlife Sanctuary (BRWLS) is located in Kodagu district. Eight rivers and streams

originate in BWLS and flow into the Bay of Bengal (Laxmanthirtha, Ramthirtha and Kabini) and

the Arabian sea (Borapole, Segamanihole, Somahole, Miugorehole and Urtihole). BWLS buffer

region is populated by tiny agricultural villages with coffee, cardamom and commercial

plantations. The projected land use depicts loss of evergreen forest cover from 39.19 to 34.4 %

(Table 10). The core portion of WLS shown positive change in its forest cover, as evident from

Figure 8. Horticulture and commercial crops constitute 30.95 % landscape by 2026 (Figure 8).

Threats to the sanctuary are poaching, encroachments by coffee estates, firewood collection and

hydroelectric projects. Invasive species like Eupatorium, Chromolaena odorata and Lantana

camara have affected the floral diversity of the sanctuary in the borders. The commercial crops in

the buffer region are another major impact on forests. The grasslands and scrub forests are being

encroached for commercial exploitation. Human animal conflicts are noticed due to illegal

collection of small timber, firewood, N.T.F.P., grass by villagers from within BWLS.

Table 10: Land use of simulated, projected BRWLS with 10 km buffer and accuracy of simulation.


Ha % Ha % Ha %

Ever green to semi


Deciduous forest 9692 6.49 11,853.56 7.94 8,039.28 5.39

Scrub_Grass 8115.36 5.44 7517.99 5.04 2,927.98 1.96





Horticulture 39811.75 26.68 42,360.74 28.39 46,151.66 30.95

Agriculture 21366.3 14.32 24,097.63 16.15 27,392.09 18.37

Water 320.39 0.21 124.47 0.08 82.26 0.06

Built-up area 1302.6 0.87 1011.6 0.68 1676.21 1.12

Open area 8633.55 5.79 10,173.80 6.82 11,394.38 7.64


Accuracy of

simulation


0.83 0.86 0.81




Figure 8: Simulated and Predicted LU of BRWLS.

9. Talacauvery Wildlife Sanctuary (TWLS)

TWLS is located in Madikeri taluk of Coorg district, covers an area of 105.59 sq. km. Since the

region is in heart of Western Ghats with more undulating terrain, not prevailing abrupt changes.

The PA is also origin of Cauvery River, which lies on the eastern edge of the sanctuary. Predicted

map of 2026 shows (Figure 9) that the evergreen forest remains almost same while the deciduous

forest will be increased to 6.7% (buffer region). This shows the good management by the forest




department with in core area. Buffer region has commercial activities and conversion of coffee

estates to rubber plantations, due to which agricultural activities may come down (Table 11) by

8.39% (2026). The scrub forest and horticultural activities are shown as steady state. Built-up area

is predicted to increase by a marginal value. Even though there are 6 villages (Bhagamandala,

Thannimani, Cherangala, Sannapulikotu, Aiyangeri and Perur) within the wildlife sanctuary but

due to lesser population the impact is minimum.

Table 11: LU analysis of Talacauvery WLS considering 10 km Buffer

Category ACTUAL LU_2016 SIMULATED_2016 PREDICTED_2026

Ha % Ha % Ha %

Ever green to semi



Scrub_Grass 1587.15 1.66 3977.64 4.16 1460.97 1.50

Forest Plantations 0 0 0 0 0 0

Horticulture 29304.92 30.65 27449.19 28.71 29214.00 30.09

Agriculture 2418.97 2.53 3897.27 4.08 2600.37 2.68

Water 0 0 0 0 0 0

Built-up area 573.67 0.60 582.93 0.61 725.94 0.75

Open area 4742.33 4.96 6516.90 6.82 4665.60 4.81

Total Area 95611.50

Accuracy of

simulation


0.83 0.77 0.74




Figure 9: Simulated and Predicted LU of TWLS.

10. Nagarhole National Park (NNP)

Nagarahole National Park is located in Mysore and Coorg district and comprises of three forest

ranges namely Nagarhole, Kallahalla and Thitimathi. In past, the park was an exclusive hunting

reserve of the king of Mysore. During 1870-1980, around 15% of the forest was cleared and was

replaced by teak plantation. Predicted map of 2026 shows the degradation of deciduous forest

cover by 3.35% while the scrub forest is expected to be increased. The evergreen forest patches

are retained. The built up area is expected to increase due to which the forest plantation as well as




commercial plantation shows an increasing trend while the agricultural activities shows no change.

The horticulture region shown a positive change in is cover due to commercial activities and land

transformation. The uncontrolled land conversion has resulted in less water availability and

drought like conditions towards Mysore district side (East). The protected area faces various

problems such as poaching activities, invasion of weed, increasing human habitation, smuggling

of timber, frequent forest fires (Natural and human induced). The tribal people in the region

depends on forest for grazing, timber and fuel-wood and also practice agriculture. Due to rich flora

and fauna, the tourism activity in the region is another cause of degradation and increased human-

animal conflict.

Table 12: LU analysis of NNP considering 10 km Buffer


Ha % Ha % Ha %

Ever green to semi



Scrub_Grass 16492.32 6.83 16179.21 6.58 17414.37 7.16


Horticulture 40560.03 16.79 32501.61 13.21 41734.44 17.16

Agriculture 61877.34 25.62 53759.52 21.85 61123.05 25.13

Water 4841.64 2.00 4925.97 2.00 4806.45 1.98

Built-up area 2228.31 0.92 3411.81 1.39 2388.78 0.98

Open area 1358.73 0.56 14355.00 5.84 2340.90 0.96


Accuracy of

simulation


0.89 0.86 0.81




Figure 10: Simulated and Predicted LU of NNP

11. Bandipur National Park (BANP)

Bandipur National Park is located in Mysore and Chamarajanagar district of Karnataka, at foot

hills of Western Ghats with an area of 874.20 sq. km. Prediction map of 2026 shows that the

deciduous forest follows the degradation trend and will reduce by 3.21%. The scrub forest tends

to increase by 5.42% in the south-eastern part of the national park (Table 13). The agricultural

activities and the commercial plantations remains same while the built-up area increases signifying

growth in human population within the 10 km buffer region of the protected area. BNP faces forest




fire (natural and man-made) frequently in 2014 2427.707 Ha of forest are lost due to fire. Recently,

in 2017 1000 Ha of forest area lost due to fire, emphasizing need of effective management. The

protected area faces many human induced problems such as poaching activities, encroachments,

illegal logging of fuel-wood, weed infestation, grazing by animals within the national park. Due

to non-effective eco-management, the human animal conflicts is more and loss of crop is high.

Fire, weeds infestation and deforestation has resulted in barren hill tops in eastern side of buffer

and some core portion. Tourism activities are creating pressure on the forest, wild life. All these

problems if continued will enhance the degradation the forest in future. The analysis also highlights

how these cumulative drivers impact forest cover by 2026 with loss from 47.30 to 44.09 %. The

deciduous cover has lost due to fire and land transformation in eastern side of buffer region.

Table 13: LU analysis of BANP considering 10 km Buffer


Ha % Ha % Ha %

Ever green to semi



Scrub_Grass 93593.87 25.58 64878.21 17.72 113419.08 31.00


Horticulture 10607.78 2.90 12280.68 3.35 12135.96 3.32

Agriculture 65011.30 17.77 112497.75 30.73 53478.18 14.62

Water 4992.39 1.36 5027.31 1.37 5034.51 1.38

Built-up area 3942.05 1.08 2757.06 0.75 4414.59 1.21

Open area 10956.82 2.99 1467.99 0.40 11728.44 3.21


Accuracy of

simulation


0.82 0.86 0.75




Figure 11: Simulated and Predicted LU of BANP




12. MM-Hills Wildlife Sanctuary (MMHWLS)

MMHWLS is located in Kollegal taluk of Chamarajanagar, serves as a corridor between BRT

Tiger Reserve and Cauvery Wildlife Sanctuary. Predicted map of 2026 shows that the deciduous

forest continues to decrease (2.26%) and convert into scrub forest. The horticultural and

agricultural activities remain steady state. Prediction shows that there is a slight increase in built-

up area (351.36 Ha). This can be considered as the main reason of continuous degradation of forest

and decrement in the water bodies (0.09%). Soligas and Lingayats are the dominating communities

present in the MM Hills. Temple festivals every year draws millions of devotees to come, and this

causes degradation. Non-timber products are the main source of livelihood for these communities.

Other than religious tourism, there were mining activities for black granite practiced in the region

till 1996 and at present there is a huge pressure to restart the quarry work in the sanctuary which

will enhance the degradation. Considering degradation from 2016-2026 as %, regulatory agencies

should not allow any activities that harm ecosystem. The buffer region has gomla lands, which can

be used for grazing through effective management by community.

Table 14: LU analysis of MMHWLS considering 10 km Buffer


Ha % Ha % Ha %

Ever green to semi



Scrub_Grass 34725.69 11.33 40350.28 12.88 42428.60 13.65

Forest Plantations 0 0 0 0 0 0

Horticulture 3114.37 1.02 36.54 0.01 2880.33 0.93

Agriculture 54838.09 17.89 53898.54 17.20 55290.05 17.78

Water 1026.68 0.33 2023.60 0.65 740.99 0.24

Built-up area 1623.19 0.53 696.34 0.22 1974.55 0.64

Open area 5097.91 1.66 1516.62 0.48 4614.76 1.48

Total Area 313317.66 Ha

Accuracy of

simulation


0.85 0.80 0.77




Figure 12: Simulated and Predicted LU of MMHWLS.

13. Biligirirangaswamy Temple Tiger Reserve (BRT)

BRT Tiger Reserve is located in Chamarajanagar district of Karnataka, acts as a connector between

Eastern and Western Ghats. The crest and troughs in the protected area allows diverge flora and

fauna in the region. Prediction map of 2026 shows that there is a reduction in deciduous forest by

1.60% and increase in forest plantations. The built-up area increases while open area reduces. The

agricultural activities and horticultural practices remains unchanged. There is a slight increment in

evergreen forest at the top of the hill because of less interference by humans. The soligas who were

hunters and shifting cultivators are residents of BRT. They are given a land after the declaration




of the sanctuary. Extraction of non-timber forest product is the main source of income for them.

Increment in their population has led to more fragmentation of forest. Also BR temple is present

on the hill top which comes in the core region of the tiger reserve. Religious tourism also plays a

major role in degradation of forest. The terminal point of two rich biodiversity hotspots should be

protected from further degradation. The effective management of forest, gomala lands in the buffer

region should be taken by forest department under various schemes exist. The barren regions,

vaccent forest blocks should enriched by native plantations can reduce impact on core area.

Table 15: LU analysis of BRT considering 10 km Buffer


Ha % Ha % Ha %

Ever green to semi



Scrub_Grass 50613.39 22.66 45587.61 20.01 51550.56 22.92


Horticulture 13340.21 5.97 6687.63 2.94 10184.94 4.53

Agriculture 48939.48 21.91 69544.89 30.53 49586.76 22.05

Water 3069.41 1.37 1292.49 0.57 4061.16 1.81

Built-up area 5255.93 2.35 1077.93 0.47 5701.14 2.54

Open area 8526.84 3.82 4738.68 2.08 10246.59 4.56


Accuracy of

simulation


0.87 0.82 0.75




Figure 13: Simulated and Predicted LU of BRT.

14. Cauvery Wildlife Sanctuary (CWLS)

Cauvery Wildlife Sanctuary is located in the district of Chamarajanagar, Ramanagar and Mandya

in Karnataka, located at the banks of river Cauvery. The predicted 2026 map shows the continued

decrement in the deciduous forest (3.26%) and increment in the scrub forest (7.44%). Prediction

shows that the agricultural and commercial plantation remains same. The built-up area remains

almost same while there is a slight decrease in the open area (0.68%). Forest plantations are

predicted to come up in future. The forest degradation continues because of more human

intervention as various tourist spots such as Mekedhatu and hogenakal falls are present within the




buffer region of CWLS. Another reason for degradation is practising the religious activities inside

the protected area (Muthathi – present in North-western part of the CWLS). Due to pressure on

forest for various human needs, fragmentation in the region has increased which has led to drought

conditions and will further affect the biodiversity in the protected area.

Table 16: LU analysis of CWLS considering 10 km Buffer


Ha % Ha % Ha %

Ever green to semi

evergreen forest 0 0 0 0 126 0.03


Scrub_Grass 69789.38 17.19 102679.65 24.93 100359.54 24.63

Forest Plantations 0 0 0 0 126 0.03

Horticulture 7102.26 1.75 8737.56 2.12 7669.62 1.88

Agriculture 95303.98 23.48 75405.24 18.31 81528.75 20.01

Water 3266.79 0.8 4471.11 1.09 3154.95 0.77

Built-up area 3959.89 0.98 4979.61 1.21 3152.79 0.77

Open area 28872.60 7.11 6527.61 1.59 26211.15 6.43

Total Area 411805.80 Ha

Accuracy of

simulation


0.93 0.96 0.91




Figure 9: Simulated and Predicted LU of CWLS.

15. Bannerghatta National Park (BNP)

Based on the evaluation the land use map of 2026 has generated by using 2009-2015. The projected

land use of 2027 depicts the loss of forest cover from 41.38% to 35.59% and shows an increase in

urban area from 4.49 to 9.62 % due to various layouts in the periphery (Figure 15). The barren

land has reduced from 4.17 to 3.36 % by conversion of open spaces to urban (Table 17). The forest

cover present in southern parts represents minimal disturbances (connected to Tali reserve forest

and Cauvery wild life sanctuary), whereas northern portion shows more transitions. The




uncontrolled and unplanned growth of greater Bangalore would have irreversible impact on forests

of BNP. Kolifram gate, Batterayana Doddi village, Weavers colony, Kalkere region and

Bannerghatta village, Anekal road represents higher loss in forest cover due to new paved surfaces

created in the form of townships highlights the region likely to undergo major transition in 2027.

The built-up area expansion in Anekal industrial area, Kalkere, Basavanapura and Weavers colony,

Uttarahalli Manavartha Kaval are the major regions represents urban growth.

Table 17: Simulated and projected land use of BNP from 2015-2027.

Categories ACTUAL LU_2015 SIMULATED_2015 PREDICTED_2027

Ha % Ha % Ha %

Forest 50372.60 41.38 49444.88 40.62 43315.05 35.59

Agriculture 58977.00 48.45 58208.18 48.64 60207.03 49.46

Barren land 5075.30 4.17 6668.57 4.66 4460.76 3.66

Water 1834.00 1.51 2011.68 1.65 2031.11 1.67

Urban 5462.10 4.49 5387.75 4.43 11707.15 9.62

Total area 121721.06

Accuracy of simulation Kno Klocation Kstandard

0.93 0.86 0.87




Figure 15: Simulated and projected LU of BNP with 5km buffer.

References:

1. Amato, A.W., Bradford, J.B., Fraver, S. & Palik, B.J. 2011. Forest management for mitigation

and adaptation to climate change: insights from long-term silvicultural experiments. Forest

Ecology and Management 262 (5): 803-816.

2. Bharath Setturu, Rajan, K.S., Ramchandra, T.V., 2014. Status and future transition of rapid

urbanizing landscape in Central Western Ghats – CA based approach, ISPRS Technical

Commission VIII Symposium, 09-12 December 2014.

3. Bharath Setturu, Rajan, K.S., Ramchandra, T.V., 2016. Geo-visualisation of landscape

dynamics in Sirsi and Haliyal forest divisions of Central Western Ghats, proceedings of




National Conference on Challenges of Civil Engineering Innovations (NCCCEI 2016), At

Bangalore from 4-5 May, 2016; Sri Venkateshwara College of Engineering NH 7, Vidyanagar,

Bengaluru International Airport Road, Bengaluru - 562157.

4. Bharath Setturu, Rajan, K.S., Ramchandra, T.V., 2017. Geo-Visualization of land cover

transitions in Karwar region of Central Western Ghats, International symposium on Water

Urbanism and Infrastructure Development in Eco-Sensitive Zones, Hyatt Regency, Kolkata,

India 6-7 January 2017,

5. Dang, A.N. and Kawasaki, A., 2017. Integrating biophysical and socio-economic factors for

land-use and land-cover change projection in agricultural economic regions. Ecological

Modelling, 344, pp.29-37.

6. Guan, D., Li, H., Inohae, T., Su, W., Nagaie, T. and Hokao, K., 2011. Modeling urban land

use change by the integration of cellular automaton and Markov model. Ecological Modelling,

222(20), pp.3761-3772.

7. Halmy, M.W.A., Gessler, P.E., Hicke, J.A. and Salem, B.B., 2015. Land use/land cover change

detection and prediction in the north-western coastal desert of Egypt using Markov-CA.

Applied Geography, 63, pp.101-112.

8. Hyandye, C. and Martz, L.W., 2017. A Markovian and cellular automata land-use change

predictive model of the Usangu Catchment. International Journal of Remote Sensing, 38(1),

pp.64-81.

9. Liu, X., Ou, J., Li, X. and Ai, B., 2013. Combining system dynamics and hybrid particle swarm

optimization for land use allocation. Ecological Modelling, 257, pp.11-24.

10. Overmars, K.P., De Koning, G.H.J. and Veldkamp, A., 2003. Spatial autocorrelation in multi-

scale land use models. Ecological modelling, 164(2), pp.257-270.

11. Pontius, G.R. and Malanson, J., 2005. Comparison of the structure and accuracy of two land

change models. International Journal of Geographical Information Science, 19(2), pp.243-265.

12. Ramachandra, T.V., Setturu, B., Rajan, K.S. and Chandran, M.S., Modelling the forest

transition in Central Western Ghats, India. Spatial Information Research, pp.1-14.

13. Saaty, T.L., 2008. Decision making with the analytic hierarchy process. International journal

of services sciences, 1(1), pp.83-98.




14. Sinha, P. and Kumar, L., 2013. Markov land cover change modeling using pairs of time-series

satellite images. Photogramm. Eng. Remote Sens, 79(11), p.11.

15. Tayyebi, A., Perry, P.C. and Tayyebi, A.H., 2014. Predicting the expansion of an urban

boundary using spatial logistic regression and hybrid raster–vector routines with remote

sensing and GIS. International Journal of Geographical Information Science, 28(4), pp.639-

659.

16. Von Neumann, J., 1966. Theory of Selfreproducing Automata (edited and completed by A. W.

Burks), Univ. of Illinois Press, 388 pp.

17. Yuan, H., Van Der Wiele, C.F. and Khorram, S., 2009. An automated artificial neural network

system for land use/land cover classification from Landsat TM imagery. Remote Sensing, 1(3),

pp.243-265.

modeling and visualization of forest cover transition in...

Documents