Results and Discussion

Land-use changes in the study region (proposed railway lines with buffer) are depicted in Fig. 3. The results highlight that built up and monoculture cultivation are increasing with reductions in forests and agriculture areas. Improved infrastructure and industrialization in the vicinity of Mysore has led to population influx, thereby leading to urban sprawl with the increase in built-up surfaces (concentrated growth, edge growth). Similar patterns of urbanization were noticed in and around Kushalnagar due to developing industrial areas in its vicinity. The expansion of small towns such as Ponnampete, Husur, is due to industrialization at Kushalnagara and Mysore. Land-use changes in the study regions are discussed below:

Mysore and Kushalnagar (Alignment-1): Built-up and horticulture land uses have increased from 8.4% to 13.9% and 5.7% to 7.9% respectively, with the decline of agriculture (from 69.9% to 63.4%) and forests (12.9% to 11.2%), while other land use classes showed least variations. Urban area intensification and spread was observed at the core and outskirts of Mysore city during 2010 and 2019. Kushalnagar, Hunsur, Periyapattana have witnessed linear urban growth along the direction of major roads.

Mysore and Kutta (Alignment-2): Built-up has increased at Mysore city and its suburbs, Hunsuru with new industrial layout in Mysore and enhanced job opportunities. Reduction in agriculture area by 24.6% and forests by 20.2% in the last eight years (2010 - 2019) has paved the way for built-up, evident from 7.2% to 11.6% increase.



Fig. 3. Land use dynamics along the proposed railway corridor

Modeling land-use changes considering various scenarios helps in visualizing likely changes (Ramachandra et al. 2019a). The study's focus is to understand the potential implications of the proposed railway network on land-use changes, predicted considering agents/factors (fig. 4) such as railway stations, city centres, bus stops, educational institutions, industries, roads in the study region (Fig. 1). Protected areas such as Nagaraholé and wildlife sanctuary were considered as a constraint. Fuzzy logic was used to determine each factor's influence and normalize the ranges between 0 and 1 (Table 2). MCE-AHP was used to understand each of the growth factor's relative importance and determine the weightage of each factor contributing to the change across each land use class (Table 5). The outcome of AHP shows factors such as railway line, city centre, railway station and bus tops have highest influence and contributed to the growth of built up, horticulture and agriculture activities. Weightages were assigned to all factors influencing change depending on the likely level of influence. Site suitability maps were created for each land use classes considering the weights, factors and growth constraints. The site suitability maps along with the transition probabilities (Table 6) obtained from Markov Chains with Cellular Automata were used to simulate land-uses for the year 2026.



Fig. 4. Factors contributing to land use change

Table 5. Various Factors, range and type of infuse and weights across various land use class (Example of Mysore Kushalnagar railway corridor – Alignment 1)

Sl.no

Land use

Factor

Range

Influence

Weight

1

Built up

Railway Track

4500 - 5000

Decreasing *

0.25

City Centre

3500 – 7000

Decreasing #

0.19

Railway Station

0 – 3700

Increasing $#

0.19

4900 - 6500

Decreasing $#

Bus Stop

300 - 3000

Decreasing #

0.15

Roads

0 – 3000

Decreasing #

0.08

Industries

0 – 5000

Decreasing #

0.07

Education

600 -3000

Decreasing #

0.07

2

Agriculture

Railway Track

4500 - 5000

Decreasing *

0.143

City Centre

0 - 2100

Increasing $#

0.143

2100 - 16300

Decreasing $#

Railway Station

0 - 2600

Increasing $#

0.143

5100 - 9200

Decreasing $#

Bus Stop

0 – 1400

Increasing $#

0.143

3000 - 5300

Decreasing $#

Education

0 - 900

Increasing $#

0.143

2600 - 5300

Decreasing $#

Industries

0 - 4400

Increasing $#

0.143

7100 - 14300

Decreasing $#

Roads

1500 – 6500

Decreasing #

0.143

3

Horticulture

Railway Track

4500 - 5000

Decreasing *

0.143

City Centre

0 - 1500

Increasing $#

0.143

12000 - 15600

Decreasing $#

Railway Station

0 - 2300

Increasing $#

0.143

5300 - 9300

Decreasing $#

Bus Stop

0 – 1400

Increasing $#

0.143

3300 - 5900

Decreasing $#

Education

0 - 900

Increasing $#

0.143

4200 - 6200

Decreasing $#

Industries

0 – 3800

Increasing $#

0.143

10700 - 17600

Decreasing $#

Roads

0 – 7500

Decreasing #

0.143

*Liner function, #Sigmoidal function, $ Symmetric

Table 6. Probable land use changes from 2019 to 2026 (Alignment 1)

LAND USE

TO

Forest

Forest Plantation

Horticulture

Agriculture

Built up

Water

FROM

Forest

77.3%

3.2%

9.6%

7.6%

1.7%

0.7%

Forest Plantation

0.0%

83.3%

4.3%

6.0%

5.9%

0.5%

Horticulture

0.0%

0.0%

86.8%

0.0%

13.2%

0.0%

Agriculture

0.0%

0.3%

4.8%

82.9%

11.7%

0.3%

Built up

2.0%

2.0%

2.0%

2.0%

90.0%

2.0%

Water

0.0%

0.2%

9.5%

6.0%

1.3%

83.0%



Fig. 5. Simulated land use 2026

Prediction of land uses in 2026 is carried out considering 2019 as the base year and transition rules based on the probability of land-use changes during 2010 and 2019 and site suitability(for each land use). Predicted land uses for the year 2026 depict an increase in the built-up areas by almost 1.5 times at the cost of forest areas and agricultural lands (Fig. 5) with the implementation of railway projects. Other than the protected forests such as Wildlife sanctuaries (Nagaraholé.), the unprotected forests are highly vulnerable to land-use changes evident with forest loss near Kushalnagar, Hunsur and Kutta. Agriculture areas in the proximity of city centers, and railway stations tend to change to built-up areas (leapfrog and edge growth developments), as in the vicinities of Mysore, Kushalnagar, Ponnampete, Hunsur, Gonikoppa. Kutta and surrounding villages. In alignment-1, built-up and horticulture are likely to increase to 21.9% and 11.5%, respectively with the decline of forest (9.2%) and agriculture lands (53.3%). Similarly, in alignment-2 forests and agriculture would reduce to 16% and 38% respectively, while built-up and horticulture land uses increase respectively to 18.9% and 22.3%.

With the current trends of land use (2010-2019) in Kodagu and Mysore, societal demands and pressures have led to the damage of animal habitat, movement paths, forage grounds, etc. leading to biotic mortality (human and animals), crop damage (Karanth et al. 2013; Gubbi et al. 2014; Venkataramana et al. 2017). Fig. 7 highlights the distribution of flora and fauna such as tiger, panther, elephant, sloth bear, gaur, etc. based on field investigations and the information compiled from the published literatures (Jhala et al. 2011, 2019; Madhusudan et al. 2015; Shankar 2016; KFD 2018).

The proposed railway lines are in the elephant forage zones (confirmed elephant presence zones), and Mysore Thalassery line passes through dense tiger areas. Both the corridors would directly affect the animal forage grounds and movement paths beyond the protected forests. Land use patterns of 2026 show a declining forest cover and agriculture area with escalations in human activities into the vulnerable pockets of animal presence zones, particularly at Hunsur, Periyapattana, Kutta, Gonikoppa, Kushalnagar that would aggravate the problems related to human and wild.

The spatial analyses highlight the need for a holistic approach considering the region's ecological fragility while implementing developmental projects. The simulation of land-uses reveals of likely increase in urban areas at the cost of forests. These land-use changes would lead to habitat loss, loss of foraging grounds (which span beyond the protected forested areas), making it necessary to mitigate disastrous impacts by shunning projects that are likely to aid as catalyst in the largescale land cover changes. Holistic approaches in regional planning are quintessential to conserve ecologically important region and support people's livelihood with the sustenance of natural resources, mitigation of environmental impacts such as frequent occurrences of land or mudslides, human-animal conflicts, forest fires, flooding, water scarcity, etc.



Fig. 6. Distribution of Fauna and Flora