Journal of Biodiversity Management & Forestry

Jha et al., J Biodivers Manage Forestry 2014, 3:1 http://dx.doi.org/10.4172/2327-4417.1000121

Research Article

Journal of Biodiversity Management & Forestry A SCITECHNOL JOURNAL

Assessment of Elephant (Elephas Maximus) Mortality along Palakkad-Coimbatore Railway Stretch of Kerala and Tamil Nadu Using Geospatial Technology Nidhi Jha1, Kiranmay Sarma1* and Prodyut Bhattacharya1 1School

of Environment Management, Guru Gobind Singh Indraprastha University, New Delhi, India

*Corresponding author: Dr. Kiranmay Sarma, School of Environment Management, Guru Gobind Singh Indraprastha University, Sector 16C, Dwarka, New Delhi 10078, India, E-mail: [email protected] Rec date: December 17, 2013 Acc date: March 21, 2014 Pub date: March 25, 2014

Abstract Asian elephants (Elephas maximus) are facing serious threats due to the train-hits and a major concern for the environmentalists and common man. Habitat fragmentation, degradation of habitat quality, forest cover loss and lacking in management of physical barriers could be attributed for these accidents. The present study is carried out in PalakkadCoimbatore railway sector of Tamil Nadu and Kerala states of India to find out the problem sites of incidents using landscape matrices, detection of changes in land use/ cover using temporal remote sensing data and its relation with human elephant conflict and to predict the future scenario based on the present findings. The study reveals that there are noticeable conversion of forests from dense to open (7.12% and 2.75%) during 1989 to 1999 and 1999 to 2012. During 1989 to 1999 and 1999 to 2012 about 5 and 2 percent of the dense forests were changed to other non-forest type, respectively. These changes have great impacts on the forest fragmentation which leads to breaches of elephant corridors and caused deaths to the animals due to train-hits. The number of forest patches showed drastic deductions from 1989 to 2012 (2589 ha to 702 ha), also there is less number of patch density in the later years. From the future prediction study it is estimated that there will be hardly any changes in the dense forest areas by 2020. Mining areas is considered to be one of the key factors for habitat alteration in future as they are located inside the dense forests, which has been projected through matrix in the paper in relation to wildlife habitat.

International Trade in Endangered Species of Wild Fauna and Flora (CITES). Asian elephants are facing a number of threats due to human elephant conflicts. The concept of human-elephant conflict is not new as both the species have been utilizing the same space from thousands of years [3,4]. Habitat fragmentation, degradation of habitat quality, forest cover loss and lacking in management of physical barriers could be attributed as the main reasons of this conflict in India [5-11]. This results in the injury and death of the humans, crop raiding, damage to the properties, and death and displacement of elephants. During the past 50 years many forest areas have undergone drastic changes mainly due to huge amount of anthropogenic and developmental activities and lead to the degradation of natural corridors for animal movements [12,13]. The situation has deteriorated at an alarming rate in the last few decades due to large scale mining operations carried out in the forest areas to cater the needs of various industries [14,15]. The major threat to the elephant life has been railway tracks and national highways, which are passing between the forests [13,16,17]. Linear intrusions like these are becoming the death ground for the elephants and becoming increasing threats to the wildlife in India, due to fragmented forest landscape. Railway lines cause direct loss of habitat, degradation of habitat quality, habitat fragmentation, and population isolation and reduced excess to vital habitats are reported in various places [18]. In India elephant mortality due to train hits are considered as major difficulties in different states viz., Assam, West Bengal, Uttarakhand, Jharkhand, Tamil Nadu, Kerala, Uttar Pradesh, Odisha and Tripura [11]. Tamil Nadu and Kerala stand 5th and 6th on that list and the major accident prone stretch is from Palakkad station in Kerala to the Coimbatore station in Tamil Nadu. PalakkadCoimbatore railway section is identified to be one of the most problematic areas in the country as it passes through the elephant corridors in the Palakkad (Palghat) gap near Walayar. In between 2002 and 2013 thirteen elephants were died in this sector and in 2009 itself three accidents took place in which four elephants lost their lives [19]. In south India wild Asian elephants are presently confined to the forested hilly tracts of the Western Ghats and its adjacent Eastern Ghats in the states of Karnataka, Kerala and Tamil Nadu, and more recently in a small area of Andhra Pradesh, Maharashtra and Goa [20,21] (Figure 1).

Keywords: Elephants; Change detection; Forest fragmentation; Railway track; Remote sensing

Introduction The Asian elephant (Elephas maximus) is an important flagship species for conservation of biodiversity as it is the only living species of the genus Elephas (Elephantidea) and hence Ministry of Environment and Forest, Government of India declared them as the national heritage of India [1,2]. The species is globally categorized in endangered (A1 cd) (IUCN, 1996) list of the Convention on

Figure 1: Map showing the present distribution of Asian elephants in India (Source: ANCF, 2011)

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Citation:

Jha N, Sarma K, Bhattacharya P (2014) Assessment of Elephant (Elephas Maximus) Mortality along Palakkad-Coimbatore Railway Stretch of Kerala and Tamil Nadu Using Geospatial Technology. J Biodivers Manage Forestry 3:1.

doi:http://dx.doi.org/10.4172/2327-4417.1000121 Human-elephant conflict is a global issue and many scholars have put their efforts to bring out the problems and solutions through their research works [22-29]. A good numbers of research works have been carried out in India, and suggest that elephant mortality occurs due to various reasons of conflicts with human being [1-3,12,13,15-17,19,20].

catchers, parrots, peacocks, pigeons and so on. Poisonous and non– poisonous snakes are found commonly.

The main objectives of the present research include the identification of problem sites using landscape matrices, detection of changes in land use/ cover using temporal remote sensing data and its relation with human elephant conflict, enumerate various reasons of elephant mortality and to predict the future scenario based on the present findings.

Material and Methods Study area The Palakkad-Coimbatore section of southern railway of Kerala and Tamil Nadu falls between 10°49'39'' to 11°01'08'' N latitudes and 76°37'59''to 77°01'20'' E longitudes near the Palghat gap which is a natural depression through the Western Ghats ranges. The section has two railway lines (Line A and line B) passing through the area and is considered one of the busy traffic sectors. Line A is an old establishment (came in existence in 1861) while Line B was constructed by railways in 1974 (Figure 2). This region of eastern Kerala and western Tamil Nadu consist of high mountains, gorges and deep-cut valleys. Forty one of Kerala’s west-flowing rivers and three of its east-flowing ones originate in this region. The elevation varies from 20 to 2386 m above msl. The Palghat gap is one of the important physiographic features with the biggest mountain pass in the world (30 to 40 km.) separating the two folds of the Western Ghats on the border of Kerala with Tamil Nadu. The important peaks of the region are Anginda (2386 m), Padagiri (1585 m) and KarimalaGopuram (1440 m). The soil of the region is composed of mainly laterite soil, virgin forest soil, black cotton soil and alluvial soil and the area is drained by two major river systems viz., Malampuzha and Walayar and its tributaries. The study area comprises of several lakes scattered over the region. CoimbatorePalakkad area experiences the humid equatorial tropic climate. The maximum temperature ranges from 28.1 to 37.40 °C whereas the minimum temperature ranges from 22.2 to 25.30 °C. The annual rainfall varies from 1757.6 to 2849.5 mm. Major rainfall is received during June to September in the southwest monsoon (71%). The northeast monsoon contributes about 18%. Humidity is higher during this period and it averages 90%. The forest types found is mainly tropical moist forests and tropical dry forests. The important tree species found in the area are Dalbergia latifolia, Erythrina indica, Cassia sp., Alstonia scholaris, Macaranga indica, Mangifera sp., Terminalia latifolia, Terminalia chebula, Lannea coromondelica, Adina cordifolia, Schleichera oleosa, Ancardium occidentale, Artocarpus integriofolia, Cieba pentandra and Enterolobium saman. Other plant species found in the area are bamboo, wild black pepper, wild cardamom, calamusrattan palm (a type of climbing palm) and aromatic vetiver grass (Vetiveria zizanioides). Elephants are common in almost all parts of the forest whereas other important animals like tiger, leopard, gaur, bear, sambar, spotted deer, Nilgirislangur, bonnet monkey, slender loris, jungle cat, different types of mongoose, jackals, squirrels and hares are also found in the area. The bird species found are jungle crow, king crow, myna, woodpeckers, sunbird, king fishers, skylark, paradise fly

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Figure 2: Location of Study Area with critical section of railway track For the present study temporal remote sensing imagery utilized are for the years 1998, 1999 and 2012 (Table 1). Year

Imagery

Path & Row

Date of acquisition

1989

Landsat TM

144 & 52

21.11.1989

1999

Landsat TM

144 & 52

07.04.1999

1012

Landsat ETM+

144 & 52

01.03.2012

Table 1: Details of the imagery used The satellite images with bands (7) were stacked to prepare an FCC (False Color Composite) of bands 3(Red), 2(Green) and 1(Blue). The relevant Survey of India topographic maps and image were geometrically rectified in 1:50,000 scale using geographic projection system UTM; speroid and datum used were WGS 84 with UTM zone 43N. The GIS and image processing software used are ArcGIS 10, Erdas Imagine 2011, Fragstats and Quantum GIS 1.6. The paradigm for the study is described in figure 3. Field study was carried out during1st February to 11th April 2012. During the field verification, questionnaire survey was conducted covering the stakeholders like State Forest Department, Wildlife, local communities, Railway Department and Agriculture Department. Accuracy assessment of the classification schema is given in table 2.

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doi:http://dx.doi.org/10.4172/2327-4417.1000121

Figure 3: Paradigm for the study

Year

Overall classification accuracy

Overall kappa statistics

1989

84.71%

0.7838

1999

76.28%

0.6715

2012

85.35%

0.8040

Table 2: Classification accuracy

Results and Discussion Landuse/cover distribution and changes From the classification it is found that most of the study area is dominated by agriculture (39.56%-46.17%) and the area increased during the period. The next dominant land cover is forests (both open and dense) and are in decreasing trend (37.53%-33.27%). There is increase in areas under settlement and mining (Figures 4 and 5).

Figure 4: Area under various land use/ cover from 1989 to 2012

Figure 5: Land use/ cover in 1989, 1999 and 2012

Change detection analysis and future prediction It is apparent that there are noticeable changes of forests from dense to open (7.12% and 2.75%) during 1989 to 1999 and 1999 to 2012. Also remarkable conversion of dense forests to non-forest uses such as cash crop farming of coffee and tea, agricultural lands, settlements and open area/ rocky cover (5% and 1.9%) has occurred during the periods. This shows that during this decade indiscriminating cutting of trees took place which leads to these changes. That is why crop raiding and train-hit cases increased rapidly. Increased in dense forests from open forests was observed (1.73% and 2.2%) might be due to the birth of various conservation measures taken by the government and other organizations to safeguard the natural habitat of wildlife during the period (Figure 6, Tables 3 and 4). Change Type

1989-1999

1999-2012

Dense to open forest

69.04 (7.12%)

26.67 (2.75%)

Dense forest to non-forest

48.44 (5%)

18.70 (1.93%)

Dense forest to mining

0.49 (0.05%)

0.043 (0.004%)

Open forest to dense forest

16.7715 (1.73%)

21.33 (2.20)

Open forest to non-forest

17.67 (1.82%)

13.41 (1.38%)

Open forest to mining

0.30 (0.03%)

0.0135 (0.001%)

Non forests to others

193.61 (19.98%)

176.45 (18.12%)

No change

622.78 (64.27%)

713.35 (73.62%)

Table 3: Change matrix during 1989 to 1999 and 1999 to 2012

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1989

1999

2012

Class Matrix Dense

Open

Dense

Open

Dense

Open

Class Area (in ha)

30953.22

5421.525

22513.59

10404.6

21258.27

10974.1

Percentage of landscape (%)

27.2332

4.77

18.1743

8.3992

17.1609

8.8589

Number of patches

1215

1374

405

475

264

438

Patch density

1.069

1.2089

0.3269

0.3834

0.2131

0.3536

Landscape shape Index

25.6267

39.5783

14.7522

18.8884

15.9949

19.0572

Area-Weighted mean

24414.07

276.46

19512.58

2652.98

19826.74

2831.08

Shape area-weighted mean

12.20

276.46

9.3144

5.1963

13.0544

4.4152

Clumpiness

0.9451

0.8425

0.9663

0.9423

0.9626

0.943

Table 4: Class matrix result for dense and open forests during 1989, 1999 and 2012 fragmented in the total landscape area. Results also showed fewer fragmentations in dense forests. Increase in the number of patches leads to steady degradation of already fragmented forests and increases the biotic pressure [11]. There is gradual decrease in the dense forest cover (27%, 18% and 17%) whereas the class matrix for open forests showed the increasing trend (4.7%, 8.4% and 8.8%). In contrast to this the number of patches showed less in the later years for both dense and open forests. This suggests the possible conversion of dense forest into open forest and also the large number of forest patches are been omitted in a period of a decade (Table 6).

Figure 6: Change matrix during 1989 to 1999 and 1999 to 2012

Prediction for 2020 The future of the land cover/use rests centrally on how best the habitat is secured. From the map it is evident that the settlement area will be much bigger than before. Dense forest would be more or less same (Figure 7). The determining factor will depend on the change in mining area in the future because most the mining areas which are located in the study area actually lie inside the dense forest. So any change in mining will adversely affect the wildlife in the forests. This will also lead to the increased human-elephant conflict because of the shrinkage of the natural habitat of elephants which will push them to the edges of the forests near the villages and this will put pressure on the carrying capacity of animals. The probability of change from dense to open and agriculture will be less as compared to the probability of change from open to dense. Also in the year 2020, the probability of change from agriculture to settlement is also high i.e., about 0.1624 (Table 5).

Figure 7: Prediction for 2020

Forest fragmentation The coverage of dense forests in 1989 was about 27 percent while open forests occupied only about 6 percent and were mostly

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Given:2012

Probability of changes in 2020 Dense forest

Open forest

Water body

Agriculture

Rocky surface

Settlement

Mining

Dense forest

0.8556

0.0841

0.0007

0.0216

0.0379

0

0.0001

Open forest

0.1449

0.7575

0.0002

0.0203

0.058

0.019

0

Water body

0.03

0.0214

0.8693

0.0227

0.0566

0

0

Agriculture

0

0.0092

0.0005

0.8184

0.0095

0.1624

0

Open land/ rocky cover

0.2559

0.2133

0

0.1195

0.3402

0.0672

0.0038

Settlement

0

0

0.0004

0.191

0.0024

0.8042

0.002

Mining

0.0742

0.0818

0

0.0221

0.0409

0.0505

0.7305

Table 5: Future prediction matrix for year 2020 Year

Number of patches

Patch density

landscape shape index

1989

2589

2.2779

6.6845

1999

880

0.7104

4.0467

2012

702

0.5667

4.7088

Table 6: Land matrix for different years The depletion of forests and increase in other man made features like agricultural lands, mining areas, transportation establishments, and formation of abandoned open lands were noticed to be concurrent with the increase in the settlements [30]. Prakash and Gupta [31] and Ghosh [32] studied the impacts of mining activities on land use/ cover changes in different parts of India and reported similar type of results Components

to the present study. Goretti [33] and Schejbal [34] have reported vegetation loss and its growth severely hampered due to the establishments of infrastructures and other allied activities. They emphasized dumping of waste materials in the mining areas is one of the biggest problems for forest fragmentation and similar types of results have also been reported by others [35,36]. Large mammals like elephants move long distances both daily and seasonally infact they can move 10 to 15 km a day or have a home range of over 100 square kilometers [6]. The limestone mining activities located in the prime elephant habitat both in Kerala and Tamil Nadu had created lot of disturbances and blocked the natural movement of elephants through such old corridors. Industrial developments like Malabar & ACC cement factories and their associated infrastructure and activities have contributed to the habitat fragmentation to a certain extent (Table 7).

Potential impact

Scale

Remarks

Surface and ground water

High run off, drying out of water sources

***

Soil

Soil erosion due to change in cropping pattern and forest degradation

***

Systematic soil and water conservation efforts will contribute to overcome such situation, water scarcity is one of the major threats for elephants.

Climate

Climate vulnerability -long spell of draught periods

**

Logging operations

High soil erosion, more open spaces within forest

***

Change in forest type due to preferential (economic) species selection

Due to selection of fact growing economic species, ecological association disturbed

*

Destruction of corridor

Limited movement of elephant obstacle in free movements of herds

***

Introduction expansion railway tracks

Railway tracks often disturb wildlife habitats

****

Expansion of road networks

Construction of roads between reserve forests disturb forest ecosystem

****

A. Degradation of physical environment (Soil and water)

B. Vegetation and forest cover Proper implementation of management plan prescription is required, full care should be taken in the high conservation valued forest areas.

C. Infrastructure development (transport, mining, urbanization)

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Elephant corridors should be taken into consideration while designing road and railway networks. Rigorous EIA process

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Unorganized mining and air, water and sound pollutions

Heavy impact on flora and fauna reduce food availability

*****

required before mining, R&R should be mandatory.

Introduction of coffee plantation in new areas

Gradual change in natural ecosystem

**

Expansion of tea gardens and cashew nut plantations

Selective species introduced and nurture reduce food supply for wildlife

**

Historically this area adopted several monoculture cash crop, concern for biodiversity needs to be incorporated in the land use planning process to support wildlife along with commercial crop.

Introduction of monoculture crop: banana, betel nut, mango orchard, cassava and palm oil

Selective species introduced and nurture reduce food supply for wildlife

**

D. Changes in cropping pattern

*Low impact**Moderate impact ***Medium impact ****High impact *****Severe impact

Table 7: Matrix comparing environmental, socio-economic and biological factors for elephant habitat degradation This is also one of the reasons that elephants started using the area close to railway track [19]. Mining produces a lot of noise pollution and elephants is sensitive to loud noises. So mining is not only using the habitat of the wildlife but are also scaring them. The fragmentation of forests leads to dividing the space into smaller units of disjoint landscape and reduces the area of habitat for wild animals. Furthermore, fragmentation isolates species and inhibits movement and reduces the probability of recolonization in the event that a species disappears from a given patch of habitat [37]. These conditions are fulfilling in the present research where destruction of natural elephant corridors are the main factors for elephant death because of train-hit. Theobald and Riebsame [38] and Riebsame et al. [39] stressed that habitat fragmentations of wild animals were highly affected in the mountainous western US due to occupations of dwellers in various ways in the natural forests. Such conditions are cited by numerous researchers throughout the globe [2,6,10,12,13,16,17,23,25-27] which support the findings of present study.

Conclusions The rapid conversions of the land use/ cover pattern is enhancing the human-elephant conflict. In India the main reasons for the conflict could be attributed as habitat fragmentation, degradation of habitat quality, forest cover loss and lacking in management of physical barriers. One of the major threats to the elephant lives is due to train hits where railway tracts were constructed amidst the existing corridors which, leads to forest fragmentation. In the present study it is concluded that conversions of intact forests into scattered patches are one of the main reasons for the mortality of elephant. Relentless population increase and related developmental activities within the forest areas are the main reasons for the forest degradation and fragmentation which lead to the breach of the natural corridors and the natural connectivity. The present study area i.e., near the Palghat Gap is a natural depression through the Western Ghats ranges is ecologically rich in terms of flora and fauna. As the existing two busy railway sectors between Coimbatore and Palakkad are responsible for large number of elephant mortality it is time for the authority to take remedial measures to save this threatened species. The study using geospatial technology could be useful for finding out the spatiotemporal changes in the habitat of any wild animals and could be efficiently utilized for predicting the future scenario.

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Acknowledgement The authors acknowledge the support and cooperation provided by Wildlife Trust of India, Noida during the course of the study. Authors are also thankful to Kerala and Tamil Nadu Forest Departments for their kind permissions and supports for the study.

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