Conservation & Sustainable Use of the Dry Grassland Ecosystem in Peninsular India: A Quantitative Framework for Conservation Landscape Planning

Transcription

1 Conservation & Sustainable Use of the Dry Grassland Ecosystem in Peninsular India: A Quantitative Framework for Conservation Landscape Planning Submitted to the Ministry of Environment and Forests, Government of India Report: April 1 st 2012 March 31 st 2013

2 Abi Tamim Vanak, Ph.D. National Environmental Sciences Fellow, Ashoka Trust for Research in Ecology and the Environment, Bangalore, India Research Associates: Ameya Gode, Abhijeet Kulkarni and Chintan Sheth, Ashoka Trust for Research in Ecology and the Environment, Bangalore, India Host: Jagdish Krishnaswamy, Ph.D., ATREE, Bangalore, India Collaborators: Enrico Di Minin, Ph.D., University of Helsinki, Finland Kriti K. Karanth, Ph.D., Centre for Wildlife Studies, Bangalore, India 2

3 Conservation & Sustainable Use of the Dry Grassland Ecosystem in Peninsular India: A Quantitative Framework for Conservation Landscape Planning Abi Tamim Vanak * Executive Summary: Dry grassland biomes of peninsular India are prime habitat for several endangered species. The patchy distribution of grasslands in the hot and semi-arid states of India requires urgent, but systematic planning and management as they are diminishing at an unprecedented rate. Conservation of dry grasslands is a global challenge and the key to executing strategies lies in accurate mapping of areas using remote sensing data, zonation exercises and implementation of model systems for management and conservation. Country-wide grassland occurrence probability maps were prepared using MODIS (250m) 2011 time series data to identify large grassland areas for further fine-scale mapping in four states (Andhra Pradesh, Karnataka, Madhya Pradesh and Maharashtra). Fine-scale maps were generated using LANDSAT Enhanced Thematic Mapper plus (30m) data. Thirty one tiles from four states were classified into several land cover types. Surveys were carried out for collection of ground truth points of various land cover types, which were used for a more accurate classification of remotely sensed data. Species-distribution models of grassland species were coupled with preliminary presence data from sign-based surveys of key faunal species in all large grassland polygons. These data were then used to identify key grassland sites for the next phase of the project. In the next phase of the project, LISS IV satellite imageries are being acquired and processed to produce high-resolution (5.8m) landcover maps of the key grassland sites. We will also conduct a landcover change analysis over the past 15 years of all the selected sites. Occupancy surveys for key faunal species in grassland mosaics are ongoing. Surveys of key floral species will be undertaken to determine the vegetation composition of the different grassland sites. Finally human-wildlife conflict surveys are ongoing to feed into the Zonation software to identify priority areas using species distributions, threats, disturbance and costs. * National Environmental Sciences Fellow, Ashoka Trust for Research in Ecology and the Environment 3

4 TABLE OF CONTENTS Project Highlights... 5 Introduction... 6 Objectives... 6 Study Site... 7 Methodology... 7 Data Acquisition... 7 Data processing... 9 Field survey Results From MODIS data From LANDSAT ETM+ data Field survey results Proposed work in the next six months Publications in preparation:

5 Project Highlights First large-scale identification, mapping and analysis of grassland habitats in India Multi-tiered approaches for identification of grassland biomes on a national scale Large-scale ground surveys Fine scale (30m) district-wise land cover maps Ultra high-resolution (5.8m) land cover maps of model grassland sites for conservation Utilisation of latest (2013) satellite imagery for land cover analysis Large-scale biodiversity inventory of grassland areas encompassing four states of India Novel scientific methods: application of ZONATION algorithms for selecting model systems for conservation prioritisation 1000 household surveys for human-wildlife conflict 5

6 Introduction The dry savanna grasslands of the hot arid and semi-arid regions of peninsular India are considered as prime habitat for several critically endangered species such as the Great Indian Bustard (Ardeotis nigriceps), lesser florican (Sypheotides indica) and other endangered and endemic species such as Indian wolf (Canis lupus pallipes), Indian fox (Vulpes bengalensis) and blackbuck (Antelope carvicapra). Spread over parts of Madhya Pradesh, Andhra Pradesh, Karnataka and Maharashtra, maintenance of these grasslands depends upon careful planning and management. However Government fallacy declares much of these grasslands, scrub and thorn forests as waste or unproductive land resulting in a lack of protection for endangered and endemic wildlife which occupy this habitat. Furthermore, conversion to agriculture and urbanization, as well as fragmentation and the introduction of invasive species have considerably altered grassland biodiversity. As per the recommendation from the Task force on desert and grasslands, certain grasslands viz., Shola grasslands of Nilgiris, Sewan grasslands of Bikaner, Jodhpur and Jaisalmer, semi-arid grasslands of Deccan, Rollapadu grasslands in the semi-arid tracts of Andhra Pradesh, Banni Grasslands of Gujarat and Alpine Grasslands of Sikkim and Western Himalaya have been recognized as ecologically sensitive ecosystems and any development projects in these areas will have to undergo stringent environmental impact assessments. KEY GRASSLAND SPECIES Indian Bustard Indian Wolf Blackbuck Lesser Florican The prioritization of landscapes for conservation of multiple species in human-dominated areas is recognized as a key global challenge. Identifying key areas for conservation is a critical first step, but zonation, planning and site-level implementation are crucial to the success of any long-term conservation solution. In the first phase of the two phase planning and implementation program spread over a two year period, we were able to identify areas considered probable dry grassland sites at a countrywide scale using remote sensing data and specific sites at a state level for four target states. Objectives 1) Create a countrywide map of dry grassland biomes of India, with fine-scale maps at district level for Andhra Pradesh, Karnataka, Madhya Pradesh and Maharashtra 2) Create a classification for conservation prioritization based on objective criteria in a quantitative framework 3) Design landscape specific conservation management plans for the protection and sustainable use of grassland habitats based on a participatory consultative approach involving all stakeholders In the first year of the study, we focussed on the first two objectives 6

7 Study Site The present study focuses on the countrywide distribution of dry grassland biomes in India. Therefore, for identifying priority sites for dry grassland conservation we focussed on four states of peninsular India viz. Madhya Pradesh, Maharashtra, Andhra Pradesh and Karnataka. According to the classification of grass cover of India 1, mainly two types of grasslands are spread over the Peninsular India, (1) Sehima-Dichanthium type, (2) Dichanthium- Cenchrus-Lasiurus type. 1. Sehima-Dichanthium Type: These are spread over the Central Indian plateau, Choto-Nagpur plateau and Aravalli ranges, covering an area of about 17,40,000 km². This region has an elevation between 300 and 1200 m. There are 24 species of perennial grasses, 89 species of annual grasses and 129 species of dicots, including 56 legumes. National level threats to grassland habitats in India 2. Dichanthium-Cenchrus-Lasiurus type: These are spread over an area of about 436,000 km², including northern parts of Delhi, Aravalli ranges, parts of Punjab, almost whole Rajasthan, and Gujarat, and southern Uttar Pradesh. The elevation of this region is not high, between 150 to 300 m. There are 11 perennial grass species, 43 annual grass species, and 45 dicots including 19 legumes. This area has many protected areas, mainly in the hilly regions, but the Lasiurus sindicus dry grassland of the Thar desert is under-represented in the PA system. These grasslands are extremely important for the survival of certain bird species. Declaration as wastelands by Government policy Conversion to irrigated agriculture Urbanization Conversion to wind/solar/biofuel farms Overgrazing Forestry plantations Methodology Data Acquisition We used annual time series of Moderate Resolution Imaging Spectrometer (MODIS, 250m 16 day NDVI) data of the year 2011, downloaded from Glovis website ( for mapping. Eight different tiles were downloaded covering whole of India. Use of Normalized Difference Vegetation Index (NDVI) data was integral as an estimate of the total production, and the difference between the maximum and minimum NDVI as a measure of the seasonality. We carried out unsupervised classification using ISODATA algorithm in ERDAS Imagine 10, on individual tiles to identify areas where probability of grassland occurrence was high and low. We verified the result of unsupervised classification with true grassland points available with us. 1 Dabadghao & Shankarnarayan The Grass Cover of India. 7

8 Maps were prepared state-wise, showcasing those probable grassland patches that would feed in to the next phase of our study. The maps prepared using MODIS time series data of 2011, were used as reference for marking out large areas with high probability of grasslands for further survey. However, MODIS data has a resolution of 250 m, which often results in a mixture of class signatures, for instance, dry grasslands and fallow agriculture land. To overcome this issue, remote sensing imagery with higher resolution and higher spectral information was used. The best possible freely available data was Landsat ETM+ data, which has a resolution of 30 m. There are several advantages of using Landsat data. Firstly, 30m resolution is ideal to differentiate between adjacent land cover classes, especially in a human dominated landscape. Also, Landsat data comes in 7 spectral bands. Given the immense amount of information contained in each of these bands, differentiating between land cover types was straightforward. Various band combinations (colour composites) were tested. A comparison was done between different band combinations to arrive at that combination which alleviates differentiation of similar spectral signatures. The details and advantages of some of the best band combinations of Landsat are as follows: 1) RGB 321: This is the natural colour combination, and appears as it does to the naked human eye. For example, Figure 1 is a 321 composite of the Mhaswad grassland area in Maharashtra. 2) RGB 432: This is the false colour composite, and shows healthy vegetation in dark red hues and agriculture and grasslands in lighter red. The main advantage of using this one was that it distinguishes between dense vegetation and sparse vegetation. Built up areas and bare soils are also very clearly discernable. 3) RGB 743: This combination provides a natural-like rendition and saturates according to vegetation health. One more plus point of this combination is that it penetrates clouds and smoke. Grasslands were clearly showing a light green colour, while irrigated and non-irrigated agriculture showed clearly different signatures. Fallow lands and bare soil showed a pink colour with this combination. The Landsat data was downloaded from the Global Land cover Facility ( The maps created using MODIS time series data were referred to while selecting the tiles of the Landsat data. 8

9 Figure 1: Combination 321. ( Data processing The downloaded data was checked for relief effects on the values; given that most of the study area is without a heavy slope gradient, orthorectification of the imagery was not required. To identify areas for surveying for conservation prioritization, it was necessary to have a land cover map of high resolution. Supervised and unsupervised classification techniques were used to obtain maps of high accuracy. Both techniques of classification were tested on several tiles. Digital image interpretation along with existing ground control points were used for supervised classification. The Maximum Likelihood Classifier in ERDAS Imagine 2010 and the Fisher classification in IDRISI Taiga were used for this purpose. Post classification, some test ground control points were used to test the accuracy of the classification. The output of the classification was 7 tiles within Maharashtra state, 7 tiles within Andhra Pradesh, 11 tiles covering Madhya Pradesh and 6 tiles for Karnataka, with each pixel representing a certain class. The tiles were classified into 5 major land cover classes: Savannah, Agriculture, Water, Built up and Dense vegetation. The classified images were processed in ArcGIS 9.3 and grassland class pixels were extracted. 9

10 Figure 2 Flowchart showing steps in identifying grassland areas Objective (2) was to create a classification for conservation prioritization. Thus, it was necessary to identify large areas of grasslands which were well connected and would serve as an ideal habitat for various species. Two different approaches were used to identify such areas: i) Raster processing: This processing was done in ERDAS Imagine. One basic advantage of this process is that it deals with the pixels itself. The input was the.img file representing pixels for grassland only, and the processing steps are as follows: 1. Clump: This tool in ERDAS Imagine (Raster>Thematic>clump) creates clumps, which are contiguous groups of pixels in one thematic class. The number of neighboring pixels to be considered can be specified by the user. The output of clump provides input to the sieve function. 2. Sieve: This tool eliminates the clumps from the previous output which are lesser than a size specified by the user. In this case, a minimum size of 10 pixels was set as the threshold. 3. Eliminate: This tool works on the same lines as sieve but does not recode the smaller clumps into another class, but simply eliminates the data pixels. The output of this process was a set of raster images representing the large grassland areas. 10

11 ii) Vector processing: This approach deals with vector shape files, rather than pixels. The primary step was to convert the grassland rasters to polygons. For this, the raster to polygon tool of Arcmap 10 was used. The polygons were reprojected, and their area, perimeter and area to perimeter ratio were calculated using Field Calculator tool. Thresholds were set so as to select areas of appropriate size for further surveying. An area threshold of >1000 hectares (10 sq km) and an area to perimeter ratio >30 was set, and the select attributes by query tool was used to get polygons representing grassland areas. The polygons and the rasters were both used in field for surveying. Field survey Field surveys for collection of ground control points of various cover types was undertaken in all four states of Andhra Pradesh, Karnataka, Madhya Pradesh and Maharashtra. The various cover types collected for control points were: grassland, agriculture, scrub, forest, plantation, rocky outcrops/bare and fallow (refer Table 2). The ground control points were collected to be used as signatures for supervised classification with higher accuracy. Along with field surveys for ground control points, areas were surveyed for the presence of grassland specific species. Grassland areas were searched for species specific signs along existing trails and tracks. Carnivore scat, track impressions, herbivore pellets, opportunistic sightings and local information were used to confirm presence of certain critically endangered and grassland specialist species. Preliminary data of habitat pressures in grassland areas were also collected based on observations as well as information from local village folk. Grazing, development (windmills, roads, canals etc.), hunting, mining were considered pressures on the habitat and local species. This data can act as a guideline for systematic collection of pressure data at a later phase of the project. Species presence data from field surveys, photographs, literature surveys and information from experts was used to map grassland species across the four states using speciesdistribution models. Results From MODIS data The NDVI values derived from MODIS showed a clear seasonal pattern across the year, which was particularly evident for grasslands (Figure 1). Mean annual precipitation explained a large fraction of the spatial variability of the NDVI data. Representative true grassland sites covering most of the states falling within peninsular India achieved the highest NDVI value in the month of November following the monsoon, after which it decreased monotonically, and reached its minimum value in the month of May. 11

12 STATE COVER TYPE Grassland Agriculture Scrub Forest Plantation Fallow Bare Rock Andhra Pradesh Karnataka Madhya Pradesh Maharashtra Table 1: Number of ground truth points of the various covers types from all four survey states The seasonal pattern of NDVI for grasslands varied substantially over the season, showing differences in available grass cover across the season and depending on management practices of different sites. While identifying this particular land cover type, we came across problems of mixing of class signature in between adjacent classes, which was inevitable, as in most of the places dry grasslands occur in small patches. Keeping in mind the spatial resolution of the NDVI dataset (250 meter), and maximum possible refinement of the classification, we could give a category of high or low, depending on the occurrence of dry grassland patches in a particular class (Figure 3). 12

13 Figure 3: Map of India showing probability of occurrence of grasslands derived from MODIS NDVI data 13

14 From LANDSAT ETM+ data a) Land cover maps: Land cover maps for 6 Landsat scenes in Maharashtra, 6 in Andhra Pradesh, 5 in Karnataka and 9 in Madhya Pradesh were produced. The following major classes were made: Savannah, Agriculture, Water and Built-up area. b) Large continuous areas: From each tile, areas fulfilling the conditions (as per the methodology) were selected for surveying. After the intensive surveys, some major areas were selected for further analysis (Figure 4a, b & c). c) These areas are as follows : i. Mhaswad, Maharashtra (refer plate 1, fig. 6) ii. Bhoom, Maharashtra (plate 3, fig. 5) iii. Yeola, Maharashtra (plate 2, fig. 7) iv. Nandane/Laling. Maharashtra (fig. 7) v. Nannaj, Maharashtra (fig. 5) vi. Rollapadu, Andhra Pradesh (plate 6, fig. 10) vii. Kondapuram, Andhra Pradesh (plate 7, fig. 11) viii. Ratlam and Jhabua districts, Madhya Pradesh (plate 5, fig. 9) ix. Dokarkheda and Susner grasslands, Madhya Pradesh (plate 4, fig. 8) Figure 4a: Selected grassland areas in Madhya Pradesh 14

15 Figure 4b: Selected grassland areas in Andhra Pradesh Figure 4c: Selected grassland areas in Maharashtra 15

16 Figure 4d: Selected grassland areas in Karnataka The total area and percentage of these grasslands in the three states are given in tables 2.1, 2.2 and 2.3. Karnataka state is missing from this analysis as there were no intact semi-arid savanna grasslands that exceeded 10 sq.km. Most areas that had earlier supported grasslands were either converted to plantations or diverted for other uses (such as Challekere Science City in Chitradurga district). Further surveys are ongoing to establish the status of traditional grazing areas, such as Amrit Mahal Kavals. As certain areas are under litigation we were unable to survey them. District Grassland area (sq. km) Total area (sq. km) % Grassland Nizamabad Kurnool Anantpur Kadapa Medak Hyderabad Rangareddi Mahbubnagar Nalgonda Table 2.1: Area of grasslands in different districts of Andhra Pradesh 16

17 District Grassland area (sq. km) Total area (sq. km) % Grassland Nashik Dhule Aurangabad Jalna Buldhana Solapur Sangli Ahmednagar Dhule Nandurbar Table 2.2: Maharashtra District Grassland area Total area % Grassland (sq. km) (sq. km) Jhabua Ratlam Mandsaur Neemuch Dhar West Nimar East Nimar Morena Bhind Sheopur Gwalior Datia Tikamgarh Chattarpur Panna Satna Rewa Table 2.3: Madhya Pradesh 17

18 A total of 10 areas have been identified as potential grassland survey sites. 5 areas in Maharashtra; 900+ sq. km. 3 areas in Madhya Pradesh; sq. km. 2 areas in Andhra Pradesh totalling to 200+ sq. km. Field survey results Field surveys were carried out in 54 districts of the four states. A total of 14,485 kilometres of road survey effort and >100 km of foot survey effort were spent in surveying these areas. Table 2 highlights the major grassland/savanna areas in the three states. The preliminary data shown is only presence of species that were directly sighted or whose signs could be identified with 100% accuracy. Table 3 shows our preliminary survey results. Proposed work in the next six months In the following 6 months we plan to work based on three broad categories. (a) Mapping of species in an occupancy framework using sign surveys and remote camera-traps deployed in the areas we have selected for detailed study. Ecological niche modelling and habitat suitability maps that have been prepared are being analysed with grassland maps created from 2013 LISS IV satellite imagery of 5m resolution. (b) Threats to these areas will be mapped based on data available and collected from ground-surveys of areas. Livestock densities and pressures will also be surveyed and mapped. (c) Priority zones will be mapped by combining grassland maps, species distribution maps and threats. Categorization of grassland areas will also be carried out based on parameters such as size, fragmentation of grassland areas, confirmed presence of species, levels of anthropogenic impact and grazing pressure. The above mapping of priority zones and grassland categorization will be carried out using the software ZONATION. Identification of model for proof-of-concept and conservation potential will be based on quantitative ranking of key areas; this will determine the effectiveness of the zonation exercise. Field surveys for collection of fine scale information on key biological values, anthropogenic impact and potential for conservation planning. The LISS IV imagery will be classified in finer detail, so as to also get the exact extent of grassland areas and also of the human encroachment. The data being from 2013 will be resampled to 30m and used for land cover change analysis over a period of 13 years (2000, 2005, and 2013).Using the land change modeller of IDRISI Selva, change maps, transition maps and change prediction maps will be produced. In collaboration with Dr. Krithi Karanth, Centre for Wildlife Studies, surveys on human-wildlife conflict are ongoing in the selected sites in the three states. These surveys will determine the presence of wildlife in the selected areas, as well as crop damage, livestock loss and any other incidences of conflict that may be detrimental to the conservation of biodiversity in these areas. 18

19 Based on all these factors two model areas will be selected for detailed conservation planning and engagement with stakeholders. Publications in preparation: 1) Vanak, A. T., Gode, A., Krishnaswamy, J. (in prep), A multi-method multi-scale classification of semi-arid savanna grasslands in Peninsular India. To be submitted to Landscape Ecology or Remote Sensing of the Environment 2) Di Minin, E., Karanth, K. K. and Vanak, A. T. (in prep), Assigning conservation priorities in human-dominated semi-arid savanna ecosystems in India. To be submitted to Biological conservation or Conservation Letters 3) Karanth, K. K., Menon, S, Patwardhan, P. and Vanak, A. T. (in prep), Assessing humanwildlife conflict in three states of peninsular India using an occupancy modelling approach. To be submitted to Ecology and Society MAJOR THREATS TO GRASSLANDS Andhra Pradesh: Over-grazing, mining, wind-farms, plantations, canals and dams. Maharashtra: Over-grazing, wind farms, plantations, mining, canals/dams, hunting Madhya Pradesh: Over-grazing, plantations and wind-farms. The major threats to grassland habitats in the survey states are summarized in the above text box. A detailed account of all threats identified in selected survey areas is given in table 3; plates depict a few threats to grassland ecosystems. 19

20 Sl. No State/Grassland area Andhra Pradesh Area (sq. km) Grey Wolf Indian Fox Golden Jackal Striped Hyena Jungle cat Blackbuck Chinkara Nilgai Indian Bustard Lesser Florican Threats/ Pressures 1 Kondapuram grasslands OG, Mn, W & P 2 Rollapadu and neighbouring grasslands OG, Mn, CD & P Madhya Pradesh 3 Grasslands in Jhabua and Ratlam districts 4 Susner and Dokarkheda grasslands OG & P OG, W, SF & P Maharashtra 5 Mhaswad grasslands OG, H & P 6 Bhoom grasslands OG, W, CD & P 7 Yeola grasslands OG, Mn, W & P 8 Nandane/Songir grasslands OG 9 Laling grasslands OG, Mn & P Table 3: Species presence data from select survey sites highlighting area of grassland and the respective states. (1=detected, 0=not detected). Threats/Pressures OG = Overgrazing; W = Wind farms; P = Plantations; Mn = Mining; CD = Canals/Dams; H= Hunting; Solar farms = SF 20

21 Plate 1: Mhaswad grasslands, Satara District, Maharashtra 21

22 Plate 2: Yeola grasslands, Nashik District, Maharashtra 22

23 Plate 3: Bhoom grasslands, Osmanabad District, Maharashtra 23

24 Plate 4: Susner savanna, Shajapur District, Madhya Pradesh 24

25 Plate 5: Ratlam grasslands, Ratlam District, Madhya Pradesh 25

26 Plate 6: Rollapadu grasslands, Kurnool District, Andhra Pradesh 26

27 Plate 7: Kondapuram savanna, Kadapa District, Andhra Pradesh 27

28 Plate 8: Key grassland fauna (clockwise from left), Great Bustard, Indian Wolf and Indian Fox. 28

29 Plate 9: Chinkara in Susner grasslands 29

30 Plate 10: Trenching and mounding schemes in Mhaswad, Maharashtra. 30

31 Plate 11: Windmills on rocky plateaus are known to disrupt avian movement. 31

32 Plate 12: Solar farms such as in Susner are a severe threat to prime savanna habitat. 32

33 Plate 13: Bunds created by the Integrated Wasteland Development Programme in Mhaswad. 33

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36 Figure 7. 36

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38 Figure 9. 38

39 Figure

40 Figure