35 Applications of Remote Sensing and GIS in Wildlife Monitoring

Dr. Puneeta Pandey

 

CONTENTS

 

1.  Aim of the Module

 

2.  Introduction

 

3.  Wildlife Management in India

 

4.  Role of remote sensing in wildlife mapping

 

5.  Applications of Remote Sensing in Wildlife mapping

 

6.  Applications of GIS in Wildlife mapping

 

7.  Conclusions

 

8.  References

 

 

1.  Aim of the Module

 

I. This module will help us understand the importance of wildlife and its management in India.

 

II. Then we shall study the role of remote sensing and GIS in wildlife monitoring

 

 

2.  Introduction

 

Wildlife resources constitute a vital link in the survival of the human species and have been a subject of much interest and research all over the world. Presently, wildlife habitats are under severe pressure and a large number of species of wild fauna have become endangered. Wildlife is treated as an inexhaustible genetic resource; the conservation of which becomes essential. Human induced undesirable changes such as land encroachments leading to wildlife habitat loss, pollution and introduction of invasive species pose serious threat to wildlife health and richness. Hence in order to restore wildlife habitat, fragmentation and to prevent further local and global extinction of any species, it is imperative to understand and carry out comprehensive study of the wildlife population and pattern. But most of the wildlife habitats are located in those areas where accessibility is not easy because of difficult terrain. Also the study of wildlife conservation and management with the help of conventional methods happens to be tough, time taking, risky and requires lot of resources. Also expressing and measuring biodiversity including study of organisms and their biotic and abiotic components happens to be intricate because of the versatile nature of biodiversity. Remote sensing can answer these problems as the number of strategies for wildlife studies including investigation of biodiversity, wildlife habitation mapping and animal movement modeling can be executed with the help of remote sensing and inventory database.

 

3. Wildlife management in India

 

Today, India contains 172 species (2.9% of the world’s total number) of animals that are considered to be globally threatened by the IUCN. These include 53 species of mammals, 69 species of birds, 23 species of reptiles, and 3 species of amphibians. Extinction is somehow classified as ‘biological reality’ because no species has, as yet, existed for more than a few million years without evolving into something different or dying out completely. Extinction is threatening all species, but most of the time smaller animals, like bats and rodents, face this threat more than other animals. We, however, tend to focus on the charismatic flagship species, which we like to see and which fascinate us. Success in evolution is measured in terms of survival: failure, by extinction. Most recent extinctions can be attributed, either directly or indirectly, to human demographic and technological expansion, commercialized exploitation of species, and human-caused environmental change. These factors, in turn, have affected the reproductive rate of endangered species and their adaptability to changing environmental conditions. Concern for wildlife is, in fact, a concern for ourselves.

 

 

3.1 Project Tiger

 

The huge demand for tiger bones, destined for use in Oriental traditional medicine outside of India and as a macho supplement, is a threat to India’s tiger population. ‘Project Tiger’ was launched on 1 April

 

1973 with the following objectives:

 

  • to maintain a viable population of tigers in India for scientific, economic, aesthetic, cultural, and ecological values; and
  • to preserve, for all times, areas of biological importance as a national heritage for the benefit, education, and enjoyment of the people.

 

At the beginning of the project, 9 tiger reserves were created. Currently, there are 50 tiger reserves covering a total area of 71027.10 km2. Also, international trade in tiger products has been banned under the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES).

 

 

 

3.2 Blackbuck /Indian Antelope

 

The blackbuck (Antilope cervicapra), or Indian antelope, is exclusive to the Indian subcontinent. The blackbuck was listed under Schedule I of the Wildlife (Protection) Act, 1972, and Punjab and Haryana honored it as their state animal. Presently, there are five wildlife sanctuaries in Punjab. The Bishnoi community does not allow felling of trees as well as killing of all wild animals, including birds. The strict policy of local cooperation and noninterference towards the local wildlife has provided protection to peafowl, partridges, hares, jungle cats, nilgai, and other wild animals.

 

 

3.3 Great Indian Bustard

 

The great Indian bustard (Ardeotis nigriceps) is the state bird of Rajasthan and an indicator of the health of the grassland ecosystem of the Indian plains. In the early 1980s, five states undertook conservation measures for the great Indian bustard, and eight protected areas were declared; however, the state of the bustard has deteriorated sharply during the last 10 years. The major problems that face the survival of the great Indian bustard include:

 

  • Habitat destruction and deterioration – Too many domestic animals are causing disturbances during the breeding season, and habitat has been lost due to the conversion of grasslands and wastelands to crop fields.
  • Poaching
  • Mismanagement of bustard sanctuaries

 

The Bombay Natural History Society is a pioneer in promoting the conservation of the great Indian bustard.

 

4. Application of remote sensing in wildlife mapping

 

Human induced undesirable changes such as land encroachments leading to wildlife habitat loss, pollution and introduction of invasive species pose serious threat to wildlife health and richness. Hence in order to restore wildlife habitat, fragmentation and to prevent further local and global extinction of any species, it is imperative to understand and carry out comprehensive study of the wildlife population and pattern. But most of the wildlife habitats are located in those areas where accessibility is not easy because of difficult terrain. Also the study of wildlife conservation and management including wildlife densities, living pattern, population and habitat with the help of conventional methods happens to be tough, time taking, risky and requires lot of resources. Also expressing and measuring biodiversity including study of organisms and their biotic and abiotic components happens to be intricate because of the versatile nature of biodiversity. Remote sensing can answer these problems as the number of strategies for wildlife studies including investigation of biodiversity, wildlife habitation mapping and animal movement modeling can be executed with the help of remote sensing and inventory database.

 

Remote sensing is a computer based software application which obtains and processes geographic information from satellite or air born sensors. Remote sensing measures the reflected and emitted electromagnetic radiations from the objects. The spatial coverage provided by the remote sensing occurs across wide range of electromagnetic wavelength.

 

Remote sensing is capable of providing uniform consistent spatial observation data at wide scale domain. The images and photographs obtained from the remote sensing helps greatly in the investigation of physical conditions. It can be further enhanced for better accuracy using remotely sensed data and field study (multi stage approach). Remote sensing can be classified based on either direct approach or indirect approach (Chambers et al., 2009). The direct approach suggests direct observation of spatial features, objects or communities using satellites or air born sensors using high resolution spatial sensors and hyperspectral sensors (Turner et al., 2003). The indirect parameters are dependent on the environmental parameters such as land use, land cover, species composition etc., obtained from remotely sensed data as surrogate for precise measurement of the potential species verities and patterns (Collingwood et al., 2009).

 

Satellite Remote Sensing offers information on vegetation type, forest cover, and their changes at global, regional, national, or micro level studies (Roy et al. 1987, Unni at al. 1985, Porwal and Pant, 1986). Remote Sensing plays an important role in forest management with reference to wildlife management, fire control, grazing land management, soil and water conservation, mapping of sites suitable for social forestry and afforestation programmes.

 

Some of the areas where remote sensing can be useful for wildlife studies are:

 

o   Revision and updating of stock maps

 

o   Fire risk Zonation

 

o   Planning response routes

 

o   Protected area management

 

o   Site suitability analysis for Afforestation

 

o   Soil and water conservation

 

o   Mapping wildlife corridors

 

o   Habitat suitability Mapping

 

o   Prediction Analysis

 

o   Change Detection Analysis

 

o   Mapping Required Resources for Wildlife

 

o   Real time tracking

 

o   Population Mapping

 

o   Developing and updating Web Portal of particular Wildlife

 

Wide varieties of satellite data sets are available commercially including digital data sets obtained from LANDSAT-5 (Land Observation Satellite), TM (Thematic Mapper), LISS-3 (Linear Imaging and Self Scanning Sensor), IRSID (Indian Remote Sensing Satellite Series 1D), SPOT (Système Probatoire Pour l’Observation de la Terre) and XS (Multi-Spectra). TM sensors helps in availability of multi temporal data with replicated coverage of 16 days for examining temporal changes occurring in the wildlife habitat and communities. Latest series of Indian Remote Sensing Satellites and SPOT series (French satellites) come with the advantages of stereo data acquisition competence with ±26° off-nadir viewing potential of and higher spatial resolutions of 6 (IRS1C/IRSID PAN data) to 10m (SPOT PAN data). The sensors LISS-3 on board IRS1C/D satellites give multi-spectral data obtained in four bands of visible and the near infrared (VNIR) and short wave infrared (SWIR) zone. LISS-3 images contain region of 124/141 km for the VNIR bands (B2, B3, B4) and 133/148 km for the SWIR band (B5) perceived from an altitude of 817 km (IRS1C) to 780 km (IRS1D) with recurring coverage of 25 days. The VNIR bands have spatial resolution of 24m and SWIR has nearly 71m of resolution. The spatial resolution of LISS-3 of the IRS satellite series and XS of the SPOT satellite series are superior to LANDSAT- TM.

 

In order to conserve and manage wildlife system, many countries maintain an inclusive forest account databases of protected areas. These vegetation inventory databases are important for the wildlife studies as they are extensive at comparatively larger spatial scales (example, 1:20,000), reduce the cost of production and they are generally allocated in convenient GIS format (McDermid et al., 2009). Generally different management and conservation strategies cover only particular species and protected areas, which happens to be only 5.19% (7.74 million km2) of the total earth’s land surface (WCMC 1992). Many of these biological reserves and protected areas are designed for aesthetic purpose and tourist attraction, rather than wildlife conservation purpose. In these areas, sometimes wildlife is exposed to unsuitable land use practices such as grazing livestock, agriculture, mining etc. Poaching of some species makes them vulnerable and sometimes some deceases and invasive species invade wildlife population (Prins 1996). Therefore thriving wildlife resource require up keeping of optimal conditions within wildlife reserve as well as outside it. The successful management and conservation of wildlife reserve can be carried out well if there is complete availability of information and relevant knowledge about the spatial and temporal distribution of wildlife population. The successful mapping of wildlife distribution can be accomplished using satellite remote sensing.

 

 

Coral reef mapping of 9 reef classes was done with 37% accuracy with LANDSAT TM, 67% with aerial photography and 81% with an airborn CASI hyperspectral scanner by Mumby and his co workers (1998a). Thermal scanners have been used to measure the population of deer, elk, bison and moose in Canada by comparing ground counts with aerial count, as thermal scanners are known to determine the presence or absence of those species which are not easily observable during certain climatic conditions (Intera Environmental Consultants, 1976). Error can sometimes occur during thermal scanning because of sunlight heated objects and presence of non- target animals. Many of the species like earthworms and termites are known to cause interference because of the roughness caused either by their exoskeleton or by their impact to the soil surface. Certain species which readily modify their environment hamper the applicability of remote sensing satellite as the sensors are incapable to capture the impact of such species on the environment. In such conditions radar can be helpful to map such animals as it is sensitive to micro topography (Weeks et al. 1996; Van Zyl et al. 1991).

 

Figure 1: Statistical Model to Predict Animal Location and Movement for Merriam’s Turkey

(Source: https://nau.edu/LCI/LLECB/Wildlife-Habitat-Modeling/ )

In the figure 1, GPS satellites and radio tags provide key data on animal location and movement of Merriam’s Turkey. This data would be useful to derive statistical models of space and resource use.

 

Integrated Normalized Difference Vegetation Index (NDVI) or ‘greenness index’ was reported to be associated with biome averages of net primary productivity (Goward et al., 1985). It has also been demonstrated that there exist sharp linear relationship between vegetation indices observed from the satellite and seasonal primary production (Prince, 1991). Similarly several studies have been carried out which demonstrate relationship between NDVI and biomass production but fewer studies have been carried out to show the linkage between NDVI and wildlife (Muchoki, 1995; Omullo, 1996; Oindo, 1998).

 

Since more than three decades remote sensing has been widely used to confine the distribution of zones appropriate for the specific wildlife habitat. Landsat MSS was used for mapping suitable areas for prairie chicken (Cannon et al., 1982) and Wiersema (1983) used it to map snow cover to detect south facing snow free slopes which forms winter habitat of alpine ibex. Landsat TM was used to map wetland suitable for foraging wood stork by Hodgson et al., (1987) and it was used by Congalton et al., (1993) for mapping suitability of land for deer identification. Landsat TM was used to evaluate availability of habitat for wood thrush (Rappole et al., 1994). These studies relied on the vegetation map acquired from the remote sensing as the sole explanatory variable but land suitability for the wildlife may be found out using more than one factor for better representation.

 

5. Application of Geographic Information System (GIS) in wildlife mapping

 

GIS is computer based system designed for capturing, managing, manipulating, analyzing, modeling and displaying spatially geo-referenced data and for solving complex management problems. GIS helps in easy management of natural and man- made resources at wider scales extending from local to global scale. GIS is capable of overlaying information from different thematic maps depending on user specific logic and derived map outputs. Because of the wide array of GIS application, task defined systems have been created which include engineering specific, land based information, generic thematic, statistical and property lot mapping, environmental planning systems and image processing systems related with remotely sensed data and landsat.

 

In GIS, the attribute data are stored in relational database and geospatial data are saved in map layers, map themes and map coverages. These layers geographically referenced to one another happen to be the foundation of GIS. The gist of map layers refers to spatial as well as attributes data. GIS database sourced map coverages and GIS analysis based results can be displayed and printed in maps, tables and figures and shared various GIS software packages.

 

The increasing use of geospatial technology that involves the use of remote sensing, GIS and GPS have helped vastly in research pertaining to ecological domain. In the context of wildlife management, GIS is used for mapping, monitoring, analysing and modelling the nesting behaviour and habitats of wildlife populations; wildlife distributions; movement patterns; and to identify potential nesting habitats (Lawler & Edwards 2002; Harvey & Hill 2003; Gibson et al. 2004). which can provide valuable information for the development of management strategies (Lawler & Edwards 2002; Harvey&   Hill 2003; Fornes 2004; Gibson et al. 2004; Greaves et al. 2006; Shanahan et al. 2007; McLennan 1998; Maktav et al. 2000; Fornes 2004; Beggs 2005).

Source: Landres et al., 2001

Figure 2: A Digital Elevation Model showing continuous coverage of slope, aspect, and elevation in a raster grid across an entire area. This area is the east side of the Cascade Mountains in west-central Washington State. Each regular grid cell is 1 km on a side. Figure developed and provided by Steve Brown at the University of Montana

 

GIS easily helps in creating maps that cannot be created by using traditional cartographic method. Moreover GIS software packages offering modeling tools can easily create measurements and analyze attribute data. The information in GIS is stored digitally hence it is easily accessible for evaluation and analysis making it easy to be shared among wildlife managers and public. GIS particularly offer potential to enhance the accuracy and precision and long term inexpensive basic actions of wildlife management and conservation such as inventorying, analysis, monitoring, planning and communication.

 

Wildlife management actions are ideally based on intimate information of natural landscape, land use and mass of interior and exterior threats to it. GIS and similar type of computer based technologies such as remote sensing provide means to acquire huge amount of geospatial data and offer powerful analysis tools for understanding linkages between different types of data and help in manipulating these data over larger areas for various development goals for wildlife. Geographic information on the population scattering of wildlife forms a basic source of data in wildlife management. Usually the distribution is derivative from observations on the ground. Radio-telemetry and satellite pathway have been used to evidence the distribution of a diversity of animal species (Thouless and Dyer 1992).

 

Figure 3: Mechanism of GIS

 

Aerial inspection process based on direct observation increased by use of photography have been used to map the distribution of a range mammals (Norton-Griffiths 1978), birds (Drewien et al. 1996; Butler et al. 1995) and sea turtles and marine mammals (Wamukoya et al. 1995).

 

GIS mapping is progressively being used for wildlife density mapping and dispersion mapping derived from ground observation or aerial survey. Habitat studies based on GIS commonly merge information on vegetation type or different area descriptor, with other land feature reflecting the reserve base factors and other significant factors. A model for Florida scrub jay developed included vegetation type and soil drainage to differentiate primary habitation, secondary habitation and unsuitable areas (Breiniger et al., 1991). A GIs-based model was developed to categorize prospective nesting habitation for cranes in Minnesota (Herr and Queen, 1993).

Figure 4: Sketch for GIS based suitability mapping.

 

GIS sometimes faces basic issues such as in case of determining if GIS is suitable for given situation, finding which data layer is essential and adequate to achieve the planned task. These basic problems need to be resolved before taking any action. constrictions and limitations of GIS applicability consist of the simplification of data for mixed areas due to inadequate scale resolution, data incoherence from integrating data from different sources without due regard to reliability of each source, and lack of quality data.

Source: ESRI, October 2010

Figure 5: An overview of the Web Valley and Morebawa, showing wolf deaths caused by rabies,

and the ensuing vaccination effort. The circled numbers represent the number of wolves

vaccinated at each trapping location. The carcasses found to the south of the vaccination points

were caused by a second rabies outbreak.

In Bale Mountains National Park (BMNP) in south central Ethiopia, most wolves were split into three linked subpopulations, Sanetti Plateau, Morebawa, and the Web Valley. In late August 2008, researchers in the Web Valley found dead Ethiopian wolves and the laboratory tests confirmed rabies cases. It was then added to a rapidly rising GIS layer of the area, in order to understand the likely origin of the outbreak and the direction of its dispersal through the population.

Map sources: Bat Conservation International, National Atlas, Natural Earth, North American Atlas, Ontario Ministry of Natural Resources, Pennsylvania Game Commission, U.S. Fish and Wildlife Service, and West Virginia Division of Natural Resources

Figure 6: Map showing the future impact of White nose syndrome (WNS) on gray bat and Indiana bat hibernation sites

 

Bat Conservation International (BCI) created a geodatabase of critical hibernation sites and mapped the likely spread of the disease using GIS. A few past projects had focused on developing geospatial datasets, but no long-term plan was in place for setting GIS as part of customary operations. Now, GIS technology is helping biologists to improve understanding the threats prevailing in the wildlife communities. Spatial analysis of the affected areas and possible future spread is essential for focusing efforts to increase awareness and endorse vigilance.

 

6. Conclusions

 

Wildlife habitat and species around the world are facing a crisis. It is estimated that global warming may cause the extinction of 15–37% of species by 2050. India has launched an extensive protected area network of research institutions in which legislation, socio-economic factors, and wildlife research are playing a great role. Planned research activities include studies on disease diagnosis, site suitability, animal behavior, as well as tracking the animal movement in its habitat by means of GPS. The future depends on interaction between habitat and wild animals as well as preservation of genetic diversity and biodiversity. The potential of RS and GIS tools in mapping wildlife, planning natural resource and management are indeed huge. These technologies at present fully developed and they are progressively being useful in natural resource mapping, planning and management. However, their application, mainly in developing countries, is still lacking because of the shortage of suitable scale of data, software, hardware and expertise. Future research in wildlife modelling should focus designing more practical dynamic models of wildlife.

 

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