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Application of Geospatial Techniques for Conservation and Management of Biodiversity

P. K. Yadav*#, Kiranmay Sarma, Sumit Dookia
University School of Environment Management, Guru Gobind Singh Indraprastha University
Dwarka 16C, New Delhi - 110078, India
*E-Mail: pramod.yadav31@gmail.com
#Current Address: G. B. Pant Institute of Himalayan Environment and Development
Kosi-Katarmal, Almora-263643, Uttarakhand, India


Abstract
Geospatial techniques have been used to monitor land use changes; this has an important role in conservation and management of biodiversity as well as determination of natural resources. It is also very useful for the production of land use and land cover information which can be useful to determine the distribution of biodiversity and their natural habitat. Using remote sensing techniques to develop land use classification mapping is a useful and detailed way to improve the management of areas designed to wildlife habitat and biodiversity assessment for a region. Understanding the complex mechanism of biodiversity necessitates its spatial and temporal dynamics management of protected areas and synergetic adoption of measurement approaches with long-term plot inventories. In observation of this, importance of geospatial techniques, which can be seen as a combination of integrating tools such as Geographic Information System (GIS), Remote Sensing (RS), Global Positioning System (GPS), and information and communication technologies, are realized as complimentary systems to ground-based studies. This paper addresses how wide range of geospatial tools can be used in conservation, management, monitoring and assessment of biodiversity. Remote sensing data and techniques address these needs, which include identifying and detailing the biophysical characteristics of species� habitats, predicting the distribution of species and spatial variability in species richness, and detecting natural and human-caused changes at scales ranging from individual landscapes to the entire world. However, advancement in the spatial and spectral resolution of sensors are now available to conservation biologist, which is making the direct use of remote sensing at certain aspects of biodiversity increasingly feasible, for example distinguishing species assemblages or even identifying species of individual trees.      

Keywords: Remote sensing, GIS, GPS, Biodiversity, Conservation, Management, Natural resources   

INTRODUCTION
Over the last century humans have been changing ecosystems more rapidly than in any comparable period in history (Vitousek et al.1997). As a result biodiversity, or the variety of genes, species and ecosystems, has declined rapidly (Balmford et al. 2003). The loss and degradation of biological diversity reduces our ability to adapt to the change. This loss is compounded by the loss of knowledge of biodiversity especially among people with close relationship with the natural ecosystem. Global Biodiversity Assessment (Heywood et al. 1995) estimates the total number of animal and plant species to be between 13 and 14 million. It further records that so far only 1.75 million species have been described and studied (Heywood et al. 1995). Incidentally, many of the species are getting extinct even without being recognized their presence and importance in the ecosystem. Recently there is a perceptible change in understanding the priorities of biodiversity conservation and management mainly through spatial delineation of the biodiversity rich and poor areas (Behera et al. 2005, Roy et al. 2005).

For many of the conservation ecologists, question remains unclear to estimate species richness, as there is rapid decline in species diversity. Scientifically sound biodiversity management requires frequent and spatially detailed assessments of the species diversity and distribution. Such information can be prohibitively expensive to collect directly. Measuring the distribution and status of biodiversity remotely, with airborne or satellite sensors, seems to be an ideal way to gather these crucial data (Gross et al. 2009)[7]. This remote sensing based information on vegetation and land cover provides a potential spatial framework and works as one of the vital input layers in assessing and monitoring of biodiversity. 

Satellite remote sensing (RS)
Acquirement of remote sensing images of earth from space has opened new frontiers for conservation and management of biodiversity. The multispectral satellite images provide definitions of vegetation patches, which are related to phonological types, gregarious formations and communities occurring in unique biodiversity setup (Behera 1999). The temporal satellite images provide information for vegetation mapping, monitoring and understanding ecosystem functions, primarily through the relationship between reflectance of vegetation structure and composition (Joshi et al. 2003). Such an approach allows monitoring the forest condition and degradation processes of earth surface. The images also provide digital mosaic of the spatial arrangement of land cover and vegetation types amenable to computer processing and difference in surface phenomenon over time can be determined and evaluated by visual interpretation with local knowledge (Garg et al. 1988). The other approach to analyze the conservation and management of biodiversity is based on nominal scale classified maps. These maps can also be analyzed using various indices quantitatively, which measure the heterogeneity of landscape within a specific radius. Diversity and dominance are well known examples of those indices (Baker and Cai 1992). They are ordinarily computed from samples of relatively homogenous cover types, named patches. Size, shapes, perimeter, connectivity, orientation, presence of corridors, visibility or diversity of patches are critical variables for describing the landscape mosaic.  Analysis of biodiversity fragmentation and have been common goals in the use of satellite data for landscape pattern analysis. 


Geographic information system (GIS)
Geographic information systems are used to collect, store, analyze, disseminate and manipulate information that can be referenced to a geographical location. It provides the way to overlay different �layers� of data like the ecological conditions, the actual vegetation physiognomy and human pressure indices and helps to assess disturbance levels; the spatial distribution of several species in order to determine biodiversity status; past and present maps for monitoring land cover and land use changes. The most widely used definition of GIS is a computer-based system that captures, stores, manages, analyses, and displays geo-referenced data (geographic data). It provides possibilities to extrapolate observations e.g., to automatically define and map the potential area of a given species and to compare it with the locations where, it has been actually observed; or to combine different data sets for defining the potential list of species for a given forest type. GIS provides a database structure for efficiently storing and managing ecosystem related data over large regions. It also assists in location of study plots and ecologically sensitive areas. GIS supports spatial statistical analysis of ecological distributions. It improves remote sensing information extraction capabilities, and provides input data and parameters for conservation and management of biodiversity. The data generated through ground truthing and integration of related attributes when used in GIS application result into significant features of biodiversity resources.

Global positioning system (GPS)
Global positioning system receivers have received much attention in the past several decades, due to their broad appeal across a wide spectrum of both industrial and researcher�s users. It is a satellite-based positioning system operated by the U.S. Department of Defense. GPS allows the collection of information about the geographical position of any location using a network of satellites. It has a great potential in conservation and management of biodiversity, as well as in many other related disciplines requiring geographic locations of the objects in the conservation and management of biodiversity. Instigated with GIS, it acts as a powerful tool to describe the geographical characteristics of ecological systems. A practical use of GPS has been in locating the sample plots and this information was used for mapping and spatio-statistical analysis (Behera et al. 2000).

For biodiversity assessment
Geospatial technology combines of geospatial analysis and modeling, development of geospatial database and information systems using satellite remote sensing, GIS, in-situ and models. The holistic understanding of the complex mechanisms that control biodiversity, as well as their spatial and temporal dynamics, requires synergetic adoption of measurement approaches, sampling designs and technologies (Gross et al. 2009). These technologies include Geographic Information Systems (GIS), Remote Sensing (RS), and Global Positioning System (GPS). There are various types of remote sensing satellite data, having different spatial and temporal resolutions in generating inputs for assessing the biodiversity. It is very clear for managing and conserving biodiversity that the data requirements are both of spatial and non-spatial nature and also of various time scales. Subsequently under a national level project on biodiversity characterization at landscape level, vegetation type maps were analyzed in conjunction with climate and topography using GIS to identify habitats a priori and then to determine the relationship between remotely sensed habitat categories and species distribution patterns (Ramesh et al.1997, Amarnath et al. 2003).
 
Sarma and Barik (2010) analyze the relationship between vegetation type and the geo-morphological features such as, slope and drainage density using geospatial techniques to integrate biodiversity information and the vegetation maps with existing spatial environmental data to establish main concern areas for biodiversity conservation of the Nokrek Biosphere Reserve of Meghalaya, India. The method provided a novel cooperative mechanism to aid spatial knowledge management and building consensus between remote sensing inputs and field observation on biodiversity conservation. Similarly, Land use cover (LULC) is an important component to understand global land status; it shows present as well past status of the earth surface (Yadav et al. 2012). On global to local scales, the only feasible way to monitor the Earth�s surface is to prioritize and assess the success of conservation efforts through remote sensing (Murthy et al. 2003). The remote sensing-based vegetation type maps and species distribution maps help in prioritizing the areas of bioprospecting and mapping of target economically useful species (Joshi et al. 2006).  Currently a suite of remote sensing satellites, having various resolutions, is available to generate spatial information on vegetation and land cover from global to local level.

For landscape dynamics
Landscapes are dynamic and an extremely complex concept used in different ways. Its holistic and dynamic nature has been recognized in many geographical and landscape ecological studies. Spatial presentation of landscape dynamics can be used to understand disturbance circumstances smoothly and in this scenario development of models to study landscape dynamics using geospatial technology would be great importance of conservation biologist (Talukdar et al. 2004)   In landscape ecology, biodiversity is considered an integral part of the broader concept of landscape heterogeneity for management and conservation. Therefore, to characterize a landscape, diversity plays an important role; it acts as indemnity for the system by increasing its ability to withstand change. As a complex phenomenon it can be analyzed in many different ways. For example Yadav (2012) analyzed spatial and temporal model of dynamics in the Garo Hills landscape and identified different drivers of deforestation in variant slope and elevation pattern.  The range of influences on landscape change is vast and includes climate, geology, topography, plant succession, and species extinction and species evolution. Disturbance like floods, windstorms, earthquakes, fire, volcanism and the ecosystem modification are responsible for landscape dynamics. Landscape management policies do not always take the dynamic nature of ecosystem. Landscape ecology is the study of patterns and structures across dynamic temporal and spatial analysis scales. Spatial patterns observed in landscape results from complex interactions between biotic and abiotic processes and disturbances that occur within environment (Turner et al. 2001). As change occurs throughout landscape, the overall structure and composition ecological community is affected, hence landscape study become important for maintain ecological diversity. There are three basic characteristics of landscape that affect their diversity i.e. structure, function and dynamics. The structure is most well understood element of landscapes. It is also most noticeable viewed in the form of different land form. 

Geospatial techniques have been used extensively in the past few decades to provide digital mosaic of the spatial arrangement of land cover and vegetation types amenable to computer processing (Coulson et al.1990). Habitat loss and forest fragmentation strongly influence biodiversity conservation in landscapes that has intense land use changes. Biophysical spectral modeling techniques allow stratifying vegetation types based on the canopy closure. Such an approach allows mapping and monitoring the forest condition and degradation processes. Mapping the distributions of vegetation types and land use provides critical information for managing landscapes to sustain their biodiversity and the structure and function of their ecosystems (Helmer et al. 2002).

Several attempts have been made to use landscape structure metrics to quantify the independent and joint effects of these processes (Barbaro et al. 2007). There is a strong relationship between landscape structure and ecological processes; objectively quantifying spatial landscape structure remains an important aspect of landscape ecology (Turner, 1989). A large number of metrics and indices have been developed to characterize landscape composition and configuration based on categorical map patterns (Mc Garigal et al.1995). These metrics are used to analyze landscape structure for a wide variety of applications, including quantifying landscape change over time (O�Neill et al.1997), relating landscape structure to ecosystem (Wickham et al. 2000), population and meta-population processes (Kareiva and Wennergren, 1995). Arguably the major application of landscape structure metrics has been assessing effects of habitat loss and fragmentation on landscape connectivity (Neel et al. 2005).

For assessing natural habitat fragmentation  
Natural forest habitat is considered as one of the greatest threats to global biodiversity because the forests are the most species-rich of terrestrial ecosystems (Armenteras et al. 2003). The complex process of deforestation and fragmentation of forest habitat is a common phenomenon in tropical and temperate forests, and apart from forest degradation it also brings about several physical and biological changes in the forest environment (Jha et al. 2005). These two processes may have negative effects on biodiversity, increasing isolation of habitats, endangering species, modifying species� population dynamics, and expanding at the expense of interior habitat (Giriraj et al. 2009) and with the increased rate of deforestation, timber extraction and encroachment had exposed catchments to flash floods and landslides. The ecological consequences of fragmentation may differ depending on the patterns of spatial configuration imposed on a landscape and how it varies both temporally and spatially (Armenteras et al. 2003) [35]. Therefore, an understanding of the relationship between landscape patterns and the ecological processes influencing the distribution of species is required by resource managers to provide a basis for making land-use decisions.

Land use and land cover is a fundamental variable that impacts forest fragmentation and isolation of habitats, which is being linked with human and physical environments. While the importance of human activities is widely recognized, the relative influence of human activities on environmental factors is less understood. Remote sensing is the only feasible way to map forest fragmentation from regional to global scales. Improvements in technology and availability of imagery are rapidly increasing the importance of the field in many areas including forest ecosystem monitoring. However, land cover maps indicate only the location and type of vegetation, and further processing is needed to quantify and map forest fragmentation. These attributes can be quantified in the form of mathematical descriptors, referred to as metrics (Gustafson, 1998). FRAGSTATS is spatial pattern study tool that was implanted to assess landscape fragmentation. The corridor configuration or structure in landscape relate to a mosaic patches usually quantified by landscape metrics or measurement such FRAGSTATS, widely used landscape ecology software packages. FRAGSTATS is set of spatial statistics that were implemented by ecologists to describe the characteristics of landscape and components of landscapes (Raines, 2002). These statistics facilitate comparison of landscape and assessment processes. The advantage of FRAGSTATS is that calculations are implemented in fully integrated with GIS and consequently easy to apply to digital maps (Baskent and Keles 2005).
  
In addition, a public-domain software packages like BioCAP (BioCAP, 1999), UTOOLS (McGaughey and Ager, 1997), ATtILA (Ebert and Wade, 2004) [41] are available for computation of numerous metrics and have been extensively used by the landscape ecology community. Several authors have used these tools to provide reliable means of ecosystem monitoring and biodiversity conservation (Sarma et al. 2010, Sarma and Kushwaha 2005, Roy and Joshi, 2002). Yadav et al. (2012) employed spectral remote sensing data and GIS for change detection and conflicts analysis of Nagzira-Navegaon corridor of central India. The results addressed the conservation issue by promoting participatory deforestation and presence of human encroachment in term of urbanization, agriculture land and build-up area.   Finally, for the fragmentation assessment of a landscape, it requires incorporation of landscape metrics using remote sensing data of land cover changes and the processes driving the changes. In addition, the direct linkage of geographical information system (GIS) technologies with remote sensing and landscape ecology research allows us to integrate spatial land use land cover patterns and ecological processes in a manner which is essential for the understanding of processes of change (Forman, 1995). 

For species habitat model 
The integrated use of GIS and remote sensing can be applied to describe ecosystems, identify a species distribution and habitat use, gathering the information on physical parameters of the wildlife habitats and to organize conservation strategies for both endemic and introduced species (Scott et al.1987). Remote sensing based habitat maps in conjunction with information on species-habitat associations are generally being used to derive information on the distribution of species, although a few exceptions may exist. The degree of correspondence between habitat maps and species distributions depends on the degree of habitat map generalization, and this could be optimized to get maximum information of species diversity (Coops and Catling, 1997). Habitat maps appear to be capable of providing information on the distribution of large numbers of species in a wide variety of areas; however, this is restricted to the spatial scale to tens of square kilometers. In smaller, local areas with limited species diversity, direct mapping can provide detailed information on the distribution of certain canopy tree species or associations. Satellite datasets from IRS, Landsat, SPOT and ASTER have been used effectively in mapping the homogenous plant colonies with prior knowledge of their occurrence, and the vegetation types of the area using remote sensing techniques (Roy et al. 2001). Studies have reported on the use of hyper spectral image data for differentiation of species as well as discrimination within conifer species and several tropical species (Cochrane, 2000).

Mapping habitats requires information on species composition and indicators that include canopy cover stand density, topography, soil type and reflectance properties of vegetation type to characterize individual species or homogenous system using satellite remote sensing data are a complex process. In areas where vegetation structure varies greatly, species differences may predominate in imagery (Giriraj et al. 2009). The remote sensing data may then prove less suitable for determining species composition and delineation of specific vegetation types and habitats. Patterns of species distribution on the ground have been shown to be associated with the distribution of environmental variables, such as topography, precipitation, soil and geomorphology type, and levels of disturbance. In such cases, a GIS model based on elevation, slope, aspect, and proximity to water source, etc., in conjunction with ground-based species databases, and broad vegetation types derived from RS, will help in identifying the spatial pattern of the species assemblages and habitats. With the detail information on species occurrences and its environmental condition it is possible to identify potential plant and animal distribution for conservation planning, when primary information is lacking. Association of a particular species with specific environmental conditions has long been documented, but quantitative analyses have been possible only recently with the advent of new tools, as well as availability of continuous spatial data on various environmental parameters. Ideally, for modeling habitat distribution of species, environmental data at an appropriate scale (i.e. precipitation and temperature) and precise geo-coordinates are required. Today wide range of satellite and climate data sets is available freely to model habitat of plant and animal distribution using modeling tools like Open Modeler GARP, Maxent, Biomapper, Diva-GIS. Globally studies carried out using these tools can be found, for example in Churdhar Wildlife Sanctuary of Himachal Pradesh, India by Sarma et al. (2012) and Areendran et al. (2011) assess the elephant habitat suitability and dispersal corridor in northern part of Chhattishgarh, Central India.   Their results provide healthy and consistent predictions of geographic distribution and its ecological conditions for the species. They are important measures for monitoring threatened species spread of invasive species, potential sites for habitat restoration and conservation of biodiversity.

For assessing biodiversity conservation priorities  
Landscape level spatial data of disturbance and intensity using earth observation satellites are important for tracking responses of the biosphere to climate change and for improved resource management. Remote sensing satellite data (NOAA, MODIS, SPOT Vegetation) are highly efficient to monitor and understand major disturbance events and their historical regimes more at a regional to global scale. Change detection through Remote Sensing has now been applied widely because its quick analysis processes accurate results and visual spatial information (Zhang, 2003). LULC classification and mapping for various period images can be used to LULC type change (van Lynden and Mantel 2001), while NDVI calculation and mapping for various-period images can be applied to  the detect change in vegetation quality which has been applied in vegetation coverage assessment, crop yield estimation and crop identification (Tian and Min 1998). NDVI is a band ratio calculated using (Infrared"Red) /(Infrared+Red) (Rouse et al.1973). It is highly correlated with vegetation parameters such as green leaf biomass and green leaf area and hence is of considerable value for vegetation segmentation (Justice et al.1985). 
               
Certain combination of satellite data derived vegetation parameters like Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), Net Primary Productivity (NPP) and land properties such as Land Surface Temperature (LST), Emissivity and Albedo can be correlated to understand uncertainties in ecosystem recovering or changes in energy balance. For e.g. coupling of LST and NDVI was found to substantially improve land cover characterization for regional and continental scale land cover classification (Nemani and Running, 1997). Nemani and Running (1997) explained LST-NDVI space, an energy exchange trajectory results, where decreasing vegetation density is coupled with increasing LST can be identified as disturbed areas and increasing trends in vegetation density and decreased LST can be identified as reforestation or irrigated lands.

Some of the examples on the application of remote sensing derivative products for regular monitoring and assessment of earth systems are: applications of NDVI and EVI derived products from coarse and medium resolution satellite data to identify dynamics of crop vegetation status, crop progress, areas of drought and areas cleared by deforestations (Tao et al. 2008). LAI derived from remote sensing data can be used a variable in crop growth models, estimation of different crops and its changes, forest canopy density and index can be used to categorize different ecosystems, input for biogeochemical cycle modeling, carbon flux studies and NPP estimations (Sasai et al. 2007). Other key products like burned area, land surface temperature, chlorophyll mapping and many others can be used an end product for conservation and monitoring of ecosystems.

At landscape level disturbed areas can be identified using combination of land cover maps and landscape metrics to calculate disturbance index (DI). DI along with biodiversity information (species diversity and richness, endemism, invasiveness) human-wildlife conflict and degree of terrain complexity can spatially identify areas of biological richness and measures to monitor critical areas. Case studied using this approach was carried out as change detection and conflict analysis of Kanha-Pench and Nagzira-Navegaon corridor of central India by Yadav (2011) and concluded that establishment of corridors as protected area are necessary for promoting sustainable conservation and management of biodiversity. Others case studies using this approach were carried out in tropical and temperate forests widely (Giriraj et al.2009) to identify level of habitat fragmentation and disturbance to delineate conservation zones for the sustenance of biodiversity. Thus remote sensing and GIS based landscape approach is an emerging tool for identification of hotspots for biodiversity conservation in the mountains, and especially to appropriately include human dimension in the conservation management planning.

 For biodiversity Monitoring 
An aspect of nature conservation that deserves special attention in the context of GIS is analysis, measurement and planning related to biodiversity. Its plays an important role as a tool for conservation and management of biodiversity, with the current greater concern for sustainable use of resources, and conservation and management of biodiversity. Many data relating to biodiversity and ecological systems have been collected and stored in forms suited to management and analysis using Geospatial techniques (Aspinall, 1995). Records of species or habitat can be stored in a database and mapped to show where they occur. This geographic information can be used to target surveys and monitoring schemes (Marqules and Austin, 1991). Data on species or habitat distribution from different dates allow monitoring of the location of individuals to be identified and the amount of distribution measured. The variety of data potentially able to be entered through Geospatial technique is large (Maguire et al.1991). These data are in different forms and are either a spatial or non-spatial. Spatial data include maps, satellite imagery and aerial photographs. Maps have scales, and according to scale, information can be stored and extracted. Non-spatial data include tables of measurements, species and habitat, attributes, photographs, videos, sound, etc. Davis et al. (1990) concluded that taxonomic, ecological and cultural variables required for assessment of biological diversity and their corresponding information scales. 

Increasingly, problems in conservation and management of biodiversity are being solved with the aid of technology. One recent advance in monitoring the spatial location of wild animals is the GPS collar. Recently, tracking collars based on GPS technology have become available to wildlife researchers (Wing et al. 2005). GPS radio-collars are based on a radio receiver (rather than a transmitter) in an animal�s collar. The receiver picks up signals from a special set of satellites and uses an attached computer to calculate and store the animal�s locations periodically (e.g. once/15 minutes, once per hour, etc.). Depending on collar weight, some GPS collars store the data and drop off the animal when expired to allow data retrieval; others transmit the data to another set of satellites that relay it to the researchers; and still others send the data on a programmed schedule (e.g., daily) to biologists who must be in the field to receive them Data are automatically collected by the collar according to its programming, in that way reducing, extremely, operator effort. The data collected consists of the wearer�s identity, time of day, date and coordinates: these data are sent electronically to the researcher (Wing et al. 2005). However, such technology is dependent on the collar being able to receive satellite signals (minimum of three).

The biological and conservation databases contain several major logical entities that have a geographic property or spatial characteristic that can be mapped. Examples are species occurrences, sites, and managed areas. The biological and conservation database systems also integrate geographically hierarchical design features to support the conservation efforts at different geographic scales. For examples, the conservation status of a particular species is rarely uniform across its range but in some places a species may be critically endangered, while at a wider scale (national, regional or global), it may be secure. This hierarchical structure, through the use of Geospatial techniques, allows the setting of local priorities. To summarize, the Geospatial tools are associated with two different roles for a geographical perspective on biodiversity data and other environmental issues. Firstly it contains a powerful reference base (geographic location), for example maps of natural vegetation (endemic, multipurpose, and endangered), soil, land cover, topography, hydrology, bird migration, distribution of fauna, etc. Locating features associated with their attributes allows diverse data to be combined, compared and analyzed in a single database to produce new relationships between environmental features and associations between different biota. Secondly it is a powerful and effective way of communicating a large variety of information.

Gap analysis of protected area for conservation planning 
Conservation and management of biodiversity through the creation of protected area (PA) is one of the most proficient ways to maintain viable population in natural ecosystems. Gap analysis is a conservation tool designed to assess the representativeness of existing protected area networks and to identify conservation priorities (Scott et al.1993), other word its literally consist of overlaying a map of biodiversity on map of protected area and see where the gap are. It has been used at a variety of scales, gaining popularity in scientific studies, as well as in practical assessment and provides power approach the effectiveness of an effectiveness of a PA system in representing local biodiversity.  Gap analysis is essentially a comparison of the distributions of species (or any other feature of conservation concern) with that of protected areas, used to define the degree to which species are represented in the protected areas, and to compare the representations to prescribed targets (Margules and Pressey, 2000).

For assessing gap analysis different biological richness levels were computed by integrating disturbance index with physical (i.e., terrain complexity), ecological (i.e., species diversity), phyto-sociological (i.e., species endemism, rarity and threatened) and economical (i.e., species importance value) parameters Roy et al. (2005). Systematic conservation planning and Gap analysis began at the beginning of the 1980s and studies that adopted have been carried out globally on a continental level and in many countries worldwide (Ramesh et al.1997, Rodrigues et al.1999; Powell et al.2000, Scott et al. 2001). It provides an assessment of effectiveness of PAs and home range for the conservation of territorial vertebrates (mammals, birds, reptiles, amphibians etc.). Mathur and Padalia (2006) were undertaken gap analysis study to establish a logical and scientific basis of protected area planning to conserve the representative samples of biological diversity both in island�s landscape as well as the surrounding seascape of Andaman and Nicobar Islands. The identification of gaps was based on the level of protection offered to different vegetation/land cover types, biologically rich zones and localities of conservation importance for birds and sea turtles within PA system in Andaman and Nicobar Islands. The main outcome of gap analysis is to identify the true or partial gap species which need further protection. Species identified as covered are assumed to be well protected and this information can be used to guide the selection of new protected areas.  

Wetland Inventory
Wetlands have been playing a critical role in maintaining the balance of biodiversity in their surrounding ecosystems. It is unique and significant part of natural resources and provides vital habitats for wildlife (Tiner et al.2002). They are important feeding and breeding areas for wildlife and provide a stopping place and refuge for waterfowl. As with any natural habitat, wetlands are important in supporting species diversity and have a complex of wetland values. It is estimated that freshwater wetlands alone support 20 percent of the known range of biodiversity in India (Deepa and Ramachandra 1999). The input to wetland inventory is how information (time and spatial sequence) is assessed. Remote sensing offers important advantages over other methods of data collection that have led to its use in a wide range of applications. Geographic information system and spatial analyses are concerned with the quantitative location of important features, as well as properties and attributes of those features (Bolstad 2006). Now geospatial techniques are widely applied to wetland resources investigation, categorizing, function evaluation and protection. It gets deeply progressing especially on the dynamic investigation of wetland landscape and wetland degeneration (Yong-xing 2002).
 
Conservation and management of wetlands requires an exhaustive mapping of their distribution and determination of whether or not they have changed over time and to what extent (Jensen et al.1993). To conserve and manage wetland biodiversity, it is important to have inventory of wetlands and their catchments. Resolution Image Spectrometer (MODIS) sensor data on the project Terra Satellite data, spectral knowledge of blue red and NIR bands in conjugation in with Land Surface Temperature (LST) and NDVI have been used to define the thresholding range for wetland classes (water, mud flats, vegetation and salt flats) (Agarwal and Garg 2008). In India several workers have worked on mapping the change in wetlands using remote sensing tool (Agarwal and Garg 2007, Garg et al. 2008, Bhaskar et al. 2010). Garg et al. (1998) have carried out study to delineate wetlands of a number of districts across India using IRS IA/B, LISS I/II data. The ability to store and analyze the data is essential. Digital maps are very powerful tools to achieve this. Maps relating the feature to any given geographical location have a strong visual impact. Maps are thus essential for monitoring and quantifying change over time scale, and assist in decision making. The technique used in the preparation of map started with ground survey. Ground-based survey of wetlands of large, or even small, wetlands is very time consuming. The use of geospatial techniques offers a cost effective and time saving alternative for delineating wetlands over a large area compared to conventional field mapping. The land use information generated through geospatial techniques could be effectively used for consecration and management for biodiversity of wetlands.  

CONCLUSIONS
The outcome of this paper reveals that geospatial techniques provides a powerful tool for assessing  geospatial information for monitoring land use and land cover changes, changes in landscape, mapping potential, species distributions and monitoring, and biodiversity loss, however, a few critical areas of research need to addressed. Assessment and quantification need to be geospatial data driven, decision support system and dependent on multi-scale spatial and temporal resolution supporting multi-thematic information.

Understanding the biodiversity fragmentations, species distributions and levels of species richness and how they operate in different geospatial contexts is a fundamental challenge of modern conservation biology (Gross et al. 2009, Tuner et al. 2003). This challenge is considered important with the ongoing simplification of native ecosystems, declining populations and rising loss of biodiversity. To pursue this loss, it is necessary to understand where and why species occurring and what areas needs conserve, protect and which are rich in species and areas of high endemism. Geospatial techniques have to provide challenging task like which areas need project implementation with proven methods and clear solution in managing biodiversity. In the recent decades, tremendous increases in the initiate of earth observation satellites with better repetitively, improvement in spectral bands, spatial resolution from 50cm to 1km and also unprecedented number of remote sensing tools with which to address these challenges. These tools are found in both public and private sectors of the economy and are not limited to any particular country or region.
For sustainable conservation and management of biodiversity need to improvement and challenges conservation biologists, landscape ecologists, and biodiversity specialist should combine their datasets on vegetation types, species richness and diversity, distribution maps, areas of endemism and extinction, levels of disturbance together and analyze them from global to locals for better ways of monitoring and conserving biodiversity. For example, Mildrexler et al. (2007) combined vegetation and land surface properties to detect disturbance. Similarly Irfan-Ullah et al. (2007)  combined climate and topography along with species locations to identify potential species distribution. Finally biodiversity database can be further put to advanced niche modeling to derive species distribution and potential habitats as defined by its biophysical parameterization. Derived spatial distribution suitably integrated with coarse scale information of spatial and non-spatial nature, can be used for resolving the stakeholders interests to achieve conservation and sustainability, by geospatial query, visualization and analysis.

ACKNOWLEDGEMENTS
Authors are thankful to the Prof. Prodyut Bhattacharya (Dean) Prof. J. K. Garg, Dr. Rita singh (Course Co-coordinator, M.Sc. Biodiversity & Conservation), Dr. Sanjay K. Das and Dr. Pamposh of University School of Environment Management, Guru Gobind Singh Indraprastha University for providing necessary facilities and support to write this article.

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Zhang Y,  Guindon B (2005). Landscape analysis of human impacts on forest fragmentation in the Great Lakes region. Canadian Journal of Remote Sensing, 31(2), 153-166.



 

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