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Anais XVI Simpósio Brasileiro de Sensoriamento Remoto - SBSR, Foz do Iguaçu, PR, Brasil, 13 a 18 de abril de 2013, INPE Biodiversity Characterization at Landscape level using Geospatial Model Parth Sarathi Roy Dr K L Rao Geospatial Chair Professor, Center for Earth and Space Science, Hyderabad Central University, Hyderabad [email protected] Satya Prakash Singh Kushwaha, Arijit Roy, Harish Karnataka and Sameer Saran Indian Institute of Remote Sensing, Indian Space Research Organization, 4, Kalidas Road, Dehradun 248001, Uttarakhand, India Abstract Biodiversity is generally considered at the species level although conservation of biodiversity requires management at higher level of organization, particularly at the landscape scale. A landscape approach of conservation is the most feasible as it would be impossible to protect individual species. The information on the biodiversity characteristics such as species richness and their spatial distribution, economic and the ethno-botanical importance is of great significance to any nation. Nationwide project on the biodiversity characterization at landscape level, was carried out between 1998 and 2010 to characterize and map the flowering plants richness in the natural (forests, grasslands, scrub etc.) and man-made (forest plantations) vegetation formations. The spatial database on vegetation types generated using wet and dry season satellite imagery and ancillary data such as topographic maps and the species richness through field inventory were used to generate the spatially-explicit species distribution maps and statistics. Spatial Landscape Model (SPLAM) has been developed for landscape analysis and spatial data integration. The present study is first attempt which resulted in spatial database on vegetation types, porosity, patchiness, interspersion, juxtaposition, fragmentation, disturbance regimes, ecosystem uniqueness, terrain complexity and the species richness for biodiversity conservation. The field sampling involved 19,876 geo-referenced 0.04 ha plots across India and 7215 plant species. The geospatially-tagged species database, created in the project, provides information on the endemic, rare, endangered, threatened and medicinally/economically important species. The database, disseminated to large number of organizations has found extensive applications in policy planning, operational management, biodiversity conservation, bio-prospecting and the climate change studies. Keywords: biodiversity characterization, remote sensing, GIS, Landscape Modeling, biological richness, biodiversity information system -1- 3321 Anais XVI Simpósio Brasileiro de Sensoriamento Remoto - SBSR, Foz do Iguaçu, PR, Brasil, 13 a 18 de abril de 2013, INPE Introduction The Convention on Biological Diversity (CBD) is one of the three Rio Conventions, emerging from the UN Conference on Environment and Development, also known as the Earth Summit, held in Rio de Janeiro in 1992 (CBD, 2010). CBD has mandated the signatory nations to inventory and report the biodiversity using internationally accepted tools including remote sensing. It is now widely recognized that biodiversity is a multi-dimensional and multi-scale phenomenon encompassing different organization levels and a wide range of spatial scales (Anon., 2001). Identifying patterns of biodiversity and their causal factors is, therefore, an enormous task that requires: (i) mapping and monitoring of biological patterns across different spatial and temporal scales, and (ii) analysis of such patterns with respect to diverse aspects of the physical and human environment. Remote sensing is one of the best tools available for coping with this challenge (Roy and Tomar, 2000). Information obtained from remote sensing is intrinsically multi-dimensional (horizontally, vertically, temporally and spectrally) and may cover spatial scales ranging from a few centimeters to entire continents. Understanding the spatial distribution and the abundance of species on multiple scales has been a matter of concern to ecologists and the evolutionary biologists (Krebs, 1994; Gaston and Blackburn, 2000). Explaining patterns of diversity at species level has been one of the most complex problems in ecology since diversity is the outcome of many environmental factors, whose relative importance varies in time and space (Diamond, 1988). Although acquiring this information solely from field generates accurate information, it is limited by data collection methods, small area coverage, and the high time and cost (Heywood, 1995). Also the results of any small area study can’t be extrapolated on regional or global scales. Of late, there has been a perceptible change in understanding the priorities for biodiversity conservation and management, mainly due to the availability of spatial data showing biodiversity rich and poor areas (Behera et al., 2005; Kempf, 1993). Though biodiversity is generally appreciated at the species level, it needs to be assessed and conserved at all levels of ecological organization and spatio-temporal scales. The existing biodiversity databases are discrete, localized and rarely do they give a complete picture of the extent and distribution of the biological diversity of the entire country. Efforts are being made by various organizations in India to investigate and document the bioresource data in digital form. Some of the national databases are National Basic Forest Inventory (NBFIS), Indian Biodiversity Portal of Ashoka Trust for Research in Ecology and the Environment (ATREE) and National Knowledge Commission, PADAP of National Botanical Research institute (NBRI). At the global level, there are more than 300 digital databases of various kinds containing information on the plant resources and diversity on regional, national or continental scale. Many Asian and African countries lack such databases. Some of the important spatial biodiversity databases available at global level are Global Assessment of Endemism and Global Centers of Vascular Plant Diversity (Kier et al., 2005); Global Patterns of Plant Diversity and Floristic Knowledge (Barthlott et al., 2005); Map of Potential Species Diversity of Vascular Plants (Barthlott et al., 1996). One of the post important information conservation needs the spatial patterns of landscapes, ecosystems, land use and their organization. Earth Observing systems and geospatial techniques provide valuable data to describe the landscapes, their structure and also overlay the environmental layers for systematic analysis. -2- 3322 Anais XVI Simpósio Brasileiro de Sensoriamento Remoto - SBSR, Foz do Iguaçu, PR, Brasil, 13 a 18 de abril de 2013, INPE Methodology This study has generated spatial information at three levels viz. Satellite based primary information (vegetation type map, road layer, fire occurrence, etc.) Geospatially derived or modeled information (disturbance Index, fragmentation, Biological Richness) GPS tagged stratified field sample plot information on phytosociology. The vegetation type map was prepared using two season satellite data to take into consideration the phenology and the seasonal variations in the verioua landcover in the region, along with ancillary information on topography, temperature and precipitation regimes and biogeography. The vegetation type map has been used as base information along with other ancillary information (Fig. 1) to geospatially models the fragmentation and the disturbance regime maps. Fragmentation was computed as the number of patches of forest and non-forest types per unit area. Using a moving window approach an output layer with patch numbers is derived and a look-up table (LUT) associated with this is generated, which keeps the normalized data of the patches per cell in the range from 0 to 37. The mathematical representation of the fragmentation is: (Eq. 1) Where, Frag = fragmentation; n = number of patches; F = forest patches; NF = non-forest patches. Pixels having fragmentation index values of 1 were categorized as low fragmentation; medium fragmentation was assigned to pixels having a value of 2. All the pixels having values from 3 to 37 were categorized as high fragmentation areas. The disturbance surface was prepared as a combination of different landscape matrices, viz., fragmentation, porosity, juxtaposition, and interspersion. The spatial distribution of the anthropogenic/natural forces on the landscape was used to generate the spatial distribution of disturbance factors, viz., proximity to roads, villages, fire intensity, shifting cultivation, and mines using ground based sampling data as well as ancillary databases. The disturbance index (DI) is computed by adopting a linear combination of the defined parameters on the basis of probabilistic weightage. The mathematical equation used for computing the Disturbance Index is as follows: (Eq. 2) where DI = Disturbance Index; Frag = fragmentation, Por = porosity; Patc = Patchiness; Int = interspersion; Jux = juxtaposition; Wt = weights. The disturbance index map has a range of 0-100. The disturbance index was classified as (1) Low (11-18); (2) Medium (19-23); (3) High (24-28); (4) Very High (28-72). -3- 3323 Anais XVI Simpósio Brasileiro de Sensoriamento Remoto - SBSR, Foz do Iguaçu, PR, Brasil, 13 a 18 de abril de 2013, INPE Fig 1. Approach for biodiversity characterization The biological richness at the landscape level was computed as a function of ecosystem uniqueness, species diversity, biodiversity value, terrain complexity, and Disturbance Index (Roy et al., 2012): (Eq. 2.3) Where, BR = biological richness, DI = Disturbance Index, SR = species richness, BV = biodiversity value, EU = ecosystem uniqueness, and Wt = weights. The range of the biological richness index is 0-100 and have been categorized as low (17-33), medium (34-49), high (50-69), and very high (70-91). All these sub-models have been developed and integrated into a software package named Spatial Landscape Model (SPLAM) (Roy et al., 2005). Results The study provides vegetation types and land use and their spatial patterns. First time the spatial patterns of fragmented/ remnant patches of native flora are set in a matrix of agricultural land and settlements. Disturbance and fragmentation are two strongly-related processes and it is often difficult to distinguish the nature of the interactions between the two. The study observed that at the landscape level, disturbance is related to patch structure and spatial arrangement that determine the fate of patches, their size and the duration. Severe disturbances or lack of disturbance generally has a depressing effect on biodiversity, but intermediate disturbance seems to increase the diversity. In the present study, fragmentation, interspersion, juxtaposition and patchiness (Roy and Tomar, 2000) were used to compute the disturbance index employing as a function of porosity, juxtaposition , fragmentation, interspersion and proximity from sources of disturbance such as road, rail or settlement. Weights were assigned to these parameters empirically to emphasize their relative importance. The reliable information on spatial patterns of biological richness helps in conservation prioritization of threatened, rare, endemic, flagship and keystone species. Richness, in the present case, was calculated as a function of ecosystem uniqueness, terrain complexity and the -4- 3324 Anais XVI Simpósio Brasileiro de Sensoriamento Remoto - SBSR, Foz do Iguaçu, PR, Brasil, 13 a 18 de abril de 2013, INPE disturbance regime. The ecosystems with high number of RET and endemic species were considered as highly unique whereas those with no RET and endemic species as common ecosystems. Shannon-Weaver Index of diversity (Shannon and Weaver, 1949) was used to depict species richness. The biodiversity value was calculated as sum of the importance values of ten uses (food, fodder, fiber, oil, dye, medicine, etc., of a particular species. Spatial variance in SRTM values was taken as terrain complexity. Since complex terrains create larger number of species niches, it was considered appropriate to include terrain complexity in the equation. Several states have used the data in growing stock assessment and working plan preparation. Protected area managers and researchers have used spatial data for species conservation planning and management. The study has contributed to the scientific understanding, characterizing and deciphering the spatial patterns of Indian forest landscapes, their disturbance regimes and the biological richness. It provides spatial information on 120 vegetation types consisting of natural, semi-natural and man-made formations (forest plantations). The non-spatial database includes phytosociological data collected from 19,876 sample plots with 7215 plant species, wherein 486 species are endemics, 74 are red-listed (threatened), 3005 species are economically important, and all 7215 can be said to be ecologically important. Data Sharing and Dissemination About 130 GB spatial and non-spatial data was created and disseminated to a large number of central and state departments, research institutes, universities and the individuals for their own scientific use. Advanced GIS work was facilitated by high computing capabilities and advanced visualization system using state-of-art information and communication technologies. The high speed network access has given a new dimension to geospatial domain where larger data sets may be processed, more complex models can be established, more complex analysis for decision-making can be performed, and better methods of display and visualization for virtual reality can be achieved. These technological advancements in GIS can play an important role in the area of biodiversity conservation, management and climate studies. The information services implemented using OGC WMS under BIS are freely accessible by the users after formal registration while the digital spatial data is shared with user organizations for further value addition and scientific studies (http://bis.iirs.gov.in) Fig.2. The outputs of this project also make an important component of the Indian Bioresource Information Network (IBIN) of the Department of Biotechnology, Govt. of India. IBIN is being developed as a distributed national network infrastructure using open source software solutions to provide relevant information on diverse themes and of issues related to country’s bioresources to a wide range of end users in an interoperable environment. IBIN portal (www.ibin.gov.in) aims to offer a platform for all the data holders in the country to host their data while retaining data ownership (Fig. 3). Its major goal is to promote an open-ended, coevolutionary growth among all the digital databases related to biological resources of the country and to add value to the databases through multi-source integration. The IBIN web portal is being developed in open source GIS environment and implemented using OGC web service specifications for interoperable GIS solutions. The database created has immense potential to be utilized in varied areas of research, and policy making processes namely climate change, conservation, prioritization, identifying gaps in research etc and identification of potential ecological and wildlife corridors, development of international monitoring protocols and forest management. -5- 3325 Anais XVI Simpósio Brasileiro de Sensoriamento Remoto - SBSR, Foz do Iguaçu, PR, Brasil, 13 a 18 de abril de 2013, INPE Fig. 2. Webpage of Indian Biodiversity Information Network Fig 3. Webpage of Indian Bioresource Information network -6- 3326 Anais XVI Simpósio Brasileiro de Sensoriamento Remoto - SBSR, Foz do Iguaçu, PR, Brasil, 13 a 18 de abril de 2013, INPE Inputs to Climate Change Study Most of the climate change studies take a relatively coarse resolution vegetation database for calibrating the various climate forcing, which sometimes give erroneous results due to the various factors like orography (Renssena and Lautenschlagerc, 2000). This database can be used as input to climate models requiring digital land cover information. Apart from running the climate models, the impact of the various climate change scenarios are also important to understand the dynamics of the various natural and anthropogenic forcing. This database is only of its kind database for future climate change-related studies- be it species or habitat transitions (Gastner et al., 2009) and loss or tree-line shifts. The results are useful for monitoring of invasive species and gap analysis. The key requirement in invasive species mapping is delineation of spatial extent of invasion to understand the severity of invasion. The data from inventory as well as modeled output of fragmentation and disturbance index is essential for prioritizing the initiatives for invasive species control, monitoring species spread, and evaluation (Kinezaki et al., 2003; Kushwaha, 2011). A few critical areas where databases have been effectively utilized are in forest working plan preparation, protected area management, people’s biodiversity register, bio-prospecting, species niche modeling, prioritization of local habitats for studies using high resolution databases, biodiversity change analysis, economic evaluation and inputs for international protocols like CBD (2010). Conclusions The outcome of the nationwide project on biodiversity characterization at landscape level have been quite significant, particularly the wall to wall holistic database on the key inputs describing the quality and quantity on the vegetation and biodiversity at different spatial levels. Although there are a few biodiversity databases in many parts of the world in public domain like Biodiversity Information System of Europe, Atrium Biodiversity Information System of Botanical Research Institute of Texas etc., none of them covers the complete gamut of spatial databases starting from vegetation type, fragmentation, biological richness coupled with geospatially-tagged field plot data. Our database, on the contrary, is a baseline database on vegetation types, fragmentation status and biological richness of Indian landscape, which is key to biodiversity conservation planning and developing future management strategies for conservation efforts. The biologically rich areas identified in the present study can also act as the baseline for the creation of future protected areas, national parks and sanctuaries. The uniqueness of the present study lies in the central database repository of locale-specific information of 7215 plant species recorded from India and their status with respect to endemic, rare, endangered, threatened status as well as the economic/medicinal importance. The wider dissemination and an open software environment for further value additions by integration of other available data sets. Acknowledgements The authors express their sincere gratitude to the Chairman, Indian Space Research Organization and Secretary, Department of Biotechnology for financial support and the encouragement. The participation and contribution of Phase-I, Phase-II and Phase-III project teams is duly acknowledged. -7- 3327 Anais XVI Simpósio Brasileiro de Sensoriamento Remoto - SBSR, Foz do Iguaçu, PR, Brasil, 13 a 18 de abril de 2013, INPE References Anon., 2001. Encyclopedia of Biodiversity. Academic Press, London. Behera, M.D., Kushwaha, S.P.S. and Roy, P.S., 2005. Geo-spatial modeling for rapid biodiversity assessment in Eastern Himalayan region. Forest Ecology and Management, v. 207, 363-384. Barthlott, W., Mutke, J., Rafiqpoor, M.D., Kier, G. and Kreft, H., 2005. Global centres of vascular plant diversity. Nova Acta Leopoldina v. 92, 61-83 Barthlott, W., Lauer, W. and Placke, A., 1996. Global distribution of species diversity in vascular plants: towards a world map of phytodiversity. Erdkunde, v. 50, 317-328. CBD, 2010. Global Biodiversity Outlook 3. Secretariat of the Convention on Biological Diversity. Champion, H.G. and Seth, S.K., 1968. Revised Forest Types of India. 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Roy, P.S., Kushwaha, S.P.S., Murhty, M.S.R., Roy, A., Kushwaha, D., Reddy, C.S., Behera, M.D., Mathur, V.B., Padalia, H., Saran, S., Singh, S., Jha, C.S. & Porwal, M.C. (2012) Biodiversity Characterisation at Landscape Level: National Assessment, Indian Institute of Remote Sensing, Dehradun, India, pp. 140. ISBN 81-901418-8-0. -8- 3328