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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
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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.
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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).
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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
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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.
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Fig. 2. Webpage of Indian Biodiversity Information Network
Fig 3. Webpage of Indian Bioresource Information network
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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.
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