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Remote Sensing And Image Interpretation

Large connected landscapes are paramount to maintain top predator populations. Across their range, tiger (Panthera tigris) populations occur in small fragmented patches of habitat, often isolated by large distances in human-dominated landscapes. We assessed connectivity between 16 protected areas (PAs) in central India, a global priority landscape for tiger conservation, using data on land use and land cover, human population density, and transportation infrastructure. We identified and prioritized movement routes using a combination of least-cost corridor modeling and circuit theory. Our analyses suggest that there are several opportunities to maintain connectivity in this landscape. We mapped a total of thirty-five linkages in the region and calculated metrics to estimate their quality and importance. The highest quality linkages as measured by the ratio of cost-weighted distance to Euclidean distance are Kanha–Phen/Bandhavgarh–SanjayGhasidas/Melghat–Satpura, and cost-weighted distance to least-cost path length are Nawegaon–Tadoba/Achanakmar–SanjayGhasidas/Kanha–Phen. We used current flow centrality to evaluate the contribution of each PA and linkage toward facilitating animal movement. Values are highest for Kanha and Pench tiger reserves, and the linkages between Kanha–Phen, Kanha–Pench, and Pench–Satpura, suggesting that these PAs and linkages play a critical role in maintaining connectivity in central India. In addition, smaller areas such as Bor, Nawegaon, and Phen have high centrality scores relative to their areas and thus may act as important stepping stones. We mapped pinch points, which are sections of the linkages where tiger movement is restricted due to unfavorable habitat, transportation networks, human habitation, or a combination of factors. Currently, very limited data exist on tiger movement outside of PAs to validate model results. Regional-scale connectivity mapping efforts can assist managers and policy makers to develop strategic plans for balancing wildlife conservation and other land uses in the landscape.

Connecting the dots: mapping habitat connectivity for tigers in central India

India, a mega-diverse country, possesses a wide range of climate and vegetation types along with a varied topography. The present study has classified forest types of India based on multi-season IRS Resourcesat-2 Advanced Wide Field Sensor (AWiFS) data. The study has characterized 29 land use/land cover classes including 14 forest types and seven scrub types. Hybrid classification approach has been used for the classification of forest types. The classification of vegetation has been carried out based on the ecological rule bases followed by Champion and Seth's (1968) scheme of forest types in India. The present classification scheme has been compared with the available global and national level land cover products. The natural vegetation cover was estimated to be 29.36% of total geographical area of India. The predominant forest types of India are tropical dry deciduous and tropical moist deciduous. Of the total forest cover, tropical dry deciduous forests occupy an area of 2,17,713 km(2) (34.80%) followed by 2,07,649 km(2) (33.19%) under tropical moist deciduous forests, 48,295 km(2) (7.72%) under tropical semi-evergreen forests and 47,192 km(2) (7.54%) under tropical wet evergreen forests. The study has brought out a comprehensive vegetation cover and forest type maps based on inputs critical in defining the various categories of vegetation and forest types. This spatially explicit database will be highly useful for the studies related to changes in various forest types, carbon stocks, climate-vegetation modeling and biogeochemical cycles.

Nationwide classification of forest types of India using remote sensing and GIS

Mapping and assessment of vegetation types in the tropical rainforests of the Western Ghats using multispectral Sentinel-2 and SAR Sentinel-1 satellite imagery

Multi-Data Approach for remote sensing-based regional crop rotation mapping: a case study for the Rur catchment, Germany.

https://geoagro.icarda.org/downloads/publications/papers/With%20SuplimentaryNew%20vegetation%20type%20map%20of%20India%20prepared%20using%20satellite%20remotesensing%20Comparison%20with%20global%20vegetation%20maps%20and%20utilities.pdf

Pushpa Dash

Papers

New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities