Your search found 3 records
1 Babar, S.; Amarnath, Giriraj; Reddy, C. S.; Jurasinski, G.; Jentsch, A. 2011. Spatial patterns of phytodiversity - assessing vegetation using (Dis) similarity measures. In Grillo, O.; Venora, G. (Eds.). The dynamical processes of biodiversity - case studies of evolution and spatial distribution. Rijeka, Croatia: InTech. pp.147-186.
Vegetation ; Species ; Biodiversity ; Ecosystems ; Spatial information ; Statistical methods ; Plant ecology ; Forests / India / Andhra Pradesh / Eastern Ghats
(Location: IWMI HQ Call no: e-copy only Record No: H044596)
http://www.intechopen.com/source/pdfs/24414/InTech-Spatial_patterns_of_phytodiversity_assessing_vegetation_using_dis_similarity_measures.pdf
https://vlibrary.iwmi.org/pdf/H044596.pdf
https://vlibrary.iwmi.org/pdf/H044596.pdf
(0.71 MB) (630.62KB)
2 Babar, S.; Amarnath, Giriraj; Reddy, C. S.; Jentsch, A.; Sudhakar, S. 2012. Species distribution models: ecological explanation and prediction of an endemic and endangered plant species (Pterocarpus santalinus L.f.). Current Science, 102(8):1157-1165.
Ecology ; Species ; Pterocarpus santalinus ; Indigenous organisms ; Endangered species ; Models ; Geographical distribution ; Biodiversity / India / Andhra Pradesh / Eastern Ghats
(Location: IWMI HQ Call no: e-copy only Record No: H044856)
(0.87 MB) (893KB)
Pterocarpus santalinus L.f. (Red Sanders) is an endemic and endangered species largely confined to the southern portion of the Eastern Ghats, Andhra Pradesh, India. To understand its ecological and geographic distribution, we used ecological niche modelling (ENM) based on field sample-based istributional information, in relation to climatic and topographic datasets. Before modelling, hierarchical partitioning was used to optimize the choice of variables for better prediction and reliability. We used three ENM approaches, namely GARP, Maxent and BIOCLIM for predicting potential areas of occurrence. The ENM successfully reconstructed key features of the species geographic distribution, mainly in the forest tracts of Chittoor and Kadapa districts. GARP appeared to be more robust in prediction capabilities compared to BIOCLIM. The potential distributional area identified by these models falls mainly in regions not protected and experiencing high anthropogenic pressure owing to economic and medicinal use. The success of this model indicates that ENM-based approaches provide a promising tool for exploring various scenarios useful in the study of ecology, biogeography and conservation.
3 Amarnath, Giriraj; Babar, S.; Murthy, M. S. R. 2017. Evaluating MODIS-vegetation continuous field products to assess tree cover change and forest fragmentation in India: a multi-scale satellite remote sensing approach. The Egyptian Journal of Remote Sensing and Space Sciences, 20:157-168. [doi: https://doi.org/10.1016/j.ejrs.2017.05.004]
Remote sensing ; Models ; Vegetation ; Satellite imagery ; Forest fragmentation ; Forest ecosystems ; Trees ; Canopy ; Time series analysis ; Deforestation ; Landscape ; Climate change / India
(Location: IWMI HQ Call no: e-copy only Record No: H048220)
http://www.sciencedirect.com/science/article/pii/S1110982317302132/pdfft?md5=272802c5ea945f049718e7f6501c83bf&pid=1-s2.0-S1110982317302132-main.pdf
https://vlibrary.iwmi.org/pdf/H048220.pdf
https://vlibrary.iwmi.org/pdf/H048220.pdf
(3.34 MB)
Monitoring the changes in forest-cover and understanding the dynamics of the forest is becoming increasingly important for the sustainable management of forest ecosystems. This paper uses temporal MODIS Vegetation Continuous Field (MODIS-VCF) to monitor the tree cover change in the Indian region over a period of 6 years (2000–2005). Pixel-based linear regression model is developed to identify rate of deforestation and fragmentation at landscape level. The regression parameters viz., slope, offset and variance are used to identify threshold between forest and non-forest classes. The classification algorithm resulted into change area, no change area, positive change and negative changes. MODIS-VCF raw product of 2005 was validated using the field data and showed a coefficient of determination (R2 = 0.85) between percent tree cover and individual plot wise canopy cover information. The results were overlaid with UNEP protected area boundary. On a long-term basis, the forest cover change was monitored using medium spatial resolution (Landsat and IRS) satellite data to identify the rate of deforestation and fragmentation at landscape level. The developed approach is efficient and effective for regional monitoring of forest cover change. It could be automated for regular usage and monitoring.
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