Your search found 2 records
1 Roy, P. S.; Behera, M. D.; Murthy, M. S. R.; Roy, A.; Singh, S.; Kushwaha, S. P. S.; Jha, C. S.; Sudhakar, S.; Joshi, P. K.; Reddy, S.; Gupta, S.; Pujar, G.; Dutt, C. B. S.; Srivastava, V. K.; Porwal, M. C.; Tripathi, P.; Singh, J. S.; Chitale, V.; Skidmore, A. K.; Rajshekhar, G.; Kushwaha, D.; Karnatak, H.; Saran, S.; Amarnath, Giriraj; Padalia, H.; Kale, M.; Nandy, S.; Jeganathan, C.; Singh, C. P.; Biradar, C. M.; Pattanaik, C.; Singh, D. K.; Devagiri, G. M.; Talukdar, G.; Panigrahy, R. K.; Singh, H.; Sharma, J. R.; Haridasan, K.; Trivedi, S.; Singh, K. P.; Kannan, L.; Daniel, M.; Misra, M. K.; Niphadkar, M.; Nagabhatla, N.; Prasad, N.; Tripathi, O. P.; Prasad, P. R. C.; Dash, P.; Qureshi, Q.; Tripathi, S. K.; Ramesh, B. R.; Gowda, B.; Tomar, S.; Romshoo, S.; Giriraj, S.; Ravan, S. A.; Behera, S. K.; Paul, S.; Das, A. K.; Ranganath, B. K.; Singh, T. P.; Sahu, T. R.; Shankar, U.; Menon, A. R. R.; Srivastava, G.; Sharma, N. S.; Mohapatra, U. B.; Peddi, A.; Rashid, H.; Salroo, I.; Krishna, P. H.; Hajra, P. K.; Vergheese, A. O.; Matin, S.; Chaudhary, S. A.; Ghosh, S.; Lakshmi, U.; Rawat, D.; Ambastha, K.; Malik, A. H.; Devi, B. S. S.; Gowda, B.; Sharma, K. C.; Mukharjee, P.; Sharma, A.; Davidar, P.; Raju, R. R. V.; Katewa, S. S.; Kant, S.; Raju, V. S.; Uniyal, B. P.; Debnath, B.; Rout, D. K.; Thapa, R.; Joseph, S.; Chhetri, P.; Ramachandran, R. M. 2015. New vegetation type map of India prepared using satellite remote sensing: comparison with global vegetation maps and utilities. International Journal of Applied Earth Observation and Geoinformation, 39:142-159. [doi: https://doi.org/10.1016/j.jag.2015.03.003]
Satellite imagery ; Remote sensing ; Vegetation ; Climate change ; Temperature ; Precipitation ; Scrublands ; Grasslands ; Ecology ; Global positioning systems ; Land cover ; Assessment ; Cultivation / India
(Location: IWMI HQ Call no: e-copy only Record No: H047008)
(2.48 MB)
A seamless vegetation type map of India (scale 1: 50,000) prepared using medium-resolution IRS LISS-III images is presented. The map was created using an on-screen visual interpretation technique and has an accuracy of 90%, as assessed using 15,565 ground control points. India has hitherto been using potential vegetation/forest type map prepared by Champion and Seth in 1968. We characterized and mapped further the vegetation type distribution in the country in terms of occurrence and distribution, area occupancy, percentage of protected area (PA) covered by each vegetation type, range of elevation, mean annual temperature and precipitation over the past 100 years. A remote sensing-amenable hierarchical classification scheme that accommodates natural and semi-natural systems was conceptualized, and the natural vegetation was classified into forests, scrub/shrub lands and grasslands on the basis of extent of vegetation cover. We discuss the distribution and potential utility of the vegetation type map in a broad range of ecological, climatic and conservation applications from global, national and local perspectives. Weused 15,565 ground control points to assess the accuracy of products available globally (i.e., GlobCover, Holdridge’s life zone map and potential natural vegetation (PNV) maps). Hence we recommend that the map prepared herein be used widely. This vegetation type map is the most comprehensive one developed for India so far. It was prepared using 23.5m seasonal satellite remote sensing data, field samples and information relating to the biogeography, climate and soil. The digital map is now available through a web portal (http://bis.iirs.gov.in).
2 Bordoloi, R.; Das, B.; Tripathi, O. P.; Sahoo, U. K.; Nath, A. J.; Deb, S.; Das, D. J.; Gupta, A.; Devi, N. B.; Charturvedi, S. S.; Tiwari, B. K.; Paul, A.; Tajo, L. 2022. Satellite based integrated approaches to modelling spatial carbon stock and carbon sequestration potential of different land uses of Northeast India. Environmental and Sustainability Indicators, 13:100166. [doi: https://doi.org/10.1016/j.indic.2021.100166]
Carbon sequestration ; Carbon stock assessments ; Land use ; Land cover ; Satellite imagery ; Landsat ; Vegetation index ; Regression analysis ; Biomass ; Climate change mitigation ; Forest cover ; Remote sensing ; Modelling ; Simulation / India / Arunachal Pradesh / Assam / Manipur / Meghalaya / Mizoram / Nagaland / Sikkim / Tripura
(Location: IWMI HQ Call no: e-copy only Record No: H050887)
https://www.sciencedirect.com/science/article/pii/S2665972721000672/pdfft?md5=2b0c924ff6ef3156dbcfe3c57e940f61&pid=1-s2.0-S2665972721000672-main.pdf
https://vlibrary.iwmi.org/pdf/H050887.pdf
https://vlibrary.iwmi.org/pdf/H050887.pdf
(4.25 MB) (4.25 MB)
The study aims to estimate and predict the aboveground biomass, carbon stock and carbon sequestration potential of different land uses of Northeast India and relate these estimates with the land use changes. Many applications such as carbon stock and sequestration monitoring, forest degradation monitoring, and climate change mitigation, require precise and timely estimation of forest biomass. Although traditional field inventory can reliably estimate forest biomass, remote sensing is emerging as an alternate and fast approach to cover larger area with relative precision for biomass estimation. In this study, a combined approach of field inventory and Landsat OLI derived vegetation indices were used in spatial modelling of aboveground biomass and carbon stock in different land uses. A stepwise multilinear regression algorithm was used to derive the model that used Landsat derived NDVI, SAVI and ARVI as predicators. The predicted AGB ranged from 14.32 to 185.95 Mg ha-1 with an average of 148.78 Mg ha-1. The developed model that used combined vegetation indices showed correlation of R2 = 0.79 with an RMSE of 51.04 Mg ha-1. The present study also applied the empirical model (CO2FIX) to simulate the future scenario of carbon stock and carbon sequestration potential of the different land uses. The carbon stock potential of different land uses were 182.31 Mg ha-1, 158.91 Mg ha-1, 134.98 Mg ha-1, 169.26 Mg ha-1, 133.84, 89.95 Mg ha-1, 128.3 Mg ha-1 and 61.36 Mg ha-1 in Tropical forest, Subtropical forest, Temperate forest, Tropical plantation, Subtropical plantation, Temperate plantation, Shifting fallows and Agricultural land, respectively.
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