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Assessment of Carbon Storage Potential and Area under Agroforestry Systems in Gujarat Plains by Co2fix Model and Remote Sensing Techniques
Agroforestry is a traditional and ancient land use practice, having deliberate integration of trees with crop and livestock components. In India, agroforestry practices are prevalent in different agro-ecological zones and occupy sizeable areas. These practices have great potential for climate change mitigation through sequestration of atmospheric CO2. Carbon sequestration potential was studied in four districts of Gujarat (Anand, Dahod, Patan and Junagarh), for which field survey was conducted to collect primary data on existing agroforestry systems. The extent of agroforestry area in these districts was estimated by sub-pixel classifier using medium resolution remote sensing data (RS-2/LISS III). By sub-pixel classifier, the highest area under agroforestry was estimated in Dahod (12.48%) followed by Junagarh district (10.95%) with an average of 9.12%. Sapota (Manilkara zapota) based agroforestry was also mapped in Junagarh district, which occupied an area of 1.13%. An accuracy of 87.2% was found by sub-pixel classifier in delineation of sapota-based agroforestry in the district. Dynamic CO2FIX model has been used to estimate total carbon (biomass + soils) and net carbon sequestered in existing agroforestry systems. Net carbon sequestered over a simulated period of 30 years in Anand, Dahod, Patan and Junagarh districts was found to be 2.70, 6.26, 1.61 and 1.50 Mg C ha-1 respectively. Total carbon stock in all four districts for baseline and simulated period of 30 years was estimated to be 2.907 and 3.251 million tonnes respectively. Thus, agroforestry systems in Gujarat have significant potential in carbon storage and trapping atmospheric CO2 into biomass and soils. Hence, CO2FIX model in conjunction with remote sensing techniques can be successfully applied for estimating carbon sequestration potential of agroforestry systems in a district or a region.
Keywords
Agroforestry, Geospatial, Remote Sensing, Sub-Pixel, Tree Cover.
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