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Global forest AGB estimation using SAR remote sensing & AIML

Global forest AGB estimation using SAR remote sensing & AIML

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Forest biomass is a key factor in assessing total carbon sequestration and maintaining a positive... View more

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Forest biomass is a key factor in assessing total carbon sequestration and maintaining a positive carbon balance within the ecosystem. Above-ground biomass (AGB) is one of the 54 essential climate variables documented by the Global Climate Observing System, which is being assessed primarily through traditional field measurements using vegetation type- and species-specific allometric equations. Although AGB estimation using field measurements is more precise, it is impractical to employ at regional, national, and global levels, as it would be too expensive, labor- and time-intensive. Various researchers have worked on forest AGB estimation employing various techniques on different local to global scales, while were limited to local (plot level to forest boundary), to regional (country to the continent).
This project will utilize large-scale field-based information and different remote sensing products, especially various polarizations of Synthetic Aperture Radar (SAR; ALOS PALSAR, Sentinel-C)) for Global Forest AGB estimation in combination with other earth observation satellite based inputs including optical (Sentinel 2, Landsat, MODIS), and LiDAR (GEDI) etc., each of this has distinct advantages over the others with unique spectral, spatial, and temporal properties with extensive coverage, and excellent timeliness. The study will also aim to estimate the dynamics in global AGB and determine the hotspots of forest degradation and its underlying linkages with climate-induced factors. The importance of the project is the global forest above-ground biomass assessment that helps in understanding the global carbon budget including its sequestration and sink to maintain a positive carbon balance within different types of global forest ecosystems. Looking into the recent advancement in sensor resolution, and machine learning techniques, the aim of the proposed research is to map the global forest AGB and productivity using large-scale field inventory datasets and fine resolution optical-SAR remote sensing imageries. The study will also aim to estimate the dynamics in global AGB and determine the hotspots of forest degradation and its underlying causes and linkages with climate-induced factors. The importance of the project is the global forest above ground biomass assessment that helps in understanding the global carbon budget including its sequestration and sink to maintain a positive carbon balance within different types of global forest ecosystems.

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