Development of a spatially explicit model of blue carbon storages in tropical mudflat environment through integrated radar-optical approach and ground-based measurements

Abstract

Tropical coastal wetlands have been well recognized for their role in mitigating climate change apart from possessing high biodiversity values as well as providing important habitats for fisheries and other ecosystem services. Although these wetlands play key roles in sequestering carbon (C) in the lithosphere and hydrosphere, there is a notable dearth of comprehensive yet spatially explicit accounts of their total blue carbon (TBC) stocks. The anthropogenic factors including development stressors have fundamentally altered the global C dynamics. In this respect, holistic accounting of the total C stocks of the coastal wetlands becomes the foremost task in emphasizing their recognition as integral components of climate change mitigation strategies. This study modeled TBC stocks of an intertidal mudflat of eastern India across different land use/ land covers (LULCs) by quantifying the above ground biomass (AGB), below ground biomass (BGB), and soil organic C (SOC). The distribution of SOC was modeled through spatial interpolation of point-specific C densities (R2 = 0.77; RMSE = 22.91 Mg ha−1). AGB was estimated by establishing allometric relationships between geospatial data, both of active (PALSAR-2) and passive (Pleiades-1B) nature and in-situ dendrometric variables using the artificial neural network (R2 = 0.85; RMSE = 48.91 Mg ha−1). BGB was estimated from the modeled AGB. Spatial integration of these datasets estimated the TBC stock of mudflat as 27,469.30 Mg. Overall, open mangrove represented highest mean TBC density (163.29 Mg ha−1) while herbaceous vegetation recorded largest TBC stock (9818.94 Mg). Mean TBC densities across LULCs were significantly different (p < 0.05) from each other except for the herbaceous vegetation and built-up categories, indicating the inertial effects of paleo-land covers. Accordingly, this study could be inferred as a pioneering one in developing a comprehensive geospatial model of TBC accounting in India towards strengthening the regional and national C inventories.