This project leverages interpretable machine learning techniques to analyze and assess the impacts of various environmental factors on the vegetation within the Sundarbans, a unique mangrove area in the delta region of the Padma, Meghna, and Brahmaputra river basins. The Sundarbans is a UNESCO World Heritage Site and a vital area for biodiversity. Understanding how environmental factors affect its vegetation can provide crucial insights for conservation efforts.
Kamel Didan - University of Arizona, Alfredo Huete - University of Technology Sydney and MODAPS SIPS - NASA. (2015). MOD13A2 MODIS/Terra Vegetation Indices 16-Day L3 Global 1km SIN Grid. NASA LP DAAC. http://doi.org/10.5067/MODIS/MOD13A2.0061.
Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store (CDS), (date of access), https://cds.climate.copernicus.eu/cdsapp#!/home.
J. A. Cummings and O. M. Smedstad. 2013: Variational Data Assimilation for the Global Ocean. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications vol II, chapter 13, 303-343.
J. A. Cummings and O. M. Smedstad. 2013: Variational Data Assimilation for the Global Ocean. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications vol II, chapter 13, 303-343.