Estimation and mapping of above ground biomass and carbon of Bwindi impenetrable National Park using ALOS PALSAR data

Biomass is an important parameter for bioenergy modelling, food security, environmental assessment and climate change research. However, estimation of biomass is still a challenging task especially in areas with complex forest stand structures and environmental conditions. The aim of this research was to estimate the Above Ground Biomass (AGB) and carbon stocks in the Bwindi Impenetrable National Park (BINP) using the Horizontal-Horizontal (HH) and Horizontal-Vertical (HV) polarizations acquired by the L-band ALOS PALSAR instrument. In addition, field data from 35 sample plots comprising of the Diameter at Breast Height (DBH), co-ordinates of centroids and angles to the top and bottom of the individual trees was used for the analysis. The relationship between biomass and radar backscatter for selected sample plots was established using pairwise correlation. A strong correlation between the radar backscatter and AGB was found using the HV polarization compared with the HH polarization. Overall, ALOS PALSAR data has a strong potential for biomass and carbon estimation in the tropical areas such as the BINP

Land cover and land use analysis in coastal Bangladesh

Land cover and land use (LCLU) analysis is a central determinant of the current and future relationship between people and local ecosystem services. It provides the spatial basis for the integrated analysis of the study area. Historical change (1989–2010) is identified using classification techniques based on available satellite imagery supported by other data. Nine categories of LULC are identified; water, Bagda (brackish shrimp farming), Galda (freshwater prawn farming), agriculture (non-waterlogged), agriculture (waterlogged), wetlands and mudflats, mangrove, rural settlements and major urban areas. The analysis shows an increase in aquaculture (mainly replacing rice fields), with agriculture becoming more intermixed with settlements. Future LULC scenarios are determined based on stakeholder narratives