Aboveground biomass estimation using multisource remote sensing & gis techniques: a case study in Faculty of Earth Science (FSB)

Aboveground biomass (AGB) assessment is an important step towards better understanding forest carbon dynamics, environmental conservation, and climate change mitigation. This study was carried out in the forested area around the Faculty of Earth Science at University Malaysia Kelantan, which is part of Malaysia's biodiverse tropical ecosystem. Traditional methods of calculating biomass are frequently damaging, labour-intensive, and time-consuming, rendering them unsuitable for large-scale applications. Remote sensing and GIS technologies, on the other hand, appear to be promising instruments for estimating biomass on a big scale, without causing damage, and continuously. As a result, the goal of this study is to estimate AGB using a combination of remote sensing data from Sentinel-2, Landsat 8, and LiDAR, as well as field measurements of tree height, diameter at breast height (DBH), and wood density. The methodological approach includes pre-processing and analysing optical and LiDAR datasets to generate vegetation indices (NDVI and EVI) and a canopy height model (CHM). These inputs were combined with ground truth data via GIS and regression modelling. Three regression models were created to determine the predictive power of different combinations of these factors. CHM consistently emerged as the most reliable predictor of AGB, beating vegetation indices that demonstrated saturation effects in dense canopy locations.