Above-Ground Forest Biomass Estimation using Multispectral LiDAR Data in a Multilayered Coniferous Forest

Abstract

Above-ground biomass and carbon stock are fundamental components of the global carbon cycle, essential for climate change mitigation. Remote sensing data can provide timely and accurate estimates of various forest attributes, especially over large and remote forested areas. The objective of this research was to investigate the potential of multispectral LiDAR data for estimating the stem biomass (SB) and total biomass (TB) in a multi-layered fir forest using an Edge-tree corrected Area Based Approach (EABA). Subsequently, a Random Forest (RF) regression analysis was performed to develop SB and TB predictive models using LiDAR-derived height metrics. Two RF models were produced and evaluated in terms of their predictive performance. Overall, our work demonstrates the capability of multispectral LiDAR data to provide reliable SB and TB estimates in a complex structured forest, contributing significantly to sustainable forest management.