Total and component forest aboveground biomass inversion via LiDAR-derived features and machine learning algorithms.

ABSTRACT: Forest aboveground biomass (AGB) and its biomass components are key indicators for assessing forest ecosystem health, productivity, and carbon stocks. Light Detection and Ranging (LiDAR) technology has great advantages in acquiring the vertical structure of forests and the spatial distribution characteristics of vegetation. In this study, the 56 features extracted from airborne LiDAR point cloud data were used to estimate forest total and component AGB. Variable importance-in-projection values calculated through a partial least squares regression algorithm were utilized for LiDAR-derived feature ranking and optimization. Both leave-one-out cross-validation (LOOCV) and cross-validation methods were applied for validation of the estimated results. The results showed that four cumulative heig

SUBMITTER: Ma J 

PROVIDER: S-EPMC10639141 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature