Tree-centric mapping of forest carbon density from airborne laser scanning and hyperspectral data.

ABSTRACT: Forests are a major component of the global carbon cycle, and accurate estimation of forest carbon stocks and fluxes is important in the context of anthropogenic global change. Airborne laser scanning (ALS) data sets are increasingly recognized as outstanding data sources for high-fidelity mapping of carbon stocks at regional scales.We develop a tree-centric approach to carbon mapping, based on identifying individual tree crowns (ITCs) and species from airborne remote sensing data, from which individual tree carbon stocks are calculated. We identify ITCs from the laser scanning point cloud using a region-growing algorithm and identifying species from airborne hyperspectral data by machine learning. For each detected tree, we predict stem diameter from its height and crown-width estimate. F

SUBMITTER: Dalponte M 

PROVIDER: S-EPMC5137341 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature