%0 Journal Article
%T Impact of data model and point density on aboveground forest biomass estimation from airborne LiDAR
%A Alexander Koltunov
%A Antonio Ferraz
%A Carlos Alberto Silva
%A Heiko Balzter
%A Mariano Garcia
%A Sassan Saatchi
%A Susan Ustin
%J Archive of "Carbon Balance and Management".
%D 2017
%R 10.1186/s13021-017-0073-1
%X Accurate estimation of aboveground forest biomass (AGB) and its dynamics is of paramount importance in understanding the role of forest in the carbon cycle and the effective implementation of climate change mitigation policies. LiDAR is currently the most accurate technology for AGB estimation. LiDAR metrics can be derived from the 3D point cloud (echo-based) or from the canopy height model (CHM). Different sensors and survey configurations can affect the metrics derived from the LiDAR data. We evaluate the ability of the metrics derived from the echo-based and CHM data models to estimate AGB in three different biomes, as well as the impact of point density on the metrics derived from them
%K Airborne LiDAR data
%K Aboveground biomass
%K Point density
%K Data thinning
%K Echo-based
%K Canopy height model
%U https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5311013/