Airborne LIDAR-Derived aboveground biomass estimates using a Hierarchical Bayesian approach

M Wang, Q Liu, L Fu, G Wang, X Zhang - Remote Sensing, 2019 - mdpi.com
Conventional ground survey data are very accurate, but expensive. Airborne lidar data can
reduce the costs and effort required to conduct large-scale forest surveys. It is critical to
improve biomass estimation and evaluate carbon stock when we use lidar data. Bayesian
methods integrate prior information about unknown parameters, reduce the parameter
estimation uncertainty, and improve model performance. This study focused on predicting
the independent tree aboveground biomass (AGB) with a hierarchical Bayesian model using …

Airborne LIDAR-derived aboveground biomass estimates using a hierarchical Bayesian approach.

WMX Wang MengXi, LQW Liu QingWang… - 2019 - cabidigitallibrary.org
Conventional ground survey data are very accurate, but expensive. Airborne lidar data can
reduce the costs and effort required to conduct large-scale forest surveys. It is critical to
improve biomass estimation and evaluate carbon stock when we use lidar data. Bayesian
methods integrate prior information about unknown parameters, reduce the parameter
estimation uncertainty, and improve model performance. This study focused on predicting
the independent tree aboveground biomass (AGB) with a hierarchical Bayesian model using …
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