Aboveground biomass estimation in linear forest objects: 2D- vs. 3D-data

MODIS Based Estimation of Forest Aboveground Biomass in China

Accurate estimation of forest biomass C stock is essential to understand carbon cycles. However, current estimates of Chinese forest biomass are mostly based on inventory-based timber volumes and empirical conversion factors at the provincial scale, which could introduce large uncertainties in forest biomass estimation. Here we provide a data-driven estimate of Chinese forest aboveground biomas...

Estimation of linear deformations of 2D and 3D fuzzy objects

Impact of data model and point density on aboveground forest biomass estimation from airborne LiDAR

BACKGROUND 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 heigh...

Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates

Landsat Thematic mapper (TM) image has long been the dominate data source, and recently LiDAR has offered an important new structural data stream for forest biomass estimations. On the other hand, forest biomass uncertainty analysis research has only recently obtained sufficient attention due to the difficulty in collecting reference data. This paper provides a brief overview of current forest ...

Estimation of aboveground biomass using airborne LiDAR data

In this study a semi-empirical model that was originally developed for stem volume estimation is used for aboveground biomass (AGB) estimation. The semi-empirical model is based on the relative heights of first echo LiDAR point cloud data and assumes a linear relationship between AGB and canopy volume. However, the usage of point cloud data leads to a computationally demanding task when process...