Mapping Forest Aboveground Biomass Using Multisource Remotely Sensed Data
The majority of the aboveground biomass on the Earth’s land surface is stored in forests.
Thus, forest biomass plays a critical role in the global carbon cycle. Yet accurate estimate of forest
aboveground biomass (FAGB) remains elusive. This study proposed a new conceptual model to map
FAGB using remotely sensed data from multiple sensors. The conceptual model, which provides
guidance for selecting remotely sensed data, is based on the principle of estimating FAGB on the
ground using allometry, which needs species, diameter at breast height (DBH), and tree height as
inputs. Based on the conceptual model, we used multiseasonal Landsat images to provide information
about species composition for the forests in the study area, LiDAR data for canopy height, and the
image texture and image texture ratio at two spatial resolutions for tree crown size, which is related
to DBH. Moreover, we added RaDAR data to provide canopy volume information to the model. All
the data layers were fed to a Random Forest (RF) regression model. The study was carried out in
eastern North Carolina. We used biomass from the USFS Forest Inventory and Analysis plots to train
and test the model performance. The best model achieved an R2 of 0.625 with a root mean squared
error (RMSE) of 18.8 Mg/ha (47.6%) with the “out-of-bag” samples at 30 30 m spatial resolution.
The top five most important variables include the 95th, 85th, 75th, and 50th percentile heights of the
LiDAR points and their standard deviations of 85th heights. Numerous features from multiseasonal
Sentinel-1 C-Band SAR, multiseasonal Landsat 8 imagery along with image texture features from
very high-resolution imagery were selected. But the importance of the height metrics dwarfed all
other variables. More tests of the conceptual model in places with a broader range of biomass and
more diverse species composition are needed to evaluate the importance of other input variables.