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.

The meaning of compatibility in systems of taper and volume equations has been extended. It is desirable and possible to develop completely compatible taper and volume equation systems that have algebraic compatibility and numeric consistency among all the component equations. Two such taper and volume systems were developed for slash pine in the southeastern United States. One was derived from a recently developed merchantable volume equation (Zhao and Kane 2017), and the other was derived from the well-known Max and Burkhart segmented polynomial taper equation (1976). Three fitting methods were used to obtain numerically consistent estimates of parameters in these two systems. The performance of the systems associated with fitting methods were evaluated with taper, merchantable volume and total volume predictions, and bias trends over different dbh classes, total height classes, and different relative height intervals. The new system outperformed the Max-Burkhart taper-based system for merchantable and total volume predictions and was competitive in diameter prediction. Optimization of parameters of the system for both taper and cumulative volume simultaneously is preferable to separate optimization for taper or volume only.