Estimating the aboveground biomass of coniferous forest in Northeast China using spectral variables, land surface temperature and soil moisture.

As a crucial indicator of forest growth and quality, estimating aboveground biomass (AGB) plays a key role in monitoring the global carbon cycle and forest health assessments. Novel methods and applications in remote sensing technology can greatly reduce the investigation time and cost and therefore have the potential to efficiently estimate AGB. Random forest (RF), combined with remote sensing images, is a popular machine learning method that has been widely used for AGB estimation. However, the accuracy of the ordinary linear variable selection method in the AGB estimation of coniferous forests is challenging due to the complexity of these forest biomes. In this study, spectral variables (spectral reflectance and vegetation index), land surface temperature (LST) and soil moisture were extracted from the operational land imager (OLI) and thermal infrared sensor (TIRS) of Landsat 8, and optimized RF regressions were established to estimate the AGB of coniferous forests in the Wangyedian forest farm, Inner Mongolia, Northeast China. We applied one linear (Pearson correlation coefficient (PC)) and four nonlinear (Kendall's τ coefficient (KC), Spearman coefficient (SC), distance correlation coefficient (DC) and the importance index) indices to select variables and establish optimized RF regressions for AGB estimation. The results showed that all the nonlinear indices provided significantly lower estimation errors than the linear index, in which the minimum root mean square error (RMSE) of 40.92 Mg/ha was obtained by the importance index in the nonlinear indices. In addition, the inclusion of LST and soil moisture significantly improved AGB estimation. The RMSE of the models constructed through the five indices decreased by 12.93%, 7.31%, 8.33%, 6.28% and 10.78%, respectively, following the application of the LST variable. In particular, when LST and soil moisture were both added into the model, the RMSE decreased by 31.47%. This study demonstrates that combining the nonlinear variable selection method with optimized RF regression can improve the efficiency of AGB estimation to support regional forest resource management and monitoring.