Prediction of Regional Forest Biomass Using Machine Learning: A Case Study of Beijing, China

Abstract

Dynamic changes in forest biomass are closely related to the carbon cycle, climate change, forest productivity and biodiversity. However, most previous studies mainly focused on the calculation of current forest biomass, and only a few studies attempted to predict future dynamic changes in forest biomass which obtained uncertain results. Therefore, this study comprehensively considered the effects of multi-stage continuous survey data of forest permanent sample plots, site condition factors and corresponding meteorological factors using Beijing as an example. The geographic detector method was used to screen the key interfering factors that affect the growth of forest biomass. Then, based on the back-propagation artificial neural network (BP-ANN) and support vector machine (SVM) learning methods, 80% of the sample data were extracted to train the model, and thereby verify the prediction accuracy of different modeling methods using different training samples. The results showed that the forest biomass prediction models based on both the machine learning algorithms had good fitting accuracy, and there was no significant difference in the prediction results between the two models. However, the SVM model was better than the BP-ANN. While the BP-ANN model provided more volatile predictions, and the accuracy was above 80%, the prediction results of the SVM model were relatively stable, and the accuracy was above 90%. This study not only provides good technical support for the scientific estimation of regional forest biomass in the future, but also offers reliable basic data for sustainable forest management, planning decisions, forest carbon sequestration and sustainable development.

Examining the Potential of Sentinel Imagery and Ensemble Algorithms for Estimating Aboveground Biomass in a Tropical Dry Forest

“…Parametric methods have traditionally been used to estimate AGB models, but they are unable to deal with the complicated non-linearity in complex tropical forests [3]. Non-parametric algorithms, such as SVM [40][41][42], ANN [35,43,44], K-NN [45][46][47], RF [39,[48][49][50][51][52], and XGBoosting [53], have been shown to provide more accurate results, as they are less affected by forest factors, can handle high data dimensionality, and can effectively establish complicated non-linear relationships between AGB plot measurements and RS predictors [3,54]. Ghosh and Behera [52] evaluated the estimation effectiveness of RF and stochastic gradient boosting (SGB) models to estimate the AGB of two plantations in a dense tropical forest in India using S-1 and S-2 data and products (i.e., vegetation indices and SAR textures).…”