Comparing Machine Learning and Statistical Models for Remote Sensing-Based Forest Aboveground Biomass Estimations

Understanding the distribution of forest aboveground biomass (AGB) is pivotal for carbon monitoring. Field-based inventorying is time-consuming and costly for large-area AGB estimations. The integration of multimodal remote sensing (RS) observations with single-year, field-based forest inventory analysis (FIA) data has the potential to improve the efficiency of large-scale AGB modeling and carbon monitoring initiatives. Our main objective was to systematically compare the AGB prediction accuracies of machine learning algorithms (e.g., random forest (RF) and support vector machine (SVM)) with those of conventional statistical methods (e.g., multiple linear regression (MLR)) using multimodal RS variables as predictors. We implemented a method combining AGB estimates of actual FIA subplot locations with airborne LiDAR, National Agriculture Imagery Program (NAIP) aerial imagery, and Sentinel-2 satellite images for model training, validation, and testing. The hyperparameter-tuned RF model produced a root mean square error (RMSE) of 27.19 Mgha−1 and an R2 of 0.41, which outperformed the evaluation metrics of SVM and MLR models. Among the 28 most important explanatory variables used to build the best RF model, 68% of variables were derived from the LiDAR height data. The hyperparameter-tuned linear SVM model exhibited an R2 of 0.10 and an RMSE of 32.17 Mgha−1. Additionally, we developed an MLR using eight explanatory variables, which yielded an RMSE of 22.59 Mgha−1 and an R2 of 0.22. The linear ensemble model, which was developed using the predictions of all three models, yielded an R2 of 0.79. Our results suggested that more field data are required to better generalize the ensemble model. Overall, our findings highlight the importance of variable selection methods, the hyperparameter tuning of ML algorithms, and the integration of multimodal RS data in improving large-area AGB prediction models.