Machine Learning-Based Forest Type Mapping from Multi-Temporal Remote Sensing Data: Performance and Comparative Analysis

This paper presents a meticulous exploration of advanced machine learning techniques for precise forest type classification using multi-temporal remote sensing data within a woodland environment. The study comprehensively evaluates a diverse range of models, spanning from advanced ensemble learning methods to several finely tuned support vector machine (SVM) variants, with a specific focus on Bayesian-optimized SVM with radial basis function (RBF) kernel. Our findings highlight the robust performance of the Bayesian-optimized SVM, achieving a high accuracy of up to 94.27%, and average precision and recall of 94.46% and 94.27% respectively. Notably, this accuracy aligns with the levels attained by acclaimed ensemble techniques such as Random Forest and CatBoost while also surpassing those of XGBoost and LightGBM. These results highlight the potential of these methodologies to significantly enhance forest type mapping accuracy compared to tradition (Linear) SVM and black-box neural networks. This, in turn, can enable reliable identification and quantification of key services, including carbon storage and erosion protection, intrinsic to the forest ecosystem. Finding of our comparative study emphasizes the profound impact of employing and fine-tuning advanced machine learning approaches in the realm of remote sensing-based environmental analysis.