Enhancing Tree Species Mapping in Arkansas’ Forests Through Machine Learning and Satellite Data Fusion: A Google Earth Engine–Based Approach

Abstract Arkansas’ subtropical climate nurtures extensive forested regions, particularly within the Ozark-St. Francis and Ouachita National Forests. Despite this, the state lacks an up-to-date high-resolution map detailing the distribution of tree species within its forests. This study harnesses the power of machine learning, specifically the Random Forest (RF), Gradient Tree Boosting (GTB), Support Vector Machine (SVM), and K-Nearest Neighbors (K-NN) classifiers, within the Google Earth Engine (GEE) framework. These classifiers are applied to classify tree species distribution in Arkansas’ forests by integrating data from various sources, including Sentinel-1/-2, Landsat-8, and the National Agriculture Imagery Program (NAIP). The study evaluates the classification accuracy of single-sensor images against fused composites, revealing that the fused Landsat-8 and Sentinel-1 data achieve the highest validation accuracy at 0.8875, followed by single-sensor Sentinel-1 and Landsat-8, which yield validation accuracies of 0.8863 and 0.8859, respectively. Among the classifiers, RF demonstrates the highest accuracy, followed by GTB, K-NN, and SVM when applied to fused Landsat-8 and Sentinel-1 images. This study incorporates the Shapley Additive Explanations (SHAP) to elucidate feature importance, and results show that elevation is the most influential variable across all forest classes, followed by vegetation indices. The study introduces a weighted ensemble method, resulting in a remarkably accurate tree species distribution map with an accuracy score of 0.9772. This research demonstrates the effectiveness of combining machine learning and satellite image fusion with explainable AI (XAI) and GEE to enhance tree species classification and produce a precise, high-resolution regional tree cover map.