Evaluating The Potential of Satellite Data and Machine Learning Models For Land Cover Classification in Support of Emission Inventory - A Pilot Study in Dak Nong

Abstract Land use change in agriculture and forestry is a key sector in the IPCC reports on climate change, as it significantly impacts greenhouse gas emissions and carbon sequestration. Advances in remote sensing technology and new satellite generations provide opportunities for more frequent and detailed monitoring of land cover. This study focuses on analyzing and evaluating the effectiveness of machine learning models, including Random Forest (RF), Boosted Tree (BT), and Support Vector Machine (SVM), for land cover classification in Dak Nong. Sentinel-2 MSI and ALOS World 3D data were used in machine learning classification models. Results indicate that RF is the most effective classification model, achieving the highest overall accuracy of 78.6% with strong stability, and Kappa value of 0.75. BT classifier also performed well on dry-season imagery, while SVM exhibited lower accuracy due to its sensitivity to parameter settings. The findings highlight the importance of integrating multi-seasonal imagery with spectral indices and topographic data to improve classification accuracy in complex landscapes as well as their potential application to support AFOLU-related emissions inventory.