Multi-Machine Learning Approach for Mapping Drought-Driven Fires in Kalimantan Using Geo-AI Data

This paper presents a comparative analysis of four machine learning algorithms-Random Forest (RF), Gradient Boosting (GTB), Support Vector Machine (SVM), and Classification and Regression Trees (CART)-for mapping drought-driven forest and land fires in Kalimantan. We utilized multi-source Geo-Artificial Intelligence data, including satellite remote sensing products and spatial data, incorporating physical, environmental, climatological, biological, and socio-economic (anthropogenic) factors as inputs. The EI Niño year of 2019 was selected as the study period for data analysis. The Global Annual Burned Area Maps (GABAM) product was refined using nighttime active fire data, which represent actual fire occurrences and served as our reference map. We applied the Graph Regularization fusion method to all the classified images to enhance mapping accuracy. Our results indicate that RF and GTB significantly outperformed SVM and CART in terms of precision, recall, Kappa, and overall accuracy, with both RF and GTB achieving an overall accuracy of 97%. The fused image yielded results similar to those of the RF and GTB images. These findings offer valuable insights for developing a national early warning system for drought-related forest and land fires, using Geo-AI data to support forest management and the goal of achieving net-zero emissions.