This study presents the information on the dynamics of changes in land use/land cover (LULC) spatially and temporally related to the causes of flooding in the study area. The dynamics of LULC changes have been derived based on the classification of Landsat imagery for the period between 1990 and 2016. Terrain surface classification (TSC) was proposed as a micro-landform classification approach in this study to create flood hazard assessment and mapping that was produced based on the integration of TSC with a probability map for flood inundation, and flood depth information derived from field observation. TSC as the micro-landform classification approach was derived from SRTM30 DEM data. Multi-temporal Sentinel-1 data were used to construct a pattern of historical inundation or past flooding in the study area and also to produce the flood probability map. The results of the study indicate that the proposed flood hazard mapping (FHM) from the TSC as a micro-landform classification approach has the same pattern with the results of the integration of historical inundation or previous floods, as well as field investigations in the study area. This research will remain an important benchmark for planners, policymakers and researchers regarding spatial planning in the study area. In addition, the results can provide important input for sustainable land use plans and strategies for mitigating flood hazards.

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.

This study develops a geomorphological identification methodology for wall reefs in the microcontinental environment of Wangiwangi Island, Indonesia, using medium-resolution Landsat 8 satellite imagery and morphological analysis based on Maxwell’s geomorphological framework. The uniqueness of the wall reef landform lies in the fact that the lagoon elongates on limestone, resulting in a habitat and ecosystem that develops differently from those of other shelf reefs, namely, platform reefs and plug reefs. Using Optimum Index Factor (OIF) optimization and RGB image composites, four reef types were successfully identified: cuspate reefs, open ring reefs, closed ring reefs, and resorbed reefs. A field check was conducted at fifteen observation sites, which included measurements of depth, turbidity, and water quality parameters, as well as an in situ benthic habitat inventory. The analysis results showed a strong correlation between image composites, geomorphological reef classes, and ecological conditions, confirming the successful adaptation of Maxwell’s classification to the Indonesian reef system. This hybrid integrated approach successfully maps the distribution of reefs on a complex continental shelf, providing an essential database for shallow-water spatial planning, ecosystem-based conservation, and sustainable management in the Coral Triangle region. Policy recommendations include zoning schemes for protected areas based on reef landform morphology, strengthening integrative monitoring systems, and utilizing high-resolution imagery and machine learning algorithms in further research.