Flooding frequently affects rivers and low-lying areas of the Phong River, making flood risk assessment essential for mitigation and loss reduction.This study developed flood risk maps for the Lower Nam Phong basin using a fuzzy analytic hierarchy process (FAHP) integrated with GIS.Hazard (flood depth, slope, soil drainage) and vulnerability (land use) weights were derived through FAHP.Results show that under baseline conditions, 19.44% of Nong Bua, Bung Nium, and Khok Sri Sub-Districts were classified as high risk, consistent with historical flood records.Future scenarios indicate downstream expansion of high-risk zones: 10.45% under RCP 4.5 (Representative Concentration Pathways) and 12.00% under RCP 8.5, particularly in Bung Nium and Khok Sri.Across all scenarios, medium-risk areas dominated (8.13, 36.45, and 72.67 km 2 for baseline, RCP 4.5, and RCP 8.5, respectively).The maps provide actionable insights for flood management and planning in the basin.

Precise Digital Terrain Models (DTMs) are essential for mangrove carbon accounting and Measurement, Reporting, and Verification (MRV) frameworks that facilitate climate change mitigation.Although airborne photogrammetry and LiDAR are frequently employed for terrain extraction, their utilization in dense mangrove wetlands is limited by elevated expenses and operational intricacies.This work offers a novel assessment of the iPhone 12 Pro LiDAR sensor as an economical and portable option for Digital Terrain Model (DTM) development in mangrove ecosystems, filling a significant void in accessible carbon monitoring technology.Utilizing the 3D Scanner App for iOS, the study gathered data from 12 locations (55 points per location) encompassing open regions, sparse canopies, and dense canopies, corroborated by 660 NRTK positioning points.The precision of the LiDAR-derived Digital Terrain Model (DTM) was assessed by extensive statistical criteria, such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Bias, Pearson correlation coefficient, and 95% confidence intervals, thereby ensuring rigorous validation of consumer-grade mobile LiDAR for high-resolution topographic mapping.The results indicated exceptional accuracy in open environments (MAE < 0.1 m, negligible RMSE and bias), moderate precision in sparse canopies (MAE 0.086-0.381m), and diminished performance in dense vegetation (MAE up to 1.064 m, positive bias of 0.928 m).Statistical analysis indicated negligible elevation fluctuation in open areas, however substantial outliers were seen in dense canopies.Regression analysis indicated a robust association in open areas (R = 0.956), a moderate correlation in sparse vegetation (R = 0.799), and a lesser correlation in dense canopies (R = 0.663), implying possibility for enhancement by allometric modifications.Although professional LiDAR systems exhibit superior accuracy, the iPhone LiDAR is a pragmatic and cost-effective option for Digital Terrain Model production in open to moderately vegetated regions.The system's portability and affordability render it especially advantageous for ecological monitoring and geoscientific applications in resource-constrained environments, however dense vegetation poses significant challenges.These findings underscore the capability of consumergrade LiDAR technology to facilitate precise environmental monitoring while markedly decreasing expenses relative to professional systems.

The catastrophic flash flood that struck the Biluk Poh Watershed in Bali on October 16, 2022, was a complex disaster.While triggered by extreme rainfall, its catastrophic scale was critically exacerbated by landscape-level factors, particularly massive sediment mobilization.This study investigates the spatial contribution of soil erosion to the flood's severity by quantifying its extent and intensity using the Universal Soil Loss Equation (USLE).The analysis integrates CHIRPS rainfall data, ALOS PALSAR DEM, vegetation indices from Sentinel-2, and field-based measurements of soil structure, texture, and organic matter.High-resolution erosion modelling (10 m) revealed soil loss ranging from 0 to 4664.78 t ha yr, with a watershed average of 168.03 t ha yr.The results show a landscape of stark contrasts: while 39.30% of the area experiences only Slight erosion (0-15 t ha yr), a combined 55.97% suffers from Moderate (18.56%),Heavy (29.42%), and Very Heavy (7.99%) erosion.Erosion was most severe in the Penyaringan and Yeh Embang Kauh villages.These findings demonstrate that uncontrolled erosion from these specific hotspots, which are concentrated upstream and along steep river corridors, functioned as the key exacerbating factor.They supplied the massive sediment loads that blocked the main river channel, catastrophically worsening the flood event triggered by high rainfall.This study provides the first high-resolution erosion assessment for the Biluk Poh Watershed, offering critical evidence for targeted mitigation.The urgency of these findings is underscored by the catastrophic September 2025 Bali floods, which demonstrated that the sedimentdriven mechanisms modeled in this study are a primary, escalating, and regional-scale threat.

Food security is a pressing issue in Indonesia, where challenges in land suitability and crop productivity are exacerbated by rice diseases and pests.This study aims to detect and analyze rice diseases and pests in southeastern Mount Merapi, Klaten Regency, using UAV technology to support sustainable agriculture.The research establishes an integrated framework for precision agriculture by combining a region Land Potency Index (LPI) with UAV-based multispectral monitoring to address these challenges.The LPI, assessing slope, lithology, soil, water, and hazard exposure, strategically identified the central zone of Klaten Regency as the area with the highest agricultural potential, thereby optimizing the focus for subsequent detailed analysis.Within these high-potential zones, UAV-derived vegetation indices (NDVI and NDCI) served as effective early-warning indicators.The analysis revealed a clear distinction where healthy rice plants consistently exhibited NDVI > 0.6 and NDCI > 0.5, while significant clusters fell below these thresholds.Crucially, field validation confirmed that these low-value clusters were predominantly associated with leaf blast disease, demonstrating the method's efficacy in pinpointing specific physiological stress.The widespread prevalence of blast in high-potential areas underscores a direct and significant threat to regional yield.Therefore, this study demonstrates that the integration of LPI for targeting and UAVs for diagnosis provides a scalable, datadriven workflow.The findings highlight the critical need for management strategies that leverage this early detection capability to implement timely interventions, such as the use of resistant varieties and balanced fertilization, thereby enhancing the sustainability and resilience of rice production systems in Indonesia and similar agro-ecological contexts.

International audience

The Covid-19 outbreak rapidly became a global pandemic in December 2019, spreading through droplets, direct contact, and possibly airborne transmission. Southeast Asian nations like Malaysia and Indonesia experienced delayed outbreaks but saw a surge in cases. Analysing Covid-19 spatial patterns, especially concerning temperature and humidity, provides valuable insights. Utilizing remote sensing, allows studying the correlation between temperature conditions and Covid-19 outbreak patterns. This study focuses on investigating the impact of urban heat islands (UHIs) on Covid-19 outbreaks in Jakarta and Greater Kuala Lumpur, given their significant caseloads in Indonesia and Malaysia, respectively. The research integrates remote sensing, secondary data, and statistical analysis methods. Remote sensing was used to acquire land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI). The analysis revealed that industrial and commercial areas were hotter than others during normal times, but during the pandemic, LST and UHI shifted from industrial to settlement areas due to large-scale social restrictions. This shift corresponded with the cessation of office, tourism, and industrial activities during lockdowns in March and July 2020 in Jakarta and Greater Kuala Lumpur, respectively. The concentration of people shifted from central business and industrial areas to residential areas during lockdowns, resulting in changes in UHI patterns.