The upper Lam Nam Chi and Lam Saphung subwatershed in Thailand is an area at risk of drought and water scarcity, with climate change having the potential to increase the severity of these problems. Therefore, this study evaluated streamflow and investigated the impact of climate change on streamflow in the research area, applying the Soil and Water Assessment Tool model under climate change scenarios RCP4.5 and RCP8.5. Streamflow data were used from two stations of the Royal Irrigation Department during 2012-2022 for model calibration, with model accuracy being analyzed using the Nash-Sutcliffe efficiency and PBIAS. The findings showed that the average annual streamflow of the upper Lam Nam Chi and Lam Saphung Subwatershed during 2012-2022 was 856.40 million cubic meters. The highest discharge occurred in September and the lowest in January. There was a considerable increase in the predicted annual streamflow under the climate change scenarios RCP4.5 and RCP8.5 in the near-term (2041-2060), medium-term (2061-2080), and long-term (2081-2100). The highest streamflow occurred in July and August and the lowest in April and May. In particular, the estimated annual streamflow under the long-term scenario without adopting any measures or policies for GHG emission control (RCP8.5) was notably different, being more than 4 times higher than the current streamflow. In conclusion, climate change influenced the streamflow in this watershed, which could lead to severe flooding and water scarcity in this watershed in the future.
The Rungan Watershed is facing an alarming rate of environmental degradation yet continuing to support the livelihoods of local communities. Therefore, this study is essential for addressing these issues and formulating adaptive management model. This research aims to assess environmental conditions and social economic aspects to formulate management model that is expected to serve as a framework for sustainable development in Palangka Raya City. By applying both qualitative and quantitative approaches, the study collects ecological, economic, and socio-cultural data to inform the development of management strategies. Land cover, land use, and vegetation density are assessed using remote sensing imagery and land cover maps. The analysis of forest vegetation density classes is conducted through the Normalized Difference Vegetation Index (NDVI), utilizing data from Landsat imagery. Socio-cultural information is gathered through interviews and observations. The study identifies various land cover condition, information on social economic condition that serve as a basis for developing a zoning based management model that categorizes the Rungan Watershed into conservation, buffer, and general utilization zones. This model accommodates the diverse interests of multiple stakeholders within a sustainable development framework. The study concludes that the zonation management of the sub-basin must align with the biophysical ecological conditions of the area, address existing challenges, and promote community welfare.
This study develops an integrated framework for monitoring eutrophication in Xuan Khanh Reservoir (XKR), Hanoi, Vietnam, by combining in-situ observations with Landsat 8 (L8) reflectance data. A total of 66 field measurements collected during four surveys (October 2024-July 2025) were used to calibrate an empirical model for estimating the Trophic State Index (TSI) from L8-derived reflectance. The near-infrared to red ratio (B5/B4) achieved the best performance (R²=0.81; RMSE=2.4). Nineteen cloud-free L8 images acquired between July 2021 and July 2025 were then applied to map spatiotemporal TSI variations. Results show that XKR consistently remained in a highly eutrophic to hypereutrophic state, with TSI values ranging from 64 to 82. Spatial patterns revealed persistent hotspots in shallow northern zones and inlet-connected areas, driven by external nutrient inputs and surrounding land-use pressures. These findings demonstrate the value of L8 as a cost-effective monitoring tool for small, polluted reservoirs, offering evidence to guide nutrient reduction, wastewater management, and land-use regulation. The proposed approach provides transferable insights for long-term trophic state assessment and can support policy decisions aimed at safeguarding freshwater resources in data-limited regions.
Anthropogenic land use and cover changes pose a significant threat to small island ecosystems, yet systematic monitoring remains limited. This study analyzes LULC dynamics over 20 years (2004-2024) on three inhabited islands in South Bangka, Bangka Belitung, Indonesia. The objectives were to assess LULC changes, evaluate their impacts on biodiversity, and examine the effectiveness of existing policies using multi-temporal Landsat and Sentinel-2 imagery. The supervised classification achieved an overall accuracy of 85.3-91.2% with Kappa coefficients of 0.82-0.89, indicating substantial to nearly perfect agreement. The results revealed distinct transformation patterns across the islands. Tinggi Island demonstrated resilience through the recovery of secondary dryland forests (from 68.52 ha to 113.48 ha), despite the presence of illegal tin mining activities that created artificial wetland systems. In contrast, Kelapan Island faced severe degradation, with extensive forest loss and habitat fragmentation. This resulted in a 29.5% decline in mean patch size and a critical loss of connectivity. Conversely, Pongok Island showcased successful conservation efforts, significantly expanding its mangrove ecosystem, which enhanced its blue carbon capacity, even amid agricultural intensification. The assessment of biodiversity impacts confirmed that maintaining patch sizes above 20 hectares and ensuring high connectivity are essential thresholds for species conservation. However, current LULC management policies demonstrate limited effectiveness due to regulatory gaps between marine and terrestrial governance frameworks. The study concludes that integrated conservation strategies, including adaptive management and corridor restoration, are urgently needed to balance development pressures with the preservation of ecosystem integrity.
Air pollution in Northern Thailand is a persistent environmental issue, particularly during the dry season, posing significant health risks. This study used multiple remote-sensing data sources to explore spatio-temporal PM2.5 variations in Chiang Mai Province from 2020 to 2024. Ground-based PM2.5 measurements were merged with Terra-Aqua aerosol optical depth (AOD) and Sentinel-5P carbon monoxide (CO) and nitrogen dioxide (NO2) data within the Google Earth Engine platform. Multiple linear regression models were employed to estimate ground-level PM2.5 concentrations and generate spatial distribution maps. Central lowland and northern areas of Chiang Mai consistently exhibited higher PM2.5 levels, particularly in March and April. The model achieved an R2 of 0.77 and a root mean square error (RMSE) of 14.60 μg/m3, with an overall correlation of 0.88 between satellite-derived and ground-based measurements. Seasonal analysis revealed enhanced model performance during the burning period (January-April; R2=0.72, RMSE=17.31 μg/m3), compared with the non-burning period (May-December; R2=0.26, RMSE=8.32 μg/m3). During the 2020-2024 burning periods, average PM2.5 concentrations were 51.33, 45.62, 25.18, 49.81, and 41.11 μg/m3, respectively, peaking at 79.18±16.90 μg/m3 in March 2023. Integrating AOD with Sentinel-5P CO and NO2 data improved estimation accuracy and hotspot identification, highlighting the potential of cloud-based geospatial platforms for comprehensive air quality monitoring.
