Search Results (239)

Coastal zones are subject to a range of natural and anthropogenic processes that result in coastal erosion and accretion, threatening essential infrastructure and straining livelihoods. Analysis of shoreline changes is thus crucial for informing coastal zone planning and management to avert the ramifications of erosion and accretion. Despite a range of literature examining coastline changes worldwide, there is a paucity of literature focusing on Southern Africa, particularly within small harbours. The paper, therefore, aims to analyse shoreline changes at four small harbour zones along the Atlantic Ocean in the Western Cape province, South Africa, over the period from 1985 to 2025. To acquire an accurate shoreline position, four spectral criteria were applied simultaneously: the Automated Water Extraction Index (AWEI), the Modified Normalised Difference Water Index (MNDWI), the Normalised Difference Vegetation Index (NDVI), and the Near Infrared (NIR). Four statistics were then used to measure shoreline changes in the USGS Digital Shoreline Analysis System (DSAS): Net Shoreline Movement (NSM), Shoreline Change Envelope (SCE), End Point Rate (EPR), and Weighted Linear Regression (WLR). Considerable variability was observed within and among the four small harbour study areas, with several erosion and accretion hotspots identified. The 20-year forecast indicated that future shoreline positions would largely maintain the 2025 curvature. Although the study did not reveal significant threats, authorities are encouraged to pay particular attention to erosion and accretion hotspots through appropriate mitigation and adaptation efforts. Full article

Mangrove deforestation is a global problem, but sediment-dominated estuaries can resist it through their own morphodynamic processes. This study reconstructs the spatiotemporal trajectory of the Ujung Pangkah estuary (1995–2025) to weigh natural progradation against anthropogenic pressure. We mapped the estuary in Google Earth Engine using Landsat archives and a Random Forest classifier with four spectral indices (NDVI, mNDWI, EVI, and MVI) and then independently validated all four epochs (Overall Accuracy 92.25–95.00%; Kappa 0.845–0.900). Error-adjusted change-detection analysis shows a non-monotonic trajectory: mangrove extent grew from 898.88 ha (1995) to a 2410.44 ha peak in 2015 and then contracted to 1816.87 ha by 2025 (error-adjusted: 1261.11 to 2626.03 to 2213.71 ha). Across the 30-year record, this is a statistically significant net expansion (z = 3.60, p < 0.001). Spatial attribution shows the 2015–2025 contraction comes mostly from landward anthropogenic conversion (78.1%), not seaward erosion (21.9%). A lagged correlation between a suspended sediment proxy and decadal net change (r = 0.92) offers quantitative support for continued sediment-driven coastal progradation. Ujung Pangkah’s resilience therefore coexists with a real, locatable anthropogenic pressure. Safeguarding this blue carbon ecosystem means targeting policy at the interior conversion zones already underway, alongside continued protection of the coastal frontier. Full article

Super-resolution (SR) of remote-sensing imagery is commonly assessed through spatial fidelity or visual sharpness, although index-based geomatics requires preservation of cross-band spectral relationships. We compare five Sentinel-2 full-band SR configurations (LDSR-S2, SRGAN, SPAN, Mamba, and SWIN) in two hazard-mapping cases: flood-water detection during the 2024 Valencia flood and burn-scar mapping after the 2025 Palisades wildfire. Each model refines four native RGB–NIR bands, while SEN2SR reconstructs the remaining bands to produce a ten-band product at $2.5$ m. We evaluate native-grid reconstruction, the introduction of high-frequency details, and downstream thematic, boundary, and edge-region metrics against a bilinear-interpolation baseline. Dynamic-threshold MNDWI and dNBR detectors are applied independently to each output. Among the learned configurations, SWIN achieves the strongest native-grid reconstruction and task-specific spectral consistency and the strongest fire agreement, but adds the least high-frequency content. Flood full-ROI gains are modest, with LDSR-S2 increasing the F1-score from 0.085 for bilinear interpolation to 0.091. All learned configurations increase flood-edge recall, F1-score, and IoU while reducing edge precision and balanced accuracy. LDSR-S2 gives the strongest final edge F1-score and IoU in both tasks, Mamba gives the lowest learned-model symmetric flood-boundary distance. SPAN yields the best spatial consistency and lowest learned flood-edge spectral error and SRGAN adds the most high-frequency content and the largest combined edge-region gain, alongside the greatest task-specific spectral deviation and the largest symmetric boundary-distance increases. Thus, increased edge activation does not establish uniformly improved delineation or recovered sub-pixel detail. These cases demonstrate feasibility rather than generalization. Operational validation requires more diverse, time-synchronous, high-resolution, spectrally compatible references. Full article

Northeast Thailand’s Chi-Mun River Basin is among Southeast Asia’s most flood-prone regions, experiencing annual monsoon inundation and extreme flood events that threaten community sustainability. Despite substantial investment in flood management infrastructure, community responses to flood hazards vary considerably, with some areas demonstrating adaptive capacity while others exhibit maladaptive development patterns that increase vulnerability. This study assesses community resilience to flood hazards by integrating flood exposure, environmental conditions (Sentinel-2 spectral indices), and population dynamics across 1157 locations in the Upper Chi River Basin, Maha Sarakham Province. A two-stage analytical framework combining K-means clustering and Random Forest classification identified five resilience classes: Slow Recovery (36.3%), Vulnerable Decline (25.8%), Unknown (17.7%), High Resilience (14.2%), and Maladaptive Growth (6.1%). Results reveal that Maladaptive Growth and High Resilience were clearly distinguished by population volatility (254.3 vs. 65.4), population change (+585.8% vs. −18.7%), and elevation (152.7 m vs. 168.1 m). Population trend emerged as the strongest predictor (importance = 0.135), followed by MNDWI (0.114) and elevation (0.113), indicating that demographic dynamics and topographic characteristics are more influential than flood frequency alone in determining resilience class membership. The findings reveal that Maladaptive Growth areas exhibit extreme population growth with high volatility in lower-elevation areas, whereas High Resilience communities maintain environmental quality and demographic stability despite population decline. These findings inform targeted interventions for sustainable flood risk management and contribute to understanding maladaptation in flood-prone regions, supporting the achievement of Sustainable Development Goals 11, 13, and 15. Full article

Urbanisation has become a significant driver of ecological transformation, resulting in the degradation of urban green and blue spaces (UGSs and UBSs) and a subsequent decrease in ecosystem services (ESs). It is imperative to evaluate the spatio-temporal dynamics of UGS and UBS in order to develop sustainable urban planning strategies, particularly in the swiftly expanding cities of the Global South. In the Siliguri Planning Area (SPA), India, this study examines the long-term variations in UGS and UBS and their associated ecosystem service values (ESVs) from 1991 to 2021. The Normalised Difference Vegetation Index (NDVI) and Modified Normalised Difference Water Index (MNDWI) were employed to delineate UGS and UBS using multi-temporal Landsat imagery, respectively). The benefit transfer method was employed to quantify ESV, and sensitivity analysis was conducted to assess the valuation’s reliability. Also, adjusted value coefficients were employed. The findings indicated that landscapes such as tea garden (58.74% reduce) and agricultural land (46.09 increase) have undergone a substantial transformation as a result of urbanisation, with the built-up areas increasing from 5798.61 ha in 1991 to 8500.23 ha in 2021 (46.59% increase). Simultaneously, UGS decreased (by 40.26%) from 12,533.04 ha (47.81% oftotal area)in 1991 to 7486.29 ha (28.55% oftotal area) in 2021, while UBS decreased (by 79.47%) from 255.69 ha (0.94% oftotal area) in 1991 to 52.48 ha (0.19% oftotal area) in 2021. Subsequently, the ESV of UGS and UBS fell significantly from 1183.05 crores (INR) to 706.67 crores (INR) and 34.72 crores (INR) to 7.12 crores (INR), respectively. This confirms the elasticity of the valuation estimates, as the sensitivity coefficients remained below one. The study contributes to understanding the crucial role of urban planners, private property owners, and builders in promoting green–blue infrastructure conservation, wetland restoration, and ecological zoning. Such ecosystem service-based planning is essential for achieving sustainable development in rapidly urbanising regions and enhancing urban ecological resilience. Full article

The increasing tourism pressure on the UNESCO World Heritage Altai Mountains calls for efficient environmental monitoring tools. This study presents a Google Earth Engine framework that couples the Remote Sensing Ecological Index (RSEI) with land use/land cover (LULC) mapping to assess ecological changes in the Lake Manzherok area between 2020 and 2025. RSEI was derived from Sentinel-2 and Landsat imagery by combining four indicators (NDVI, MNDWI, NDBSI, LST) through principal component analysis. LULC classification was carried out using Random Forest trained exclusively on Sentinel-2 spectral bands. The results confirm that RSEI effectively captures ecological gradients in complex mountainous terrain, with the first principal component explaining 57–62% of the total variance. While 92% of the study area remained stable, 5.9% showed a decline in ecological status, spatially coinciding with a near doubling of built-up and bare surfaces from 9.89 km² to 18.17 km². The largest negative RSEI changes were associated with transitions from forestland (ΔRSEI = −0.29) and grassland (ΔRSEI = −0.20) to built-up/bare land, whereas reverse transitions displayed positive ΔRSEI values. These spatial patterns are consistent with the visible development related to tourism. However, because the built-up/bare land class also includes naturally bare surfaces, and because interannual climate variability may affect the RSEI components, it is important to interpret the ΔRSEI values as relative changes rather than absolute measurements of tourism impact. The proposed framework provides a reproducible and transferable tool for monitoring ecological quality in data-scarce mountain regions, delivering spatially explicit evidence that can support conservation and land-use planning. Full article

As climate change intensifies and urbanization continues, Fuzhou faces growing public health risks from the urban heat island effect. This study develops a Heat Risk Index (HRI) by integrating the Local Climate Zone (LCZ) classification with the Crichton Risk Triangle framework. Hazard was characterized using quality-controlled Landsat LST and the proportion of valid MYD11A2 composites exceeding a citywide P90 LST threshold. Exposure was represented by normalized population density; ln(P + 1) was used only for cartographic classification. Vulnerability incorporated older population, nighttime light, NDVI, and MNDWI. The continuous HRI was calculated as H × E × V, and ln(HRI) was used only for Jenks five-class map presentation. The results show clear spatial differences among LCZ types and counties, with higher risks concentrated in densely built and populated areas of the five urban districts, Changle, and localized county centers. LCZ differences in LST and HRI were statistically significant (both p < 0.001), and HRI showed significant positive spatial autocorrelation. These findings provide a basis for LCZ-specific heat-risk management while acknowledging uncertainty associated with data-year mismatch, spatial resampling, remotely sensed surface temperature, and the absence of independent health-outcome validation. Full article

Forest fires are a major environmental concern in Northern Thailand, contributing to ecosystem degradation, biodiversity loss, and seasonal air pollution. This study identified the environmental factors influencing forest fire occurrence and developed a forest fire susceptibility map using an integrated Geographic Information System (GIS) and Logistic Regression (LR) framework in Chom Thong District, Chiang Mai Province, Thailand. Fire occurrence data were derived from Visible Infrared Imaging Radiometer Suite (VIIRS) active fire hotspots detected by the Suomi National Polar-orbiting Partnership satellite (Suomi-NPP satellite) during 2023–2025. A total of 1674 hotspots were identified (616 in 2023, 889 in 2024, and 169 in 2025). Ten environmental variables, including elevation, slope, aspect, Topographic Wetness Index (TWI), stream density, rainfall, Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Land Surface Temperature (LST), and land-use, were analyzed. The LR model was trained using 2293 training samples (70%) and validated using 983 samples (30%). The results revealed that slope, rainfall, stream density, and LST were significant predictors of forest fire occurrence, with deciduous and evergreen forests exhibiting the highest susceptibility among land-use classes. The resulting forest fire susceptibility map classified 235.12 km² (21.16%) and 204.16 km² (18.38%) of the district as very high and high susceptibility, respectively, primarily in mountainous forest areas. The model achieved an overall accuracy of 77.5% and an Area Under the Curve (AUC) value of 0.852, indicating good predictive performance. Furthermore, the proposed Geographic Information System-Logistic Regression (GIS-LR) framework provides an interpretable and transferable approach for forest fire susceptibility assessment and generates spatial information that can support forest fire prevention, resource allocation, and environmental management in Northern Thailand and other fire-prone regions. Full article

Floating photovoltaics (FPV) offer a sustainable approach to renewable energy production while enhancing water resource management by reducing land use, improving module efficiency through natural cooling, and limiting water evaporation. However, large-scale deployment requires careful site selection, operational feasibility assessment, and integration with existing water management practices. This study presents a fully cloud-native Sentinel-2-based framework to map recent water surface extents of reservoirs and reconstruct their bathymetric profiles. Developed within the Google Earth Engine (GEE) environment, the approach employs harmonized time series, automated cloud filtering, and median composites combined with adaptive NDWI/MNDWI thresholds to generate high-resolution (10 m) water masks. These dynamic surface extents are integrated with monitored water levels to estimate bathymetry through an annual multitemporal log-ratio band-switching configuration. The method has been tested across the nine main strategic reservoirs of the Basilicata region (Southern Italy). The water-surface extraction approach achieved an Overall Accuracy of 95.33% and a Kappa coefficient of 0.907 in the internal thematic assessment. The bathymetric reconstruction results led to relative volume errors for medium-to-large water bodies ranging between 13.8% and 35.1%, while larger percentage discrepancies in smaller impoundments were driven by scale-dependent normalization effects on low storage volumes. Overall, the proposed method offers a scalable, cost-effective, and Earth Observation (EO)-driven screening tool for the initial site selection, capacity assessment, and planning of FPV systems, particularly in data-scarce regions lacking updated bathymetric surveys. Full article

Rapid urbanization is reshaping land surface conditions and local thermal environments in fast-growing coastal cities. This study examines how Land Use Land Cover (LULC) transformation influenced seasonal land surface temperature (LST) dynamics in Chattogram City Corporation (CCC), Bangladesh, over 2004–2024. Multi-temporal Landsat imagery was analyzed using a Random Forest classifier, and spectral indices (NDVI, NDBI, NDBaI, MNDWI) were derived to characterize surface biophysical conditions. Built-up land expanded by 27.71 km², largely replacing agricultural and vegetated areas. Summer mean LST rose from 36.08 °C to 36.50 °C, while winter LST rose from 25.25 °C to 26.97 °C. Only the winter warming trend is statistically significant; the summer change falls within the ±1–2 °C retrieval uncertainty of Landsat-derived LST. The summer–winter thermal gap consequently narrowed from 10.83 °C to 9.53 °C, indicating that urbanization-driven warming in this tropical coastal city is disproportionately concentrated in the cool dry season. Partial correlation and multiple regression analyses confirm that built-up intensity (NDBI) is the dominant driver of surface warming, while vegetation (NDVI) exerts a consistent cooling influence. Water bodies showed contrasting seasonal trends, with winter extent declining alongside a slight summer increase. These findings highlight the critical role of vegetation and water bodies in moderating urban heat and provide data-driven insights for climate-responsive planning in rapidly urbanizing coastal cities. Full article

Drought is a recurrent hazard in the Chi River Basin, northeastern Thailand, causing agricultural losses despite its low-lying floodplain setting. This study developed a machine learning framework integrating Sentinel-2 indices (SMI, NDVI, MNDWI, VCI, NDMI) and static spatial variables (DEM, TWI, prox2river, slope) aggregated to H3 hexagonal grids (≈5.96 km²) for village-relevant analysis. Drought reports from 1482 observations (2019–2024) were aggregated to 203 grid cells. Three models—Random Forest, XGBoost, and LightGBM—were evaluated using temporal (training: 2019–2023; test: 2024) and spatial holdout validation. LightGBM achieved the best performance with AUC = 0.783 (temporal) and 0.714 (spatial), accuracy = 78.3%, and balanced accuracy = 76.4%. Five-class severity classification showed declining accuracy from 71.4% (Very Low) to 25.0% (Severe), limited by rare event sample sizes. SHAP analysis revealed static topographic variables dominated importance (76.1%) over remote sensing indices (23.9%), with weak individual correlations (|r| < 0.10). The framework is best characterized as a drought risk mapping tool for identifying persistently vulnerable areas rather than an operational early warning system. The methodology is transferable to similar floodplain environments with local re-estimation and validation. Full article

Drought is a recurring agricultural hazard in Northeast Thailand’s floodplain environments, yet the actual values of remote sensing indices at confirmed drought locations remain poorly characterized. This study characterized six remote sensing indices (NDVI, VCI, SMI, NDMI, MNDWI, NSMI) at 541 agricultural drought-reporting locations in the Chi River Basin, Maha Sarakham Province, across three years representing different ENSO phases (La Niña 2020, El Niño 2023, neutral 2024). Drought-reporting frequency was classified based on village-level alert frequency: high frequency (six alerts, n = 100 villages) and low–moderate frequency (≤3 alerts, n = 441 villages). Sentinel-2 imagery was processed for the January–May dry season. Due to non-independence of observations (repeated measurements and spatial autocorrelation), analyses focused on descriptive statistics and effect sizes (Cohen’s d) rather than formal hypothesis testing. Results revealed remarkably small mean differences between frequency classes (0.008–0.024), with uniformly small effect sizes (Cohen’s d = 0.20–0.22). VCI and MNDWI showed negligible differences (Cohen’s d = 0.124 and 0.094, respectively). Index values at high-frequency locations showed stability across years (CV < 7% for all indices except NDMI), with limited year-to-year variation. SMI and NSMI were perfectly correlated (r = 1.00), indicating mathematical redundancy. Provisional descriptive reference points were derived from the three-year dataset (NDVI ≈ 0.21, VCI ≈ 0.52, SMI ≈ 0.41, MNDWI ≈ −0.33 at high-frequency locations), but these are descriptive summaries only and require validation with longer time series before they can be considered for operational use. These findings demonstrate that individual remote sensing indices have limited discriminatory power in this sandy soil floodplain environment, where local factors—soil properties, topography, and irrigation access—dominate over regional climate forcing. Five policy-relevant observations are proposed, including re-evaluation of threshold-based early warning systems and prioritized irrigation investments based on static vulnerability factors. This study contributes to SDG 2 (Zero Hunger), SDG 6 (Clean Water), and SDG 13 (Climate Action) through improved understanding of drought monitoring limitations in floodplain environments. Full article

Lakes on the Tibetan Plateau are sensitive indicators of climate change. Their water storage variations play an important role in regional hydrological processes and ecological security. This study is based on multi-source remote sensing and meteorological data from 1988 to 2025. Lake area was extracted using the MNDWI and the Otsu threshold method. HYDROWEB water level data were used to establish an area-water level relationship. This relationship was then applied to reconstruct a long-term water level time-series and estimate changes in lake water storage. GRACE/GRACE-FO data and meteorological observations were further analyzed to identify the driving factors. The results show that Siling Co experienced a persistent expansion over the study period, with the lake area increasing by 805.83 km², water level rising by 14.33 m, and water storage increasing by 30.42 km³. Correlation analysis indicates that air temperature, precipitation, and evaporation jointly influenced lake water storage variations. Among these factors, precipitation plays a relatively more important role. During 2002–2019, lake water storage changes (LWSC) were highly consistent with terrestrial water storage (TWS) variations. However, TWS anomalies lagged approximately one year behind LWSC. These findings improve the understanding of the long-term hydrological responses of Siling Co to climate change. They also provide a scientific basis for water resource management and infrastructure planning in the region. Full article

Reliable surface water extent (SWE) delineation remains challenging in river–reservoir monitoring systems characterized by complex hydrologic variability and spectral uncertainty in land/water transition zones, leading to significant classification errors in large waterbodies. This study developed a probabilistic ensemble technique that integrates deep transfer learning (DTL) with 1000 Monte Carlo-based Modified Normalized Difference Water Index (MNDWI) runs to improve water delineation under dynamic flow regimes in the Kanji Reservoir and transboundary Niger River. Six DTL segmentation models with backbones were trained on 1562 7-band Landsat 8/9 surface reflectance using an 80/20 train/validation split. The U-shaped Residual Network (UResNet) recorded the most stable convergence and highest performance and was subsequently coupled with 1000 Monte Carlo–MNDWI runs using a pixel-level maximum positive probability rule for reservoir delineation from 2019 to 2024 and flood event detection under complex confluence conditions. The resulting UResNetMNDWI ensemble achieved the highest classification performance against seven classification techniques across all metrics, with accuracy from 0.92 to 0.99 and false detection rates below 2%. Bootstrap uncertainty analysis of 5000 resamples further confirmed the statistical reliability of ensemble predictions. The model identified key flood hotspots at Mugatare Island and Inugu Settlement, demonstrating strong potential for operational flood monitoring applications. Full article

The increasing frequency and magnitude of glacial lake outburst floods pose a severe threat to the safety of downstream communities. However, Interference from glacier shadows and mountain shading reduces the accuracy of remote sensing-based glacial lake detection. We propose a two-level nested framework that integrates global spatiotemporal aggregation and local adaptive enhancement. At the global level, the 80th temporal percentile (P80) of multi-temporal AWEI imagery is used to construct a stable water-background composite and suppress short-term seasonal noise. Multi-source physical constraints, including the Normalized Difference Snow Index (NDSI), a DEM-derived slope constraint (slope < 10°), and red-band reflectance thresholds (0.3 < BandRed < 1.6), are applied to suppress interference from land, terrain shadows, snow, and glaciers. At the local scale, an adaptive dynamic segmentation strategy is proposed by establishing an equal-area buffer for each individual lake, where the temporal occurrence frequency of MNDWI is computed to build a stable water probability composite, and the Otsu algorithm is applied to independently derive lake-specific optimal thresholds. Using Landsat imagery and meteorological data from 1990 to 2025, we quantified the spatiotemporal dynamics of typical glacial lakes in the central Himalayas, and explored the driving mechanisms of climate factors on lake area changes. Over the past 35 years, the number and area of lakes have exhibited a pronounced expansion trend under a climatic regime characterized by rising temperatures, increasing precipitation, and decreasing relative humidity. During 1990–2020, lake area variations were primarily governed by strong interactions between temperature and wind speed. Summer variability exerted a more pronounced impact than winter variability. The proposed framework provides an effective approach for glacial lake extraction in the study area and may provide useful technical support for long-term monitoring of alpine lakes. Full article