Search Results (556)

Accurately mapping surface freshwater bodies (e.g., ponds, reservoirs, and small lakes) is vital for managing insular ecosystems and communities. However, satellite-based extraction in coastal settings is challenged by seawater intrusion, complex topography, and cloud cover. Focusing on bedrock islands outside China’s Pearl River Estuary, this study developed a robust method to address these issues. We used both Gaofen-1 (GF-1) optical and Gaofen-3 (GF-3) Synthetic Aperture Radar (SAR) imagery, supported by field-collected water quality samples from surface freshwater body shorelines for model training and validation. The performance of two index-based methods (the Normalized Difference Water Index, NDWI, and the Normalized Difference Vegetation Index, NDVI), two machine learning algorithms (Random Forest, RF, and Support Vector Machine, SVM), and a U-Net convolutional neural network (U-Net) deep learning model was compared. The U-Net model achieved the highest accuracy, with Area Under the Curve (AUC) values of 0.881 (GF-1) and 0.840 (GF-3). It effectively discriminated freshwater from seawater and mitigated cloud interference, demonstrating superior precision and robustness over traditional methods. This work establishes a high-precision framework for monitoring island freshwater resources, supporting sustainable water management. The proposed framework provides a practical tool for tracking freshwater availability under climate variability and anthropogenic pressures, contributing to the monitoring of Sustainable Development Goal (SDG) indicator 6.3.2 on ambient water quality. Full article

Most remote sensing extraction studies utilizing vegetation indices typically classify and extract land cover features based on the phenological characteristics of the study area or rely on a single vegetation index. When attempting to extract multiple land cover types simultaneously, classification accuracy often declines significantly because a single vegetation index is unsuitable for all features. While some recent studies employ deep learning and neural networks for classification and extraction, their complex mechanisms and “black-box effect” hinder clear explanations for accuracy outcomes. In response to the issues outlined above, this paper proposes a simpler and more intuitive method for the hierarchical extraction of typical land cover features. This approach analyzes the difficulty of separating these features based on spectral reflectance data to determine the following extraction order: first water bodies, followed by reed, then Suaeda salsa, and finally tidal flat. Furthermore, by selecting appropriate parameters and substituting vegetation indices for bands that perform better, high extraction accuracy is achieved. The classification and interpretation results were validated using a combination of field survey data and Google imagery, together with a validation sample. Accuracy assessments using overall accuracy and Kappa coefficient demonstrate the following optimal results for the hierarchical approach: NDWI for water, S2REP for reeds, and MSAVI for Suaeda salsa. Overall accuracy reached 98.5% with a Kappa coefficient of 0.9796, validating the effectiveness of this spectral-feature-based hierarchical extraction method using diverse vegetation indices. Using a hierarchical extraction approach to classify typical land cover features in the study area from 2020 to 2025, accuracy rates exceeded 98% in all cases. Based on these classification results, the INVEST model was employed to simulate carbon stock trends in the Liaohe Estuary region over the past five years. The study found that, although factors such as tides and the date of image acquisition had a certain impact on the study area compared with the problems caused by historical development, the ecological environment in the study area is gradually stabilizing at the present stage. Full article

Soil water retention capacity (SWRC) is vital for agriculture and watersheds, but traditional measurements are hindered by destructive sampling and spatial discontinuity. This study selected Youyi and Heshan farm in Heilongjiang Province as the study area, using the time-series Normalized Difference Water Index (NDWI) from Sentinel-2 during the snowmelt-to-bare-soil window as a soil water retention signature (SWRS) for monitoring SWRC. The exponential decay fitting model (EDFM) was used to construct a Soil Moisture Decay Index (SMDI) to analyze the spatial patterns of the SWRC. Results showed that: (1) time-series NDWI exhibited distinct exponential decay signatures varying with soil textures and degradation gradients; (2) the EDFM effectively fitted the time-series NDWI (R² = 0.84–0.99), extracting decay rate and stable level to quantify SWRC; (3) SMDI showed high consistency with in situ soil moisture (R = 0.82–0.88) and measured field capacity (Youyi Farm: R² = 0.56; Heshan Farm: R² = 0.59), and correlated significantly with soil organic matter (R² = 0.61–0.71) and texture (R² =0.50–0.64), confirming the physical controls on water retention; and (4) SMDI spatial distribution revealed distinct degradation patterns across varying topographic and soil conditions. This study innovatively transformed point-scale static SWRC measurements into spatially continuous monitoring, offering new tools for precision water management and degraded-soil restoration, with strong theoretical and practical value. Full article

Accurate estimation of crop yield from remote sensing remains challenging due to the crop-specific nature of yield drivers and the difficulty of interpreting spectral indicators across agronomic systems. While many studies prioritise predictive accuracy through complex models, fewer explicitly examine the stability and physiological relevance of individual spectral and phenological indicators under controlled analytical conditions. This study investigates yield–spectral relationships in wheat and cotton using a unified Sentinel-2 indicator framework applied across multiple growing seasons in a Mediterranean agricultural environment. A consistent set of spectral and thermal indicators was derived from two phenologically targeted Sentinel-2 acquisitions per season and analysed using correlation analysis, univariate regression, constrained multivariate modelling, and recurrence analysis within an identical workflow for both crops. Distinct crop-specific patterns were observed. Wheat yield was most strongly associated with water-sensitive and canopy-related indicators, with NDWI-based metrics reaching Pearson correlations up to r = 0.85 and multivariate models explaining a substantial proportion of yield variability (up to R² ≈ 0.70) under controlled analytical conditions. In contrast, cotton yield variability was dominated by thermal accumulation, with growing degree day indicators showing correlations up to |r| = 0.59 and multivariate performance reaching R² = 0.74. Recurrence analysis indicated consistent recurrence of these indicator families across analytical stages under the examined conditions. Overall, the results indicate that parsimonious, physiologically interpretable indicator combinations can account for a meaningful proportion of yield variability without reliance on highly complex or high-dimensional modelling approaches, supporting crop-aware indicator selection for precision agriculture applications. Full article

Rice is one of the world’s most important food crops, feeding over half of the global population and being crucial for food security. Accurate, timely mapping of rice fields is essential for precision agriculture, yet conventional methods relying on static samples fail to capture dynamic farmers’ planting decisions. To address this, we propose the Multi-Source Dynamic Sample Generation and Phenology-Guided Feature Selection Framework for In-Season Rice Identification (MSDF-RiceID) using multi-source remote sensing imagery. It incorporates two key innovations: (i) a rule-based sample updating mechanism based on historical rice maps and a dynamic threshold algorithm, and (ii) phenology-guided feature optimization through exponential weighting. Developed specifically to handle complex cropping patterns and high cloud cover in Hunan Province, MSDF-RiceID integrates these innovations within a grid-search-optimized Random Forest classifier to produce reliable monthly rice distribution maps. In-season samples corresponding to transplanting dates in April (DOY 100, 120), June (DOY 160), and July (DOY 184), differentiated as early-, middle-, and late-rice crops. The optimal feature set combined Sentinel-1 (PRI, VH, VH_VV), Sentinel-2 (NDYI, PSRI, NDBI, NDWI), and MODIS (NDVI, EVI, NDBI, LSWI) indices. Accuracy increased seasonally, with F1-score rising from 0.82 in May to 0.97 at harvest. Cross-region validation in Taishan (Guangdong) and Panjin (Liaoning) showed that the earliest identifiable stage (F1-score > 0.9) occurred earlier than in Hunan due to Hunan’s more complex triple-cropping phenology, highlighting the model’s strong transferability. Furthermore, MSDF-RiceID outperformed existing products (TWDTW-Rice and EARice10), increasing overall accuracy by 0.12–0.18, Kappa by 0.23–0.35, and F1-score by 0.09–0.15. These results demonstrate its effectiveness for in-season, large-scale, and dynamic rice mapping under persistent cloud cover, thereby providing direct support for precision agricultural management in heterogeneous cropping systems. Full article

Wetlands are among the planet’s most productive ecosystems, yet they are increasingly imperiled by intersecting global challenges, particularly agricultural expansion, food security demands, and climate change. 1 This study investigated the spatial extent of floodplain wetlands and assesses Land Use/Land Cover (LULC) dynamics in the uMngeni catchment using multi-temporal Landsat imagery for the years 2000, 2010, 2020, and 2024. 2 Seven key land cover classes were classified, which included agriculture, bare land, built-up areas, forest, grassland, wetlands, and water bodies, using the Random Forest (RF) classification incorporating spectral indices (NDVI, NDWI) and topographic variables (slope and aspect) on Google Earth Engine (GEE). The overall accuracies for the respective years were 88.98% (2000), 91.23% (2010), 84.21% (2020), and 86.55% (2024), with corresponding Kappa coefficients of 0.82, 0.84, 0.78 and 0.80. 3 The findings show a significant 37% decline in wetland area from 2000 (2978 ha) to 2024 (1874 ha), with the most pronounced loss (46%) occurring between 2000 and 2010. Built-up areas increased by 38% over the same period, while agriculture peaked in 2010 (9312 ha) before declining to 7632 ha by 2024. The dominant transitions involved wetlands and grasslands being replaced by urban land and bare surfaces, particularly along the floodplain edges. 4 These patterns reflect intensifying human pressure on wetland ecosystems. Targeted interventions, such as enforcing buffer zones, regulating land use near water bodies, and restoring degraded wetlands, are critical to conserving ecosystem services and achieving sustainability outcomes aligned with the Sustainable Development Goals. Full article

Conserved land represents an important mechanism for protecting ecologically sensitive lands while maintaining working landscapes. Despite their significance, nationwide tools for continuous hydrological monitoring of conservation easement lands remain limited. This study conceptualizes surface-water inundation as an indicator of hydrologic connectivity and ecosystem function, reflecting how water dynamics influence the resilience and ecological performance of conservation easement landscapes. We present a scalable framework to assess inundation dynamics across more than 340,000 conservation sites between 2018 and 2024 by integrating Sentinel-2 satellite imagery, Dynamic World land-cover data, and machine-learning classifiers (Support Vector Machine, Random Forest, and CART) within the Google Earth Engine platform. Spectral water indices (NDWI, MNDWI, and NDMI) were combined with Dynamic World classifications to generate quarterly inundation maps at 10 m spatial resolution, enabling consistent detection of surface-water presence across space and time. Among the evaluated classifiers, the Support Vector Machine (SVM) model achieved the highest performance in surface-water detection. Results reveal strong regional and seasonal variability in inundation patterns across conservation land. Higher inundation frequencies were observed in the Midwest, Gulf Coast, and Pacific Northwest, where wetland-associated easements showed persistent inundation (>50%) during spring and early summer, highlighting their role in supporting biodiversity, groundwater recharge, and flood mitigation. Overlay analysis with the National Wetlands Inventory (NWI) and SSURGO hydric soils confirmed a strong spatial correspondence between inundation occurrence and wetland-prone landscapes, extending the same Sentinel-2 and machine-learning framework to conservation land and enabling the first systematic cross-program comparison of hydrological dynamics across two major U.S. conservation mechanisms. This work highlights the critical role of conservation lands including Conservation Reserve Program (CRP) areas and conservation easements in supporting inundation dynamics and hydrological connectivity. These functions are essential for sustaining wetland habitats, maintaining water quality, and enhancing flood mitigation at the national scale. Full article

Methodological opacity and the omission of environmental variables in forest masks can generate biased estimates. The objective of this study was to validate a reproducible workflow for quantifying forest cover change in the area adjacent to Nevado de Colima over the 2019–2025 period, subdivided into nine assessment areas with standardized sampling based on 3 × 3 pixel kernels (900 m²). An enriched Random Forest model with slope and spectral indices (NDVI, NBR, NDWI-Gao, and BSI) classified six spectral combinations derived from Sentinel-2 L2A bands B2, B3, B4, B8, B11, and B12, together with a new index proposed in this study, Red-Enhanced Normalized Burn Ratio (RE-NBR), used as a conservative classifier and auxiliary classifier output in the probabilistic cross-check estimation. Validation employed thematic and areal metrics. All combinations reached OA values between 89.44% and 92.53% and Kappa values between 0.79 and 0.85, with Shortwave Infrared (B12, B8, B4) as the most consistent configuration across dates. Allocation disagreement systematically exceeded quantity disagreement on all dates. The Seasonal Stability Index increased from 0.73 in 2019 to 0.77 in 2025, with persistent positive asymmetry between February and April. The probabilistic cross-check adjustment produced an adjusted forest loss of 1594.74 ha and an adjusted gain of 802.65 ha over 120,289.70 ha. Within the protected natural areas, expected change was distributed unevenly among vegetation types, with pine–oak forest showing the highest total expected loss, whereas high-mountain meadow showed the highest expected gain and also remained among the covers with the highest expected loss, indicating active spatial reconfiguration in the upper ecological domain where Pinus hartwegii Lindl. is the dominant species, though no species-level classification was performed. These results provide spatial evidence to support field verification, forest-health monitoring, and management decisions in the protected high-mountain study area. Full article

Mangrove ecosystems provide critical coastal protection, biodiversity support, and carbon storage, yet they remain vulnerable to degradation caused by coastal development, pollution, and climate-related pressures. This study monitors mangrove dynamics in Al-Birk, Asir Region, Saudi Arabia, using Google Earth Engine (GEE), multi-temporal Landsat imagery, spectral indices, and Random Forest (RF) classification. Landsat imagery from 2016 to 2021 was processed to derive NDVI, MSAVI2, EVI, and NDWI, and supervised RF classification was applied to map annual mangrove extent and associated land-cover classes. The RF classifier achieved an overall accuracy of 92.5% and a Kappa coefficient of 0.89. Results indicate that classified mangrove extent increased from approximately 1069 ha in 2016 to 1540 ha in 2021, representing a net gain of 471 ha and a 44% increase over the study period. A localized decline was detected between 2020 and 2021, indicating spatially uneven vegetation dynamics. The findings provide a spatial baseline for monitoring mangrove change and supporting coastal conservation planning in Saudi Arabia. While the detected expansion is temporally consistent with ongoing restoration initiatives, the study does not establish direct causality between policy interventions and observed spatial changes. Full article

Bangladesh is an extremely climate-exposed country, with erosion, accretion, tidal surges, and cyclones continuously modifying coastal districts. Shoreline change in Bangladesh is crucial for sustainable coastal management and disaster resilience. Therefore, the objectives of this research are as follows: (i) to assess accretion- and erosion-based shoreline changes of the Bangladesh delta adjacent to the Bay of Bengal for 2021–2025 using a fixed 2021 reference shoreline and a 2025 shoreline proxy extracted from Landsat 8/9 imagery, and (ii) to explore onshore change dynamics from satellite-derived NDVI, NDBI, and NDWI for 2022–2025. The study covers 14 coastal districts and integrates the 2021 baseline shoreline, Survey of Bangladesh geospatial datasets, and 17,055 Ground Reference Points (GRPs) to support geometric consistency and spatially explicit reporting at the delta scale. Three spectral indices—Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI)—were applied to assess vegetation health, surface water distribution, and built-up/exposed land characteristics. Results indicate spatial variability in coastal change, with 383.49 km² of land gained through accretion and 124.12 km² lost to erosion, resulting in a neat accretion of 259.37 km² between 2021 and 2025; 8747.91 km² remained geomorphologically stable. Spectral index trends show minimal inter-annual NDVI and NDWI variability, suggesting stable vegetation cover and no long-term expansion of surface water. In contrast, a slight increase in NDBI indicates localized exposure of new sediments or small-scale land-use transitions along emerging coastal zones. Spearman correlation analysis highlights consistent negative relationships between NDVI and NDWI and moderate contrasts between NDVI and NDBI, reinforcing the coexistence of vegetation recovery, water withdrawal, and sediment-driven land emergence. The novelty of this study lies in the provision of consistent, near-real-time coastal change inventory for the full ~710 km Bangladesh delta coastline by combining a common 2021 baseline shoreline with harmonized Landsat 8/9 OLI surface reflectance (2022–2025) and linked onshore spectral-index dynamics over the same period. Overall, this short-term assessment reveals a sedimentary system that is active but balanced, with accretion surpassing erosion despite cyclone-affected disturbances, underscoring the value of operational satellite monitoring for coastal management, hazard preparedness, and climate-adaptive planning. Full article

Plant diversity is a key indicator of ecosystem structure, function, and restoration status, yet its rapid assessment remains challenging in sandy ecosystems where vegetation is sparse, spatially heterogeneous, and strongly affected by exposed soil backgrounds. In such environments, conventional greenness-based spectral indices may not adequately capture species-level variation because plant communities are controlled not only by photosynthetic biomass but also by soil moisture, micro-topography, and dune-related habitat heterogeneity. This study evaluated the potential of Sentinel-2-derived spectral indices for estimating plant α-diversity in the Hunshandak Sandland, northern China. Based on field observations from 888 plots collected during 2017–2024, four α-diversity metrics—species richness, Shannon–Wiener index, Simpson index, and Pielou evenness index—were calculated and compared with 21 spectral indices using correlation analysis, partial least squares regression (PLSR), and random forest (RF) models. The results showed that model performance varied substantially among diversity metrics. Species richness was estimated with the highest accuracy, whereas Shannon–Wiener, Simpson, and Pielou indices showed weaker predictability, indicating that remotely sensed spectral indices were more sensitive to species number than to abundance distribution and evenness. Moisture- and soil-background-sensitive indices, including the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Bare Soil Index (BSI/BRI), and Chlorophyll Absorption Ratio Index (CARI), showed relatively stable relationships with plant diversity across different vegetation gradients. Although the overall explanatory power was moderate rather than high, the results demonstrate the practical value of Sentinel-2 spectral indices for regional screening of plant diversity patterns in sandy ecosystems. This study provides empirical evidence for biodiversity monitoring and ecological restoration assessment in semi-arid sandy landscapes and highlights the need to integrate environmental covariates, multi-source remote sensing, and phenological information in future studies. Full article

Monitoring marine outfalls is crucial for mitigating coastal pollution and protecting marine environments. Current methods rely mainly on manual inspection and satellite remote sensing interpretation, which are inefficient, inaccurate, and inadequate for large-scale real-time monitoring. Although UAV visible-light imagery has been introduced for marine outfall detection, challenges remain, including insufficient and diverse target features, small multi-scale target detection difficulties, and complex background interference. To address these limitations, this study systematically benchmarks mainstream object detection models (YOLOv8n, YOLOv9t, YOLOv10n, YOLOv11n, and RTDETR-light) on a dedicated multi-source remote sensing fusion dataset that we constructed for marine outfalls along Zhanjiang’s southern coast, incorporating NDWIs. Our comparative experiments evaluate the models’ effectiveness in this challenging scenario. Experimental results indicate that YOLOv8n is the most balanced model for marine outfall detection, achieving 84.1% precision, 68.6% recall, 77% mAP50, and an F1 score of 0.75. This benchmark provides empirical evidence and practical model selection criteria for intelligent marine outfall monitoring, thereby offering a reference framework for researchers and engineers in related fields. Full article

Apple orchards are increasingly adopting anti-hail nets to mitigate climate risks; however, these structures alter canopy reflectance and pose challenges for remote sensing. This study presents an operational framework to map apple orchards under different netting conditions in Vacaria, Brazil. Multisensor surface reflectance data from Sentinel-2 and Harmonized Landsat and Sentinel-2 were used to generate dense spectral index time series combined with field observations. Five spectral indices, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), and Bare Soil Index (BSI), were evaluated individually and in combination within a hierarchical classification framework. Random Forest (RF) and one-dimensional convolutional neural networks (1DCNN) were applied as complementary machine learning approaches. RF showed more stable performance across hierarchical levels, while indices contributed differently depending on scale: BSI and NDVI were more effective at broader levels, whereas EVI and SAVI were critical for discriminating net colors. To our knowledge, this is the first study applying multisensor time series and machine learning to map anti-hail net systems in apple orchards. Full article

Accurately quantifying the driving mechanisms of land surface temperature (LST) is fundamental to developing climate-resilient urban strategies. However, traditional linear models often fail to capture the complex nonlinear interactions and spatial non-stationarity inherent in urban thermal environments, especially when hindered by multicollinearity among morphological indicators. This study proposes a multi-scale spatial explainability attribution framework by integrating an XGBoost machine learning model with SHAP (SHapley Additive Explanations) to decipher the thermal dynamics of Changchun, a representative cold-region city in China. Utilizing a 500 m grid-based dataset, we incorporated 3D urban morphology (BVD), land cover (NDVI, NDWI), and socioeconomic factors. The results indicate that the XGBoost model achieves superior predictive performance (R² = 0.694) compared to traditional OLS models. SHAP global attribution identified Building Volume Density (BVD) as the primary warming driver, as its three-dimensional volume creates “thermal traps” through radiation trapping and reduced ventilation. Notably, NDVI exhibits a significant nonlinear “cooling threshold effect” at 0.3, beyond which its mitigation efficiency stagnates or even reverses due to vegetation fragmentation and heat-induced physiological stress. Furthermore, spatial mapping reveals a distinct “sign reversal” in NDWI’s impact, reflecting the dualistic thermal regulation of water bodies across different urban–rural gradients. These findings suggest that urban thermal management strategies should shift from merely restricting 2D surface occupancy (e.g., Building Density) to a more sophisticated approach focused on precisely controlling 3D volume intensity (BVD). This study provides a “point-to-area” diagnostic tool supporting a transition to spatially targeted urban planning interventions. Full article

Draining peatlands for peat extraction converts them into significant sources of greenhouse gas (GHG) emissions. Quantifying GHG emissions at the regional scale remains challenging because direct field measurements are spatially limited, while GHG accounting for land-use planning requires spatially explicit information. Building on the advances in remote sensing (RS) as a scalable low-cost emission accounting tool for large areas, this study presents a proof-of-concept workflow that integrates satellite-based land-cover classification with an emission-factor (EF) approach to support spatial upscaling of peatland GHG estimates. Using Sentinel-2 imagery and a supervised Random Forest classifier, peatland-related land-cover classes were mapped for selected sites in Latvia. The classification results show higher accuracy for spectrally distinct classes such as raised bogs and active peat-extraction areas, while more heterogeneous classes exhibited lower performance. The study provides an overview of how to utilize the RS approach to generate accurate land-cover maps, which can be used to upscale GHG estimation in Latvia when field data is limited. The study does not include calibration against site-level flux measurements, uncertainty propagation, or temporal variability analysis; therefore, the emission results are illustrative and consistent with current EF-based inventory practice rather than validated site-specific fluxes. Full article