Search Results (1,393)

Open-pit coal mining in arid and desert regions causes extensive and persistent surface disturbance, yet long-term monitoring of disturbance and backfilling processes remains challenging. Existing time-series change detection approaches can identify spectral changes but provide limited information on mining disturbance types and their evolution pathways. To address this issue, an automated surface disturbance detection method (Auto-SD) was developed for open-pit coal mines in arid and desert environments. This method integrates disturbance-type identification and temporal information extraction using the tasseled cap brightness (TCB) component to characterize changes associated with surface material exposure and accumulation. Using Landsat imagery from 1990 to 2023, Auto-SD was applied to 89 open-pit coal mines in China’s arid and desert regions, achieving an overall classification accuracy of 0.84. The cumulative disturbed area reached 423.10 km², while the internal dumping area reached 94.25 km², indicating limited backfilling recovery. Disturbance intensified after 2006, whereas backfilling lagged behind, forming a trajectory of rapid expansion, delayed recovery, and gradual stabilization. Spatially, mining areas exhibited a progressive transition from external dumping to internal dumping and backfilling. Furthermore, cumulative pit area generally followed an S-shaped growth pattern with mining duration. These findings provide new insights into long-term mining landscape evolution and support ecological restoration assessment and sustainable resource management in arid mining regions. Full article

Coal fires are a significant source of greenhouse gas emissions and ecological pollutants, yet their emission characteristics and carbon accounting remain poorly constrained. To reveal the pollutant generation characteristics and carbon emission levels of the typical underground coal fire area in Sulabulak, Xinjiang, this study integrated laboratory simulation, multi-source remote sensing inversion, and in situ field monitoring. Thermogravimetric analysis, a high-temperature tube furnace, HSC thermodynamic simulation, and multi-source remote sensing data from Landsat-8/9 and Sentinel-1A were employed to investigate the gaseous products and heavy metal migration mechanisms at different combustion stages, and to delineate the spatial extent of different combustion states in the fire area. A coal loss model was then constructed by coupling experimentally determined carbon emission factors with remote sensing-derived areas and was compared with an emission flux model based on field measurements. The results show that the coal oxidation process proceeds through three distinct stages, with indicator gas ratios (CO₂/CO and C₂H₄/C₂H₆) serving as effective indicators for combustion state identification. Heavy metal partitioning is governed by elemental volatility and redox conditions: As and Se partition predominantly into the gas phase, while Zn becomes enriched in fly ash. Remote sensing time series analysis documents continuous fire expansion accompanied by progressive surface subsidence. By cross-validating the indirect coal loss model (constrained by remote sensing area) against the direct emission flux model (constrained by field measurements), we estimate the current annual GHG emission of the Sulabulak fire area at approximately 0.65 × 10⁴ t CO₂ equivalent. This study proposes a coupled “micro-experiment–macro-remote sensing–field measurement” approach for carbon emission accounting, providing reliable data support for environmental pollution control and the development of carbon inventories for coal fires in arid regions. Full article

Chronic coastal pollution can drive progressive mangrove degradation, yet its spatiotemporal trajectories and post-disturbance recovery remain poorly quantified from satellite observations. In this study, Landsat time-series imagery and the LandTrendr algorithm implemented on Google Earth Engine (GEE) were used to characterize mangrove responses to a kaolin pollution event in Tieshan Port, Guangxi, China. NDVI, NDMI, and NBR trajectories were first compared to identify the most sensitive indicator of contamination-induced stress, and LandTrendr was then applied to extract the timing, magnitude, duration, and spatial extent of mangrove disturbance and recovery. Results showed that kaolin contamination imposed persistent chronic stress on mangroves from 2017 to 2021, with degradation first occurring near Langen Village and then expanding northward across the port. Moderate and severe degradation were mainly distributed along tidal creeks and patch edges, indicating strong spatial control by local hydrodynamics and geomorphology. Among the tested indices, NDVI provided the earliest and clearest response to contamination, whereas NDMI and NBR showed delayed or less consistent responses. The disturbance mapping achieved an overall accuracy of 86.5% with a Kappa coefficient of 0.73. Recovery remained limited after pollution discharge ceased, suggesting persistent environmental constraints on mangrove regeneration. These findings demonstrate that Landsat–LandTrendr trajectories provide an effective framework for monitoring chronic pollution-driven mangrove degradation and recovery in coastal wetlands. Full article

Rapid urbanization in tropical Asia has fundamentally transformed land use–land cover while intensifying urban thermal stress, yet the relationship between land cover change and thermal conditions is frequently assumed to be spatially uniform. This study challenges that assumption by demonstrating that land cover–thermal relationships in Colombo District, Sri Lanka, are highly spatially and temporally heterogeneous, with statistically significant associations detected in only 17–47% of the study area in any given year, underscoring that context, not land cover type alone, governs thermal outcomes. Using multi-temporal Landsat satellite imagery, LULC maps were derived, and the urban heat island effect (UHIE) and urban thermal field variance index (UTFVI) were calculated for seven time periods (1989, 1996, 2002, 2009, 2014, 2019, 2024). Geographically weighted regression (GWR) was applied to model local relationships between LULC classes, namely wetland vegetation, water bodies, impervious surfaces, and other pervious surfaces, and thermal indices across a 500 m spatial grid, revealing a 74% loss in wetland vegetation and a 326% increase in impervious surfaces over the study period. Water bodies exhibited spatially variable cooling effects relative to wetland vegetation, most pronounced in eastern regions during earlier periods, while impervious surfaces showed consistent, spatially persistent warming effects concentrated in western and southern urban cores. By coupling GWR with a 35-year multi-sensor time series, this study provides a spatially explicit, longitudinal account of how land cover–thermal relationships evolve as tropical urbanization intensifies, offering an evidence base for spatially targeted rather than uniform climate adaptation planning in rapidly urbanizing tropical cities. Full article

Surface vegetation dynamics in mining areas are characterized by complex non-linear processes associated with anthropogenic disturbance and ecological restoration. Existing remote sensing approaches often face limitations in balancing temporal interpretability and the characterization of long-term vegetation trajectories at regional scales. To address this issue, this study proposes a trajectory-guided weakly supervised framework that integrates parameterized curve fitting with deep temporal learning for mining vegetation monitoring. Based on the characteristic “extraction–reclamation” cycle, six representative vegetation trajectory patterns were pre-defined to describe different stages of degradation and restoration. Long-term NDVI trajectories (1990–2023) derived from Landsat time-series data were modeled using linear and parameterized Sigmoid functions to automatically generate high-quality supervision samples and temporal transition labels. These trajectory-constrained samples were subsequently incorporated into a multi-task BiLSTM-Attention network to simultaneously perform pixel-level change classification and turning-point regression. Applied to the mining clusters of the Dongting Lake Basin, China, the proposed framework achieved an overall classification accuracy of 86.64% (Kappa = 0.83), while the temporal prediction error remained within two years. Results revealed that 28.66% of the 61.20 km² of significantly degraded mining land has undergone effective ecological restoration, with restoration activities increasing sharply between 2012 and 2014 in response to regional environmental policies. By coupling ecological trajectory modeling with weakly supervised temporal learning, this study offers a promising approach for large-scale mining restoration monitoring and ecological assessment. Full article

Black-sand mining is an under-studied anthropogenic pressure on arid coastal environments, where sparse vegetation and slow natural recovery limit conventional impact assessment. Despite the recent expansion of heavy-mineral extraction along the Mauritanian coast, no spatio-temporal analysis has quantified its effects on land cover and surface-water dynamics in the N’Diago region. We analysed the N’Diago–LGUWYCHICH coastal sector (south-western Mauritania) using three Landsat scenes (2014, 2020, and 2026) in QGIS (free and open-source Geographic Information System), four spectral indices (NDVI-Normalized Difference Vegetation Index, NDWI-Normalized Difference Water Index, BSI- Bare Soil Index, and CI -Coloration Index), supervised Support Vector Machine classification and a 30 m SRTM Digital Elevation Model over a 97.82 ha area of interest. Bare soil dominated the landscape at every date (92.6–95.7%) and vegetation stayed below 7%, indicating that canopy-based metrics underestimate disturbance in this setting. The clearest change was hydrological: an inland water body shrank from 1.02 ha in 2020 to 0.40 ha in 2026, a 60.6% loss. This individual inland water body, measured directly and independently from the NDWI index, is our primary hydrological observation: the wet feature was smaller in the 2026 image than in the 2020 image and was located near the mapped concession areas, but the available data do not establish the cause of this change. Similar bare-soil and colour index values (BSI ≈ 0.23, CI ≈ 0.79–0.80) were observed in the processed images, while residual seasonal and radiometric differences between the Landsat 8 and DOS-corrected Landsat 9 products cannot be excluded; BSI and CI are treated only as candidate or contextual spectral patterns, so any delineation of the disturbed footprint is provisional and requires confirmation from independent field data. This study illustrates a low-cost exploratory workflow that may support preliminary monitoring in data-scarce arid coastal settings, pending validation with denser time series and field observations. Full article

Accurate prediction of plough layer thickness (PLT) in cropland is essential for soil quality assessment and sustainable land management, yet regional-scale PLT mapping remains challenging because PLT is a subsurface structural attribute that cannot be directly retrieved from surface spectral signals. This study developed an interpretable framework for province-scale mapping of cropland plough layer thickness (PLT) in Hubei Province, China, by integrating multi-source remote sensing observations, including Landsat 8 optical spectral bands, Sentinel-1 SAR backscatter, and crop dynamic spectral response metrics derived from multi-year Enhanced Vegetation Index (EVI) time series, together with topographic, climatic, soil physicochemical, and land-use variables. A total of 1926 cropland soil samples were used to train and validate random forest (RF), extreme gradient boosting (XGBoost), and Cubist models, while prediction uncertainty was quantified using 90% prediction intervals. The relative contributions of different environmental variable groups were assessed, and Shapley Additive Explanations (SHAP) were used to interpret key predictors. The all-variable scenario achieved the best overall performance, with RF showing the highest accuracy (R² = 0.46; RMSE = 3.12 cm) and the narrowest prediction interval. Climatic and topographic factors dominated PLT spatial variability, whereas other variable groups provided complementary predictive information. These findings demonstrate the potential of integrating multi-source environmental data and interpretable machine learning for regional PLT mapping, and the mapped distribution of cropland PLT provides a spatial basis for cropland quality assessment and targeted soil management, although further improvements will require spatially explicit agricultural management information and more direct PLT-related predictors. Full article

Urban greening is a cornerstone of climate adaptation strategies, yet its long-term sustainability under changing vegetation dynamics remains poorly understood. This study analyzes 40 years of Landsat data (1984–2024) using normalized difference vegetation index (NDVI) time series to quantify changes in vegetation extent, phenology and interannual stability across four European cities (Paris, Graz, Barcelona and Birmingham). The study investigates long-term phenological trends in heterogeneous urban green spaces. Results showed similar developments in greening measures at all four study sites: In Birmingham, Paris and Barcelona, vegetation extent increased by 12 to 23 absolute percentage points, while Graz showed a moderate increase of 4 percentage points. A general tendency towards a longer growing season was observed across the study sites, driven by directional changes towards an earlier Start of Season (SOS) and a delayed End of Season (EOS), although the magnitude and statistical significance varied between cities. Barcelona exhibited pronounced summer NDVI declines, whereas peak vegetation activity occurred earlier in Paris and slightly later in Birmingham. Interannual variation showed greater differences in smaller, fragmented open green spaces (Coefficient of Variation ≈ 0.3) compared to larger tree-dominated parks (Coefficient of Variation ≈ 0.1). These findings provide a harmonized long-term assessment of urban vegetation dynamics. European cities are becoming greener while simultaneously experiencing shifts in vegetation activity and, depending on location, strong declines during summer months. These long-term changes highlight the importance of considering vegetation dynamics when planning resilient urban green spaces under a changing climate. Full article

Coastal dune ecosystems play an essential role in shoreline stabilization, biodiversity conservation, and carbon storage, but they are increasingly threatened by human activities and climate-related disturbances. This study assessed long-term forest vegetation dynamics in restored coastal dune ecosystems in northwestern Tunisia, integrating a 30-year Landsat dataset (1994–2024) with Random Forest classification. We quantified changes in forest and shrubland cover, evaluated the effectiveness of dune stabilization reforestation (Pinus pinea and Acacia spp.), and assessed the impact of the 2023 wildfire on forest biomass and carbon stocks. The findings demonstrate that reforestation efforts reduced mobile sand areas by 42.9% over three decades, with 78% of the sand loss attributable to vegetation stabilization. When infrastructure-affected areas were excluded, sand decreased by 64.7%, confirming genuine reforestation effectiveness. The 2023 wildfire caused substantial forest biomass losses in the reforested pine stands (264.19 t·ha⁻¹) and carbon reductions (124.17 t·ha⁻¹). These losses reflect the high vulnerability of Mediterranean coastal forests to wildfire disturbances under recurrent summer drought and increasing temperatures. The study emphasizes the importance of long-term remote sensing time-series for coastal forest management and restoration planning in Mediterranean ecosystems. Full article

Most studies of sandy shoreline forecasting address relatively short time scales. Under limited annual observations and strong shoreline persistence, the added value of a Transformer over simple baselines and the influence of remotely sensed shoreline definitions remain insufficiently tested. Using Xichong Beach, Shenzhen, we constructed 40-year shoreline series for 80 transects from 284 quality-controlled Landsat waterlines acquired during 1986–2025. We compared a quality-controlled annual landward-envelope composite waterline with annual median waterlines and examined the effects of transect spacing and positional error on long-term change rates. We then developed a residual spatiotemporal Transformer that uses 10 years of shoreline states and historical wind–wave exposure to directly predict five future horizons, and compared it with persistence, rolling linear trend, and random forest models. The annual landward-envelope composite waterline was systematically landward of the annual median waterline (positional RMSE, 12.78 m), but their alongshore LRR patterns were strongly correlated (r = 0.982) and identified consistent major erosion–accretion zones. After Monte Carlo error propagation, the beach-mean LRR was −0.250 m yr⁻¹ (95% interval, −0.299 to −0.203 m yr⁻¹), whereas the direction of change remained uncertain at 39 local transects. Across 400 year–transect locations in the independent 2021–2025 evaluation period, the Transformer produced the lowest RMSE, MAE, and Dynamic RMSE (9.403, 7.399, and 12.042 m, respectively), with an RMSE skill of 27.0% relative to persistence. Environmental features yielded a small gain during rolling validation but no stable improvement in the independent evaluation period. SHAP attribution identified recent shoreline state as the dominant predictive information, followed by wind–wave exposure. Direct forecasts for 2026–2030 gave a beach-mean displacement of −8.527 m in 2030 (95% conditional residual bootstrap interval, −12.514 to −4.950 m), although every local-transect interval crossed zero. Multi-year predictability is therefore scale dependent: beach-mean trends are more resolvable, whereas local change directions remain constrained by observation error and model residuals. Full article

Changes in lake area directly reflect the state of regional hydrological cycles and ecological balance. However, the lack of long-term monitoring data and the complexity of driving factors make it difficult to formulate effective management strategies. This study used landsat imagery and the random forest (RF) algorithm to construct a time series of the lake area of Chirui Lake from 1990 to 2024, revealing a counter-seasonal phenomenon in which the lake area during the low-flow period was significantly larger than that during the high-flow period. To clarify the driving mechanisms behind this phenomenon, a comprehensive analysis of climatic and hydrological factors and lake area was conducted using methods such as the Pettitt test, Morlet wavelet analysis, principal component analysis (PCA), structural equation modeling (SEM), and long short-term memory (LSTM) neural networks. The results indicate that, from the perspectives of climate and hydrology, sub surface runoff is likely to have played a significant role in the area changes of Chirui Lake, and that between 2025 and 2029, the lake’s surface area is likely to continue to fluctuate significantly between 0.4 km² and 0.6 km². This study explored the influence of climate and hydrology on the changes in the area of Chirui Lake and provides five-year forecast of lake surface area, however, given the inherent uncertainties in the forecast, these findings should be viewed as preliminary references rather than direct decision-making tools for lake management. Full article

Long-term monitoring of impervious surface dynamics is essential for understanding urbanization processes and supporting sustainable land-use planning. However, existing studies often lack comprehensive analyses that integrate both spatial patterns and underlying driving mechanisms. In this study, we first generate a consistent long-term impervious surface dataset for Changsha County from 2000 to 2025 using Landsat time-series imagery, incorporating feature selection and temporal grouping to ensure reliability. Leveraging the generated long-term impervious surface dataset, we perform a detailed and multi-dimensional analysis of urban expansion, systematically quantifying changes in area, expansion intensity, and growth rate, while simultaneously examining spatial dynamics through centroid shifts and kernel density estimation, and uncovering underlying driving factors through rigorous modelling. The results indicate that impervious surface area increased continuously over the study period, with a clear phased pattern characterized by rapid growth (2000–2010), decelerated expansion (2010–2020), and further slowdown (2020–2025). Spatially, edge expansion dominates the overall growth (approximately 80%), while infilling gradually increases, indicating a transition from outward sprawl to more compact development. Moreover, expansion hotspots shift from the urban core to peripheral and rural areas, accompanied by a northward migration of the center of gravity. Driving factor analysis further indicates that topographic constraints weakened progressively over the study period, road proximity retained a stable spatial association with new impervious expansion as an accessibility-related proxy, and policy-related influences gained increasing importance in later stages. These findings provide new insights into the spatiotemporal evolution and driving mechanisms of county-level urbanization in central China, and offer a reliable methodological framework and scientific basis for long-term land-use monitoring and sustainable urban planning. Full article

Long-duration flood inundation can substantially suppress crop growth and cause yield loss, particularly in semi-arid agricultural regions increasingly affected by extreme rainfall. Timely crop damage assessment is critical for disaster response and insurance-related decision-making, but direct yield-loss observations are often unavailable during or shortly after flooding. This study proposes a phenology-guided regression framework for early crop damage assessment using multi-source SAR–optical observations. The study was conducted on the Tumochuan Plateau, Inner Mongolia, China, where severe rainfall beginning on 23 July 2025 caused widespread cropland inundation. Sentinel-2 EVI time series from 2022 to 2025 were fitted using a Savitzky–Golay (SG) filter, and annual area under the EVI curve (AUC) loss in 2025 relative to the 2022–2024 historical mean was used as a proxy for flood-induced crop damage. Optical features from Landsat-8/9 and Sentinel-2, together with SAR backscatter features from Sentinel-1, Lutan-1, and Gaofen-3, were incorporated into machine learning regression models. SAR features improved pixel-wise prediction, with the Random Forest model achieving the highest $R^2$ of 0.62 using early-period features and 0.77 using later-period features. Village-scale aggregation further improved performance, yielding an early-period $R^2$ of 0.84 across 123 and 0.78 across 122 villages. These results demonstrate the feasibility of SAR–optical and phenology-guided regression for early crop damage assessment under long-duration inundation. Full article

Rapid urban expansion and agricultural development have substantially altered land use/land cover (LULC), surface thermal regimes, and ecosystem conditions in semi-arid cities. This study investigates the spatiotemporal dynamics of LULC and evaluates their associations with land surface temperature (LST), vegetation health, soil moisture, and drought conditions in Ghazni City, Afghanistan, between 2013 and 2023 using a Google Earth Engine (GEE)-based framework. Landsat 8 OLI/TIRS imagery was classified using a Random Forest (RF) algorithm, while the Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), and LST were derived to evaluate environmental responses. To provide a comprehensive evaluation of urban–climate interactions, monthly Landsat-derived LST time series were analyzed and compared to MODIS and ERA5 datasets through a multi-source consistency assessment framework. The RF classification achieved overall accuracies of 95.56% (2013) and 97.77% (2023), with Kappa coefficients of 0.89 and 0.94, respectively. Results revealed a substantial expansion of built-up areas (5.4%) and vegetation/agricultural land (7.2%), accompanied by a decline in bare land. Urban and barren surfaces consistently exhibited higher LST values, whereas vegetated areas demonstrated a pronounced cooling effect. NDVI and SAVI analyses indicated improving vegetation conditions and soil moisture status over the study period. LST exhibited strong seasonal variability, with summer maxima reaching 49.74 °C and winter minima declining to −8.39 °C. Comparisons among the Landsat, MODIS, and ERA5 datasets demonstrated strong agreement, with a high correlation between Landsat- and MODIS-derived LST (R = 0.84), supporting the reliability of the Landsat-derived LST estimates. Generally, the findings demonstrate the critical role of vegetation in moderating surface temperatures and enhancing urban climate resilience, providing scientific evidence for sustainable land use planning and climate adaptation strategies in semi-arid cities. Full article

Accurate crop evapotranspiration (ET_c) estimation at high spatial and temporal resolution remains a major challenge for precision irrigation. This study presents a multi-sensor data fusion framework combining daily MODIS (250 m), Sentinel-2 (10 m), and Landsat 8/9 (30 m) imagery with FAO-56 Penman–Monteith reference evapotranspiration (ET₀) to generate pixel-wise daily ET_c maps for irrigated maize (Zea mays L.) near Nyírbátor, Hungary, over six growing seasons (2020–2025). The proposed Median Time Series Model exploits field-scale MODIS NDVI as a temporal backbone and derives pixel-wise linear transfer functions to reconstruct daily NDVI at 10–30 m resolution. Three gap-filling strategies were compared; the median approach yielded the highest agreement (NDVI reconstruction R² = 0.81; RMSE = 0.19 (NDVI units); pixel-wise correlation 0.70–0.85) and effectively suppressed sub-pixel spectral mixture artefacts. Sentinel-2 consistently outperformed Landsat 8/9 (pixel-wise R² = 0.36–0.78 vs. 0.001–0.91). A nonlinear power crop coefficient model (K_c = a · NDVI^b) proved more robust than linear rescaling (mean validation R² of 0.80 (power) vs. 0.71 (rescale) across Sentinel-2 seasons; both methods were positive in all six seasons after correcting an unconstrained-fit artefact). Seasonal ET_c ranged from 313 to 545 mm, with cumulative water deficits reaching −334 mm during the 2021 drought. Six-year mean seasonal ET_c (428–483 mm for Sentinel-2) falls within the 400–600 mm range published for irrigated maize under comparable continental conditions, with season-integrated ET_c/ET₀ ratios (rescale method mean 0.86; power method mean 0.84) consistent with expected FAO-56 K_c trajectories. Cross-validation against an independent MATLAB implementation confirmed algorithmic consistency (reference ET₀ (R² = 0.88–0.91, Pearson r = 0.97–1.00)) and daily ET_c while identifying meteorological input as the dominant source of absolute ET_c uncertainty (estimated at ±15–30% through first-order error propagation). Plausibility assessment was limited to comparison with published seasonal benchmarks and an independent algorithmic implementation; no eddy covariance or lysimeter measurements were available for direct ET_c validation. Full article