Search Results (1,118)

Subsurface combustion in coal mines poses a significant threat to ecosystem integrity, geological stability, and public safety. Effective risk mitigation requires continuous monitoring and accurate detection of combustion dynamics. In this study, an improved subsurface combustion index (SCI) was developed based on multisource remote sensing indicators, and long-term time series observations (2010–2025) were used to characterize its spatiotemporal evolution. Results show that dREGI achieved the highest anomaly discrimination among all evaluated vegetation indices, with an M-statistic of 1.4186, outperforming NDVI (1.1073) and EVI (0.8226). Adaptive principal component analysis identified dREGI and H as the dominant contributors to SCI construction. Separability analysis further demonstrated that integrating dREGI with LST and H improved the performance of the composite SCI by 16.3%, increasing its M-statistic from 0.959 to 1.115 relative to the dREGI-only baseline. Temporally, subsurface combustion exhibits a multi-stage evolution, with initial anomalies emerging around 2013, followed by a transitional phase during 2014–2018. Activity intensifies during 2019–2023, peaks in 2023, and declines in 2024, indicating residual combustion. Spatially, high-risk areas are concentrated in the eastern region, while moderate and low-risk zones occur in the central and western regions, respectively. These results demonstrate that the proposed indices provide a more robust and sensitive framework for early warning and spatial delineation of subsurface combustion zones. Full article

Rainfall-induced shallow landslides strongly affect slope stability and hazard potential in mountainous areas. However, the spatiotemporal evolution of landslide scars under repeated rainfall events remains poorly understood. Using Beijing mountainous areas as a case study, we combined remote sensing, time-series NDVI analysis, and a visual foundation model to quantify landslide scar evolution and identify its controlling mechanisms. Two typical patterns have been found. Pattern I follows a “decline–outburst–overcompensation–scarring” sequence: pre-event NDVI declines by 25.1%; during the event, NDVI drops to extreme lows and 21.7% of pixels are masked; after the event, surviving vegetation shows 8.5% overcompensatory growth, but permanent scars form. Pattern II follows a “growth–acceleration–stabilization–masking” sequence: pre-event NDVI increases by 13.6%, reducing landslide risk; rainfall drives NDVI to a peak (+23.4%); post-event NDVI remains high, and landslide areas account for only 0.53%, with damage masked within a new, higher steady state. These findings demonstrate that topographic conditions, vegetation type, and phenological stage jointly control landslide scar characteristics. Steep slopes with shallow-rooted vegetation tend toward Pattern I (explicit damage, persistent scars), while gentle slopes with vegetation in active growing seasons tend toward Pattern II (masked damage, rapid recovery). Pre-event NDVI anomalies provide identifiable precursory information and should be incorporated into early warning and risk assessment systems. Full article

Vegetation phenology refers to the cyclical growth patterns of vegetation in nature, which are influenced by climatic conditions, human activities, and genetic factors. It plays an irreplaceable role in regulating carbon cycling and energy flow within natural ecosystems. However, the combination of a cloudy and rainy climate with a landscape characterized by the interplay of land and water and fragmented patches has long posed challenges for remote sensing phenological monitoring data, including a scarcity of valid observations, frequent temporal gaps, and spectral distortion in mixed pixels. These issues make it difficult to reliably support the needs of wetland phenological inversion and mapping. To address this issue, this study uses vegetation inversion in the Poyang Lake wetlands as a case study and reconstructs high-spatiotemporal-resolution time-series kNDVI data based on multi-source remote sensing data. Methodologically, we propose an improved and enhanced spatiotemporal adaptive reflectance fusion model, IESTARFM. This model enhances the homogeneity of similar pixel selection through adaptive matching windows and land cover constraints. Additionally, it explicitly incorporates cloud probability and time-lag factors into the weighting structure to systematically downweight unreliable observations, and further employs quadratic term corrections to account for the nonlinear growth response of kNDVI. Using the reconstructed dataset, key phenological information is extracted by combining third-order harmonic analysis with a dynamic thresholding method, thereby enhancing the robust characterization of seasonal trajectories under conditions of missing data and noise. Accuracy evaluation results show that the 10m/8d high-frequency kNDVI dataset reconstructed by IESTARFM achieves at least a 12.61% improvement in fusion accuracy compared to classical methods such as ESTARFM, STARFM, and FSDAF, with a maximum reduction in RMSE of 0.026, and effectively restores details in areas with thin cloud cover. The reconstructed kNDVI series achieved a coefficient of determination $R^2$ = 0.875 and RMSE = 0.066 relative to Sentinel-2 observations, indicating that the reconstructed series closely reproduces the reference imagery in both amplitude and spatial structure. The phenological parameters derived from kNDVI exhibit an RMSE of 4.81 days compared to field observations, demonstrating that the reconstructed time series reliably captures the timing of key phenological events. It should be noted that the proposed approach is designed for post-event time-series reconstruction and is not intended for real-time forecasting. In summary, this study collaboratively enhanced the reliability of high-resolution index time-series reconstruction and phenological identification in cloudy and rainy wetlands through three key aspects: cloud noise suppression, heterogeneous boundary preservation, and nonlinear growth characterization. It provides a generalizable technical foundation for dynamic monitoring of wetland vegetation, ecological restoration assessment, and refined management in regions with frequent cloud and rainfall. Full article

Planted forests are increasingly promoted to meet rising demand for forest products and restore degraded lands, but their extent and ecological implications are often misrepresented because tree crops (e.g., orchards, plantation agriculture) exhibit similar spectral and spatial signatures to planted forests. This study aims to improve differentiation between planted forests and tree crops within national-scale restoration programs. We combined Landsat-derived NDVI time series targeting disturbance-related phenological windows with the LandTrendr algorithm to map planting/clearcutting events and fused in situ spectral measurements with Sentinel-2 to develop a modified orchard flowering index (MOFI). Random forest models evaluated classification performance using combinations of spatiotemporal spectral features, biomass accumulation proxies, and the MOFI. Incorporating the MOFI improved discrimination of tree crops versus planted forests, raising the planted forest F1 from 0.751 to 0.843. The combination of the MOFI and spatiotemporal spectral features achieved the highest accuracy (F1 = 0.843). The results show tree crops are concentrated on plains and gentle mountain slopes, while plantations occur mostly on slopes > 15°, with tree crops comprising 27.1% of mapped planted tree area. These findings imply that many national planted forest map estimates may be biased without phenology- and biomass-informed methods and that integrating Landsat and Sentinel phenology metrics supports more accurate monitoring for management and policy. Full article

Timely and accurate assessment of rice productivity, encompassing grain yield, aboveground biomass (AGB), and harvest index (HI), is essential for harvest planning, supply chain coordination, and food security. This study evaluates the feasibility of predicting all three productivity components using satellite and weather time series data while examining trade-offs between forecast accuracy and operational lead time. Five machine learning models (CatBoost, Gaussian Process Regression (GPR), Random Forest, Ridge regression, and TabPFN) were compared across six in-season prediction windows (December to May) using Sentinel-2 vegetation indices (Normalized Difference Vegetation Index (NDVI), Chlorophyll Index Red Edge 2 (CIRE2), Land Surface Water Index (LSWI)), weather variables (minimum and maximum temperature and radiation), and agronomic records from 256 commercial and experimental rice fields in southern New South Wales, Australia, over four growing seasons (2022–2025) using leave-one-year-out cross-validation. Rolling in-season forecasts were evaluated across December–May; March was selected for further analysis as a practical window that balances accuracy and timeliness for decision-making, with minimal additional error reduction in later months closer to harvest. TabPFN had the lowest RMSE for yield prediction (RMSE = 1.85 t ha⁻¹, $r=0.72$), Ridge had the lowest RMSE for AGB (RMSE = 3.05 t ha⁻¹, $r=0.77$), while tree-based models yielded the lowest RMSE for derived HI (RMSE ≈ 0.07). HI prediction showed weak regional relationships, with direct prediction yielding $\left|r\right|\approx 0.24$ and derived HI (predicted yield divided by predicted AGB) showing $r\approx 0$. Although strong correlations ($r>0.9$) between HI and vegetation indices were observed within individual site-seasons, consistent with other studies, these relationships were highly variable across site-seasons, reflecting the difficulty of inferring HI from canopy reflectance when biotic and/or abiotic stresses decouple AGB accumulation from grain filling. Both direct and derived HI approaches yielded comparable errors, indicating that satellite and weather data lack information content for regional-scale HI prediction. These findings support satellite-based yield and AGB forecasting for operational use. Full article

As a crucial grassland management practice, mowing plays a key role in maintaining the stability, productivity, and economic value of grassland ecosystems. The development of large-scale monitoring techniques for detecting whether mowing has occurred is of significant scientific and practical importance for improving the understanding of grassland ecosystem response mechanisms and optimizing management strategies. This study focuses on the concentrated grassland area of the Xilingol League in Inner Mongolia, restricted to the SAR-covered western sub-region. All classification accuracies reported here are obtained under spatially random train/test splits and represent an upper bound; generalization to geographically disjoint blocks remains unverified. By utilizing Sentinel-1, Sentinel-2, and Landsat-8 remote sensing images during the mowing season (August to September 2023) along with field survey data, we first applied the random forest-SHAP algorithm to select the optimal features from 70 texture features and construct a multimodal remote sensing dataset. Subsequently, we proposed the MAD-Net (Multi-Modal Attention Fusion Network with Dynamic Weighting) model to fully exploit information related to mowing identification from both optical and SAR data and conducted comparative analyses with other models. The results indicate that the CNN_LSTM_Attention model, which integrates convolutional neural networks, long short-term memory networks, and convolutional block attention modules, performed best in terms of capturing spatiotemporal variations in time series NDVI data. The U-Net model achieved the highest performance on the optimized texture dataset, while the MAD-Net model, which consists of three subnetworks that target different feature data, reached an identification accuracy of 92.59% in the SAR-covered western sub-region under a spatially random train/test split. This result represents an optimistic upper bound, as generalization to geographically independent blocks has not been evaluated. Ablation studies reveal that NDVI time series is the most informative single modality, while texture and SAR features provide complementary information; the proposed dynamic weighting module outperforms conventional fusion strategies. This study provides a new perspective for the large-scale binary classification of mown vs. non-mown grassland and effectively combines multimodal remote sensing data with deep learning models. Thus, this work not only offers a comparative basis for timely and effective identification of mowed grasslands but also provides insights for formulating optimized regional grassland management policies. Full article

Degraded rangelands in semi-arid pastoral systems are widely associated with declining vegetation, soil carbon loss, and worsening household livelihoods. However, the mechanisms linking rangeland degradation to household income remain poorly understood, particularly in a panel-data context. This study examines how rangeland condition, vegetation dynamics, and livestock by-product underutilization are related to household income in Öndörshireet Soum, Töv Aimag, Mongolia. The analysis is based on a multi-source panel dataset covering 2018 to 2024, combining Sentinel-2 NDVI time series, soil organic carbon measurements from 120 permanent plots, and a five-wave survey of 114 households. The results indicate widespread and persistent degradation. Nearly 90 percent of monitored plots are at least moderately degraded; NDVI shows a steady decline over time; and average soil carbon levels remain well below those observed at a managed reference site. Over the same period, real household income declined despite a gradual increase in herd size. Econometric estimates show that vegetation condition is positively associated with income, whereas higher levels of by-product waste are associated with lower income, even after accounting for precipitation variability. The interaction results further suggest that the benefits of herd expansion weaken when production losses remains high. Taken together, these findings indicate that ecological decline and low value capture from livestock operate simultaneously to constrain pastoral livelihoods. Improvements in pasture condition alone appear insufficient to offset these pressures when a substantial share of livestock value is not recovered. While the results offer useful insights for rangeland policy, further evidence from multiple sites would be needed to assess causality and the extent to which these patterns apply beyond a single soum. Full article

Climate change is a major hazard to the agricultural systems of the world, as it is changing the temperature regimes, precipitation patterns, and soil dynamics, which are weakening crop production and the stability of ecosystems. The proposed research is a hybrid modeling framework that combines Multiple Linear Regression (MLR) with a deep learning architecture (PatchTST) based on the Transformer to quantify and predict the effect of climate variability on the productivity of agriculture. Multi-source data, including global weather data, crop data, and ISRIC-WISE soil data, were harmonized through stringent preprocessing steps that included imputation, normalization, and spatial-temporal alignment. The regression analysis reveals a statistically significant negative impact of temperature on crop yield, while precipitation and soil fertility exhibit positive contributions. To capture complex non-linear dependencies and long-term temporal patterns, the PatchTST model was trained using time-series inputs enriched with satellite-derived vegetation indices. The proposed model significantly outperforms conventional deep learning approaches, achieving an R² of 0.98, RMSE of 0.0172, and MAE of 0.0134. Attention-based interpretability highlights soil moisture and NDVI as dominant predictors, reinforcing the model’s physical and agronomic relevance. The findings indicate that integrating interpretable statistical models with advanced deep learning improves predictive accuracy while addressing the transparency limitations of black-box approaches. The framework supports practical deployment across regional crop planning, climate risk policymaking, and farm-level decision support systems, demonstrating its direct applicability to real-world agricultural management. Full article

Municipal solid waste landfills may remain sources of environmental concern long after closure because heavy metals can persist in soils and affect ecosystem recovery. This study presents an integrated assessment of a closed municipal solid waste landfill in Kokshetau, Northern Kazakhstan, by combining field-based soil geochemical analysis with remote sensing monitoring of vegetation dynamics. A radial-gradient sampling design was used to characterize spatial patterns of contamination and to distinguish zones with different levels of anthropogenic impact. The results showed a clear concentration of heavy metals, particularly Zn and Pb, in the central part of the landfill, where integrated pollution and ecological risk indices indicated the highest levels of technogenic pressure. Time-series analysis of Landsat-derived vegetation indices for 2017–2025 revealed poorer vegetation condition in the most contaminated areas, with NDVI and EVI values increasing toward the landfill periphery. The observed negative association between vegetation indices and ecological risk suggests that remote sensing indicators can provide useful information on the ecological condition of closed landfill sites, although they should be interpreted together with field measurements. The novelty of this study lies in the combined use of geochemical contamination indices and long-term vegetation-index monitoring to assess post-closure landfill conditions in an arid continental region of Central Asia, where such integrated studies remain limited. The findings highlight the persistence of environmental risks after landfill closure and support the use of vegetation indices as non-invasive tools for monitoring rehabilitation and prioritizing further field investigations. Full article

Ongoing global climate change and intensified human activities have increased the frequency and intensity of droughts, posing a serious threat to global ecosystems and agricultural sustainability. However, the seasonally differentiated effects of droughts on cropland phenology and productivity, especially in Northeast China, remain insufficiently understood, limiting the assessment of agro-ecosystem vulnerability and the development of effective adaptation strategies. In this study, the standardized precipitation evapotranspiration index (SPEI) was used to assess the frequency and severity of extreme drought in Northeast China based on run theory. Cropland phenology parameters and productivity were derived from time-series MODIS normalized difference vegetation index (NDVI), and gross primary productivity (GPP) products, which were smoothed using a Savitzky–Golay (S–G) filter. Correlation analyses were conducted to examine regional associations between SPEI-defined drought conditions and cropland phenology and productivity. Results show that: (1) Drought events occurred frequently in the central and southern parts of Northeast China, particularly in the Songnen Plain (5.22 events per decade) and the Liaohe Plain (4.89 events per decade); (2) the Songnen Plain showed significant increases (Sen’s slope > 0, p < 0.05) across all drought metrics over 2001–2020, which coincided with LOS shortening (−0.18 d a⁻¹) and GPP decline (−9.12 g C m⁻² a⁻¹); in contrast, the Sanjiang Plain exhibited slight declines (Sen’s slope, p > 0.05) in drought metrics, resulting in LOS lengthening (0.06 d a⁻¹) and GPP increases (7.84 g C m⁻² a⁻¹); and (3) drought impacts were strongly season-dependent, with autumn droughts showing a stronger association with reductions in crop productivity in local areas of Northeast China. These findings highlight the need to account for crop responses to drought events, which is essential for developing measures to cope with drought and protecting regional food security. Full article

Large-scale excavation for pumped-storage hydropower stations (PSPSs) in mountainous areas substantially alters slope soils and accelerates ecological degradation, yet quantitative multi-indicator assessments for such projects remain limited. This study integrates field surveys, laboratory analyses, and multi-temporal remote-sensing data to evaluate the disturbance-induced evolution of soil properties at two representative PSPSs in China. Soil bulk density and porosity measurements revealed significant compaction on disturbed slope surfaces, particularly on soil-dominated slopes. Key nutrient indicators, including organic matter, alkali-hydrolysable nitrogen, available phosphorus, and available potassium, showed consistent declines relative to adjacent undisturbed habitats. A comprehensive ecological degradation indicator (EDI) was constructed using five vegetation and soil spectral indices (RVI, NDVI, SAVI, SBI, and SM) weighted through the analytic hierarchy process. Time-series EDI mapping (2019–2023) demonstrated a progressive increase in moderately to extremely degraded areas during intensive construction stages. The results highlight the strong spatial heterogeneity of disturbance effects and underscore the necessity of soil-focused restoration strategies. This integrated assessment framework provides a scientific basis for guiding near-natural restoration and long-term soil–vegetation management in PSPS infrastructure landscapes. Full article

Accurate evapotranspiration (ET) estimation is critical for sustainable water management in arid environments. This study estimates actual ET over the Al-Hofuf region, Al-Ahsa Oasis, Saudi Arabia, during 2024 using a cloud-based remote sensing approach. Landsat 9 Level-2 imagery was combined with ERA5-Land meteorological data to quantify spatial and temporal ET variations across a 25 km buffer. Vegetation dynamics were characterized using the Normalized Difference Vegetation Index (NDVI) to derive crop coefficients (Kc) within a Kc–ET₀ framework, where reference ET (ET₀) was obtained from ERA5-Land potential evaporation. All processing utilized Python (Version 3.14) on Google Colab and Google Earth Engine for scalable computation. Eighty-eight cloud-free Landsat 9 scenes were processed following cloud and shadow masking. Mean NDVI, Kc, and daily ET values were compiled into a comprehensive time-series dataset. Model performance was evaluated through cross-validation with MODIS MOD16A2 and internal consistency checks, demonstrating strong statistical agreement (R² = 0.82, NSE = 0.71, PBIAS = +8.3%). Results revealed pronounced seasonal variability closely linked to vegetation activity and atmospheric demand, with peak ET occurring during summer months (June–July: 7.2–7.5 mm day⁻¹) and minima in winter (January–February: 2.0–2.6 mm day⁻¹). Findings demonstrate that cloud-based techniques provide reliable, cost-effective ET monitoring in data-scarce, groundwater-dependent regions. Validation confirms Kc-ET₀ estimates reliably capture spatial and temporal patterns, supporting practical irrigation management applications. This approach aids precision irrigation and long-term water sustainability planning in Al-Hofuf, contributing significantly to national water conservation objectives under Saudi Arabia’s Vision 2030 and National Water Strategy. Full article

Early-spring ephemeral plants act as pioneer species on stabilized dunes in cold arid deserts; they are capable of rapid growth under extreme drought and low-temperature conditions while sustaining dune ecosystem functions. These species are highly sensitive to climate change, yet their spatiotemporal dynamics and the mechanisms by which climatic factors regulate their growth remain poorly understood. In this study, we investigated the Gurbantunggut Desert, China, using long-term NDVI time series to extract phenological traits associated with their life cycle and developed a remote-sensing-based analytical framework to quantify the distribution patterns of early-spring ephemeral plants and their environmental drivers. We combined random forest (RF), structural equation modeling (SEM), and convolutional neural networks (CNN) to assess the relative importance and pathways of key climatic drivers and to predict future distribution changes. Our results indicate that: (1) the life cycle extraction method achieved a classification accuracy exceeding 80%, and from 2001 to 2022, the overall distribution of early-spring ephemeral plants exhibited an increasing trend; (2) snowend, snowday, and precipitation during the driest quarter were the primary drivers of ephemeral plant distribution, collectively explaining over 60% of the observed variation, and structural equation modeling further revealed that snow and precipitation had significant positive effects on their distribution; and (3) under future climate scenarios, Medium-NDVI areas are projected to expand northward and westward, with the potential emergence of new suitable habitats in northern localities by mid-century. Climate warming may facilitate the dispersal and latitudinal migration of early-spring ephemeral plants. Based on these findings, biodiversity conservation efforts should prioritize ecologically sensitive transitional zones and promote species migration and establishment under climate change through the construction of ecological corridors. Full article

This study analyzes the interannual dynamics of fallow land extent in the North Kazakhstan Region during 2021–2025 using Sentinel-2 data. Fallow fields were identified through a rule-based multi-temporal approach in which the adapted Plowed Land Index (PLI) was used as the principal indicator of mechanically processed soil surfaces, while the NDVI was applied as a supporting indicator to exclude actively vegetated fields and control for vegetation overgrowth. Annual fallow masks were generated within a unified seasonal observation window and subsequently analyzed at the district level. The results revealed pronounced interannual variability in fallow land extent, including both reductions and partial recovery over the study period, together with substantial spatial heterogeneity among districts. The largest fallow area was recorded in 2021, whereas the minimum was observed in 2024. Comparison with district-level hydrothermal coefficient (HTC) values showed that lower moisture availability generally corresponded to larger fallow areas, while relatively more favorable hydrothermal conditions were associated with their reduction. At the same time, district-specific trajectories indicate that interannual dynamics were controlled not only by agroclimatic variability but also by differences in land-use structure and agricultural management. The proposed approach confirms the applicability of Sentinel-2 time series for regional monitoring of fallow lands and demonstrates the methodological value of the adapted PLI for identifying mechanically processed fallow surfaces under the heterogeneous agricultural conditions of North Kazakhstan. Full article

Land productivity assessments used in SDG 15.3.1 commonly rely on NDVI trends, which may be strongly influenced by precipitation variability and can therefore misrepresent actual land condition change, particularly in dryland environments where vegetation productivity responds rapidly to rainfall fluctuations. To address this issue, this study presents a land degradation assessment (2000–2022) using a fully reproducible Google Earth Engine workflow integrating high-resolution 30 m Landsat time-series NDVI, precipitation, land cover, and soil organic carbon datasets. The core methodological contribution is a precipitation-conditioned hybrid productivity framework that dynamically selects among NDVI trends, Rain-Use Efficiency (RUE), and Residual Trends (RESTREND) according to local rainfall dynamics. By adapting productivity metrics to precipitation conditions, the framework reduces precipitation-driven misinterpretation of vegetation trends, operationalizes a more climate-aware implementation of the land productivity (LP) sub-indicator within SDG 15.3.1, and enables systematic comparison of productivity metrics under contrasting rainfall regimes. Results for the 2015–2022 monitoring period, which included multiple drought years, indicate that $18\%$ of land showed declining productivity, $75\%$ remained stable, and $6\%$ showed improvement. Decline was spatially concentrated in arid and semi-arid regions, whereas irrigated and managed landscapes exhibited localized improvements. The hybrid indicator provides an additional option for LP assessment that explicitly accounts for precipitation variability, supporting more climate-sensitive interpretation of productivity trends. This transferable, reproducible methodology strengthens national capacity for SDG 15.3.1 reporting and offers a scalable framework for land degradation assessments in other drought-prone regions. Full article