Search Results (1,306)

China’s coastal wetlands play a crucial role in maintaining biodiversity and providing essential ecosystem services. However, the absence of high-resolution wetland type maps poses substantial challenges for effective conservation and management. This study proposes a two-step classification framework that integrates pixel-based Random Forest algorithms with object-based hierarchical decision trees, utilizing Landsat-8 time-series imagery to generate a detailed wetland map comprising 10 wetland types and 5 non-wetland categories. The results reveal distinct spatial patterns along China’s coastline: freshwater wetlands and riverine systems dominate the northern regions, whereas southern coastal zones feature extensive tidal flats, aquaculture ponds, and mangrove ecosystems. The proposed method achieved an overall accuracy of 89.76% and a Kappa coefficient of 0.891, demonstrating its effectiveness for large-scale wetland mapping. This study provides robust technical support for the sustainable conservation and ecological management of coastal wetlands. Full article

Globally, lakes are increasingly recognized as sensitive indicators of climate change and ecosystem stress. Qaraoun Lake, Lebanon’s largest artificial reservoir, is a critical resource for irrigation, hydropower generation, and domestic water supply. Over the past 25 years, satellite remote sensing has enabled consistent monitoring of its hydrological and environmental dynamics. This study leverages the advanced cloud-based processing capabilities of Google Earth Engine (GEE) to analyze over 180 cloud-free scenes from Landsat 7 (Enhanced Thematic Mapper Plus) (ETM+) from 2000 to present, Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) from 2013 to present, and Landsat 9 OLI-2/TIRS-2 from 2021 to present, quantifying changes in lake surface area, water volume, and pollution levels. Water extent was delineated using the Modified Normalized Difference Water Index (MNDWI), enhanced through pansharpening to improve spatial resolution from 30 m to 15 m. Water quality was evaluated using a composite pollution index that integrates three spectral indicators—the Normalized Difference Chlorophyll Index (NDCI), the Floating Algae Index (FAI), and a normalized Shortwave Infrared (SWIR) band—which serves as a proxy for turbidity and organic matter. This index was further standardized against a conservative Normalized Difference Vegetation Index (NDVI) threshold to reduce vegetation interference. The resulting index ranges from near-zero (minimal pollution) to values exceeding 1.0 (severe pollution), with higher values indicating elevated chlorophyll concentrations, surface reflectance anomalies, and suspended particulate matter. Results indicate a significant decline in mean annual water volume, from a peak of 174.07 million m³ in 2003 to a low of 106.62 million m³ in 2025 (until mid-November). Concurrently, pollution levels increased markedly, with the average index rising from 0.0028 in 2000 to a peak of 0.2465 in 2024. Episodic spikes exceeding 1.0 were detected in 2005, 2016, and 2024, corresponding to documented contamination events. These findings were validated against multiple institutional and international reports, confirming the reliability and efficiency of the GEE-based methodology. Time-series visualizations generated through GEE underscore a dual deterioration, both hydrological and qualitative, highlighting the lake’s growing vulnerability to anthropogenic pressures and climate variability. The study emphasizes the urgent need for integrated watershed management, pollution control measures, and long-term environmental monitoring to safeguard Lebanon’s water security and ecological resilience. Full article

This study presents an integrated stochastic framework for assessing and forecasting drought dynamics in the western Bhagirathi–Hooghly River Basin, encompassing the districts of Bankura, Birbhum, Burdwan, Medinipur, and Purulia. Employing multiple probabilistic and statistical techniques, including the gamma-based standardized precipitation index (SPI), effective drought index (EDI), rainfall anomaly index (RAI), and the auto-regressive integrated moving average (ARIMA) model, the research quantifies spatio-temporal variability and projects drought risk under non-stationary climatic conditions. The analysis of century-long rainfall records (1905–2023), coupled with LANDSAT-derived vegetation and moisture indices, reveals escalating drought frequency and severity, particularly in Purulia, where recurrent droughts occur at roughly four-year intervals. Stochastic evaluation of rainfall anomalies and SPI distributions indicates significant inter-annual variability and complex temporal dependencies across all districts. ARIMA-based forecasts (2025–2045) suggest persistent negative SPI trends, with Bankura and Purulia exhibiting heightened drought probability and reduced predictability at longer timescales. The integration of remote sensing and time-series modelling enhances the robustness of drought prediction by combining climatic stochasticity with land-surface responses. The findings demonstrate that a hybrid stochastic modelling approach effectively captures uncertainty in drought evolution and supports climate-resilient water resource management. This research contributes a novel, region-specific stochastic framework that advances risk-based drought assessment, aligning with the broader goal of developing adaptive and probabilistic environmental management strategies under changing climatic regimes. Full article

Wildfires can drastically alter ecological landscapes in just a few days, while it takes years of post-fire recovery for vegetation to return to its former pre-fire state. Assessing changes in vegetation can help with understanding how the hydrological components in the wildfire-affected areas contribute to potential vegetation shifts. This case study of the Los Angeles Bobcat Fire in 2020 uses Google Earth Engine (GEE) and Python 3.10.18 to access and visualize variations in Difference Normalized Burn Ratio (dNBR) area, Normalized Difference Vegetation Index (NDVI), and OpenET’s evapotranspiration (ET) across three dominant National Land Cover Database (NLCD) vegetation classes and dNBR classes via monthly time series and seasonal analysis from 2016 to 2024. Burn severity was determined based on Landsat-derived dNBR thresholds defined by the United Nations Office for Outer Space Affairs UN-Spider Knowledge Portal. Our study showed a general reduction in dNBR class area percentages, with High Severity (HS) dropping from 15% to 0% and Moderate Severity (MS) dropping from 45% to 10%. Low-Severity (LS) areas returned to 25% after increasing to 49% in May of 2022, led by vegetation growth. The remaining area was classified as Unburned and Enhanced Regrowth. Within our time series analysis, HS areas showed rapid growth compared to MS and LS areas for both ET and NDVI. Seasonal analysis showed most burn severity levels and vegetation classes increasing in median ET and NDVI values while 2024’s wet season median NDVI decreased compared to 2023’s wet season. Despite ET and NDVI continuing to increase post-fire, recent 2024 NLCD data shows most Forests and Shrubs remain as Grasslands, with small patches recovering to pre-fire vegetation. Using GEE, Python, and available satellite imagery demonstrates how accessible analytical tools and data layers enable wide-ranging wildfire vegetation studies, advancing our understanding of the impact wildfires have on ecosystems. Full article

Terraces are the main engineering of soil erosion control on the Loess Plateau, offering measures for sediment reduction and water conservation, as well as the potential for increasing agricultural productivity. Over the years, large-scale terrace construction has been undertaken; however, the management has been inadequate, especially in terms of long-term monitoring and mapping. Moreover, the sediment reduction effect of terrace construction is not yet fully understood. Therefore, this study utilizes Landsat series data, integrating remote sensing imaging principles with machine learning techniques to achieve long–term temporal sequence mapping of terraces at a 30 m spatial resolution on the Loess Plateau. The sediment reduction effect brought about by terrace construction on the Loess Plateau is quantified using a sediment reduction formula. The results show that Elevation (Ele.), red band (R), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Near-infrared Reflectance of Vegetation (NIRv) are key parameters for remote sensing identification of terraces. These five remote sensing variables explain 88% of the terrace recognition variance. Coupling the Random Forest classification model with the LandTrendr algorithm allows for rapid time-series mapping of terrace spatial distribution characteristics on the Loess Plateau. The producer’s accuracy of terrace identification is 93.49%, the user’s accuracy is 93.81%, the overall accuracy is 88.61%, and the Kappa coefficient is 0.87. The LandTrendr algorithm effectively removes terraces affected by human activities. Terraces are mainly distributed in the southeastern Loess areas, including provinces such as Gansu, Shaanxi, and Ningxia. Over the past 30 years, the terrace area on the Loess Plateau has increased from 0.9790 million hectares in 1990 to 9.8981 million hectares in 2020. The sediment reduction effect is particularly notable, with an average reduction of 49.75% in soil erosion across the region. This indicates that terraces are a key measure for soil erosion control in the region and a critical strategy for improving farmland productivity. The data from this study provides scientific evidence for soil erosion control on the Loess Plateau and enhances the precision of terrace management. Full article

This study proposes a “zonal inversion–fusion mosaicking” technical framework to address the challenge of acquiring continuous full-profile topography data in coastal wetland intertidal zones. The framework integrates and synergistically analyzes data from multi-platform sensors, including satellite, unmanned aerial vehicle (UAV), and ground-based instruments. Applied to the Min River Estuary wetland, this framework employs zone-specific optimization strategies: in the inundated zone, the topography was inverted using Landsat-9 OLI imagery and a Random Forest algorithm (R² = 0.79, RMSE = 2.08 m); in the bare flat zone, a linear model was developed based on Sentinel-2 time-series imagery using the inundation frequency method, and it achieved an accuracy of R² = 0.86 and RMSE = 0.34 m; and in the vegetated zone, high-precision topography was derived from UAV oblique photography with Kriging interpolation (RMSE = 0.10 m). The key innovation is the successful generation of a seamless full-profile digital elevation model (DEM) with an overall RMSE of 0.54 m through benchmark unification and precision-weighted fusion algorithms from the sensor data fusion perspective. This study demonstrates that the synergistic sensors framework effectively overcomes the limitations of single-sensor observations, providing a reliable and generalizable integrated solution for the full-profile topographic monitoring of tidal flats, which offers crucial support for coastal wetland research and management. Full article

Rangeland degradation poses a serious challenge for the sustainable management of free-ranging livestock in Mediterranean wetlands. In Doñana National Park, Spain, the endangered Marismeño horse depends exclusively on natural forage, making it essential to monitor vegetation productivity and grazing suitability under increasing climate variability. This study presents a satellite-based assessment of rangeland carrying capacity to support the adaptive management of this iconic breed. A six-year time series (2015–2020) of 1242 images from Landsat 8 OLI/TIRS and Sentinel-2 (L1C/L2A) was processed using ILWIS and Python-based workflows to derive vegetation indices (GNDVI, NDMI) and model aboveground biomass, forage energy, and grazing pressure across five grazing units. Results revealed strong seasonal cycles, with biomass and nutritive value peaking in spring and declining sharply in summer. Ecotonal zones such as La Vera y Sotos acted as crucial refuges during drought-induced resource shortages. The harmonized multi-sensor approach demonstrated high reliability for mapping forage dynamics and assessing carrying capacity at fine scales. This remote sensing framework offers an effective, scalable tool for sustainable livestock management in Doñana, directly supporting biodiversity conservation and the long-term resilience of Mediterranean rangeland ecosystems. Full article

Wind erosion is one of the most severe environmental problems in arid and semi-arid regions, posing a serious threat to ecological security and human settlements. Afforestation is widely acknowledged as a practical strategy for mitigating wind erosion. However, quantitative assessments of the relationship between forest restoration and wind erosion control remain limited, particularly over long temporal scales and at fine spatial resolutions. This study takes Duolun County, Inner Mongolia, as a representative case to examine the role of large-scale forest restoration in controlling wind erosion. Specifically, land use dynamics from 1985 to 2024 were mapped using a time series of Landsat imagery to identify forest expansion. Then, the Revised Wind Erosion Equation (RWEQ) was applied to simulate the spatiotemporal variations in wind erosion and sand fixation. Finally, a scenario-based framework contrasting forested and non-forested conditions was used to isolate and quantify the contribution of forest restoration to wind erosion control. Results showed that forest cover increased significantly from 3.95% to 36.19% over the past 40 years, with expansion primarily concentrated in the central desertified regions and the northern hilly areas. Sand fixation increased from $8.70\times {10}^5$ t to $8.20\times {10}^6$ t, with an average annual growth of $9.06\times {10}^4$ t/year. Spatially, growth rates were more pronounced in the central and northern regions than in the south. Ecological restoration programs contributed substantially to wind erosion control, with their attributable sand fixation increasing from near zero to $6.61\times {10}^5$ t, with an average annual rate of $8.21\times {10}^3$ t/year. These findings provide new insights into the role of large-scale forest restoration in enhancing sand fixation and mitigating wind erosion. Full article

This study develops a satellite-based, machine-learning-based prediction algorithm to predict optimal irrigation scheduling for cotton cultivation within Rahim Yar Khan, Pakistan. The framework leverages multispectral satellite imagery (Landsat 8 and Sentinel-2), GIS-derived climatic, land surface data and real-time weather information obtained from a freely accessible weather API, eliminating the need for ground-based IoT sensors. The proposed algorithm integrates FAO-56 evapotranspiration principles and water stress indices to accurately forecast irrigation requirements across the four critical growth stages of cotton. Supervised learning algorithms, including Gradient Boosting, Random Forest, and Logistic Regression, were evaluated, with Random Forest indicating better predictive accuracy with a coefficient of determination (R²) exceeding 0.92 and a root mean square error (RMSE) of approximately 415 kg/ha, owed its capacity to handle complex, non-linear relations, and feature interactions. The model was trained on data collected during 2023 and 2024, and its predictions for 2025 were validated against observed irrigation requirements. The proposed model enabled an average 12–18% reduction in total water application between 2023 and 2025, optimizing water use deprived of compromising crop yield. By merging satellite imagery, GIS data, and weather API information, this approach provides a cost-effective, scalable solution that enables precise, stage-specific irrigation scheduling. Cloud masking was executed by applying the built-in QA bands with the Fmask algorithm to eliminate cloud and cloud-shadow pixels in satellite imagery statistics. Time series were generated by compositing monthly median values to ensure consistency across images. The novelty of our study primarily focuses on its end-to-end integration framework, its application within semi-arid agronomic conditions, and its empirical validation and accuracy calculation over direct association of multi-source statistics with FAO-guided irrigation scheduling to support sustainable cotton cultivation. The quantification of irrigation capacity, determining how much water to apply, is identified as a focus for future research. Full article

Abandoned cropland has been expanding due to complex socio-economic factors such as urbanization, demographic shifts, and declining agricultural profitability. As abandoned cropland simultaneously brings ecological, environmental, and social risks and benefits, quantitative monitoring is essential to assess its overall impact. Satellite image-based spatial data are suitable for identifying spectral characteristics related to crop phenology, and recent research has advanced in detecting large-scale abandoned cropland through changes in time-series spectral characteristics. However, frequent cloud covers and highly fragmented croplands, which vary across regions and climatic conditions, still pose significant challenges for satellite-based detection. This study combined Harmonized Landsat and Sentinel-2 (HLS) imagery, offering high temporal (2–3 days) and spatial (30 m) resolution, with the eXtreme Gradient Boosting (XGBoost) algorithm to capture seasonal spectral variations among rice paddy, upland fields, and abandoned croplands. An XGBoost model with a Balanced Bagging Classifier (BBC) was used to mitigate class imbalance. The model achieved an accuracy of 0.84, $Cohen’s kappa$ 0.71, and $F2 score$ 0.84. SHapley Additive exPlanations (SHAP) analysis identified major features such as NIR (May–June), SWIR2 (January), MCARI (September), and BSI (January–April), reflecting phenological differences among cropland types. Overall, this study establishes a robust framework for large-scale cropland monitoring that can be adapted to different regional and climatic settings. Full article

Understanding soybean cultivation in Northeast China is essential for informing policies related to national food security. However, long-term, high-resolution soybean maps are still lacking, largely due to persistent cloud cover, limited availability of high-quality field labels, and the difficulty of capturing crop phenological dynamics using traditional remote sensing methods. To address this gap, this study aims to develop a robust framework for generating decade-long soybean distribution maps by integrating medium-resolution Landsat imagery with advanced deep learning techniques. We mapped the soybean distribution across Northeast China from 2013 to 2022 by constructing a bi-monthly NDVI-based composite and applying a deep learning model that combines the Transformer architecture with fully connected neural networks. The model was trained using a large set of field-surveyed samples collected between 2017 and 2019. Validation results demonstrate strong classification performance, with a user accuracy of 89.77% and a producer accuracy of 88.59%, sufficient for reliable spatiotemporal analysis. When compared with prefecture-level statistical yearbook data, the predicted annual soybean areas show a high degree of agreement (R² = 0.9226). Overall, this study not only fills an important gap in long-term soybean mapping for Northeast China, but also provides a replicable methodological framework for large-scale, time-series crop mapping. The approach has strong potential for broader application in agricultural monitoring and food security assessment. Full article

Mangrove forests are critical coastal ecosystems that store carbon, support marine life, and serve as natural barriers, protecting shorelines from erosion and reducing the impact of storms by absorbing wave energy. However, the rise of human activities and sea levels has led to their destruction over the past decades. It is important to know how the areas of mangrove forests change and adapt every year to plan for their restoration and protection and to support future trends like using carbon credits to help developing countries generate income. This study aims to map and monitor mangrove forest area changes over four decades in the Mekong region, comprising Myanmar, Thailand, Cambodia, and Vietnam, from 1984 to 2023 using a time series of Landsat data together with random forest (RF) classification. This analysis implemented multiple approaches, including creating stabilized Landsat imagery composites from the LandTrendr algorithm, Otsu edge detection, Minimum Mapping Unit (MMU), and RF classifier. The study found the map accuracy based on the RF model classifier achieved an overall accuracy between 86.2% and 88.8%, providing reliable data for analysis. Country-level analysis revealed increasing mangrove forest cover in Thailand (12.9%) and Vietnam (28.4%) since 1984. Conversely, mangrove areas in Cambodia and Myanmar have decreased significantly from 1984 to 2023 by about 14.6% and 22.7%, respectively. These findings have significant implications for resource allocation, investment strategies, and the development of carbon credits to support mangrove conservation efforts. This comprehensive dataset offers valuable insights for stakeholders involved in mangrove management and restoration in the Mekong region. By understanding the spatial-temporal distribution patterns of mangrove forest change, decision-makers can make informed decisions to safeguard these critical ecosystems for future generations. Full article

Mangrove ecosystems play a critical role in global carbon cycling, serving as significant carbon sinks by storing carbon in both aboveground biomass (ACG) and soil carbon stock (SOC). However, the temporal dynamics of ACG and SOC, as well as their spatial variations across different mangrove age stages, remain poorly understood, particularly under the influence of introduced species such as Sonneratia apetala Buch.-Ham. To address these gaps, our study used a long-term series of NDVI from Landsat (from 1990 to 2024) and the mangrove product of China (1990, 2000, 2010, and 2018) to estimate the mangrove age stage ($\mathrm{Stage}\mathrm{I}$ 10–24 years, $\mathrm{Stage}\mathrm{II}$ 24–34 years, and $\mathrm{Stage}\mathrm{III}$ > 34 years). UAV-LiDAR and in-situ surveys were applied to measure mangrove canopy height to calculate ACG and measure the belowground soil carbon stock, respectively. Combined with the mangrove age stage, ACG, and SOC, our results reveal that ACG accumulates rapidly in younger mangroves dominated by Sonneratia apetala, peaking early (<20 years) and then stabilizing as mangroves, indicating that the introduction of Sonneratia apetala changed the increase in ACG with age. In contrast, SOC increases more gradually over time, with only older mangroves (over 30 years) storing significantly higher SOC. Root structure, TN, and TP were sensitive to the SOC. The different root structures (pneumatophore, plank, pop, and knee root) had different SOC results, and the pneumatophore had the lowest SOC. Remote sensing data revealed that the introduction of Sonneratia apetala altered the species composition of younger mangroves, leading to its predominance within these ecosystems. This shift in species composition not only altered the temporal dynamics of aboveground carbon (ACG) but also favored pneumatophore-dominated root structures, which were associated with the lowest soil organic carbon (SOC). Consequently, younger stands may require more time to accumulate SOC to levels comparable to older mangrove forests. These results suggest that restoration targets for vegetation carbon and soil carbon should be set on different timelines, explicitly accounting for stand age, species composition, and root functional types. Full article

Remote sensing of tidal flats and their dynamic changes is essential for understanding and conserving intertidal ecosystems. As a highly dynamic land cover type influenced by tidal variations, tidal flats present challenges for consistent long-term monitoring. The tidal flat area may be inflated in long-term remote sensing datasets due to the increasing observation frequency in recent decades. Although significant progress has been made in time-series mapping of tidal flats using Landsat imagery, the relationship between tidal flat dynamics and satellite observation frequency remains poorly understood. In this study, we aimed to quantify the impact of increased Landsat observations on long-term time series of tidal flat area changes using two widely used global tidal flat products (GTF30 and Murray’s product). Specifically, we first used a regression analysis to investigate the relationship between observation frequency, tide level, and tidal flat area; the result revealed that higher observation frequency is more likely to capture lower tides and thus detect larger tidal flat areas. Next, we developed a weighted statistical regression method to quantify the influence of observation frequency on the mapped tidal flat area at the selected 45 tidal stations. Our analysis indicates that both products exhibit significant inflated increases due to the increased observation frequency during 2000–2022. Specifically, the GTF30 product shows a spurious increase of 12.83 ± 6.51 km² attributable to the increased observation frequency, accounting for 17.57% of the total observed change. Similarly, the Murray product also exhibits a spurious increase of 13.92 ± 7.45 km², which is approximately 1.95 times the mapped change in tidal flat area. Therefore, this study emphasizes the presence of substantial inflation effects in long-term tidal flat remote sensing datasets caused by the increasing observation frequency. Quantifying this bias is essential for accurate interpretation of the long-term tidal flat dynamics and ecological assessments. Full article

This study presents an integrated framework for spatiotemporal mapping of carbon stocks in rubber plantations in Rayong Province, Eastern Thailand—an area undergoing rapid agricultural transformation and rubber expansion. Unlike most existing assessments that rely on Tier 1 IPCC defaults or coarse plantation age classes, our framework combines annual plantation age derived from Landsat time series, age-specific allometric growth models, and Tier 2 soil organic carbon (SOC) accounting. This enables fine-scale, age- and site-sensitive estimation of both tree and soil carbon. Results show that tree biomass dominates the carbon pool, with mean tree carbon stocks of 66.94 ± 13.1% t C ha⁻¹, broadly consistent with national field studies. SOC stocks averaged 45.20 ± 0.043% t C ha⁻¹, but were overwhelmingly inherited from pre-conversion land use (43.7 ± 0.042% t C ha⁻¹). Modeled SOC changes ($\mathsf{\Delta }\mathrm{SOC}$) were modest, with small gains (2.06 t C ha⁻¹) and localized losses (−9.96 t C ha⁻¹), producing a net mean increase of only 1.44 t C ha⁻¹. These values are substantially lower than field-based estimates (5–15 t C ha⁻¹), reflecting structural limitations of the global empirical ΔSOC model and reliance on generalized default parameters. Uncertainties also arise from allometric assumptions, generalized soil factors, and Landsat resolution constraints in smallholder landscapes. Beyond carbon, ecological trade-offs of rubber expansion—including biodiversity loss, soil fertility decline, and hydrological impacts—must be considered. By integrating methodological innovation with explicit acknowledgment of uncertainties, this framework provides a conservative but policy-relevant basis for carbon accounting, subnational GHG reporting, and sustainable land-use planning in tropical agroecosystems. Full article