Search Results (4,283)

An effective understanding of sediment deposition and erosion in river basins, particularly floodplains, is critical for modeling geomorphic evolution, managing flood risks, and maintaining ecological integrity. However, most related studies have been limited to hydraulic or hydrodynamic modeling approaches. Therefore, this study integrated Sentinel-1 differential interferometric synthetic aperture radar (DInSAR) coherence, Sentinel-2 normalized difference vegetation index, and soil surface moisture index data with one-dimensional hydraulic modeling to assess flood-induced sediment deposition and erosion in the Gamcheon River basin under non-flood, short flood, and long flood scenarios. The DInSAR deformation analysis revealed a clear pattern of upstream erosion and downstream deposition during flood events, indicating a total depositional uplift of 0.33 m during the long flood scenario but dominant erosion with a total measured surface lowering of −2.03 m during the non-flood scenario. These results were highly consistent with the predictions from the hydraulic model and supported by the hysteresis curves for in situ suspended sediment concentration. The findings of this study demonstrate the effectiveness of the proposed integrated approach for quantifying floodplain sediment dynamics, offering particular application value in data-scarce or inaccessible floodplains. Furthermore, the proposed approach provides practical insights into sediment management, flood risk assessment, and ecosystem restoration efforts. Full article

Drought is a critical hazard to agricultural productivity in semi-arid regions such as the Antalya Agricultural Basin of Türkiye. This study assessed agricultural drought from 2001 to 2023 using multiple remote sensing-based indices processed in Google Earth Engine (GEE). Vegetation indicators (Normalized Difference Vegetation Index, Normalized Difference Water Index, Normalized Difference Drought Index, Vegetation Condition Index, Temperature Condition Index, and Vegetation Health Index) were derived from MODIS datasets, while the Precipitation Condition Index was calculated from CHIRPS precipitation data. Composite indicators included the Scaled Drought Composite Index, integrating vegetation, temperature, and precipitation factors, and the Soil Moisture Condition Index derived from reanalysis soil moisture data. Results revealed recurrent moderate drought with strong seasonal and interannual variability, with 2008 identified as the driest year and 2009 and 2012 as wet years. Summer was the most drought-prone season, with precipitation averaging 5.5 mm, PCI 1.1, SDCI 15.6, and SMCI 38.4, while winter exhibited recharge conditions (precipitation 197 mm, PCI 40.9, SDCI 57.3, SMCI 89.6). Interannual extremes were detected in 2008 (severe drought) and wetter conditions in 2009 and 2012. Vegetation stress was also notable in 2016 and 2018. The integration of multi-source datasets ensured consistency and robustness across indices. Overall, the findings improve understanding of agricultural drought dynamics and provide practical insights for irrigation modernization, efficient water allocation, and drought-resilient planning in line with Türkiye’s National Water Efficiency Strategy (2023–2033). Full article

Nitrogen fertilization is essential for balancing maize yield, grain composition, and environmental sustainability. This study aimed to evaluate the relationship between nitrogen (N) supply, grain quality traits, and yield potential using UAV-based Normalized Difference Vegetation Index (NDVI) monitoring in a long-term fertilization field experiment in Eastern Hungary. Six N levels (0–300 kg ha⁻¹) were tested during two consecutive growing seasons (2023–2024) under varying climatic conditions. The obtained results showed that moderate N doses (120–180 kg ha⁻¹) provided the optimal nutrition level for maize, significantly increasing yield compared to the control (+5.086 t ha⁻¹ in 2024), while excessive fertilization above 180 kg ha⁻¹ did not result in any substantial yield gains; however, it significantly modified grain composition. Higher N supply enhanced protein content (+0.95% between 0 and 300 kg ha⁻¹) and reduced starch percentage, confirming the protein–starch trade-off, whereas oil content was less affected by nitrogen fertilization, similarly to previous results. The strongest correlation between NDVI values and yield was measured at the post-silking stage (112 DAS; R = 0.638 in 2023, R = 0.634 in 2024), indicating the suitability of NDVI monitoring for in-season yield prediction. Overall, NDVI-based monitoring proved effective not just for optimizing N management but also for supporting site specific fertilization strategies to enhance maize productivity and nutrient use efficiency. Full article

Rapid urbanization elevates land surface temperature (LST) through complex urban spatial relationships, intensifying the urban heat island (UHI) effect. This necessitates efficient methods to analyze surface urban heat island (SUHI) factors to help develop mitigation strategies. In this study, we propose an efficient global–local regression (EGLR) framework by integrating XGBoost-SHAP with global–local regression (GLR), enabling accelerated estimation of LST. In a case study of Wuhan, the EGLR reduces the computation time of GLR by 44.21%. The main contribution of computational efficiency improvement lies in the procedure of Moran eigenvector selecting executed by XGBoost-SHAP. Results of validation experiments also show significant time decrease of the EGLR for a larger sample size; in addition, transplanting the framework of the EGLR to two machine learning models not only reduces the executing time, but also increases model fitting. Furthermore, the inherent merits of XGBoost-SHAP and GLR also enables the EGLR to simultaneously capture nonlinear causal relationships and decompose spatial effects. Results identify population density as the most sensitive LST-increasing factor. Impervious surface percentage, building height, elevation, and distance to the nearest water body are positively correlated with LST, while water area, normalized difference vegetation index, and the number of bus stops have significant negative relationships with LST. In contrast, the impact of the number of points of interest, gross domestic product, and road length on LST is not significant overall. Full article

Tea Anthracnose (TA), caused by fungi of the genus Colletotrichum, is one of the major threats to global tea production. UAV remote sensing has been explored for non-destructive and high-efficiency monitoring of diseases in tea plantations. However, variations in illumination, background, and meteorological factors undermine the stability of cross-temporal data. Data processing and modeling complexity further limits model generalizability and practical application. This study introduced a cross-temporal, generalizable disease monitoring approach based on UAV multimodal data coupled with relative-difference standardization. In an experimental tea garden, we collected multispectral, thermal infrared, and RGB images and extracted four classes of features: spectral (Sp), thermal (Th), texture (Te), and color (Co). The Normalized Difference Vegetation Index (NDVI) was used to identify reference areas and standardize features, which significantly reduced the relative differences in cross-temporal features. Additionally, we developed a vegetation–soil relative temperature (VSRT) index, which exhibits higher temporal-phase consistency than the conventional normalized relative canopy temperature (NRCT). A multimodal optimal feature set was constructed through sensitivity analysis based on the four feature categories. For different modality combinations (single and fused), three machine learning algorithms, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Multi-layer Perceptron (MLP), were selected to evaluate disease classification performance due to their low computational burden and ease of deployment. Results indicate that the “Sp + Th” combination achieved the highest accuracy (95.51%), with KNN (95.51%) outperforming SVM (94.23%) and MLP (92.95%). Moreover, under the optimal feature combination and KNN algorithm, the model achieved high generalizability (86.41%) on independent temporal data. This study demonstrates that fusing spectral and thermal features with temporal standardization, combined with the simple and effective KNN algorithm, achieves accurate and robust tea anthracnose monitoring, providing a practical solution for efficient and generalizable disease management in tea plantations. Full article

As a vital carbon reservoir in terrestrial ecosystems, forest canopy height plays a pivotal role in determining the precision of biomass estimation and carbon storage calculations. Acquiring an accurate Canopy Height Map (CHM) is crucial for building carbon budget models at regional and global scales. A novel UNet++ deep-learning model was constructed using Sentinel-1 and Sentinel-2 multispectral remote sensing images to estimate forest canopy height data based on full-waveform LiDAR measurements from the Global Ecosystem Dynamics Investigation (GEDI) satellite. A 10 m resolution CHM was generated for Chaling County, China. The model was evaluated using independent validation samples, achieving an R² of 0.58 and a Root Mean Square Error (RMSE) of 3.38 m. The relationships between multiple Relative Height (RH) metrics and field validation data are examined. It was found that RH98 showed the strongest correlation, with an R² of 0.56 and RMSE of 5.83 m. Six different preprocessing algorithms for GEDI data were evaluated, and the results demonstrated that RH98 processed using the ‘a1’ algorithm achieved the best agreement with the validation data, yielding an R² of 0.55 and RMSE of 5.54 m. The impacts of vegetation coverage, assessed through Normalized Difference Vegetation Index (NDVI), and terrain slope on inversion accuracy are explored. The highest accuracy was observed in areas where NDVI ranged from 0.25 to 0.50 (R² = 0.77, RMSE = 2.27 m) and in regions with slopes between 0° and 10° (R² = 0.61, RMSE = 2.99 m). These results highlight that the selection of GEDI data preprocessing methods, RH metrics, vegetation density, and terrain characteristics (slope) all have significant impacts on the accuracy of canopy height estimation. Full article

Urban heat islands (UHIs) intensify thermal stress in cities, particularly in arid and semi-arid regions undergoing rapid urban expansion. The main objectives of this study are to quantify and compare UHI intensity in six major Saudi Arabian cities (Dammam, Makkah, Madinah, Jeddah, Riyadh, and Abha) representing diverse climatic zones and to examine how UHI patterns vary between urban, suburban, and rural zones over a 30-year period. Understanding the magnitude and spatial variability of UHIs across different climatic settings is crucial for developing effective urban planning and climate adaptation strategies in Saudi Arabia’s rapidly expanding cities. Except for Abha, these cities are the five most populous cities in the Kingdom. Each city was categorized into urban (>1500 people km⁻²), suburban (300–1500 people km⁻²), and rural (<300 people km⁻²) zones using high-resolution population density data. Two independent temperature datasets (ERA5-land and CHIRTS-ERA5) were analyzed for the years 1994, 2004, 2014, and 2024. Both datasets revealed consistent spatial patterns and a general warming trend across all zones and cities over the 30-year period. The UHI effect was most pronounced for minimum temperatures, with urban zones warmer than rural zones by 0.85 °C (ERA5-land) and 1.10 °C (CHIRTS-ERA5), likely due to greater heat retention and slower cooling rates in built-up areas. Mean temperature differences were smaller but still indicated positive UHI. Conversely, both datasets exhibited a reversed UHI pattern for maximum temperatures, with rural zones warmer than urban zones by 1.73 °C (ERA5-land) and 1.52 °C (CHIRTS-ERA5). This reversed pattern is attributed to the surrounding desert landscapes with minimal vegetation, indicated by low normalized difference vegetation index (NDVI), while urban areas have increasingly benefited from greening and landscaping initiatives. City-level analysis showed the strongest reversed UHI in maximum temperatures in Abha, while Jeddah exhibited the weakest. These findings highlight the need for localized urban planning strategies, particularly the expansion of vegetation cover and sustainable land use, to mitigate extreme thermal conditions in Saudi Arabia. Full article

An optical remote sensing approach was developed to identify areas with high and low salinity within the mangrove swamp rice system in West Africa. Conducted between 2019 and 2024 in Guinea-Bissau, this study examined two contrasting rice-growing environments, tidal mangrove (TM) and associated mangrove (AM), to assess changes in vegetation dynamics, soil salinity concentration, and soil chemical properties. Field sampling was conducted during the dry season to avoid waterlogging, and soil analyses included texture, cation exchange capacity, micronutrients, and electrical conductivity (EC_e). Meteorological stations recorded rainfall and environmental conditions over the period. Moreover, orthorectified and atmospherically corrected surface reflectance satellite imagery from PlanetScope and Sentinel-2 was selected due to their high spatial resolution and revisit frequency. From this data, vegetation dynamics were monitored using the Normalized Difference Vegetation Index (NDVI), with change detection calculated as the difference in NDVI between sequential images (ΔNDVI). Thresholds of 0.15 ≤ NDVI ≤ 0.5 and ΔNDVI > 0.1 were tested to identify significant vegetation growth, with smaller polygons (<1000 m²) removed to reduce noise. In this process, at least three temporal images per season were analyzed, and multi-year intersections were done to enhance accuracy. Our parameter optimization tests found that a locally calibrated NDVI threshold of 0.26 improved site classification. Thus, this integrated field–remote sensing approach proved to be a reproducible and cost-effective tool for detecting AM and TM environments and assessing vegetation responses to seasonal changes, contributing to improved land and water management in the salinity-affected mangrove swamp rice system. Full article

Crop rotation is a cornerstone of sustainable agronomy, whereas continuous monoculture can degrade soil fertility and crop vigor. A three-year field experiment (2023–2025) in Lithuania compared winter rye grown in a long-term field experiment of continuous monoculture (with and without fertilizer/herbicide inputs) with five diversified rotation treatments that included manure, forage, or cover crop phases. Unmanned aerial vehicle (UAV) multispectral imaging was used to monitor crop health via the Normalized Difference Vegetation Index (NDVI, an indicator of plant vigor). NDVI measurements at three key developmental stages (flowering to ripening, BBCH 61–89) showed that diversified rotations consistently achieved higher NDVI than monoculture, indicating more robust crop growth. Notably, the most intensive and row-crop rotations had the highest canopy vigor, whereas continuous monocultures had the lowest. An anomalous weather year (2024) temporarily reduced NDVI differences, but rotation benefits re-emerged in 2025. Overall, UAV-based NDVI effectively captured rotation-induced differences in rye canopy vigor, highlighting the agronomic advantages of diversified cropping systems and the value of UAV remote sensing for crop monitoring. Full article

Current lake and reservoir water body extraction algorithms are confronted with two critical challenges: (1) design dependency on specific geographical features, leading to constrained cross-regional adaptability (e.g., the JRC Global Water Body Dataset achieves ~90% overall accuracy globally, while the ESA WorldCover 2020 reaches ~92% for water body classification, both showing degraded performance in complex karst terrains); (2) information loss due to cloud occlusion, compromising dynamic monitoring accuracy. To address these limitations, this study presents a multi-feature fusion and multi-level hierarchical extraction algorithm for lake and reservoir water bodies, leveraging the Google Earth Engine (GEE) cloud platform and Sentinel-2 multispectral imagery in the karst landscape of Bijie City. The proposed method integrates the Automated Water Extraction Index (AWEIsh) and Modified Normalized Difference Water Index (MNDWI) for initial water body extraction, followed by a comprehensive fusion of multi-source data—including Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Red-Edge Index (NDREI), Sentinel-2 B8/B9 spectral bands, and Digital Elevation Model (DEM). This strategy hierarchically mitigates vegetation shadows, topographic shadows, and artificial feature non-water targets. A temporal flood frequency algorithm is employed to restore cloud-occluded water bodies, complemented by morphological filtering to exclude non-target water features (e.g., rivers and canals). Experimental validation using high-resolution reference data demonstrates that the algorithm achieves an overall extraction accuracy exceeding 96% in Bijie City, effectively suppressing dark object interference (e.g., false positives due to topographic and anthropogenic features) while preserving water body boundary integrity. Compared with single-index methods (e.g., MNDWI), this method reduces false positive rates caused by building shadows and terrain shadows by 15–20%, and improves the IoU (Intersection over Union) by 6–13% in typical karst sub-regions. This research provides a universal technical framework for large-scale dynamic monitoring of lakes and reservoirs, particularly addressing the challenges of regional adaptability and cloud compositing in karst environments. Full article

Grassland ecosystems in arid regions are critical for ecological balance and human livelihoods but face threats from degradation and climate change. Weideverbot (grazing prohibition) is widely adopted for restoration, yet its impact on fire risk in extreme arid environments remains unclear. This study investigates how grazing prohibition affects fire risk in Turpan, China—a hyper-arid region with 16 mm annual precipitation—by analyzing vegetation dynamics (2000–2023) and fire records. To quantify changes in fuel properties and fire risk, we integrated remote sensing data (MODIS-derived Net Primary Productivity [NPP], Fractional Vegetation Cover [FVC], and Normalized Difference Moisture Index [NDMI]) and field observations, complemented by meteorological data (temperature, precipitation, potential evapotranspiration) and local fire records. We used paired-sample t-tests to compare vegetation metrics before (2000–2010) and after (2011–2023) Weideverbot, with Cohen’s d to assess effect sizes. The results show that Weideverbot significantly increases net primary productivity (NPP: 92 to 109 g C·m⁻²·yr⁻¹, Cohen’s d > 0.8) and fractional vegetation cover (FVC: 18% to 22%, Cohen’s d > 0.8), enhancing fuel load and connectivity. Vegetation water content shows no significant change (Cohen’s d < 0.2). Post-prohibition, fire frequency increased ~8-fold, driven by elevated fuel availability and regional warming/aridification. These findings indicate that Weideverbot exacerbates fire risk in hyper-arid grasslands by altering fuel dynamics. Balancing restoration and fire management requires adaptive strategies like moderate grazing, tailored to local aridity and vegetation traits. Full article

Amid rapid urbanization, Chinese cities face mounting ecological pressure, making it critical to balance environmental protection with public well-being. As visual perception accounts for over 80% of environmental information acquisition, it plays a key role in shaping experiences and evaluations of ecological space. However, current ecological planning often overlooks public perception, leading to increasing mismatches between ecological conditions and spatial experiences. While previous studies have attempted to introduce public perspectives, a systematic framework for analyzing the spatial relationship between ecological and visual sensitivity remains lacking. This study takes 56,210 street-level points in Tianjin’s central urban area to construct a coordinated analysis framework of ecological and perceptual sensitivity. Visual sensitivity is derived from social media sentiment analysis (via GPT-4o) and street-view image semantic features extracted using the ADE20K semantic segmentation model, and subsequently processed through a Multilayer Perceptron (MLP) model. Ecological sensitivity is calculated using the Analytic Hierarchy Process (AHP)—based model integrating elevation, slope, normalized difference vegetation index (NDVI), land use, and nighttime light data. A coupling coordination model and bivariate Moran’s I are employed to examine spatial synergy and mismatches between the two dimensions. Results indicate that while 72.82% of points show good coupling, spatial mismatches are widespread. The dominant types include “HL” (high visual–low ecological) areas (e.g., Wudadao) with high visual attention but low ecological resilience, and “LH” (low visual–high ecological) areas (e.g., Huaiyuanli) with strong ecological value but low public perception. This study provides a systematic path for analyzing the spatial divergence between ecological and perceptual sensitivity, offering insights into ecological landscape optimization and perception-driven street design. Full article

Urban Form (UF), as a synthesis of urban functions and socioeconomic elements, is closely associated with Anthropogenic Carbon Emissions (ACE) and has important implications for low-carbon urban planning. As a key national economic strategy region, the Yangtze River Economic Belt (YREB) exhibits pronounced heterogeneity in urban development, highlighting the urgent need to elucidate the interaction mechanisms between UF and ACE to support carbon reduction strategies. This study employs nighttime light data and carbon emission records from 2002 to 2022 in the YREB. By integrating Support Vector Regression (SVR), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT), we developed a neural network ensemble model (RSG-Net) to analyze the impacts and driving mechanisms of UF on ACE. The results indicate the following: (1) Over the past two decades, total ACE in the YREB increased by 196%, displaying a three-phase trajectory of rapid growth, deceleration, and rebound. (2) The RSG-Net model achieved superior predictive performance, with an R² of 0.93, an RMSE of 1.96 × 10⁶ t, an RPD of 3.69, and a PBIAS of 4.53%. (3) Based on Pearson correlation analysis and SHAP (Shapley Additive Explanations) feature importance, beyond economic and demographic indicators, the most influential UF indicators are ranked as Number of Urban Patches (NP), Normalized Difference Vegetation Index (NDVI), and Construction Land Concentration (CLC). These findings demonstrate that the RSG-Net model can not only predict ACE but also identify key UF factors and explain their interrelationships, thereby providing technical support for the formulation of urban carbon reduction strategies. Full article

Advances in Earth observation technology using multispectral imagery from satellite Earth observation systems and sensors mounted on unmanned aerial vehicles (UAVs) are enabling more accurate crop monitoring. These images, once processed, facilitate the analysis of crop health by enabling the study of crop vigour, the calculation of biomass indices, and the continuous temporal monitoring using vegetation indices (VIs). These indicators allow for the identification of diseases, pests, or water stress, among others. This study compares images acquired with the Altum PT sensor (UAV) and Super Dove (satellite) to evaluate their ability to detect specific problems in super-intensive olive groves at two critical times: January, during pruning, and April, at the beginning of fruit development. Four different VIs were used, and multispectral maps were generated for each: the Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), the Normalized Difference Red Edge Index (NDRE) and the Leaf Chlorophyll Index (LCI). Data for each plant (n = 11,104) were obtained for analysis across all dates and sensors. A combined methodology (Spearman’s correlation coefficient, Student’s t-test and decision trees) was used to validate the behaviour of the variables and propose predictive models. The results showed significant differences between the sensors, with a common trend in spatial patterns and a correlation range between 0.45 and 0.68. Integrating both technologies enables multiscale assessment, optimizing agronomic management and supporting more sustainable precision agriculture. Full article

Vegetation phenology (VP) is a crucial biological indicator for monitoring terrestrial ecosystems and global climate change. However, VP monitoring using traditional remote sensing vegetation indices has significant limitations in precise analysis. Furthermore, most studies have overlooked the distinction between stable and short-term VP in relation to climate change and have failed to clearly identify the seasonal variation in the impact of climatic factors on stable VP (SVP). This study compared the accuracy of solar-induced chlorophyll fluorescence (SIF) and three traditional vegetation indices (e.g., Normalized Difference Vegetation Index) for estimating SVP in China, using ground-based data for validation. Additionally, this study employs Sen’s slope, the Mann–Kendall (MK) test, and the Hurst index to reveal the spatiotemporal evolution of the Start of Season (SOS), End of Season (EOS), and Length of Growing Season (LOS) over the past two decades. Partial correlation analysis and random forest importance evaluation are used to accurately identify the key climatic drivers of SVP across different climate zones and to assess the seasonal contributions of climate to SVP. The results indicate that (1) phenological metrics derived from SIF data showed the strongest correlation coefficients with ground-based observations, with all correlation coefficients (R) exceeding 0.69 and an average of 0.75. (2) The spatial distribution of SVP in China has revealed three primary spatial patterns: the Tibetan Plateau, and regions north and south of the Qinling–Huaihe Line. From arid, cold-to-warm, and humid regions, the rate of SOS advancement gradually increases; EOS transitions from earlier to nearly unchanged; and the rate of LOS delay increases accordingly. (3) The spring climate primarily drives the advancement of SOS across China, contributing up to 70%, with temperatures generally having a negative effect on SOS (r = −0.53, p < 0.05). In contrast, EOS is regulated and more complex, with the vapor pressure deficit exerting a dual ‘limitation–promotion’ effect in autumn (r = −0.39, p < 0.05) and summer (r = 0.77, p < 0.05). This study contributes to a deeper scientific understanding of the interannual variability in SVP under seasonal climate change. Full article