Search Results (5,775)

The response of Arctic vegetation to climate warming exhibits pronounced spatial heterogeneity, driven partly by widespread permafrost degradation. However, the role of thermokarst lake development in mediating vegetation-climate interactions remains poorly understood, particularly across heterogeneous landscapes of northeastern Siberia. This study integrated multi-source remote sensing data (2001–2021) with trend analysis, partial correlation, and a Shapley Additive Explanation (SHAP)-interpreted random forest model to examine the drivers of normalized difference vegetation index (NDVI) variability across five levels of thermokarst lake coverage (none, low, moderate, high, very high) and two vegetation types (forest, tundra). The results show that although greening dominates the region, browning is disproportionately observed in areas with high thermokarst lake coverage (>30%), highlighting the localized reversal of regional greening trends under intensified thermokarst activity. Air temperature was identified as the dominant driver of NDVI change, whereas soil temperature and soil moisture exerted secondary but critical influences, especially in tundra ecosystems with extensive thermokarst lake development. The relative importance of these factors shifted across thermokarst lake coverage gradients, underscoring the modulatory effect of thermokarst processes on vegetation-climate feedbacks. These findings emphasize the necessity of incorporating thermokarst dynamics and landscape heterogeneity into predictive models of Arctic vegetation change, with important implications for understanding cryospheric hydrology and ecosystem responses to ongoing climate warming. Full article

Maize is one of the top three crops globally, ranking only behind rice and wheat, making it an important crop of interest. Aboveground biomass is a key indicator for assessing maize growth and its yield potential. This study developed an efficient and stable biomass prediction model to estimate the aboveground biomass (AGB) of spring maize (Zea mays L.) under subsurface drip irrigation in arid regions, based on UAV multispectral remote sensing and machine learning techniques. Focusing on typical subsurface drip-irrigated spring maize in arid Xinjiang, multispectral images and field-measured AGB data were collected from 96 sample points (selected via stratified random sampling across 24 plots) over four key phenological stages in 2024 and 2025. Sixteen vegetation indices were calculated and 40 texture features were extracted using the gray-level co-occurrence matrix method, while an integrated feature-selection strategy combining Elastic Net and Random Forest was employed to effectively screen key predictor variables. Based on the selected features, six machine learning models were constructed, including Elastic Net Regression (ENR), Gradient Boosting Decision Trees (GBDT), Gaussian Process Regression (GPR), Partial Least Squares Regression (PLSR), Random Forest (RF), and Extreme Gradient Boosting (XGB). Results showed that the fused feature set comprised four vegetation indices (GRDVI, RERVI, GRVI, NDVI) and five texture features (R_Corr, NIR_Mean, NIR_Vari, B_Mean, B_Corr), thereby retaining red-edge and visible-light texture information highly sensitive to AGB. The GPR model based on the fused features exhibited the best performance (test set R² = 0.852, RMSE = 2890.74 kg ha⁻¹, MAE = 1676.70 kg ha⁻¹), demonstrating high fitting accuracy and stable predictive ability across both the training and test sets. Spatial inversions over the two growing seasons of 2024 and 2025, derived from the fused-feature GPR optimal model at four key phenological stages, revealed pronounced spatiotemporal heterogeneity and stage-dependent dynamics of spring maize AGB: the biomass accumulates rapidly from jointing to grain filling, slows thereafter, and peaks at maturity. At a constant planting density, AGB increased markedly with nitrogen inputs from N0 to N3 (420 kg N ha⁻¹), with the high-nitrogen N3 treatment producing the greatest biomass; this successfully captured the regulatory effect of the nitrogen gradient on maize growth, provided reliable data for variable-rate fertilization, and is highly relevant for optimizing water–fertilizer coordination in subsurface drip irrigation systems. Future research may extend this integrated feature selection and modeling framework to monitor the growth and estimate the yield of other crops, such as rice and cotton, thereby validating its generalizability and robustness in diverse agricultural scenarios. Full article

Under the intensifying water shortages in the vegetation season, early identification of Ips typographus L. damage is crucial for preventing wide outbreaks, which undermine the economic potential of commercial stands of Norway spruce (Picea abies Karst.) across Europe. For this purpose, remote sensing based on satellite images is considered one of the most efficient methods, particularly in homogenous and wide forested landscapes. However, under highly heterogeneous seminatural managed forest landscapes in lowland Central and Northern Europe, as illustrated by the eastern Baltic region and Latvia in particular, the efficiency of such an approach can lack the desired accuracy. Hence, the identification of smaller damage patches by I. typographus, which can act as a source of wider outbreaks, can be overlooked, and situational awareness can be further aggravated by infrastructure artefacts. In this study, the accuracy of satellite imaging for the identification of I. typographus damage was evaluated, focusing on the occurrence of false positives and particularly false negatives obtained from the comparison with UAV imaging. Across the studied landscapes, correct or partially correct identification of damage patches larger than 30 m² occurred in 73% of cases. Still, the satellite image analysis of the highly heterogeneous landscape resulted in quite a common occurrence of false negatives (up to one-third of cases), which were related to stand and patch properties. The high rate of false negatives, however, is crucial for the prevention of outbreaks, as the sources of outbreaks can be underestimated, burdening prompt and hence effective implication of countermeasures. Accordingly, elaborating an analysis of satellite images by incorporating stand inventory data could improve the efficiency of early detection systems, especially when coupled with UAV reconnaissance of heterogeneous landscapes, as in the eastern Baltic region. Full article

Driven by the globalization tide, urbanization and cross-border economic cooperation have intensified challenges to ecological conservation, with border regions increasingly confronting irreversible habitat degradation risks. As a globally recognized biodiversity hotspot, Xishuangbanna acts as a strategic hub for cross-border ecological security between China and Southeast Asia, having long been confronted with dual pressures from economic development and ecological conservation. By analyzing the spatiotemporal evolution of the Remote Sensing Ecological Index (RSEI) during 2003–2023, this study simulates its multi-scenario dynamics, develops the “RSEI-ESP-PLUS” framework, presents a novel assessment mechanism for ecological security patterns (ESP), and provides a scientific basis for regional sustainable development. Results indicate that integrating RSEI improves the accuracy of ecological source identification. Over the past two decades, regional Ecological Environmental Quality has exhibited an overall improvement trend, yet persistent ecological pressures remain—including vegetation degradation and climate warming. Concurrently, high-quality ecological areas have contracted while moderate-quality ones have expanded. In the 2033 simulation, the ecological conservation scenario delivered the most favorable ecological network assessment outcomes, identifying 16 stable and 15 potential ecological sources. Accordingly, this study establishes an ecological security pattern centered on the core structure of the “One Axis, Two Corridors, and Three Zones”, which provides a spatial planning scheme for regional sustainable development. Full article

Land surface temperature (LST) is fundamental for monitoring surface energy balance and environmental dynamics, with remote sensing providing the primary means of acquisition. However, directional anisotropy (DA) introduces systematic bias in satellite-derived LST products, particularly over complex landscapes. This study examines the impact of angular effects on LST retrievals from three leading East Asian geostationary satellites (FengYun 4A, FengYun 4B, and Himawari 9) across Hunan Province, China, using integrated comparison with in situ measurements and reanalysis data. Results show that all products exhibit a systematic cold bias, with FY4B achieving the highest accuracy. Diurnal retrieval precision increases with higher solar zenith angles (SZA), while no consistent relationship is observed between viewing zenith angle (VZA) and retrieval accuracy. Notably, the retrieval bias of the FY4 series increases significantly when the sun and sensor are aligned in azimuth, particularly when the relative azimuth angle (RAA) is less than or equal to 30°. Parametric modeling reveals that emissivity kernel-induced anisotropy is the principal driver of significant LST deviations in central Hunan, while solar kernel effects result in LST overestimation in mountainous regions and underestimation in plains. Increases in elevation or vegetation density reduce emissivity-induced errors but amplify errors caused by shadowing and sunlit effects. Emissivity anisotropy is thus identified as the primary source of LST DA. These findings deepen the understanding of LST DA in remote sensing and provide essential guidance for refining retrieval algorithms and improving the applicability of LST products in complex terrains. Full article

Soybean is a key global crop for food and oil production, playing a vital role in ensuring food security and supplying plant-based proteins and oils. Accurate information on soybean distribution is essential for yield forecasting, agricultural management, and policymaking. In this study, we developed an Enhanced Chlorophyll Index (NRLI) to improve the separability between soybean and maize—two spectrally similar crops that often confound traditional vegetation indices. The proposed NRLI integrates red-edge, near-infrared, and green spectral information, effectively capturing variations in chlorophyll and canopy water content during key phenological stages, particularly from flowering to pod setting and maturity. Building upon this foundation, we further introduce a pixel-wise compositing strategy based on the peak phase of NRLI to enhance the temporal adaptability and spectral discriminability in crop classification. Unlike conventional approaches that rely on imagery from fixed dates, this strategy dynamically analyzes annual time-series data, enabling phenology-adaptive alignment at the pixel level. Comparative analysis reveals that NRLI consistently outperforms existing vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Greenness and Water Content Composite Index (GWCCI), across representative soybean-producing regions in multiple countries. It improves overall accuracy (OA) by approximately 10–20 percentage points, achieving accuracy rates exceeding 90% in large, contiguous cultivation areas. To further validate the robustness of the proposed index, benchmark comparisons were conducted against the Random Forest (RF) machine learning algorithm. The results demonstrated that the single-index NRLI approach achieved competitive performance, comparable to the multi-feature RF model, with accuracy differences generally within 1–2%. In some regions, NRLI even outperformed RF. This finding highlights NRLI as a computationally efficient alternative to complex machine learning models without compromising mapping precision. This study provides a robust, scalable, and transferable single-index approach for large-scale soybean mapping and monitoring using remote sensing. Full article

This follow-up study investigates the long-term environmental sustainability and remediation outcomes of the UPPER (‘Urban Productive Parks for Sustainable Urban Regeneration’-UIA04-252) project in Latina, Italy, focusing on Nature-Based Solutions (NbS) applied to urban green infrastructure. By integrating proximal and satellite-based remote sensing methodologies, the research evaluates persistent improvements in vegetation health, soil moisture dynamics, and overall environmental quality over multiple years. Building upon the initial monitoring framework, this case study incorporates updated data and refined techniques to quantify temporal changes and assess the ecological performance of NbS interventions. In more detail, ground-based data from meteo-climatic, air quality stations and remote satellite data from the Sentinel-2 mission are adopted. Ground-based measurements such as temperature, humidity, radiation, rainfall intensity, PM10 and PM2.5 are carried out to monitor the overall environmental quality. Updated satellite imagery from Sentinel-2 is analyzed using advanced band ratio indices, including the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI) and the Normalized Difference Moisture Index (NDMI). Comparative temporal analysis revealed consistent enhancements in vegetation health, with NDVI values significantly exceeding baseline levels (NDVI 2022–2024: +0.096, p = 0.024), demonstrating successful vegetation establishment with larger gains in green areas (+27.0%) than parking retrofits (+11.4%, p = 0.041). However, concurrent NDWI decline (−0.066, p = 0.063) indicates increased vegetation water stress despite irrigation infrastructure. NDMI improvements (+0.098, p = 0.016) suggest physiological adaptation through stomatal regulation. Principal Component Analysis (PCA) of meteo-climatic variables reveals temperature as the dominant environmental driver (PC2 loadings > 0.8), with municipality-wide NDVI-temperature correlations of r = −0.87. These multi-scale findings validate sustained NbS effectiveness in enhancing vegetation density and ecosystem services, yet simultaneously expose critical water-limitation trade-offs in Mediterranean semi-arid contexts, necessitating adaptive irrigation management and continued monitoring for long-term urban climate resilience. The integrated monitoring approach underscores the critical role of continuous, multi-scale assessment in ensuring long-term success and adaptive management of NbS-based interventions. Full article

Wetlands are a critical component of the global biogeochemical cycle and have great potential for carbon sequestration under the changing climate. However, previous studies have mainly focused on the dynamics of soil organic carbon while paying little attention to the vegetation carbon storage in wetlands. Poyang Lake is the largest freshwater lake in China, where intra-annual and inter-annual variations in water levels significantly affect the vegetation carbon storage in the floodplain wetland. Therefore, we assessed the seasonal distribution and carbon storage of six typical plant communities (Arundinella hirta, Carex cinerascens, Miscanthus lutarioriparius, Persicaria hydropiper, Phalaris arundinacea, and Phragmites australis) in Poyang Lake wetlands from 2019 to 2024 based on field surveys, the literature, and remote sensing data. Then, we used 16 preseason meteorological and hydrological variables for two growing seasons to investigate the impacts of environmental factors on vegetation carbon storage based on four correlation and regression methods (including Pearson and partial correlation, ridge, and elastic net regression). The results show that the C. cinerascens community was the most dominant contributor to vegetation carbon storage, occupying 12.68% to 44.22% of the Poyang Lake wetland area. The vegetation carbon storage in the Poyang Lake wetland was significantly (p < 0.01) higher in spring (87.75 × 10⁴ t to 239.10 × 10⁴ t) than in autumn (77.32 × 10⁴ t to 154.78 × 10⁴ t). Water body area emerged as a key explanatory factor, as it directly constrains the spatial extent available for vegetation colonization and growth by alternating inundation and exposure. In addition, an earlier start or end to floods could both enhance vegetation carbon storage in spring or autumn. However, preseason precipitation and temperature are negative to carbon storage in spring but exhibited opposite effects in autumn. These results assessed the seasonal dynamics of dominant vegetation communities and helped understand the response of the wetland carbon cycle under the changing climate. Full article

Coastal wetland vegetation exhibits pronounced spectral mixing, complex mosaic spatial patterns, and small target sizes, posing considerable challenges for fine-grained classification in high-resolution UAV imagery. At present, remote sensing classification of ground objects based on deep learning mainly relies on spectral and structural features, while the frequency domain features of ground objects are not fully considered. To address these issues, this study proposes a vegetation classification model that integrates spatial-domain and frequency-domain features. The model enhances global contextual modeling through a large-kernel convolution branch, while a frequency-domain interaction branch separates and fuses low-frequency structural information with high-frequency details. In addition, a shallow auxiliary supervision module is introduced to improve local detail learning and stabilize training. With a compact parameter scale suitable for real-world deployment, the proposed framework effectively adapts to high-resolution remote sensing scenarios. Experiments on typical coastal wetland vegetation including Reeds, Spartina alterniflora, and Suaeda salsa demonstrate that the proposed method consistently outperforms representative segmentation models such as UNet, DeepLabV3, TransUNet, SegFormer, D-LinkNet, and MCCA across multiple metrics including Accuracy, Recall, F1 Score, and mIoU. Overall, the results show that the proposed model effectively addresses the challenges of subtle spectral differences, pervasive species mixture, and intricate structural details, offering a robust and efficient solution for UAV-based wetland vegetation mapping and ecological monitoring. Full article

The Soil and Plant Analyzer Development (SPAD) value is a reliable proxy for chlorophyll, yet conventional field measurement remains labor-intensive and spatially limited. Current remote sensing inversion models typically depend on costly multispectral sensors and rarely account for phenological changes, restricting their applicability across orchards and seasons. To overcome these limitations, this study introduces a stage-aware and low-cost SPAD inversion framework for jujube trees, integrating multi-source data fusion and an optimized ensemble model. A two-year experiment (2023–2024) combined UAV multispectral vegetation indices (VI) with RGB-derived color indices (CI) across leaf expansion, flowering, and fruit-setting stages. Rather than using static features, stage-specific predictors were systematically identified through a hybrid selection mechanism combining Random Forest Cumulative Feature Importance (RF-CFI), Recursive Feature Elimination (RFE), and F-tests. Building on these tailored features, XGBoost, decision tree (DT), CatBoost, and an Optimized Integrated Architecture (OIA) were developed, with all hyperparameters globally tuned using a genetic algorithm (GA). The RFI-CFI-OIA-GA model delivered superior accuracy (R² = 0.758–0.828; MSE = 0.214–2.593; MAPE = 0.01–0.045 in 2024) in the training dataset, and robust cross-year transferability (R² = 0.541–0.608; MSE = 0.698–5.139; MAPE = 0.015–0.058 in 2023). These results demonstrate that incorporating phenological perception into multi-source data fusion substantially reduces interference and enhances generalizability, providing a scalable and reusable strategy for precision orchard management and spatiotemporal SPAD mapping. Full article

Assessing post-fire disturbance in Mediterranean ecosystems is essential for quantifying ecological impacts and guiding restoration strategies. This study evaluates fire severity following an extreme wildfire event (~28,000 ha) in northwestern Spain using vegetation indices (VIs) derived from PRISMA hyperspectral imagery, validated against field-based Composite Burn Index (CBI) measurements at the vegetation, soil, and site levels across three vegetation formations (coniferous forests, broadleaf forests, and shrublands). Hyperspectral VIs were benchmarked against multispectral VIs derived from Sentinel-2. Hyperspectral VIs yielded stronger correlations with CBI values than multispectral VIs. Vegetation-level CBI showed the highest correlations, reflecting the sensitivity of most VIs to canopy structural and compositional changes. Indices incorporating red-edge, near-infrared (NIR), and shortwave infrared (SWIR) bands demonstrated the greatest explanatory power. Among hyperspectral indices, DVIRED, EVI, and especially CAI performed best. For multispectral data, NDRE, CIREDGE, ENDVI, and GNDVI were the most effective. These findings highlight the strong potential of hyperspectral remote sensing for accurate, scalable post-fire severity assessment in heterogeneous Mediterranean ecosystems. Full article

Global environmental changes significantly alter ecosystem services (ESs), particularly in fragile regions like the Tibetan Plateau. While methodological advances have improved spatial assessment capabilities, understanding of how multiple drivers interact to shape ecosystem service heterogeneity remains limited to regional scales, especially across complex alpine landscapes. This study aims to clarify whether multi-factor interactions produce nonlinear enhancements in ES explanatory power and how these driver–response relationships vary across heterogeneous terrains. We quantified spatiotemporal patterns of four key ecosystem services—water yield (WY), soil conservation (SC), carbon sequestration (CS), and habitat quality (HQ)—across the southeastern Tibetan Plateau from 2000 to 2020 using multi-source remote sensing data and spatial econometric modeling. Our analysis reveals that SC increased by 0.43 t·hm⁻²·yr⁻¹, CS rose by 1.67 g·m⁻²·yr⁻¹, and HQ improved by 0.09 over this period, while WY decreased by 3.70 mm·yr⁻¹. ES variations are predominantly shaped by potent synergies, where interactive explanatory power consistently surpasses individual drivers. Hydrothermal coupling (precipitation ∩ potential evapotranspiration) reached 0.52 for WY and SC, while climate–vegetation synergy (precipitation ∩ normalized difference vegetation index) achieved 0.76 for CS. Such climate–restoration synergies now fundamentally shape the region’s ESs. Geographically weighted regression (GWR) further revealed distinct spatial dependencies, with southeastern regions experiencing strong negative effects of land use type and elevation on WY, while northwestern areas showed a positive elevation associated with WY but negative effects on SC and HQ. These findings highlight the critical importance of accounting for spatial non-stationarity in driver–ecosystem service relationships when designing conservation strategies for vulnerable alpine ecosystems. Full article

Timely and accurate crop yield estimation is vital for food security and management decision-making. Integrating remote sensing with machine learning provides an effective solution. In this study, based on canopy hyperspectral data collected by an ASD FieldSpec 3 handheld spectrometer during the critical growth stages of winter wheat, 18 vegetation indices (VIs) were systematically calculated, and their correlation with yield was analyzed. At the same time, a continuous projection algorithm, Successive Projections Algorithm (SPA), was used to screen the characteristic bands. Recursive Feature Elimination (RFE) was employed to select optimal features from VIs and characteristic spectral bands, facilitating the construction of a multi-temporal fusion feature set. To identify the superior yield estimation approach, a comparative analysis was conducted among four machine learning models: Deep Forest (DF), Support Vector Regression (SVR), Random Forest (RF), and Gaussian Process Regression (GPR). Performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (rRMSE). Results indicate that the highest correlations between VIs and grain yield were observed during the flowering and grain-filling stages. Independent analysis showed that VIs reached absolute correlations of 0.713 and 0.730 with winter wheat yield during the flowering and grain-filling stages, respectively, while the SPA further identified key bands primarily in the near-infrared and short-wave infrared regions. On this basis, integrating multi-temporal features through RFE significantly improved the accuracy of yield estimation. Among them, the DF model with the fusion of flowering and filling stage features performed best (R² = 0.786, RMSE = 641.470 kg·hm⁻², rRMSE = 15.67%). This study demonstrates that combining hyperspectral data and VIs from different growth stages provides complementary information. These findings provide an effective method for crop yield estimation in precision agriculture. Full article

Wildfires are increasing globally and pose major challenges for assessing post-fire vegetation recovery and ecosystem resilience. We analyzed long-term Landsat time series in two contrasting fire-prone ecosystems in the United States and Australia. Vegetation area was extracted using the Enhanced Vegetation Index (EVI) with Otsu thresholding. Recovery to pre-fire baseline levels was modeled using linear, logistic, locally estimated scatterplot smoothing (LOESS), and generalized additive models (GAM), and their performance was compared using multiple metrics. The results indicated rapid recovery of Australian forests to baseline levels, whereas this was not the case for forests in the United States. Among climatic factors, temperature was the dominant parameter in Australia (Spearman ρ = 0.513, p < 10⁻⁸), while no climatic variable significantly influenced recovery in California. Methodologically, GAM consistently performed best in both regions due to its success in capturing multiphase and heterogeneous recovery patterns, yielding the lowest values of AIC (United States: 142.89; Australia: 46.70) and RMSE_cv (United States: 112.86; Australia: 2.26). Linear and logistic models failed to capture complex recovery dynamics, whereas LOESS was highly sensitive to noise and unstable for long-term prediction. These findings indicate that post-fire recovery is inherently nonlinear and ecosystem-specific and that simple models are insufficient for accurate estimation, with GAM emerging as an appropriate method for assessing vegetation recovery using remote sensing data. This study provides a transferable approach using remote sensing and GAM to monitor forest resilience under accelerating global fire regimes. Full article

Fractional Vegetation Cover (FVC) dynamics on the Qinghai–Tibetan Plateau (QTP) are critical indicators for assessing ecosystem condition. However, uncertainties persist in the accuracy of existing FVC products over the QTP due to retrieval differences, scale effects, and limited validation data. This study utilized the Google Earth Engine platform to integrate unmanned aerial vehicle (UAV) observations, Sentinel-2, MODIS, climate, and topography datasets, and proposed a comprehensive framework incorporating dual-index screening, machine learning optimization, and multidimensional validation to systematically assess the accuracy of GEOV3, GLASS, and MuSyQ FVC products in the alpine grasslands. The dual-index screening reduced validation uncertainty by improving the spatial representativeness of ground data. To build a high-precision evaluation dataset with limited inter-class coverage, recursive feature elimination and grid search were applied to optimize five ML models, and CatBoost achieved the superior performance (R² = 0.880, RMSE = 0.122), followed by XGBoost, GBM, LightGBM, and RF models. Four validation scenarios were implemented, including direct validation using 250 m UAV plot FVC and multi-scale validation using a 10 m FVC reference aggregated to product grids. Results show that GEOV3 (R² = 0.909–0.925, RMSE = 0.082–0.103) outperformed GLASS (R² = 0.742–0.771, RMSE = 0.138–0.175) and MuSyQ (R² = 0.739–0.746, RMSE = 0.138–0.181), both of which exhibited systematic underestimation. This framework significantly enhances FVC product validation reliability, providing a robust solution for remote sensing product validation in alpine grassland ecosystems. Full article