Search Results (153)

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

Soil salinization is a major constraint on sustainable agricultural development in arid regions, yet soil salinity dynamics are commonly assessed using pixel-based remote sensing products that are difficult to relate to agricultural management units. To solve this problem, we developed an agricultural parcel-scale framework for soil salinity monitoring and mitigation assessment in Delingha City, Qinghai Province, China. Cropland parcels were extracted using a Recurrent Residual U-Net (R2U-Net) model, and soil salinity inversion for April during 2021–2025 was conducted by integrating Sentinel-1/2 imagery with a stacking ensemble learning model. The model incorporated Random Forest Regression (RFR), Gradient Boosting Regression (GBR), Multi-Layer Perceptron (MLP), and Support Vector Regression (SVR) to improve prediction accuracy and robustness. The selected features included vegetation indices, salinity indices, and SAR backscatter parameters. Among them, the Soil Adjusted Vegetation Index (SAVI) showed the strongest correlation with soil salinity, while Salinity Index 2 (SI2) exhibited the highest contribution to model performance. The R2U-Net achieved an F1-score of 0.8574 for parcel extraction. The ensemble model produced the best inversion results with an R² of 0.52 and reduced prediction errors compared with individual models. Results indicated an overall decline in soil salinity from 2021 to 2025, suggesting an improvement in soil salinity conditions during the study period. Parcel-scale aggregation reduced spatial noise; improved temporal stability; and revealed heterogeneous field responses, including salinity-declining, fluctuating, and increasing trends. The proposed framework enhances the interpretability and management relevance of soil salinity monitoring and provides practical support for precision agricultural management in arid regions. Full article

Soil spatial variability is a key driver of tree development in perennial crops, and its characterisation is essential for precision orchard management. Against this background, soil–canopy relationships were investigated in a Citrus reticulata Blanco cv. Tango orchard under Mediterranean conditions. Electromagnetic induction (EMI), unmanned aerial vehicle (UAV) multispectral imagery, and mobile LiDAR data registered using a Simultaneous Localisation and Mapping (SLAM) workflow were integrated at individual-tree level. A previously validated EMI-derived apparent electrical conductivity (EC_a) layer was used as a baseline descriptor of soil variability. UAV and mobile LiDAR acquisitions were harmonised for 40 trees: LiDAR point clouds were voxelised to derive canopy structural traits, while UAV imagery provided Soil-Adjusted Vegetation Index (SAVI) values. EC_a at 14 kHz was negatively correlated with canopy volume (r = −0.605, R² = 0.365) and canopy volume-to-projected area ratio (r = −0.571, R² = 0.326), both significant at p < 0.001. Conversely, SAVI showed a weaker, non-significant relationship with EC_a (r = −0.285, R² = 0.081, p = 0.0749). The reduced multiple linear regression model explained canopy volume variability (R² = 0.804), retaining canopy diameter and EC_a as significant predictors. These findings highlight the value of LiDAR-derived structural traits as sensitive indicators of soil-related canopy variability, supporting the integration of structural, spectral, and soil-sensing data for site-specific orchard management. Full article

Built-up area extraction is important for monitoring urban development and land-use change. Index-based methods are widely used for extracting built-up areas from Landsat imagery because of their simplicity and efficiency. However, conventional built-up indices often enhance bare land together with built-up areas due to their similar spectral characteristics, which reduces extraction accuracy and limits automatic threshold selection. To address this problem, this study proposes a built-up area extraction method based on multi-index synthesis and principal component analysis (PCA). First, NDBI (Normalization Differential Building Index), SAVI (Soil-Adjusted Vegetation Index), MNDWI (Modified Normalized Difference Water Index), and the brightness, greenness, and wetness components of the Tasseled Cap transformation were stacked to construct a six-band synthetic index image, enhancing the contrast among built-up areas, bare land, vegetation, and water bodies. PCA was then applied to the synthetic image using both correlation and covariance matrices, and the second principal component was used to enhance built-up area information. The resulting CorPC2 and CovPC2 methods were evaluated and compared with conventional built-up indices. The results showed that both PC2-based methods improved the separability between built-up areas and background features, while CovPC2 achieved the best performance by more effectively suppressing bare-land interference without requiring an additional bare-land mask. In the main experimental area, CovPC2 achieved higher accuracy than the comparison methods, and its Otsu-based result remained close to the optimal-threshold result. Validation in three typical cities further demonstrated the applicability of the proposed method across different Landsat sensors and urban environments. The proposed PC2-based method, particularly CovPC2, provides an effective and more automated approach for Landsat-based built-up area extraction under bare-land interference. Additionally, by using a threshold optimizing algorithm, built-up areas can be automatically extracted with high accuracy. Full article

Apple orchards are increasingly adopting anti-hail nets to mitigate climate risks; however, these structures alter canopy reflectance and pose challenges for remote sensing. This study presents an operational framework to map apple orchards under different netting conditions in Vacaria, Brazil. Multisensor surface reflectance data from Sentinel-2 and Harmonized Landsat and Sentinel-2 were used to generate dense spectral index time series combined with field observations. Five spectral indices, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), and Bare Soil Index (BSI), were evaluated individually and in combination within a hierarchical classification framework. Random Forest (RF) and one-dimensional convolutional neural networks (1DCNN) were applied as complementary machine learning approaches. RF showed more stable performance across hierarchical levels, while indices contributed differently depending on scale: BSI and NDVI were more effective at broader levels, whereas EVI and SAVI were critical for discriminating net colors. To our knowledge, this is the first study applying multisensor time series and machine learning to map anti-hail net systems in apple orchards. Full article

Open-pit lignite mining causes significant environmental alterations, particularly through the removal of soil deposits and the creation of external dumps, which necessitate effective reclamation to restore landscape structures. This study evaluates the potential of using multi-temporal remote sensing data to assess the effectiveness of forest reclamation on selected external dumps of the Adamów, Bełchatów, and Turów Lignite Mines in Poland. Using Landsat imagery spanning five decades from 1976 to 2023, the study monitors vegetation development through the Normalized Difference Vegetation Index (NDVI) and the Soil-Adjusted Vegetation Index (SAVI). Reclaimed forest stands were compared against undisturbed reference forests within a 30 km buffer zone, with recovery defined as achieving 95% of the reference values. The results indicate that most studied sites reached a state of recovery, with success closely linked to the specific reclamation measures implemented and the age of the forest stands. Notably, the Adamów mine, which utilized Bender’s target species method, demonstrated rapid results, achieving high similarity to reference forests early in the analyzed period. In contrast, recovery in Bełchatów and Turów was more gradual, following trajectories influenced by pioneer and biodynamic afforestation methods. Ultimately, the study confirms that remote sensing is a highly efficient tool for monitoring extensive post-mining areas over long periods, providing a general assessment of biological restoration success. Full article

Operational agricultural monitoring in the Central European lowlands requires timely parcel boundaries; however, unmarked field edges produce minimal spectral contrast in single-date imagery. Previous works demonstrated that harmonic NDVI encoding enables zero-shot field delineation using foundational models, but the influence of the spectral index choice on temporal boundaries remained unquantified. This study systematically evaluates eleven vegetation indices—NDVI, GNDVI, NDRE, EVI, EVI2, SAVI, MSAVI, NDWI, CIg, CIre, and NDYVI—within a fixed harmonic phenology encoding pipeline. A one-year PlanetScope time series (15 × 15 km, Slavonija, Croatia) was decomposed via annual sinusoidal regression to extract per-pixel phase, amplitude, and mean parameters. These harmonic descriptors were mapped to HSV colour channels and segmented using the Segment Anything Model without fine-tuning. Official agricultural parcels (PAAFRD, 2025) provided ground truth for pixel-wise, object-wise, and size-stratified evaluation. Performance stratified into three tiers based on object-wise metrics. Soil-adjusted and enhanced-greenness indices (MSAVI, EVI, EVI2, and SAVI) achieved F1 = 0.51–0.52, and mIoU = 0.70–0.71, statistically outperforming standard ratio formulations (NDVI: F1 = 0.49) and chlorophyll indices (CIg, CIre: F1 = 0.45–0.47). Pixel-wise scores remained compressed (F1 > 0.88 across all indices), indicating consistent interior coverage but index-dependent boundary precision. Error analysis revealed scale-dependent patterns: merging dominated small parcels (<10,000 m²), while fragmentation increased with parcel size. Results demonstrate that spectral formulation is a systematic design factor in phenology-based delineation, with soil background correction and dynamic range compression improving seasonal trajectory separability. The harmonic parameters generated by this framework provide feature-ready input for crop classification, suggesting that integrated boundary extraction and crop mapping workflows merit further investigation. Full article

Urban green infrastructure plays a crucial role in enhancing environmental resilience in cities, particularly in arid regions characterized by water scarcity, soil salinity, and high climatic stress. However, arid coastal cities remain insufficiently studied with regard to spatially explicit assessments of the structure and dynamics of green infrastructure. This study evaluates the state and spatial organization of urban green infrastructure in Aktau, Kazakhstan, over the period 2015–2025, with the most recent satellite observations obtained in June 2025. Sentinel-2 satellite imagery was used to calculate seasonal Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI) values, and zonal statistics were applied to assess intra-urban differentiation across functional zones. In addition, inventory-based indicators—Green Planting Density (GPD), Structural Composition of Greenery (SCG), and Protective Green Infrastructure (PGI)—were integrated to complement the remote sensing analysis. The results indicate a moderate overall increase in mean NDVI values (from 0.21 to 0.28), with the most significant growth observed in central and coastal areas (ΔNDVI = +0.12; ΔSAVI = +0.21), while industrial and newly developed zones exhibit only limited changes. Despite these localized improvements, the spatial configuration of green infrastructure remains fragmented, reflecting a persistent center–periphery asymmetry in urban greening. These results underline the importance of irrigation practices and spatially targeted greening strategies for improving vegetation conditions in arid urban environments. The proposed integrated approach combining satellite-derived vegetation indices and inventory-based indicators can serve as a useful tool for monitoring urban green infrastructure and supporting evidence-based planning in arid coastal cities. Full article

To address the limitations of traditional forest above-ground biomass (AGB) retrieval methods—namely, the restricted accuracy of physical models and the limited generalization ability of purely data-driven models—this study proposes a differentiable physical modeling (DPM) approach for forest AGB estimation. The method adopts the water cloud model (WCM) as a physics-based framework, grounded in radiative transfer theory, and integrates C-band synthetic aperture radar (SAR) data with multispectral imagery. Within the PyTorch tensor computation framework, automatic differentiation (AD) is employed to seamlessly couple the WCM with the deep fully connected neural network (DFCNN), enabling a differentiable implementation of the WCM. Using mean squared error (MSE) as the loss function, the neural network parameters are optimized through backpropagation and gradient descent, thereby constructing an end-to-end trainable DPM model that effectively retrieves forest AGB while preserving physical interpretability and generalization capability. To validate the proposed method, two representative test sites were selected: Simao in Pu’er, Yunnan Province, and Genhe in Inner Mongolia. GF-3 PolSAR and RADARSAT-2 data were used to extract backscattering coefficients and compute the radar vegetation index (RVI), while Landsat 8 OLI imagery was employed to calculate the normalized difference vegetation index (NDVI), difference vegetation index (DVI), and soil-adjusted vegetation index (SAVI). These datasets, together with ASTER GDEM, field-measured biomass, and other relevant datasets, were integrated to construct a multisource dataset combining remote sensing and ground observations. The performance of the DPM model was then compared with the traditional WCM and several data-driven models, including the fully connected neural network (FNN), generalized regression neural network (GRNN), RF, and Adaptive Boosting (AdaBoost). The results indicate that the DPM model achieved R² = 0.60, RMSE = 24.23 Mg/ha, Bias = 0.4 Mg/ha, and ubRMSE = 22.43 Mg/ha in Simao, and R² = 0.48, RMSE = 33.29 Mg/ha, Bias = 0.87 Mg/ha, and ubRMSE = 33.28 Mg/ha in Genhe, demonstrating consistently better performance than both the WCM and all tested data-driven models. The DPM model demonstrated consistent performance across ecologically contrasting forest regions. It alleviated the systematic overestimation bias of purely data-driven models and overcame the limitations in predictive accuracy resulting from the simplified structure of the WCM. The differentiability of the WCM enables the loss function errors to be backpropagated through the neural network, thereby allowing the optimization of the physical model parameters. Overall, the DPM framework integrates the advantages of both physical models and data-driven approaches, providing an estimation method with acceptable accuracy for forest AGB retrieval. It also offers theoretical and practical insights for the integration of deep learning and physical knowledge in other research fields. Full article

This study assessed the effectiveness of vegetation index change metrics (ΔVI = Post − Pre) derived from Sentinel-2 imagery for detecting landslide-affected areas and evaluating their relationship with rainfall intensity, thereby enhancing the early-warning potential. The analysis focused on Sancheong-gun, Gyeongsangnam-do, South Korea, where intense rainfall in July 2025 triggered multiple landslides. Pre- and post-event Sentinel-2 Level-2A images (10 m spatial resolution) were used to compute changes in the Normalized Difference Vegetation Index (ΔNDVI), Soil-Adjusted Vegetation Index (ΔSAVI), Modified Soil-Adjusted Vegetation Index (ΔMSAVI), Normalized Difference Moisture Index (ΔNDMI), and Global Vegetation Moisture Index (ΔGVMI) over the landslide-affected post-disaster (PD) and non-damaged (ND) areas. Sensitivity was assessed based on the differences in mean ΔVI between the PD and ND areas, Welch’s t-statistics, and Cohen’s d values. All indices exhibited significant differences between the PD and ND areas (p < 0.001), with ΔMSAVI showing the highest sensitivity (MSAVI > GVMI ≈ SAVI > NDVI > NDMI). Correlation analysis revealed that ΔMSAVI had the strongest positive association with rainfall accumulation (72 h: r = 0.54; 7 days: r = 0.49), indicating that greater rainfall corresponded to stronger vegetation degradation signals. These findings highlight ΔMSAVI as a robust and responsive indicator of rainfall-triggered landslides, supporting its integration into satellite-based early-warning and rapid damage detection systems for improved landslide monitoring and response. Full article

The accurate estimation of grapevine biophysical parameters is important for decision support in precision viticulture. This study addresses the use of unmanned aerial vehicle (UAV) multispectral data and machine learning (ML) techniques to estimate leaf area index (LAI), pruning wood biomass, and yield, across mixed-variety vineyards in the Douro Region of Portugal. Data were collected at three phenological stages, from veraison to maturation and two modeling approaches were tested: one using only spectral features, and another combining spectral and geometric features derived from photogrammetric elevation data. Multiple linear regression (MLR) and five ML algorithms were applied, with feature selection performed using both forward and backward selection procedures. Logarithmic transformations were used to mitigate data skewness. Overall, ML algorithms provided better predictive performance than MLR, particularly when geometric features were included. At harvest-ready, Random Forest achieved the highest accuracy for LAI (R² = 0.83) and yield (R² = 0.75), while MLR produced the most accurate estimates for pruning wood biomass (R² = 0.83). Among geometric variables, canopy area was the most informative. For spectral data, the Modified Soil-Adjusted Vegetation Index (MSAVI) and the Soil-Adjusted Vegetation Index (SAVI) were the most relevant. The models performed well across grapevine varieties, indicating that UAV-based monitoring can serve as a practical, non-invasive, and scalable approach for vineyard management in heterogeneous vineyards. Full article

Wheat (Triticum aestivum L.) is a key crop in Spain, especially in Castilla and León Region. However, there are few studies evaluating predictive models based on spectral indices and multivariate analysis to estimate yield in direct seeding (DS) and conventional seeding (CS) systems. This study addresses this need by implementing a split-plot experimental design in the city of Palencia, Spain, analyzing crop physiological data and nine spectral indices derived from multispectral aerial images captured by drones. The analysis included multivariate techniques such as Principal Component Analysis (PCA) and Random Forest (RF), supplemented with statistical tests, ROC curves, and prediction analysis. The results showed that the RF model successfully classified treatments with 93.75% accuracy and a Kappa index of 0.875, highlighting performance, nitrogen, and protein as key variables. Among the vegetation indices, the Soil-Adjusted Vegetation Index (SAVI) and the Advanced Vegetation Index (AVI) were the most relevant in the flowering stage, with ROC curve values of 0.7778 and 0.8025, respectively. Spearman’s correlations confirmed a significant relationship between these indices and key physiological variables, allowing to distinguish between DS and CS systems. The RF-based prediction model for performance showed R² values above 91% in the indices with the highest correlation. However, predictive capacity was higher in DS, suggesting that conditions inherent in non-mechanized handling significantly influence model performance. This highlights the importance of using non-destructive procedures to estimate production, enabling the development of adaptive and sustainable strategies that contribute to efficient agricultural production, since it is possible to anticipate crop yields before harvest, optimizing resources such as fertilizers and water. Full article

Wetland ecosystems, particularly marsh wetlands, are vital for carbon cycling, yet the accurate estimation of the fraction of absorbed photosynthetically active radiation (FPAR) in these environments is challenging due to their complex structure and limited field data. This study employs the large-scale remote sensing data and image simulation framework (LESS), a 3D radiative transfer model, to simulate FPAR and vegetation indices (VIs) under controlled conditions, including variations in vegetation types, soil types, chlorophyll content, solar and observation angles, and plant density. By simulating 8064 wetland scenes, we overcame the limitations of field measurements and conducted comprehensive quantitative analyses of the relationship between the FPAR and VI (which is essential for remote sensing-based FPAR estimation). Nine VIs (NDVI, GNDVI, SAVI, RVI, EVI, MTCI, DVI, kNDVI, RDVI) effectively characterized FPAR, with the following saturation thresholds quantified: inflection points (FPAR.inf, where saturation begins) ranged from 0.423 to 0.762 (mean = 0.594) and critical saturation points (FPAR.sat, where saturation is complete) from 0.654 to 0.889 (mean = 0.817). The Enhanced Vegetation Index (EVI) and Soil-Adjusted Vegetation Index (SAVI) showed the highest robustness against saturation and environmental variability for FPAR estimation in reed (Phragmites australis) marshes. These findings provide essential support for FPAR estimation in marsh wetlands and contribute to quantitative studies of wetland carbon cycling. Full article

Leaf area index (LAI) is a crucial parameter reflecting the crown structure of the grassland. Accurately obtaining LAI is of great significance for estimating carbon sinks in grassland ecosystems. However, spectral noise interference and pronounced spatial heterogeneity within vegetation canopies constitute significant impediments to achieving high-precision LAI retrieval. This study used hyperspectral sensor mounted on an unmanned aerial vehicle (UAV) to estimate LAI in a typical grassland, Hulun Lake Basin. Multiple machine learning (ML) models were constructed to reveal a relationship between hyperspectral data and grassland LAI using two input datasets, namely spectral transformations and vegetation indices (VIs), while SHAP (SHapley Additive ExPlanation) interpretability analysis was further employed to identify high-contribution features in the ML models. The analysis revealed that grassland LAI has good correlations with the original spectrum at 550 nm and 750 nm–1000 nm, first and second derivatives at 506 nm–574 nm, 649 nm–784 nm, and vegetation indices including the triangular vegetation index (TVI), enhanced vegetation index 2 (EVI2), and soil-adjusted vegetation index (SAVI). In the models using spectral transformations and VIs, the random forest (RF) models outperformed other models (testing R² = 0.89/0.88, RMSE = 0.20/0.21, and RRMSE = 27.34%/28.98%). The prediction error of the random forest model exhibited a positive correlation with measured LAI magnitude but demonstrated an inverse relationship with quadrat-level species richness, quantified by Margalef’s richness index (MRI). We also found that at the quadrat level, the spectral response curve pattern is influenced by attributes within the quadrat, like dominant species and vegetation cover, and that LAI has positive relationship with quadrat vegetation cover. The LAI inversion results in this study were also compared to main LAI products, showing a good correlation (r = 0.71). This study successfully established a high-fidelity inversion framework for hyperspectral-derived LAI estimation in mid-to-high latitude grasslands of the Hulun Lake Basin, supporting the spatial refinement of continental-scale carbon sink models at a regional scale. Full article

The red imported fire ant (RIFA; Solenopsis invicta) is an invasive species that severely threatens ecology, agriculture, and public health in Taiwan. In this study, the feasibility of applying multispectral imagery captured by unmanned aerial vehicles (UAVs) to detect red fire ant mounds was evaluated in Fenlin Township, Hualien, Taiwan. A DJI Phantom 4 multispectral drone collected reflectance in five bands (blue, green, red, red-edge, and near-infrared), derived indices (normalized difference vegetation index, NDVI, soil-adjusted vegetation index, SAVI, and photochemical pigment reflectance index, PPR), and textural features. According to analysis of variance F-scores and random forest recursive feature elimination, vegetation indices and spectral features (e.g., NDVI, NIR, SAVI, and PPR) were the most significant predictors of ecological characteristics such as vegetation density and soil visibility. Texture features exhibited moderate importance and the potential to capture intricate spatial patterns in nonlinear models. Despite limitations in the analytics, including trade-offs related to flight height and environmental variability, the study findings suggest that UAVs are an inexpensive, high-precision means of obtaining multispectral data for RIFA monitoring. These findings can be used to develop efficient mass-detection protocols for integrated pest control, with broader implications for invasive species monitoring. Full article