Search Results (158)

Photovoltaic (PV) power generation is essential for achieving carbon neutrality and advancing renewable energy development. In Northwest China, the rapid expansion of PV installations requires accurate and timely spatial data to support effective monitoring and planning. Addressing the limitations of existing datasets in spatiotemporal resolution and driver analysis, this study develops a scalable solar facility inventory framework on the Google Earth Engine (GEE) platform. The framework integrates Sentinel-1 SAR, Sentinel-2 multispectral imagery, and interpretable machine learning. Feature redundancy is first assessed using correlation-based metrics, after which a Random Forest classifier is applied to generate a 10 m resolution distribution map of utility-scale photovoltaic power plants as of December 2023. To elucidate model behavior, SHAP (SHapley Additive exPlanations) is used to identify key predictors, and MaxEnt is incorporated to provide a preliminary quantitative assessment of spatial drivers of PV deployment. The RFECV-optimized model, retaining 44 key features, achieves an overall accuracy of 98.4% and a Kappa coefficient of 0.96. The study region contains approximately 2560 km² of PV installations, with pronounced clusters in northern Ningxia, central Shaanxi, and parts of Xinjiang and Gansu. SHAP analysis highlights the Enhanced Photovoltaic Index (EPVI), the Normalized Difference Built-up Index (NDBI), Sentinel-2 Band 8A, and related texture metrics as primary contributors to model predictions. High EPVI, NDBI, and Sentinel-2 Band 8A values contribute positively to PV classification, whereas vegetation-related indices (e.g., NDVI) exhibit predominantly negative contributions; these results indicate that PV mapping relies on the integrated discrimination of multiple spectral and texture features rather than on a single dominant variable. MaxEnt results indicate that grid accessibility and land-use constraints (e.g., nighttime light intensity reflecting human activity) are dominant drivers of PV clustering, often exerting more influence than solar irradiance alone. This framework provides robust technical support for PV monitoring and offers high-resolution spatial distribution data and driver insights to inform sustainable energy management and regional renewable-energy planning. Full article

Developing effective evaluation frameworks for urban regeneration in non-metropolitan areas is increasingly challenging, particularly for small-scale projects where conventional administrative indicators are often insufficient on their own. This study examines 46 regeneration projects in Republic of Korea and integrates nighttime lights (NTL), Sentinel-2 indices, and administrative statistics to identify how different project types produce observable changes. The results show that NTL is effective mainly in economy-based and central commercial area projects, where increases in radiance correspond to the expansion of commercial functions, higher business activity, and stronger evening economic operations. In contrast, NTL shows limited responsiveness in residential-support projects, reflecting the low baseline illumination and weak lighting elasticity of residential environments. For these areas, Sentinel-2 NDVI and NDBI provide clearer evidence of improvements, capturing localized changes in vegetation, built surfaces, and pedestrian environments that are not detectable through nighttime radiance. Comparative assessments indicate that most changes are concentrated within project boundaries, though external development projects occasionally influence spectral patterns in adjacent areas. These findings demonstrate that combining NTL and Sentinel-2 data offers a more context-sensitive approach to evaluating small-scale regeneration and highlights the importance of selecting indicators suited to specific project types. The study provides an empirical foundation for more adaptable, data-driven evaluation frameworks in non-metropolitan regeneration policy. Full article

Climate change represents a critical challenge for viticulture worldwide, primarily through increased heat stress, more frequent and severe drought periods, and unseasonal rainfall events. There is increasing evidence of its negative effects on both thermal regimes—potentially leading to accelerated phenology and unbalanced sugar-to-acid ratios—and hydric regimes—causing water stress that impacts berry development and final yield. The use of plastic covering in vineyards is a widespread technique, particularly in regions with high climatic variability such as the Mediterranean Basin (e.g., Southern Italy, Spain, Greece), aimed at protecting both vegetation and grapes from external factors such as hail, heavy rainfall, wind, and extreme solar radiation, which can cause physical damage, promote fungal diseases, and lead to berry sunburn. This study explores the impact of six distinct commercial plastic films, with varying optical properties, on the retrieval and accuracy of vegetation indices derived from Sentinel-2 imagery in a mid-season table grape vineyard (Autumn Crisp^®) in Southern Italy during the 2024 growing season. Laboratory spectroradiometric analyses were conducted to measure film-specific transmittance and reflectance factors from 200 to 1500 nm, enabling the development of a first-order linear spectral correction model applied to Sentinel-2 imagery. Vegetation indices (NDVI, CVI, GNDVI, LWCI) were corrected for plastic interference and analysed through univariate statistics and Principal Component Analysis. Results showed that after applying the spectral correction model, film T2 displayed the higher NDVI value (0.73). Films T3 and T4—characterised by high visible light transmittance (>39%) and low reflectance (<11% in the Red/NIR)—resulted in lower vine vigour and photosynthetic activity, with mean corrected NDVI values equal to 0.70, though still significantly higher than those of films T1 (0.65) and T5 (0.67). Films T6 and T1 were associated with greater water conservation, as indicated by the highest mean LWCI values (T6: 0.59; T1: 0.52), but lower chlorophyll-related signals, evidenced by the lowest mean CVI values (T6: 1.31; T1: 1.74) and GNDVI values (T6: 0.46; T1: 0.48). Among the corrected indices, NDVI demonstrated strong positive correlations with yield (r = 0.900) and total soluble solids per vine (TSS*vine, in kg), a key quality parameter representing the total sugar yield (r = 0.883), supporting its suitability as an index for vine productivity and fruit quality. The proposed correction method significantly improves the reliability of remote sensing in covered vineyards, as demonstrated by the strong correlations between corrected NDVI and yield (R² = 0.810) and sugar content (R² = 0.779), relationships that were not analysable with the uncorrected data; may guide film selection—opting for high-transmittance films (e.g., T2, T3) for yield or water-conserving films (e.g., T6) for stress mitigation—and irrigation strategies, such as using the corrected LWCI for precision scheduling. Future efforts should include angular effects and ground-truth validation to enhance correction accuracy and operational relevance. Full article

Mining affects groundwater and surface water both during an active mining operation and after its termination. Continuous monitoring and both quantitative and qualitative assessment of water dynamics are crucial for the sustainable management of the mining and post-mining environment. This paper provides an extensive overview of water in the mining industry and of remote sensing methods for surface water monitoring. Moreover, selected spectral water indices are compared to assess their performance and usefulness in surface water monitoring. The Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Modified Normalized Difference Water Index (MNDWI) are applied to different case study areas affected by mining-induced multitemporal surface water changes. All the selected indices were found useful as proxies for surface water identification; however, their effectiveness and accuracy varied in subsequent case studies. Full article

This study investigates the application of unmanned aerial vehicles with multispectral cameras for monitoring the condition of olive groves with high accuracy. Multispectral images of olive groves provided detailed insight into the spectral data required for the analysis of vegetation indices. Using deep learning-based object detection, individual olive trees were identified within the images, which allowed the extraction of parts corresponding to each tree. To separate the background from the canopy, segmentation based on the monocular depth estimation algorithm, Depth Anything, was applied. In this way, elements that are not part of the tree’s crown were removed for more accurate analysis and calculation of the NDVI (Normalized Difference Vegetation Index) and NDRE (Normalized Difference Red Edge Index) indices. The obtained results were compared with the results obtained for unsegmented patches, threshold-based patches, and manually segmented patches. The comparison and analysis carried out shows that the proposed segmentation approach improved the accuracy of NDVI and NDRE by focusing exclusively on the crowns of the observed trees, excluding the noise of the surrounding vegetation and soil. In addition, measurements were carried out on three observed olive groves at different parts of the vegetation cycle, and the values of the vegetation indices were compared. This integrated method combining drone-based multispectral imaging, deep learning object detection, and advanced segmentation techniques highlights a robust approach to olive tree health monitoring and provides insight into seasonal vegetation dynamics, for winter and spring, to capture differences in vegetative activity. Full article

Despite successful land reclamation efforts, post-mining areas are still prone to secondary effects of mineral extraction. These effects include surface deformations, damage to infrastructure and buildings, and periodic or permanent changes to surface water resources. This study focused on analyzing a former copper mine in southwest Poland in terms of surface water changes, which may be caused by the restoration of groundwater conditions in the region after mine closure. The main objective of the study was to detect areas with statistically significant changes in surface water between 2015 and 2024, as well as to identify the main factors influencing the observed changes. The methodology integrated open remote sensing datasets from Landsat and Sentinel-1 missions for deriving spectral indices—Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Moisture Index (NDMI), as well as Surface Soil Moisture index (SSM); spatial statistics methods, including Emerging Hot Spot analysis; and regression models—Random Forest Regression (RFR) and Geographically Weighted Regression (GWR). The results obtained indicated a general increase in vegetation water content, a reduction in the extent of surface water, and minor soil moisture changes during the analyzed period. The Emerging Hot Spot analysis revealed a number of new hot spots, indicating regions with statistically significant increases in surface water content in the study area. Out of the investigated regression models, global regression (RFR) outperformed local (GWR) models, with R2 ranging between 74.7% and 87.3% for the studied dependent variables. The most important factors in terms of influence were the distance from groundwater wells, surface topography, vegetation conditions and distance from active mining areas, while surface geology conditions and permeability had the least importance in the regression models. Overall, this study offers a comprehensive framework for integrating multi-source data to support the analysis of environmental changes in post-mining regions. Full article

The use of remote sensing technologies for mapping forest fires has experienced significant growth in recent decades, driven by advancements in remote sensors, processing platforms, and artificial intelligence algorithms. This study presents a review of 192 scientific articles published between 1990 and 2024, selected using PRISMA criteria from the Scopus database. Trends in the use of active and passive sensors, spectral indices, software, and processing platforms as well as machine learning and deep learning approaches are analyzed. Bibliometric analysis reveals a concentration of publications in Northern Hemisphere countries such as the United States, Spain, and China as well as in Brazil in the Southern Hemisphere, with sustained growth since 2015. Additionally, the publishers, journals, and authors with the highest scientific output are identified. The normalized burn ratio (NBR) and the normalized difference vegetation index (NDVI) were the most frequently used indices in fire mapping, while random forest (RF) and convolutional neural networks (CNN) were prominent among the applied algorithms. Finally, the main technological and methodological limitations as well as emerging opportunities to enhance fire detection, monitoring, and prediction in various regions are discussed. This review provides a foundation for future research in remote sensing applied to fire management. Full article

Accurate and non-destructive monitoring of leaf chlorophyll content (LCC) is essential for assessing crop photosynthetic activity and nitrogen status in precision agriculture. This study introduces a phenology-aware machine learning framework that combines hyperspectral reflectance data with various regression models to estimate leaf chlorophyll content (LCC) in cotton at six key reproductive stages. Field experiments utilized synchronized spectral and SPAD measurements, incorporating spectral transformations—such as vegetation indices (VIs), first-order derivatives, and trilateration edge parameters (TEPs, a new set of geometric metrics for red-edge characterization)—for evaluation. Five regression approaches were evaluated, including univariate and multivariate linear models, along with three machine learning algorithms: Random Forest, K-Nearest Neighbor, and Support Vector Regression. Random Forest consistently outperformed the other models, achieving the highest R² (0.85) and the lowest RMSE (4.1) during the bud stage. Notably, the optimal prediction accuracy was achieved with fewer than five spectral features. The proposed framework demonstrates the potential for scalable, stage-specific monitoring of chlorophyll dynamics and offers valuable insights for large-scale crop management applications. Full article

Leaf phenology is key to ecosystem functioning by regulating carbon, water, and energy fluxes and influencing vegetation productivity. Yet, detecting land surface phenology (LSP) in drylands using remote sensing remains particularly challenging due to sparse and heterogeneous vegetation cover, high spatiotemporal variability, and complex spectral signals. Unlike the Northern Hemisphere, these challenges are further compounded in the Southern Hemisphere (SH), where several regions experience year-round moderate temperatures. When combined with irregular rainfall, this leads to highly variable vegetation activity throughout the year. However, LSP dynamics in the SH remain poorly understood. This study presents a review of remote sensing-based phenology research in drylands, integrating (i) a synthesis of global methodological advances and (ii) a systematic analysis of peer-reviewed studies published from 2015 through April 2025 focused on SH drylands. This review reveals a research landscape still dominated by conventional vegetation indices (e.g., NDVI) and moderate-spatial-resolution sensors (e.g., MODIS), though a gradual shift toward higher-resolution sensors such as PlanetScope and Sentinel-2 has emerged since 2020. Despite the widespread use of start- and end-of-season metrics, their accuracy varies greatly, especially in heterogeneous landscapes. Yet, advanced products such as solar-induced chlorophyll fluorescence or the fraction of absorbed photosynthetically active radiation were rarely employed. Gaps remain in the representation of hyperarid zones, grass- and shrub-dominated landscapes, and large regions of Africa and South America. Our findings highlight the need for multi-sensor approaches and expanded field validation to improve phenological assessments in dryland environments. The accurate differentiation of vegetation responses in LSP is essential not only for refining phenological metrics but also for enabling more realistic assessments of ecosystem functioning in the context of climate change and its impact on vegetation dynamics. Full article

Innovative applications of high-resolution airborne imaging are explored for detecting grapevine diseases. Driven by the motivation to enhance early disease detection, the method’s effectiveness lies in its capacity to identify isolated cases of grapevine yellows (Flavescence dorée and Bois Noir) and trunk disease (Esca complex), crucial for preventing the disease from spreading to unaffected areas. Conducted over a 17 ha vineyard in the Forlì municipality in Emilia-Romagna (Italy), the aerial survey utilized a photogrammetric camera capturing centimeter-level resolution images of the whole area in 17 minutes. These images were then processed through an automated analysis leveraging RGB-based spectral indices (Green–Red Vegetation Index—GRVI, Green–Blue Vegetation Index—GBVI, and Blue–Red Vegetation Index—BRVI). The analysis scanned the 1.24 · 10⁹ pixels of the orthomosaic, detecting 0.4% of the vineyard area showing evidence of disease. The instances, density, and incidence maps provide insights into symptoms’ spatial distribution and facilitate precise interventions. High specificity (0.96) and good sensitivity (0.56) emerged from the ground field observation campaign. Statistical analysis revealed a significant edge effect in symptom distribution, with higher disease occurrence near vineyard borders. This pattern, confirmed by spatial autocorrelation and non-parametric tests, likely reflects increased vector activity and environmental stress at the vineyard margins. The presented pilot study not only provides a reliable detection tool for grapevine diseases but also lays the groundwork for an early warning system that, if extended to larger areas, could offer a valuable system to guide on-the-ground monitoring and facilitate strategic decision-making by the authorities. Full article

Land desertification represents a significant and sensitive global ecological issue. In the Inner Mongolia region of China, soil desertification and salinization are widespread, resulting from the combined effects of extreme drought conditions and human activities. Using Gaofen 5B AHSI imagery as our data source, we collected spectral data for seven distinct land cover types: lush vegetation, yellow sand, white sand, saline soil, saline shell, saline soil with saline vegetation, and sandy soil. We applied Particle Swarm Optimization (PSO) to fine-tune the Wavelet Packet (WP) decomposition levels, thresholds, and wavelet basis function, ensuring optimal spectral decomposition and reconstruction. Subsequently, PSO was deployed to optimize key hyperparameters of the Random Forest algorithm and compare its performance with the ResNet-Transformer model. Our results indicate that PSO effectively automates the search for optimal WP decomposition parameters, preserving essential spectral information while efficiently reducing high-frequency spectral noise. The Genetic Algorithm (GA) was also found to be effective in extracting feature bands relevant to land desertification, which enhances the classification accuracy of the model. Among all the models, integrating wavelet packet denoising, genetic algorithm feature selection, the first-order differential (FD), and the hybrid architecture of the ResNet-Transformer, the WP-GA-FD-ResNet-Transformer model achieved the highest accuracy in extracting soil sandification and salinization, with Kappa coefficients and validation set accuracies of 0.9746 and 97.82%, respectively. This study contributes to the field by advancing hyperspectral desertification monitoring techniques and suggests that the approach could be valuable for broader ecological conservation and land management efforts. Full article

Understanding spatial variations in land surface temperature (LST) is critical for analyzing urban climate dynamics, especially within the framework of two-dimensional (2D) turbulence theory. This study assesses the spatial sampling behavior of urban thermal fields across eight metropolitan areas, encompassing diverse morphologies, surface materials, and Köppen–Geiger climate zones. We analyzed thermal infrared (TIR) imagery from two remote sensing platforms—MODIS (1 km) and Landsat (30 m)—to evaluate resolution-dependent turbulence indicators such as spectral slopes and breakpoints. Power spectral analysis revealed systematic divergences across spatial scales. Landsat exhibited more negative breakpoint values, indicating a greater ability to capture fine-scale thermal heterogeneity tied to vegetation, buildings, and surface cover. MODIS, in contrast, emphasized broader thermal gradients, suitable for regional-scale assessments. Seasonal differences reinforced the turbulence framework: summer spectra displayed steeper, more variable slopes, reflecting increased thermal activity and surface–atmosphere decoupling. Despite occasional agreement between sensors, spectral metrics remain inherently resolution-dependent. MODIS is better suited for macro-scale thermal structures, while Landsat provides detailed insights into intra-urban processes. Our findings confirm that 2D turbulence indicators are not fully scale-invariant and vary with sensor resolution, season, and urban form. This multi-sensor comparison offers a framework for interpreting LST data in support of climate adaptation, urban design, and remote sensing integration. Full article

Farming is an important development direction of agriculture in the future, which is affected by various environmental factors, among which light plays an important role, and it is essential for the growth of organisms in nature. LED technology can regulate the growth and development of vegetables by adjusting the spectral composition of light. In order to explore light quality formulation with the aim of improving the quality and yield of celery, we set up six experimental treatments: W (white light), R (red light), B (blue light), 3R1B (red light/blue light = 3:1), 4R1B (red light/blue light = 4:1), and 5R1B (red light/blue light = 5:1). The results indicated that the 3R1B and 4R1B illumination treatments were conducive to promoting the growth of celery, enhancing plant height and root length. Specifically, the 3R1B treatment optimized the nutritional quality of celery by increasing the levels of soluble protein, soluble sugar, and total flavonoids while reducing nitrate and cellulose contents and elevating the anthocyanin content in petioles. Additionally, both treatments enhanced the contents of Ca and Mg in celery leaves and petioles. Furthermore, the 3R1B treatment promoted the accumulation of photosynthetic pigments, upregulated the activities of ANS and FNS enzymes, and induced the upregulation of gene expression levels of FNS and ANS, thereby enhancing the nutritional value of celery. Full article

The use of Google Earth Engine (GEE), a cloud-based computing platform, in spatio-temporal evaluation studies has increased rapidly in natural sciences such as forestry. In this study, Sentinel-2 satellite imagery and Shuttle Radar Topography Mission (SRTM) elevation data and image classification algorithms based on two machine learning techniques were examined. Random Forest (RF) and Classification and Regression Trees (CART) were used to classify land use and land cover (LULC) in western Oregon (USA). To classify the LULC from the spectral bands of satellite images, a composition consisting of vegetation difference indices NDVI, NDWI, EVI, and BSI, and a digital elevation model (DEM) were used. The study area was selected due to a diversity of land cover types including research forest, botanical gardens, recreation area, and agricultural lands covered with diverse plant species. Five land classes (forest, agriculture, soil, water, and settlement) were delineated for LULC classification testing. Different spatial points (totaling 75, 150, 300, and 2500) were used as training and test data. The most successful model performance was RF, with an accuracy of 98% and a kappa value of 0.97, while the accuracy and kappa values for CART were 95% and 0.94, respectively. The accuracy of the generated LULC maps was evaluated using 500 independent reference points, in addition to the training and testing datasets. Based on this assessment, the RF classifier that included elevation data achieved an overall accuracy of 92% and a kappa coefficient of 0.90. The combination of vegetation difference indices with elevation data was successful in determining the areas where clear-cutting occurred in the forest. Our results present a promising technique for the detection of forests and forest openings, which was helpful in identifying clear-cut sites. In addition, the GEE and RF classifier can help identify and map storm damage, wind damage, insect defoliation, fire, and management activities in forest areas. Full article

Bermudagrass is a warm-season turfgrass commonly grown in drought-prone areas. Harnessing natural genetic variation available in germplasm is a principal strategy to enhance its resilience to drought stress. This study was carried out to assess the comparative performance of bermudagrass hybrids under drought conditions and their subsequent recovery following the drought period. A total of 48 hybrids, including 2 commercial cultivars, ‘Tifway’ and ‘TifTuf’, were established under optimum growth conditions in the greenhouse and then subjected to drought stress by withholding irrigation for four weeks. The dry-down experiment was laid out in a randomized complete block design with four replications. Turf color, visual quality, and active spectral reflectance data were collected weekly and used to assess the health and vigor of the hybrids during progression of the drought stress for four weeks and through recovery after rewatering. Analysis of variance revealed significant differences among the hybrids for color, visual quality, and spectral vegetation indices. A multivariate analysis grouped the hybrids into drought-tolerant with full recovery after rewatering, moderately tolerant, and susceptible to extended drought stress without recovery. These results showed the prevalence of genetic variation for drought tolerance and proved instrumental in the development of bermudagrass cultivars resilient to drought stress and improved water use efficiency. Full article