Search Results (3,551)

Research on monitoring forest disturbances and analyzing its driving factors is crucial for the sustainable management of forest ecosystems. To quantitatively identify the spatial distribution and dynamic changes of forest disturbance and its driving factors in Guangdong Province from 1990 to 2019, the long-term Landsat time series imagery and the LandTrendr change detection algorithm were utilized. The impact of forest disturbances on four types of landscape fragmentation (attrition, perforation, shrinkage, and subdivision) was analyzed using the Forman index. The Geodetector model was used to analyze the driving factors of forest disturbance from human activity and the natural environment. The results showed that the LandTrendr algorithm achieved a Kappa coefficient of 0.79, with an overall accuracy of approximately 82.59%. The findings indicate a consistent increase in shrinkage patches, both in quantity and area. Spatially, the centroids of forest fragmentation processes exhibited a clear inland migration trend, reflecting the growing ecological pressures faced by inland forest ecosystems. Furthermore, interactions among driving factors, particularly between population density and economic factors, significantly amplified their combined impacts. The correlation between forest disturbances and socio-economic factors revealed distinct regional variations, highlighting significant differences in forest disturbance dynamics across cities with varying levels of economic development. This study provides critical insights into the spatiotemporal dynamics of forest disturbances under rapid urbanization and economic development. It lays the groundwork for sustainable forest management strategies in Guangdong Province and may contribute to global discussions on managing forest ecosystems during periods of rapid socio-economic transformation. Full article

This article presents the application of novel cartographic methods of vegetation mapping with a case study of the Rif Mountains, northern Morocco. The study area is notable for varied geomorphology and diverse landscapes. The methodology includes ML modules of GRASS GIS ‘r.learn.train’, ‘r.learn.predict’, and ‘r.random’ with algorithms of supervised classification implemented from the Scikit-Learn libraries of Python. This approach provides a platform for processing spatiotemporal data and satellite image analysis. The objective is to determine the robustness of the “DecisionTreeClassifier” and “ExtraTreesClassifier” classification algorithms. The time series of satellite images covering northern Morocco consists of six Landsat scenes for 2023 with a bimonthly time interval. Land cover maps are produced based on the processed, classified, and analyzed images. The results demonstrated seasonal changes in vegetation and land cover types. The validation was performed using a land cover dataset from the Food and Agriculture Organization (FAO). This study contributes to environmental monitoring in North Africa using ML algorithms of satellite image processing. Using RS data combined with the powerful functionality of the GRASS GIS and FAO-derived datasets, the topographic variability, moderate-scale habitat heterogeneity, and bimonthly distribution of land cover types of northern Morocco in 2023 have been assessed for the first time. Full article

Rapeseed mapping is crucial for refined agricultural management and food security. However, existing remote sensing-based methods for rapeseed mapping in Southwest China are severely limited by insufficient training samples and persistent cloud cover. To address the above challenges, this study presents an automatic rapeseed mapping framework that integrates multi-source remote sensing data fusion, automated sample generation, and deep learning models. The framework was applied in Santai County, Sichuan Province, Southwest China, which has typical topographical and climatic characteristics. First, MODIS and Landsat data were used to fill the gaps in Sentinel-2 imagery, creating time-series images through the object-level processing version of the spatial and temporal adaptive reflectance fusion model (OL-STARFM). In addition, a novel spectral phenology approach was developed to automatically generate training samples, which were then input into the improved TS-ConvNeXt ECAPA-TDNN (NeXt-TDNN) deep learning model for accurate rapeseed mapping. The results demonstrated that the OL-STARFM approach was effective in rapeseed mapping. The proposed automated sample generation method proved effective in producing reliable rapeseed samples, achieving a low Dynamic Time Warping (DTW) distance (<0.81) when compared to field samples. The NeXt-TDNN model showed an overall accuracy (OA) of 90.12% and a mean Intersection over Union (mIoU) of 81.96% in Santai County, outperforming other models such as random forest, XGBoost, and UNet-LSTM. These results highlight the effectiveness of the proposed automatic rapeseed mapping framework in accurately identifying rapeseed. This framework offers a valuable reference for monitoring other crops in similar environments. Full article

Leveraging satellite monitoring and machine learning (ML) techniques for water clarity assessment addresses the critical need for sustainable water management. This study aims to assess water clarity by predicting the Secchi disk depth (SDD) using satellite images and ML techniques. The primary methods involve data preparation and SSD inference. During data preparation, AquaSat samples, originally from the L1TP collection, were updated with the Landsat 8 satellite’s latest postprocessing, L2SP, which includes atmospheric corrections, resulting in 33,261 multispectral observations and corresponding SSD measurements. For inferring the SSD, regressors such as SVR, NN, and XGB, along with an ensemble of them, were trained. The ensemble demonstrated performance with an average determination coefficient of R2 of around 0.76 and a standard deviation of around 0.03. Field data validation achieved an R2 of 0.80. Furthermore, we show that the regressors trained with L1TP imagery for predicting SSD result in a favorable performance with respect to their counterparts trained on the L2SP collection. This document contributes to the transition from semi-analytical to data-driven methods in water clarity research, using an ML ensemble to assess the clarity of water bodies through satellite imagery. Full article

Decarbonization strategies aim at increasing renewable energy source (RES) capacity, including new photovoltaic (PV) systems. Utility-scale PV installations are often placed in agricultural areas, resulting in a reduction in agricultural land and affecting the environment. To balance agricultural and energy policies, PV development should not limit agricultural purposes, allowing sustainable exploitation under specific technological and environmental conditions, particularly in areas of actual or potential abandonment. Studying agricultural abandonment is complex due to its multifaceted nature, the lack of a clear definition, and challenges in acquiring cartographic data. This study introduces and compares two methodologies to identify abandoned agricultural areas, aiming to delineate macro-areas of potential abandonment and examine patterns for conversion to energy use, with a focus on Toscana, a region (NUTS-2) in central Italy, which has experienced cropland reduction unrelated to urbanization. The first, simplified approach analyses land cover changes from 2000 to 2018, while the second method provides a more detailed abandonment detection by means of medium spatial resolution satellite imagery from the Harmonized Landsat and Sentinel-2 dataset. A Random Forest classifier combined with Object-Based Image Analysis (OBIA) is applied to satellite data to map annual active/non-active croplands. Annual maps are then validated with a trajectory-based approach to detect agricultural land abandonment. This second methodology can help in providing spatially and timely meaning estimates of abandoned agricultural areas to be recovered for energy purposes and promote a sustainable growth of PV systems. Full article

This study focuses on remote sensing and monitoring of asparagus crops in the provinces of Ica and Trujillo, highlighting their importance in global food security. Using satellite images and temperature data, productivity was compared using the NDWI, NDVI, and EVI indices. The Grad-CAM technique was used to analyze the AlexNet Convolutional Neural Network (CNN) model, seeking to improve productivity. Although AlexNet validated the satellite images, it showed some confusion in regions of medium and low productivity. The model, supported by Grad-CAM, will contribute to the monitoring of optimal climatic conditions. Full article

Gold mining is an activity that has developed in Colombia due to the great availability of mineral resources geographically distributed throughout the territory. The extraction techniques used are linked to the domain of illegality and to armed actors who have generated notable landscape impacts. This study, focused on the Municipality of Ayapel, Colombia, identifies the landscape units and analyzes the changes in land use and cover resulting from gold mining between the years 1987, 2002, and 2022, applying the CORINE Land Cover methodology, an adapted legend for Colombia, using Landsat satellite images. For this, the recognition of the physical geographical characteristics of the area was carried out in order to group homogeneous landscape units through a cartographic overlay of various layers of information, considering variables such as topography, geomorphology, and lithology. This research identifies a total of 16 landscape units, 8 of which were intervened in 1987, mainly associated with denudational hills. However, in 2022, 13 landscape units were intervened, with a considerable increase in the affected area. Particularly noteworthy is the occupation of landscape units associated with alluvial valleys, with an average of more than 30% of their total area. This demonstrates that they are the most attractive and vulnerable areas for mining exploitation, as they are the zones with the greatest potential for hosting mineral deposits. This impact has worsened over the last decade due to the introduction of other extraction techniques with machinery (dredges, dragon boats, backhoes, and bulldozers) that generate higher productive and economic yields but, at the same time, cause deep environmental liabilities due to the lack of administrative controls. The changes in extraction techniques, the increase in the international price of the commodity, and the absence of government attention have been the breeding ground that has driven gold mining activity. Full article

High-resolution satellite imagery providing long-term, continuous information on surface water extent in highly developed regions is paramount for elucidating the spatiotemporal dynamics of water bodies. The landscape of water bodies is a key indicator of water quality and ecological services. In this study, we analyzed surface water dynamics, including rivers, lakes, and reservoirs, using Landsat images spanning from the 1980s to 2020, with a focus on the highly developed Coastal Chinese Mainland (CCM) region. Our objectives were to investigate the temporal and spatial variations in surface water area extent and landscape characteristics, to explore the driving forces behind these variations, to gain insights into the complex interactions between water bodies and evolving environmental conditions, and ultimately to support sustainable development in coastal regions. Our findings revealed that reservoirs constitute the largest proportion of surface water, while lakes occupy the smallest share. Notably, a trend of expansion in surface water extent in the CCM was observed, mainly from the construction of new reservoirs. These reservoirs primarily gained new areas from agricultural land and river floodplains in the early stages (1980s–2000), while a greater proportion of construction land was encroached upon by reservoirs in later periods (2001–2020). At the landscape level, a tendency toward fragmentation and complexity in surface water, particularly in reservoirs, was evident. Human interference, particularly urbanization, played a pivotal role in driving the expansion of water surfaces. While reservoir construction benefits water resource assurance, flood control, and prevention, it also poses eco-hydrological challenges, including water quality deterioration, reduced hydrological connectivity, and aquatic ecosystem degradation. The findings of this study provide essential data support for sustainable water resource development. These insights underscore the urgency and importance of integrated water resource management strategies, particularly in efforts aimed at conservation and restoration of natural water bodies and the scientific regulation of artificial water bodies. Balancing human development needs with the preservation of ecological integrity is crucial to facilitating a water resource management strategy that integrates climatic and socio-economic dimensions, ensuring sustainable water use and protection for future generations. Full article

Manganese ore, as an important strategic metal resource for the country, was subject to unreasonable mining practices and outdated smelting technologies in early China, leading to severe ecological damage in mining areas. This study examines the trends in vegetation cover change in the historical manganese mining areas of Yongzhou under the influence of policy, providing technical references for mitigating the ecological impact of these legacy mining areas and offering a basis for adjusting mine restoration policies. This paper takes the manganese mining area in Yongzhou City, Hunan Province as a case study and selects multiple periods of Landsat satellite images from 2000 to 2023. By calculating the Normalized Difference Vegetation Index (NDVI) and the Fractional Vegetation Coverage (FVC), the spatiotemporal changes and driving factors of vegetation coverage in the Yongzhou manganese mining area from 2000 to 2023 were analyzed. The analysis results show that, in terms of time, from 2000 to 2012, the vegetation coverage in the manganese mining area decreased from 0.58 to 0.21, while from 2013 to 2023, it gradually recovered from 0.21 to 0.40. From a spatial perspective, in areas where artificial reclamation was conducted, the vegetation was mainly mildly and moderately degraded, while in areas where no artificial restoration was carried out, significant vegetation degradation was observed. Mining activities were the primary anthropogenic driving force behind the decrease in vegetation coverage, while effective ecological protection projects and proactive policy guidance were the main anthropogenic driving forces behind the increase in vegetation coverage in the mining area. Full article

One of the most significant urban challenges focuses on addressing the effects of urban overheating as a consequence of climate change. Several methods have been developed to characterize urban heat islands (UHIs); however, the most widely used involve complex planning, huge time consumption, and substantial human and technical resources on field monitoring campaigns. Therefore, this study aims to provide an easily accessible and affordable remote sensing method for locating urban hotspots and addresses a multi-criteria assessment of urban heat-related parameters, allowing for a comprehensive city-wide evaluation. The novelty is based on leveraging the potential of the last Landsat 9 satellite, the application of kernel spatial interpolation, and GIS open access data, providing very high-resolution land surface temperature images over urban spaces. Within GIS workflow, the city is divided into LCZs, thermal hotspots are detected, and finally, it is analyzed to understand how urban factors, such as urban boundaries, building density, and vegetation, affect urban scale LST, all using graphical and analytical cross-assessment. The methodology has been tested in Seville, a representative warm Mediterranean city, where variations of up to 10 °C have been found between homogeneous residential areas. Thermal hotspots have been located, representing 11% of the total residential fabric, while results indicate a clear connection between the urban factors studied and overheating. The conclusions support the possibility of generating a powerful affordable tool for future research and the design of public policy renewal actions in vulnerable areas. Full article

This study presents the selection of 20 Extended Pseudo Invariant Calibration Sites (EPICS) for radiometric calibration and the derivation of their hyperspectral profiles using the DLR Earth Sensing Imaging Spectrometer (DESIS) and Hyperion data. The hyperspectral profile of one of these clusters, the GONA-EPICS cluster, was validated against ground truth measurements from the RadCalNet Gobabeb Namibia (GONA) site, demonstrating statistical agreement within their respective uncertainties through Welch’s test. The applicability of these hyperspectral profiles was further evaluated by generating Spectral Band Adjustment Factor (SBAF) between Landsat 8 and Sentinel-2A using the GONA-EPICS hyperspectral profile and comparing them to SBAF values derived from RadCalNet GONA site measurements. SBAF results were statistically the same, while SBAF derived from the combined DESIS and Hyperion data exhibited reduced uncertainty compared to those derived using Hyperion data alone, which is attributed to DESIS’s finer spectral resolution (2.5 nm vs. 10 nm). To assess EPICS applicability in cross-calibration, Cluster 13-GTS, which includes pixels from the Libya 4 CNES ROI, was used as a target. Cross-calibration gains obtained using EPICS and the T2T cross-calibration methodology were compared to those from the traditional cross-calibration approach using Libya 4 CNES ROI. Results demonstrated statistically similar gains, with EPICS achieving an uncertainty better than 6% across all bands compared to 4.4% for the traditional method, while enabling global coverage for daily cross-calibration opportunities. This study introduces globally distributed EPICS with validated hyperspectral profiles, offering enhanced spectral resolution and reliability for radiometric calibration and stability monitoring. The methodology supports efficient global scale sensor calibration and prepares for future hyperspectral missions. Full article

Clouds and their shadows can be clearly identified from high-spatial-resolution satellite images, such as those provided by Landsat-8/9 with a spatial resolution of approximately 30 m and Sentinel-2 with a spatial resolution of around 20 m. Consequently, the difference between satellite measurements over cloud-shadowed and nearby illuminated pixels can be used to derive the aerosol optical depth (AOD), even in the absence of detailed surface optical properties. Based on this assumption, an algorithm for AOD retrieval over land is developed and tested using Landsat-8/9 images containing scattered clouds over Xuzhou, China, and Dalanzadgad, Mongolia. The retrieved AODs are compared against MODIS and ground-based sun photometer measurements. The findings reveal that, in cloudy regions, over 90% of the discrepancies between the AODs retrieved using the cloud-shadow method and ground-based measurements fall within 0.05 ± 0.20 AOD. This cloud-shadow algorithm represents a valuable complement to existing satellite aerosol retrieval methods, particularly in sparsely cloud-covered areas. Full article

Supraglacial debris cover considerably influences sub-debris ablation patterns and the surface morphology of glaciers by modulating the land–atmosphere energy exchange. Understanding its spatial distribution and temporal variations is crucial for analyzing melting processes and managing downstream disaster mitigation efforts. In recent years, the overall slightly positive mass balance or stable state of eastern Pamir glaciers has been referred to as the “Pamir-Karakoram anomaly”. It is important to note that spatial heterogeneity in glacier change has drawn widespread research attention. However, research on the spatiotemporal changes in the debris cover in this region is completely nonexistent, which has led to an inadequate understanding of debris-covered glacier variations. To address this research gap, this study employed Landsat remote sensing images within the Google Earth Engine platform, leveraging the Random Forest algorithm to classify the supraglacial debris cover. The classification algorithm integrates spectral features from Landsat images and derived indices (NDVI, NDSI, NDWI, and BAND RATIO), supplemented by auxiliary factors such as slope and aspect. By extracting the supraglacial debris cover from 1994 to 2024, this study systematically analyzed the spatiotemporal variations and investigated the underlying drivers of debris cover changes from the perspective of mass conservation. By 2024, the area of supraglacial debris in eastern Pamir reached 258.08 ± 20.65 km², accounting for 18.5 ± 1.55% of the total glacier area. It was observed that the Kungey Mountain region demonstrated the largest debris cover rate. Between 1994 and 2024, while the total glacier area decreased by −2.57 ± 0.70%, the debris-covered areas expanded upward at a rate of +1.64 ± 0.10% yr⁻¹. The expansion of debris cover is driven by several factors in the context of global warming. The rising temperature resulted in permafrost degradation, slope destabilization, and intensified weathering on supply slopes, thereby augmenting the debris supply. Additionally, the steep supply slope in the study area facilitates the rapid deposition of collapsed debris onto glacier surfaces, with frequent avalanche events accelerating the mobilization of rock fragments. Full article

Vegetation indices, especially the normalized difference vegetation index (NDVI), are widely used in urban vegetation assessments. However, estimating the vegetation abundance in urban scenes using the NDVI has constraints due to the complex spectral signature related to the urban structure, materials and other factors compared to natural ground surfaces. This paper employs the 3D discrete anisotropic radiative transfer (DART) model to simulate the spectro-directional reflectance of synthetic urban scenes with various urban geometries and building materials using a flux-tracking method under shaded and sunlit conditions. The NDVI is calculated using the spectral radiance in the red (0.6545 μm) and near-infrared bands (0.865 μm). The effects of the urban material heterogeneity and 3D structure on the NDVI, and the performance of three NDVI-based fractional vegetation cover (FVC) inversion algorithms, are evaluated. The results show that the effects of the building material heterogeneity on the NDVI are negligible under sunlit conditions but not negligible under shaded conditions. The NDVI value of building components within synthetic scenes is approximately zero. The shaded road exhibits a higher NDVI value in comparison to the illuminated road because of scattering from adjacent pixels. In order to correct the effects of scattering caused by building geometry, the reflectance of the Landsat 8/OLI image is corrected using the sky view factor (SVF) and then used to calculate the FVC. Jilin-1 satellite images with high spatial resolution (0.5 m) are used to extract the vegetation cover and then aggregated to 30 m spatial resolution to calculate the FVC for validation. The results show that the RMSE is up to 0.050 after correction, while the RMSE is 0.169 before correction. This study makes a contribution to the understanding of the effects of the urban 3D structure and material reflectance on the NDVI and provides insights into the retrieval of the FVC in different urban scenes. Full article

This article addresses the study of the governance process and the influence of urban heat islands in the city of Quevedo on the coast of Ecuador, and thus contributes to the production of technical and scientific information with a view to their mitigation. To identify the UHI pattern and visualize the temperature distribution on the soil surface, light intensity patterns on the soil surface are identified by the digital processing of the Landsat 7 ETM image. The NDVI, NDSI, and SAVI indices were also calculated, and the AQI was subsequently obtained using a weighted numerical cross-tabulation. The results show that the NDVI and SAVI indicators are correlated with each other and present a strong and positive classification with the neighborhoods and special areas in which there is a high proportion of vegetation, while the NSI and SAVI indicators are positively correlated with the areas. in which there is a greater proportion of built-up areas and roads. From a comprehensive analysis of the reviewed indicators, the authors derived an environmental quality index that explains the beneficial effects of vegetation and negatively explains the detrimental effects of a city covered in cement. Full article