Search Results (816)

Using LULC change detection analysis, it is possible to identify changes due to urbanization, deforestation, or a natural disaster in an area. As population growth and urbanization increase, real-time solutions for the effects of urbanization on land use are required to assess its implications for food security and livelihood. This study seeks to identify and quantify recent LULC changes in Limbe, Cameroon, and to measure rates of conversion between agricultural, forest, and urban lands between 1986 and 2020 using remote sensing and GIS. Also, there is a deficiency of research employing these data to evaluate the efficiency of LULC satellite data and a lack of awareness by local stakeholders regarding the impact on LULC change. The changes were identified in four classes utilizing maximum supervised classification in ENVI and ArcGIS environments. The classification result reveals that the 2020 image has the highest overall accuracy of 94.6 while the 2002 image has an overall accuracy of 89.2%. The overall gain for agriculture was approximately 4.6 km², urban had an overall gain of nearly 12.7 km², while the overall loss for forest was −16.9 km² during this period. Much of the land area previously occupied by forest is declining as pressures for urban areas and new settlements increase. This study’s findings have significant policy implications for sustainable land use and food security. It also provides a spatial method for monitoring LULC variations that can be used as a framework by stakeholders who are interested in environmentally conscious development and sustainable land use practices. 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

The rapid expansion of urban areas necessitates effective monitoring systems for sustainable development planning. Time-series change detection algorithms applied to satellite imagery offer promising solutions, but their comparative effectiveness specifically for urban land cover monitoring remains poorly understood. This study aims to systematically evaluate and optimize three widely used algorithms—LandTrendr, CCDC, and BFAST—selected for their proven capabilities in different land cover change contexts and distinct algorithmic approaches. Using Landsat 5/7/8 (TM/ETM+/OLI) time-series data from 2000 to 2020 and a globally distributed dataset of 200 sample locations spanning six continents, we assess these algorithms across multiple spectral bands and parameter settings for land cover change detection in urban areas. Our analysis reveals that CCDC achieves the highest accuracy (78.14% F1 score) when utilizing complete spectral information (bands B1–B7), outperforming both BFAST (74.32% F1 score with NDVI) and LandTrendr (71.29% F1 score with B1). We demonstrated that, contrary to conventional approaches that prioritize vegetation indices, visible light bands—particularly B1 and B2—achieve higher performance across multiple algorithms. For instance, in LandTrendr, B1 yielded an F1 score of 71.29%, whereas NDVI and EVI produced 56.19% and 53.16%, respectively. Similarly, in CCDC, B2 achieved an F1 score of 72.19%, while NDVI and EVI resulted in 68.57% and 65.33%, respectively. Our findings underscore that parameter optimization and band selection significantly impact detection accuracy, with variations up to 30% observed across different configurations. This comprehensive evaluation provides critical methodological guidance for satellite-based urban expansion monitoring and identifies specific optimization strategies to enhance the application of existing algorithms for urban land cover change detection. Full article

Waterborne illnesses, including those caused by Salmonella, are an increasing public health challenge, particularly in developing countries. Potential sources of salmonellosis include fruits and vegetables irrigated/treated with surface water, leading to human infections. Salmonella causes millions of gastroenteritis cases annually, but early detection through routine water quality surveillance is time-consuming, requires specialized equipment, and faces limitations, such as coverage gaps, delayed data, and poor accessibility. Climate change-driven extreme events such as floods and droughts further exacerbate variability in water quality. In this context, remote sensing offers an efficient and cost-effective alternative for environmental monitoring. This study evaluated the potential of Sentinel-2 satellite imagery to predict Salmonella occurrence in the Maipo and Mapocho river basins (Chile) by integrating spectral, microbiological, climatic, and land use variables. A total of 1851 water samples collected between 2019 and 2023, including 704 positive samples for Salmonella, were used to develop a predictive model. Predicting Salmonella in surface waters using remote sensing is challenging for several reasons. Satellite sensors capture environmental proxies (e.g., vegetation cover, surface moisture, and turbidity) but not pathogens. Our goal was to identify proxies that reliably correlate with Salmonella. Twelve spectral indices (e.g., NDVI, NDWI, and MNDWI) were used as predictors to develop a predictive model for the presence of the pathogen, which achieved 59.2% accuracy. By spatially interpolating the occurrences, it was possible to identify areas with the greatest potential for Salmonella presence. NDWI and AWEI were most strongly correlated with Salmonella presence in high-humidity areas, and spatial interpolation identified the higher-risk zones. These findings reveal the challenges of using remote sensing to identify environmental conditions conducive to the presence of pathogens in surface waters. This study highlights the methodological challenges that must be addressed to make satellite-based surveillance an accessible and effective public health tool. By integrating satellite data with environmental and microbiological analyses, this approach can potentially strengthen low-cost, proactive environmental monitoring for public health decision-making in the context of climate change. Full article

This article analyses the impact of urban sprawl on the Valencia Plain aquifer system from 1990 to 2018, focusing on land use and land cover (LULC) changes and their environmental implications. The study applies the Weighted Environmental Index (WEI), a composite indicator based on a functional landscape perspective, to quantify changes in the environmental value over time. The WEI combines CORINE Land Cover and World Settlement Footprint data to enhance spatial resolution and urban land detection. The results show a significant territorial transformation, with urban surfaces expanding by 70% and rainfed agricultural areas declining by over 59%. Consequently, the WEI decreased from 44.80 in 1990 to 40.68 in 2018, representing a 9.2% reduction in the environmental value. These changes threaten the sustainability of key ecosystems such as the Albufera Natural Park and indicate a reduced capacity to deliver ecosystem services, including aquifer recharging, biodiversity conservation, and climate regulation. The findings underscore the need for integrated land-use planning, the protection of peri-urban agricultural areas, and the implementation of nature-based solutions to counteract the environmental impacts of urban growth in Mediterranean metropolitan contexts. Full article

Remotely sensed cropland abandonment monitoring is crucial for providing spatially explicit references for maintaining sustainable agricultural practices and ensuring food security. However, abandoned cropland is commonly detected based on multi-date classification or the dynamics of a single vegetation index, with the interactions between vegetation and soil time series often being neglected, leading to a failure to understand its full-life-cycle succession processes. To fill this gap, we propose a new full-life-cycle modeling framework based on the interactive trajectories of vegetation–soil-related endmembers to identify abandoned and reclaimed cropland in Jinan from 2000 to 2022. In this framework, highly accurate annual fractional vegetation- and soil-related endmember time series are generated for Jinan City for the 2000–2022 period using spectral mixture models. These are then used to integrally reconstruct temporal trajectories for complex scenarios (e.g., abandonment, weed invasion, reclamation, and fallow) using logistic and double-logistic models. The parameters of the optimization model (fitting type, change magnitude, start timing, and change duration) are subsequently integrated to develop a rule-based hierarchical identification scheme for cropland abandonment based on these complex scenarios. After applying this scheme, we observed a significant decline in green vegetation (a slope of −0.40% per year) and an increase in the soil fraction (a rate of 0.53% per year). These pathways are mostly linked to a duration between 8 and 15 years, with the beginning of the change trend around 2010. Finally, the results show that our framework can effectively separate abandoned cropland from reclamation dynamics and other classes with satisfactory precision, as indicated by an overall accuracy of 86.02%. Compared to the traditional yearly land cover-based approach (with an overall accuracy of 77.39%), this algorithm can overcome the propagation of classification errors (with product accuracy from 74.47% to 85.11%), especially in terms of improving the ability to capture changes at finer spatial scales. Furthermore, it also provides a better understanding of the whole abandonment process under the influence of multi-factor interactions in the context of specific climatic backgrounds and human disturbances, thus helping to inform adaptive abandonment management and sustainable agricultural policies. Full article

Accurate information on species distributions and population sizes is essential for effective biodiversity conservation, yet such data are often lacking at national scales. This study addresses this gap by assessing the distribution and abundance of 111 bird species across Croatia, including breeding, wintering, and migratory flyway populations. We combined Species Distribution Models (SDMs) with expert-based population estimates to generate spatially explicit predictions. The modeling framework incorporated high-resolution Earth observation (EO) data and advanced spatial analysis techniques. Environmental variables, such as land cover, were derived from satellite datasets, while climate variables were interpolated from ground measurements and refined using EO-based co-variates. Model calibration and validation were based on species occurrence records and EO-derived predictors. This integrative approach enabled both national-scale population estimates and fine-scale habitat assessments. The results identified critical habitats, population hotspots, and areas likely to experience distribution shifts under changing environmental conditions. By integrating EO data with expert knowledge, this study enhances the robustness of population estimates, particularly where species monitoring data are incomplete. The findings support conservation prioritization, inform land use and resource management, and contribute to long-term biodiversity monitoring. The methodology is scalable and transferable, offering a practical framework for ecological assessments in diverse regions. We integrated expert-based population estimates with species distribution models (SDMs) by applying expert-derived density values to areas of suitable habitat predicted by SDMs. This approach enables spatially explicit population estimates by combining ecological modeling with expert knowledge, which is particularly useful in systems with limited data. Experts provided species-specific density estimates stratified by habitat type, seasonality, behavior, and detectability, aligned with habitat suitability classes derived from SDM outputs. Full article

Accurate and timely flood mapping is critical for informing emergency response and risk mitigation during extreme weather events. This study presents a synthetic aperture radar (SAR)-based approach for rapid flood extent mapping using Sentinel-1 imagery, demonstrated for Tropical Storm Imelda (17–21 September 2019) in southeastern Texas. Dual-polarization Sentinel-1 SAR data (VH and VV) were processed by computing the VH/VV backscatter ratio, and the resulting ratio image was classified using a supervised Random Forest classifier to delineate water and land. All Sentinel-1 images underwent radiometric calibration, speckle noise filtering, and terrain correction to ensure precision in flood delineation. The Random Forest classifier achieved an overall flood mapping accuracy exceeding 94%, with Cohen’s kappa coefficients of approximately 0.75–0.80, demonstrating the approach’s reliability in distinguishing transient floodwaters from permanent water bodies. The spatial distribution of flooding was strongly influenced by topography and land cover. Analysis of Shuttle Radar Topography Mission (SRTM) digital elevation data revealed that low-lying, flat terrain was most vulnerable to inundation; correspondingly, the land cover types most affected were hay/pasture, cultivated land, and emergent wetlands. Additionally, urban areas with low-intensity development experienced extensive flooding, attributed to impervious surfaces exacerbating runoff. A strong, statistically significant correlation (R² = 0.87, p < 0.01) was observed between precipitation and flood extent, indicating that heavier rainfall led to greater inundation; accordingly, the areas with the highest rainfall totals (e.g., Jefferson and Chambers counties) experienced the most extensive flooding, as confirmed by SAR-based change detection. The proposed approach eliminates the need for manual threshold selection, thereby reducing misclassification errors due to speckle noise and land cover heterogeneity. Harnessing globally available Sentinel-1 data with near-real-time processing and a robust classifier, this approach provides a scalable solution for rapid flood monitoring. These findings underscore the potential of SAR-based flood mapping under adverse weather conditions, thereby contributing to improved disaster preparedness and resilience in flood-prone regions. Full article

Cropland slope structure is a key factor influencing agricultural sustainability and ecological risk, especially in topographically complex regions. This study proposes a novel framework that integrates slope spectrum analysis with H3 hexagonal grid partitioning to examine the spatiotemporal dynamics of cropland slope across China from 1990 to 2023. Using 30 m CLCD land cover data, we derived key indicators, including the T-value, upper slope limit (ULS), peak area proportion (PaP), slope at maximum area (SMA), and cropland slope change index (CSCI). This grid-based, multi-indicator approach enables the fine-scale detection of slope structure transitions. Results show that the average slope of cropland fluctuated at around 4.12°, peaking at 4.18° in 2003, while the ULS remained stable at 17°, with 95% of cropland below this threshold. Regionally, cropland in southwest and northwest China was concentrated on steeper slopes (ULS > 26°, PaP < 10%), whereas flatter areas in north and south China had cropland mainly below 15°. From 1990 to 2023, upslope expansion was evident in south China (CSCI > 10), while downslope shifts aligned with high-slope cropland in the western regions. Geographically weighted regression revealed significant positive correlations between increasing ULS and CSCI and elevated cropland fragmentation and soil erosion in hilly areas. These findings highlight the ecological risks of cropland expansion into steep terrain. The proposed framework offers a spatially explicit perspective of cropland slope evolution and supports targeted strategies for land management and ecological restoration. Full article

Declassified Keyhole imagery partially provides multi-temporal coverage that can support land-use change analysis. However, the volume of commercial (paid) Keyhole data is much larger than that of free imagery, and the extent to which commercial data can enhance the application of Keyhole imagery for land-use change analysis remains unknown. In this work, the full archive of Keyhole images for China was obtained from the USGS to identify regions with repeated coverage automatically by using the ArcPy library in Python. The years from 1960 to 1984 were divided into five 5-year periods (T1, 1960~1964; T2, 1965~1969; T3, 1970~1974; T4, 1975~1979; and T5, 1980~1984). The Keyhole images’ metadata, including resolution, acquisition time, and image extent, were utilized to classify the images into meter level (C1), five-meter level (C2), and ten-meter level (C3). The spatial distributions of combinations of imagery at different resolutions for each period and the repeated coverage of imagery at each resolution across the five periods were investigated to extract repeated-coverage regions. The coverage proportions were nearly 100% for C1 imagery for the T3, T4, and T5 periods; C2 for T1 and T2; and C3 for T1 and T3. The T3 period featured extensive coverage at all three resolutions (66%). The T1 period was mainly covered by C2/C3 (93%), and T4 had C1/C3 coverage (68%). In contrast, T2 relied primarily on C2 imagery (100%), and T5 was only covered by C1 (96%). For C1 imagery, land-use changes in almost all areas in China in the T3/T4/T5 time span could be detected, and for C2 and C3 images, the corresponding time spans were T1/T2 and T1/T3. Although this study focused on repeated-coverage area detection within China, the methodology and Python codes provided allow for the implementation of an automated process for land-use change detection from the 1960s to the 1980s in other regions worldwide. Full article

As many areas of the world continue to grow, it is important to detect areas that are urbanizing at paces above the norm and predict future urban areas, so that optimal city planning can occur. However, methods to detect rapid urbanization are currently absent. Additionally, methods that predict future urban areas often rely on deep learning algorithms, which can be computationally expensive and require a large data volume. Furthermore, prediction methods are typically developed in a single location and are not evaluated across diverse geographies. In this study, rapid and future urbanization algorithms are developed, which are based on methods that use an ensemble of built-up spectral indices and a random forest classifier to detect built-up land cover in Sentinel-2 imagery, across ten sites that vary in their climate and population. Results show that the rapid urbanization algorithm can highlight anomalous urban growth. The future urbanization algorithm had an average overall accuracy of 0.66 (±0.11) and an average F1-score of 0.46 (±0.23). However, the method performed well in areas without seasonal vegetation changes and bare ground surroundings with overall accuracy values and F1-scores near or over 0.80. Overall, these methods provide an automated global approach to identifying rapid and future urban areas with minimal data and computational resources needed, which can enable urban planners to obtain information quickly so that decision making for city planning can be completed faster. Full article

The Southwest Region (SWR) is one of Nigeria’s six geo-political zones and comprises six distinct states. It holds considerable significance due to its unique geographical features, economic vibrancy, pastoral heritage, and fragile natural ecosystems. These ecosystems are becoming increasingly susceptible to human activities and the adverse impacts of climate change. This study analyzed the temporal and spatial variations of the Normalized Difference Vegetation Index (NDVI) in relation to key influencing factors in the SWR from 2001 to 2020. The analytical methods included Sen’s slope estimator, the Mann–Kendall trend test, and the Geographical Detector Model (GDM). The analysis revealed significant spatial variability in vegetation cover, with dense vegetation concentrated in the eastern part of the region and low vegetation coverage overall, reflected by an average NDVI value of 0.45, indicating persistent vegetation stress. Human activities, particularly land use and land cover (LULC) changes, were identified as major drivers of vegetation loss in some states such as Ekiti, Lagos, Ogun, and Ondo. Conversely, Osun and Oyo exhibited signs of vegetation recovery, suggesting the potential for restoration. The study found that topographic factors, including slope and elevation, as well as climatic variables like precipitation, influenced vegetation patterns. However, the impact of these factors was secondary to LULC dynamics. The interaction detection analysis further highlighted the cumulative effect of combined anthropogenic and environmental factors on vegetation distribution, with the interaction between LULC and topography being particularly significant. These findings provide essential insights into the biological condition of the SWR and contribute to advancing the understanding of vegetation patterns with critical implications for the sustainable management and conservation of tropical forest ecosystems. Full article

This work presents the use of remote sensing data for land cover mapping with a case of Central Apennines, Italy. The data include 8 Landsat 8-9 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) satellite images in six-year period (2018–2024). The operational workflow included satellite image processing which were classified into raster maps with automatically detected 10 classes of land cover types over the tested study. The approach was implemented by using a set of modules in Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS). To classify remote sensing (RS) data, two types of approaches were carried out. The first is unsupervised classification based on the MaxLike approach and clustering which extracted Digital Numbers (DN) of landscape feature based on the spectral reflectance of signals, and the second is supervised classification performed using several methods of Machine Learning (ML), technically realised in GRASS GIS scripting software. The latter included four ML algorithms embedded from the Python’s Scikit-Learn library. These classifiers have been implemented to detect subtle changes in land cover types as derived from the satellite images showing different vegetation conditions in spring and autumn periods in central Apennines, northern Italy. Full article

The occurrence of hurricanes in the southern U.S. is on the rise, and assessing the damage caused to forests is essential for implementing protective measures and comprehending recovery dynamics. This work aims to create a novel data integration framework that employs LANDSAT 8, drone-based images, and geographic information system data for change detection analysis for different forest types. We propose a method for change vector analysis based on a unique spectral mixture model utilizing composite spectral indices along with univariate difference imaging to create a change detection map illustrating disturbances in the areas of McDowell County in western North Carolina impacted by Hurricane Helene. The spectral indices included near-infrared-to-red ratios, a normalized difference vegetation index, Tasseled Cap indices, and a soil-adjusted vegetation index. In addition to the satellite imagery, the ground truth data of forest damage were also collected through the field investigation and interpretation of post-Helene drone images. Accuracy assessment was conducted with geographic information system (GIS) data and maps from the National Land Cover Database. Accuracy assessment was carried out using metrics such as overall accuracy, precision, recall, F score, Jaccard similarity, and kappa statistics. The proposed composite method performed well with overall accuracy and Jaccard similarity values of 73.80% and 0.6042, respectively. The results exhibit a reasonable correlation with GIS data and can be employed to assess damage severity. Full article

Currently, deep learning networks based on architectures such as CNN and Transformer have achieved significant advances in remote sensing image change detection, effectively addressing the issue of false changes due to spectral and radiometric discrepancies. However, when handling remote sensing image data from multiple sensors, different viewing angles, and extended periods, these models show limitations in modelling dynamic interactions and feature representations in change regions, restricting their ability to model the integrity and precision of irregular change areas. We propose the Context-Aware Global-Local Subspace Attention Change Detection Network (CGLCS-Net) to resolve these issues and introduce the Global-Local Context-Aware Selector (GLCAS) and the Subspace-based Self-Attention Fusion (SSAF) module. GLCAS dynamically selects receptive fields at different feature extraction stages through a joint pooling attention mechanism and depthwise separable convolution, enhancing global context and local feature extraction capabilities and improving feature representation for multi-scale and irregular change regions. The SSAF module establishes dynamic interactions between dual-temporal features via feature decomposition and self-attention mechanisms, focusing on semantic change areas to address challenges such as sensor viewpoint variations and the texture and spectral inconsistencies caused by long periods. Compared to ChangeFormer, CGLCS-Net achieved improvements in the IoU metric of 0.95%, 9.23%, and 13.16% on the three public datasets, i.e., LEVIR-CD, SYSU-CD, and S2Looking, respectively. Additionally, it reduced model parameters by 70.05%, floating-point operations by 7.5%, and inference time by 11.5%. These improvements enhance its applicability for continuous land use and land cover change monitoring. Full article