Search Results (821)

We quantified monthly changes in land surface temperature (LST) over the Sierra Madre Occidental (SMO) in Mexico from 2000 to 2024 using MODIS satellite imagery (MOD11B3). The SMO is the longest continuous mountain complex in Mexico, covering an area of 251,648 km². It is an area of great importance for biodiversity conservation, as it is home to numerous endemic flora and fauna species. The Intergovernmental Panel on Climate Change (IPCC) has stated that high mountain areas are among the regions most affected by climate change and are a key element of the water cycle. We calculated an anomaly index by vegetation type in the SMO and applied change detection to spatially identify where changes in LST had taken place. The lowest LST values were in December and January (20 to 25 °C), and the highest LST values occurred in April, May, and June (>40 °C). Change detection applied to the time series showed that the months with the highest positive LST changes were May to July, and that November was notable for increases of up to 5.86 °C. The time series that showed the greatest changes compared to 2000 was the series for 2024, where LST increases were found in all months of the year. The maximun average increase was 6.98 °C from 2000 to June 2005. In general, LST anomalies show a pattern of occurrence in the months of March through July for the three vegetation types distributed in the Sierra Madre Occidental. In the case of the pine forest, which is distributed at 2000 m above sea level, and higher, it was expected that there would be no LST anomalies; however, anomalies were present in all time series for the spring and early summer months. The LST values were validated with in situ data from weather stations using linear regression models. It was found that almost all the values were related, with R² > 0.60 (p < 0.001). In conclusion, the constant increases in LST throughout the SMO are probably related to the loss of 34% of forest cover due to forest fires, logging, land use changes, and increased forest plantations. Full article

Accurate and timely land monitoring is crucial for addressing global environmental, economic, and societal challenges, including climate change, sustainable development, and disaster mitigation. While single-source remote sensing data offers significant capabilities, inherent limitations such as cloud cover interference (optical), speckle noise (radar), or limited spectral information (LiDAR) often hinder comprehensive and robust characterization of land surfaces. Recent advancements in synergistic harmonization technology for land monitoring, along with enhanced signal processing techniques and the integration of machine learning algorithms, have significantly broadened the scope and depth of geosciences. Therefore, it is essential to summarize the comprehensive applications of synergistic harmonization technology for geosciences, with a particular focus on recent advancements. Most of the existing review papers focus on the application of a single technology in a specific area, highlighting the need for a comprehensive review that integrates synergistic harmonization technology. This review provides a comprehensive review of advancements in land monitoring achieved through the synergistic harmonization of optical, radar, and LiDAR satellite technologies. It details the unique strengths and weaknesses of each sensor type, highlighting how their integration overcomes individual limitations by leveraging complementary information. This review analyzes current data harmonization and preprocessing techniques, various data fusion levels, and the transformative role of machine learning and deep learning algorithms, including emerging foundation models. Key applications across diverse domains such as land cover/land use mapping, change detection, forest monitoring, urban monitoring, agricultural monitoring, and natural hazard assessment are discussed, demonstrating enhanced accuracy and scope. Finally, this review identifies persistent challenges such as technical complexities in data integration, issues with data availability and accessibility, validation hurdles, and the need for standardization. It proposes future research directions focusing on advanced AI, novel fusion techniques, improved data infrastructure, integrated “space–air–ground” systems, and interdisciplinary collaboration to realize the full potential of multi-sensor satellite data for robust and timely land surface monitoring. Supported by deep learning, this synergy will improve our ability to monitor land surface conditions more accurately and reliably. Full article

Peatland is a difficult landscape to map due to its challenging conditions. Remote sensing lends itself to mapping efforts, but can be hampered by common weather conditions in peatland locations. Sentinel-1 Synthetic Aperture Radar technology penetrates prevalent cloud cover. Techniques used to detect water surfaces using Sentinel-1 backscatter intensity have been applied in this study to delineate peatland land cover. This application was then extended with the aim of identifying the relative conditions of peatland within an area of interest. A peatland study site was selected at Winter Hill, near Bolton in Lancashire, UK, where a nationally significant wildfire occurred in 2018. Sentinel-1 imagery captured in the winter after the wildfire quite accurately reflected the fire damage extent. From further examination, it was found that in frozen conditions there are significant statistical differences between peatland surfaces and visually similar land cover, such as fields used for livestock grazing. Using the inter-quartile range of land cover samples to identify suitable backscatter thresholds, a surface map was produced depicting peatland of varying conditions and other land cover categories. This was compared with field visit photographic records to ascertain accuracy of representation. Further analysis detected correlation between backscatter and temperature for peatland surfaces that was not evident for other land cover classes. Steeper terrain can though affect this relationship. Conversely, no significant connection could be found in areas where surface water is most likely to be retained. Aggregating Sentinel-1 backscatter according to sub-catchment zones presented the potential to further delineate by condition within a peatland land cover sample. Therefore, the use of Sentinel-1 imagery in frozen conditions in context with terrain and sub-catchment level hydrological zoning provides the opportunity to aid environmental monitoring by delineating peatland from other land cover, identifying climate-change effects such as wildfires and assessing relative condition at scale. Full article

Observing building changes in remote sensing images plays a crucial role in monitoring urban development and promoting sustainable urbanization. Mainstream change detection methods have demonstrated promising performance in identifying building changes. However, buildings have large intra-class variance and high similarity with other objects, limiting the generalization ability of models in diverse scenarios. Moreover, most existing methods only detect whether changes have occurred but ignore change types, such as new construction and demolition. To address these issues, we present a building change-type detection network (BCTDNet) based on the Segment Anything Model (SAM) to identify newly constructed and demolished buildings. We first construct a dual-feature interaction encoder that employs SAM to extract image features, which are then refined through trainable multi-scale adapters for learning architectural structures and semantic patterns. Moreover, an interactive attention module bridges SAM with a Convolutional Neural Network, enabling seamless interaction between fine-grained structural information and deep semantic features. Furthermore, we develop a change-aware attribute decoder that integrates building semantics into the change detection process via an extraction decoding network. Subsequently, an attribute-aware strategy is adopted to explicitly generate distinct maps for newly constructed and demolished buildings, thereby establishing clear temporal relationships among different change types. To evaluate BCTDNet’s performance, we construct the JINAN-MCD dataset, which covers Jinan’s urban core area over a six-year period, capturing diverse change scenarios. Moreover, we adapt the WHU-CD dataset into WHU-MCD to include multiple types of changing. Experimental results on both datasets demonstrate the superiority of BCTDNet. On JINAN-MCD, BCTDNet achieves improvements of 12.64% in IoU and 11.95% in F1 compared to suboptimal methods. Similarly, on WHU-MCD, it outperforms second-best approaches by 2.71% in IoU and 1.62% in F1. BCTDNet’s effectiveness and robustness in complex urban scenarios highlight its potential for applications in land-use analysis and urban planning. Full article

Hyperspectral image (HSI) denoising is a crucial step in image preprocessing as its effectiveness has a direct impact on the accuracy of subsequent tasks such as land cover classification, target recognition, and change detection. However, existing methods suffer from limitations in effectively integrating multi-scale features and adaptively modeling complex noise distributions, making it difficult to construct effective spatial–spectral joint representations. This often leads to issues like detail loss and spectral distortion, especially when dealing with complex mixed noise. To address these challenges, this paper proposes a multi-scale differentiated denoising network based on spatial–spectral cooperative attention (MDSSANet). The network first constructs a multi-scale image pyramid using three downsampling operations and independently models the features at each scale to better capture noise characteristics at different levels. Additionally, a spatial–spectral cooperative attention module (SSCA) and a differentiated multi-scale feature fusion module (DMF) are introduced. The SSCA module effectively captures cross-spectral dependencies and spatial feature interactions through parallel spectral channel and spatial attention mechanisms. The DMF module adopts a multi-branch parallel structure with differentiated processing to dynamically fuse multi-scale spatial–spectral features and incorporates a cross-scale feature compensation strategy to improve feature representation and mitigate information loss. The experimental results show that the proposed method outperforms state-of-the-art methods across several public datasets, exhibiting greater robustness and superior visual performance in tasks such as handling complex noise and recovering small targets. Full article

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