Search Results (1,698)

The issue of abandoned cropland poses a significant threat to national food security and the sustainable use of land resources, highlighting the urgent need for an efficient and interpretable remote sensing identification framework. This study integrates three authoritative land cover datasets—the European Space Agency WorldCover (ESA), the Environmental Systems Research Institute Land Cover (ESRI), and the China Resource and Environment Data Cloud Platform (CRLC). Multi-source remote sensing features were extracted using the Google Earth Engine platform, and high-quality training samples were constructed by randomly selecting sample points based on these features in ArcGIS. A recursive feature cross-validation method is employed to eliminate redundant variables, thereby optimizing the feature structure without compromising classification accuracy. In terms of model construction, a multi-objective optimization strategy combining the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and eXtreme Gradient Boosting (XGBoost) is proposed. By incorporating a pruning mechanism, computational efficiency is significantly improved—accelerating the identification speed by up to 75%—while maintaining model accuracy (OA: 0.9817; Kappa: 0.9633; F1-score: 0.9817; recall: 0.9866). For result interpretation, the SHapley Additive exPlanations (SHAP) method is used to evaluate global feature importance, revealing that variables such as SAVG, B3_p25, Road, DEM, and Population contribute most significantly to the identification of abandoned cropland. Meanwhile, the Local Interpretable Model-Agnostic Explanations (LIME) method is applied to conduct local interpretability analysis on typical samples. The results show that, while some samples share consistent dominant features with the global results, others exhibit stronger local influences from features such as slope and SAVG. The combination of SHAP and LIME for global–local interpretability provides insight into the heterogeneous drivers of cropland abandonment and enhances the transparency of the classification model. This study presents a practical, scalable framework for the rapid identification and management of abandoned cropland, balancing precision, interpretability, and efficiency. Full article

In the mountainous and foothill areas of Santiago, Chile, debris flows and debris floods have been recurrent over recent decades, triggered by short-duration, high-intensity summer rainfall events. These events have caused significant damage to infrastructure and have affected the population, including loss of human lives. This study assesses the susceptibility to debris flow and debris flood generation in the Arrayán and Gualtatas stream basins, located in the Metropolitan Region, using satellite and cartographic data. A Susceptibility Index (SI) was determined through the analysis of 14 conditioning factors, grouped into three main categories: geology, geomorphology, and soil conditions. The weighting and ranking of each factor’s importance were carried out using the Analytic Hierarchy Process (AHP). The results, presented in a susceptibility map, indicate that 60.78% of the study area exhibits low to very low susceptibility, 24.64% moderate susceptibility, and 14.58% high to very high susceptibility, concentrated in stream headwaters, steep slopes, and areas with unconsolidated deposits. Recent debris flow events that have reached urban areas coincide with high-susceptibility zones, validating the methodology and cartographic products, which can support land-use planning and risk management efforts. Full article

Urbanization is a dominant force reshaping human settlements, driving socio-economic development while also causing significant environmental challenges. With over 56% of the world’s population now residing in urban areas—a figure expected to rise to two-thirds by 2050—land use changes are accelerating rapidly. The conversion of natural landscapes into impervious surfaces such as concrete and asphalt intensifies the Urban Heat Island (UHI) effect, raises urban temperatures, and strains local ecosystems. This study investigates land use and landscape changes in Ceredigion County, UK, utilizing remote sensing and GIS techniques to analyze urbanization impacts over two decades (2003–2023). Results indicate significant urban expansion of approximately 122 km², predominantly at the expense of agricultural and forested areas, leading to vegetation loss and changes in water availability. County-wide mean land surface temperature (LST) increased from 21.4 °C in 2003 to 23.65 °C in 2023, with urban areas recording higher values around 27.1 °C, reflecting a strong UHI effect. Spectral indices (NDVI, NDWI, NDBI, and NDBaI) reveal that urban sprawl adversely affects vegetation health, water resources, and land surfaces. The Urban Thermal Field Variance Index (UTFVI) further highlights areas experiencing thermal discomfort. Additionally, machine learning models, including Linear Regression and Random Forest, were employed to forecast future LST trends, projecting urban LST values to potentially reach approximately 27.4 °C by 2030. These findings underscore the urgent need for sustainable urban planning, reforestation, and climate adaptation strategies to mitigate the environmental impacts of rapid urban growth and ensure the resilience of both human and ecological systems. Full article

The long-term sustainability of conservation efforts in critical reforestation regions requires timely, spatiotemporal assessments of ecological quality. In alignment with China’s environmental initiatives, this study integrates Google Earth Engine (GEE) and MODIS data to construct an enhanced Remote Sensing Ecological Index (RSEI) for two decades of ecological monitoring. Hotspot analysis (Getis-Ord Gi*) revealed concentrated high-quality zones, particularly in Xinjiang’s Altay Prefecture, with ‘Good’ and ‘Excellent’ areas increasing from 21.64% in 2000 to 31.30% in 2020. To uncover driving forces, partial correlation and geographic detector analyses identified a transition in the Three-North Shelter Forest Program (TNSFP) from climate–topography constraints to land use–climate synergy, with land use emerging as the dominant factor. Socioeconomic influences, shaped by policy interventions, also played an important but fluctuating role. This progression—from natural constraints to active human regulation—underscores the need for climate-adaptive land use, balanced ecological–economic development, and region-specific governance. These findings validate the effectiveness of current conservation strategies and provide guidance for sustaining ecological progress and optimizing future development in the TNSFP. Full article

This study presents a validated drone-based methodology for inspecting fire protection belts in Galicia, Spain, with a focus on secondary protection belts surrounding settlements. Current manual inspection methods are limited by resource constraints and inefficiency, especially given Galicia’s steep slopes and fragmented, vegetated terrain. Our integrated approach combines high-resolution drone imagery, RTK positioning, GIS tools, and the Time2Parcel algorithm, enabling synchronized, parcel-level documentation at cadastral scale and allowing office-based technicians to directly review automatically generated video segments specific to each parcel for inspection verification. The methodology employs a hybrid classification system: automated assessments via orthophoto and LiDAR analysis and manual verification for cases with low confidence scores. Government technicians can perform office-based reviews without GIS expertise; the system automatically matches video to cadastral records, eliminating manual video review. Key results include the Time2Parcel algorithm for automatic video-to-parcel correlation, completion of inspections for 4934 parcels, and an operational efficiency increase of 68–70% reduction in inspection time compared with traditional methods. This workflow enables faster, safer, and more accurate inspections in highly fragmented rural contexts, improving legal compliance and environmental management. Full article

As coastal tidal flats—ecosystems of high ecological significance and socio-economic value—face accelerating degradation driven by climate change and intensified anthropogenic disturbances, there is an urgent need for efficient, automated, and scalable monitoring solutions. Traditional monitoring approaches are constrained by high implementation costs and limited spatial coverage, whereas remote sensing—particularly multispectral satellite imagery such as Sentinel-2—has emerged as a primary and widely adopted tool for large-scale environmental observation. Building upon recent advancements in cloud computing and WebGIS technologies, this study presents a web-based, interactive tidal flat extraction system implemented on Alibaba’s AI Earth platform. The system integrates multiple water indices (NDWI, mNDWI, and IWI) with a machine learning algorithm (Random Forest), and is deployed through a user-friendly interface developed using Vue.js and Leaflet, enabling flexible parameter configuration and real-time visualization of extraction results. Its front-end/back-end decoupled architecture enables non-programming users to conduct large-scale tidal flat mapping, thereby substantially lowering the technical barriers to coastal tidal flat monitoring and management in China. Full article

Preventive conservation (PC) of built heritage has proved to be one of the most efficient and sustainable approaches to ensure its long-term preservation. Nevertheless, the management of all the areas involved in a PC project is complex, often resulting in poor interaction between them. This research proposes a GIS-based methodology for integrating data from different PC areas into a centralised digital model, establishing a Common Data Environment (CDE) to optimise PC strategies for heritage systems in complex contexts. Applying this method to the pavilions of the 1929 Ibero-American Exhibition in Seville (Spain), the study addresses five key PC areas: active follow-up, damage detection and assessment, risk analysis, maintenance, and dissemination and valorisation. The approach involved designing a robust relational database structure—using PostgreSQL—tailored for heritage management, defining several data standardisation criteria, and testing semi-automated procedures for generating multi-scale 2D and 3D GIS (LOD2 and LOD4) entities using remote sensing data sources. The proposed spatial database has been designed to function seamlessly with major GIS platforms (QGIS and ArcGIS Pro), demonstrating successful integration and interoperability for data management, analysis, and decision-making. Geographic web services derived from the database content were created and uploaded to a WebGIS platform. While limitations exist, this research demonstrates that simplified GIS models are sufficient for managing PC data across various working scales, offering a resource-efficient alternative compared to more demanding existing methods. Full article

Tea is a prominent cash crop in global agriculture, and it is Sri Lanka’s top agricultural export known as ‘Ceylon Tea,’ employing nearly one million people, with land covering an area of 267,000 ha. However, over the past decade, many tea lands in Sri Lanka have been abandoned, leading to a gradual decline in production. This research aims to identify, map, and verify tea land abandonment over time and space by identifying and analyzing a series of land use trajectories with Landsat, Google Earth, and PlanetScope imageries to provide a substantial knowledge base. The study area covers five Divisional Secretariats Divisions in Kandy District, Central Highlands of Sri Lanka: Delthota, Doluwa, Udapalatha, Ganga Ihala Korale, and Pasbage Korale, where around 70% of the tea lands in Kandy District are covered. Six land use/cover (LULC) classes were considered: tea, Home Garden and Other Crop, forest, grass and bare land, built-up area, and Water Body. Abandoned tea lands were identified if the tea land was converted to another land use between 2015 and 2023. The results revealed the following: (1) 85% accuracy in LULC classification, revealing tea as the second-largest land use. Home Garden and Other Crop dominated, with an expanding built-up area. (2) The top 22 trajectories dominating the tea trajectories were identified, indicating that tea abandonment peaked between 2017 and 2023. (3) In total, 12% (5457 ha) of pixels were identified as abandoned tea lands during the observation period (2015–2023) at an accuracy rate of 94.7% in the validation. Significant changes were observed between the two urban centers of Gampola and Nawalapitiya towns. (4) Tea land abandonment over 7 years was the highest at 35% (1892.3 ha), while 5-year and 3-year periods accounted for 535.4 ha and 353.6 ha, respectively, highlighting a significant long-term trend. (5) The predominant conversion observed is the shift in tea towards Home Garden and Other Crop (2986.2 ha) during the timeframe. The findings underscore the extent and dynamics of tea land abandonment, providing critical insights into the patterns and characteristics of abandoned lands. This study fills a key research gap by offering a comprehensive spatial analysis of tea land abandonment in Sri Lanka. The results are valuable for stakeholders in the tea industry, providing essential information for sustainable management, policy-making, and future research on the spatial factors driving tea land abandonment. Full article

Chili (Capsicum sp.) is a high-value crop cultivated by farmers, but its production is vulnerable to weather extremes (such as irregular rainfall, high temperatures, and storms), pest and disease outbreaks, and spatially fragmented cultivation, resulting in unstable yields and income. Remote sensing (RS) and geographic information systems (GIS) offer promising tools for the timely, spatially explicit monitoring of chili crops. Despite growing interest in agricultural applications of these technologies, no systematic review has yet synthesized how RS and GIS have been used in chili production. This systematic review addresses this gap by evaluating existing literature on methodological approaches and thematic trends in the use of RS and GIS in chili crop monitoring and management. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines a comprehensive literature search was conducted using predefined keywords across Scopus, Web of Science, and Google Scholar. Sixty-five peer-reviewed articles published through January 2025 were identified and grouped into different thematic areas: crop mapping, biotic stress, abiotic stress, land suitability, crop health, soil and fertilizer management, and others. The findings indicate RS predominantly serves as the primary analytical method (82% of studies), while GIS primarily supports spatial integration and visualization. Key research gaps identified include limitations in spatial resolution, insufficient integration of intelligent predictive models, and limited scalability for smallholder farming contexts. The review highlights the need for future research incorporating high-resolution RS data, advanced modelling techniques, and spatial decision-support frameworks. These insights aim to guide researchers, agronomists, and policymakers toward enhanced precision monitoring and digital innovation in chili crop production. Full article

This study investigates urban heat vulnerabilities in Seville, Spain, using a multidimensional framework that integrates remote sensing, Space Syntax, and social vulnerability metrics. This research identifies Heat Boundaries (HBs), which are critical urban entities with elevated Land Surface Temperatures (LSTs) that act as barriers to adjacent vulnerable neighbourhoods, disrupting both physical and social continuity and environmental equity, and examines their relationship with the urban syntax and social vulnerability. The analysis spans two temporal scenarios: a Category 3 heatwave on 26 June 2023 and a normal summer day on 14 July 2024, incorporating both daytime and nighttime satellite-derived LST data (Landsat 9 and ECOSTRESS). The results reveal pronounced spatial disparities in thermal exposure. During the heatwave, peripheral zones recorded extreme LSTs exceeding 53 °C, while river-adjacent neighbourhoods recorded up to 7.28 °C less LST averages. In the non-heatwave scenario, LSTs for advantaged neighbourhoods close to the Guadalquivir River were 2.55 °C lower than vulnerable high-density zones and 3.77 °C lower than the peripheries. Nocturnal patterns showed a reversal, with central high-density districts retaining more heat than the peripheries. Correlation analyses indicate strong associations between LST and built-up intensity (NDBI) and a significant inverse correlation with vegetation cover (NDVI). Syntactic indicators revealed that higher Mean Depth values—indicative of spatial segregation—correspond with elevated thermal stress, particularly during nighttime and heatwave scenarios. HBs occupy 17% of the city, predominantly composed of barren land (42%), industrial zones (30%), and transportation infrastructure (28%), and often border areas with high social vulnerability. This study underscores the critical role of spatial configuration in shaping heat exposure and advocates for targeted climate adaptation measures, such as HB rehabilitation, greening interventions, and Connectivity-based design. It also presents preliminary insights for future deep learning applications to automate HB detection and support predictive urban heat resilience planning. Full article

Since 2005, landscape ecology has emerged as a structured scientific field in the Democratic Republic of Congo, notably shaped by the contributions of Professor Jan Bogaert. The evolution of research in this field can be divided into three main phases. The first phase (2005–2012) focused on the quantitative analysis of forest fragmentation using Geographic Information Systems and landscape metrics. From 2013 to 2019, research approaches broadened to include the social sciences, marking a shift toward a socio-ecological perspective on landscapes. Since 2020, the field has increasingly adopted holistic frameworks that integrate climatic factors and forward-looking modeling. Key research themes now include ecological flows across landscape mosaics, land-use dynamics, and the anthropogenic transformation of ecosystems. However, several challenges persist, including the lack of long-term temporal datasets, uneven geographic coverage, and limited integration of local knowledge systems. Notable advances have been made through high-resolution remote sensing and participatory methods, although their application is still limited by technical and financial constraints. This manuscript advocates for stronger interdisciplinary collaboration, improved field methodologies, and the development of context-appropriate tools to support sustainable and locally grounded landscape management in the Congolese context. Full article

Housing vacancy rate is a key indicator for evaluating urban sustainable development. Due to rapid urbanization, population outflow and insufficient industrial support, the housing vacancy problem is particularly prominent in China’s underdeveloped regions. However, the lack of official data and the limitations of traditional survey methods restrict in-depth research. This study proposes a vacancy rate estimation method based on Baidu Street View residential exterior images and deep learning technology. Taking Nanning, Guangxi as a case study, an automatic discrimination model for residential vacancy status is constructed by identifying visual clues such as window occlusion, balcony debris accumulation, and facade maintenance status. The study first uses Baidu Street View API to collect images of residential communities in Nanning. After manual annotation and field verification, a labeled dataset is constructed. A pre-trained deep learning model (ResNet50) is applied to estimate the vacancy rate of the community after fine-tuning with labeled street view images of Nanning’s residential communities. GIS spatial analysis is combined to reveal the spatial distribution pattern and influencing factors of the vacancy rate. The results show that street view images can effectively capture vacancy characteristics that are difficult to identify with traditional remote sensing and indirect indicators, providing a refined data source and method innovation for housing vacancy research in underdeveloped regions. The study further found that the residential vacancy rate in Nanning showed significant spatial differentiation, and the vacancy driving mechanism in the old urban area and the emerging area was significantly different. This study expands the application boundaries of computer vision in urban research and fills the research gap on vacancy issues in underdeveloped areas. Its results can provide a scientific basis for the government to optimize housing planning, developers to make rational investments, and residents to make housing purchase decisions, thus helping to improve urban sustainable development and governance capabilities. Full article

Avalanches occur frequently in mountainous areas and pose significant threats to roads and infrastructure. Clarifying how terrain conditions influence avalanche initiation and movement is critical to improving hazard assessment and response strategies. This study focused on a wet-snow slab avalanche that occurred on 26 March 2024, in the Simutas region of the northern Tianshan Mountains, Xinjiang, China. The authors combined remote sensing imagery, high-resolution meteorological station observations, field investigations, and numerical simulations (RAMMS::Avalanche) to analyze the avalanche initiation mechanism, dynamic behavior, and path recurrence characteristics. Results indicated that persistent heavy snowfall, rapid warming, and substantial daily temperature fluctuations triggered this avalanche. The predominant southeasterly (SE) winds and the northwest-facing (NW) shaded slopes created favorable leeward snow deposition conditions, increasing snowpack instability. High-resolution meteorological observations provided detailed wind, temperature, and precipitation data near the avalanche release zone, clearly capturing snowpack evolution and meteorological conditions before avalanche initiation. Numerical simulations showed a maximum avalanche flow velocity of 19.22 m/s, maximum flow depth of 12.42 m, and peak dynamic pressure of 129.3 kPa. The simulated avalanche deposition area and depth closely matched field observations. Multi-temporal remote sensing images indicated that avalanche paths in this area remained spatially consistent over time, with recurrence intervals of approximately 2–3 years. The findings highlight the combined role of local meteorological processes and terrain factors in controlling avalanche initiation and dynamics. This research confirmed the effectiveness of integrating remote sensing data, high-resolution meteorological observations, and dynamic modeling, providing scientific evidence for avalanche risk assessment and disaster mitigation in mountain regions. 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

Heavy rainfall events, characterized by extreme downpours that exceed 100 mm per day, pose an intensifying hazard to the densely settled valleys of the western Himalaya; however, their coupling with expanding urban land cover remains under-quantified. This study mapped the spatiotemporal exposure of built-up areas to heavy-day rainfall (HDR) across Jammu, Kashmir, and Ladakh and the adjoining areas by integrating daily Climate Hazards Group InfraRed Precipitation with Stations product (CHIRPS) precipitation (0.05°) with Global Human Settlement Layer (GHSL) built-up fractions within the Google Earth Engine (GEE). Given the limited sub-hourly observations, a daily threshold of ≥100 mm was adopted as a proxy for HDR, with sensitivity evaluated at alternative thresholds. The results showed that HDR is strongly clustered along the Kashmir Valley and the Pir Panjal flank, as demonstrated by the mean annual count of threshold-exceeding pixels increasing from 12 yr⁻¹ (2000–2010) to 18 yr⁻¹ (2011–2020), with two pixel-scale hotspots recurring southwest of Srinagar and near Baramulla regions. The cumulative high-intensity areas covered 31,555.26 km², whereas 37,897.04 km² of adjacent terrain registered no HDR events. Within this hazard belt, the exposed built-up area increased from 45 km² in 2000 to 72 km² in 2020, totaling 828 km². The years with the most expansive rainfall footprints, 344 km² (2010), 520 km² (2012), and 650 km² (2014), coincided with heavy Western Disturbances (WDs) and locally vigorous convection, producing the largest exposure increments. We also performed a forecast using a univariate long short-term memory (LSTM), outperforming Autoregressive Integrated Moving Average (ARIMA) and linear baselines on a 2017–2020 holdout (Root Mean Square Error, RMSE 0.82 km²; measure of errors, MAE 0.65 km²; R² 0.89), projecting the annual built-up area intersecting HDR to increase from ~320 km² (2021) to ~420 km² (2030); 95% prediction intervals widened from ±6 to ±11 km² and remained above the historical median (~70 km²). In the absence of a long-term increase in total annual precipitation, the projected rise most likely reflects continued urban encroachment into recurrent high-intensity zones. The resulting spatial masks and exposure trajectories provide operational evidence to guide zoning, drainage design, and early warning protocols in the region. Full article