Search Results (10,821)

Accurate and up-to-date knowledge of land use and land cover represents one of the central challenges in spatial planning and landscape sciences. In this context, the present work introduces GANCIU (Geospatial Analysis with Neural Classification and Image Understanding), an original hybrid pipeline for the automatic extraction of man-made infrastructure from high-resolution satellite imagery. The primary methodological contribution lies in the sequential integration of four technologically heterogeneous components: a per-pixel Random Forest classifier, a guided image modulation step, edge detection via the Mumford–Shah variational functional solved through the Ambrosio–Tortorelli approximation, and final object delineation via the Segment Anything Model (SAM). Each component does not operate independently but conditions and informs the next: The RF probability map guides the modulation, which in turn directs the sensitivity of the variational step exclusively towards regions of interest; the AT edges provide spatial prompts to SAM, for which its masks are finally filtered by the RF probability in an adaptive manner through a Gaussian Mixture Model. This progressive conditioning scheme constitutes the architectural core of GANCIU and distinguishes it from approaches that combine classification and segmentation in parallel or in purely sequential fashion with each stage conditioning the next but without any reverse correction between them. The Random Forest classifier was trained on 44 manually annotated scenes, geographically disjoint from the twelve independent scenes used for quantitative validation. This validation, based on an instance matching protocol (precision, recall, F1 score, and IoU), confirms the contribution of the full pipeline over a Random-Forest-only baseline: Pooled false positives fall by close to two orders of magnitude (from 8320 to 209), while true positives rise nearly twentyfold (from 5 to 95), with a mean IoU of 0.742 ± 0.060 on correctly matched objects. Notably, the entire pipeline—including SAM-based segmentation—runs end-to-end on a modest, GPU-free consumer laptop (four logical CPU cores, under 16 GB RAM), demonstrating that competitive infrastructure-extraction performance does not require specialised computing hardware. Full article

Ecological resilience is a critical indicator of forest ecosystem stability and the capacity to respond to disturbance. Under intensifying climate change and human activities, accurately evaluating forest ecological resilience is important for ecosystem restoration and sustainable management. This study developed a satellite time−series−based framework for assessing ecological resilience from the complementary perspectives of resistance and recovery. Taking the Qianshan region, a typical forest area in the northeastern forest belt, as a case study, MODIS Normalized Difference Vegetation Index (NDVI) time−series data from 2005 to 2024 were analyzed. The Breaks For Additive Season and Trend (BFAST) algorithm was used to detect vegetation breakpoints, after which ecological resistance and recovery were quantified using breakpoint magnitude and post−disturbance NDVI growth rate. The optimal−parameter−based geographical detector (OPGD) was further applied to identify the spatial drivers of resistance and recovery and their interaction effects. Approximately 21% of the pixels in the Qianshan region experienced at least one breakpoint during the study period, and more than 80% of the disturbed pixels contained only one detected breakpoint. More than 70% of the disturbed pixels subsequently exhibited vegetation recovery, and most recovered pixels had normalized recovery values between 0.40 and 1.00. In contrast, ecological resistance was generally low and varied substantially among land−cover types. Forests exhibited higher resistance but lower recovery, whereas grasslands and croplands showed lower resistance but stronger post−disturbance recovery. Among the individual factors, precipitation and slope had relatively high explanatory power for the spatial differentiation of recovery. Factor interactions substantially enhanced explanatory power, with the interaction between precipitation and elevation exerting the strongest influence on resistance and the interaction between precipitation and slope exerting the strongest influence on recovery. Although mining density had relatively limited explanatory power at the regional scale, mining activities caused non−negligible localized impacts, particularly in open−pit mining areas. The proposed framework provides a practical basis for long−term monitoring, ecological restoration, and differentiated forest management in disturbance−prone regions. Full article

Understanding habitat requirements across spatial scales is important for conserving declining migratory species such as the Cerulean warbler. This study integrates data from several forms of remote sensing platforms: automated recording units, airborne LiDAR, and satellite remote sensing, to characterize the habitat of Cerulean warbler detection sites across central Indiana. We also modeled the suitable habitat of the species across the contiguous United States. Automated recording units were deployed across four forest types, and automated classification was used to derive species detections, which were manually validated. Structural variables derived within 25 m and 50 m buffers included LiDAR-based canopy height metrics, vertical vegetation distribution, foliage height diversity, and satellite-derived Enhanced Vegetation Index (EVI). Cerulean warbler detections were identified at 14 of the 57 acoustic sensor locations. Habitat characteristics at detection and non-detection sites were compared using univariate statistical tests and logistic regression models. Habitat associations varied with spatial scale. At 25 m, detection sites had significantly lower vegetation cover within the 5–10 m height stratum, which was also the highest-ranked candidate predictor, whereas EVI was the highest-ranked predictor at 50 m. At the broad scale, occurrence records from the Global Biodiversity Information Facility (GBIF) were integrated with climatic, topographic, land-cover, and anthropogenic variables within a MaxEnt modeling framework. The model showed moderate predictive performance and identified land cover as a key predictor of habitat suitability, with deciduous forests showing the highest probability of occurrence. This study highlights how multi-modal sensor data can be integrated for biodiversity monitoring and habitat assessment. Full article

This study investigates the impacts of human activities and climate variability on hydrologic alterations in the Mobile River and Perdido River Basins of Alabama. The research uses a machine learning approach, specifically cubist models, to quantify and predict changes in flow duration curves (FDCs) under both historical (1980–2009) and future climate scenarios. Future climate projections include the Representative Concentration Pathways (RCP 4.5 and RCP 8.5) and the Shared Socioeconomic Pathways (SSP2 4.5 and SSP5 8.5), evaluated for two future periods: 1980–2069 and 1980–2099. The models incorporate a wide range of covariates, including basin geomorphology, aquifer characteristics, land cover, water storage, environmental factors, solar radiation, census data, and water use data. Under the baseline period (1980–2009), most level 12 hydrologic unit codes (HUC12s) in both basins showed alterations, with substantial differences observed between pre- and post-alteration FDCs. The model performance varied, with a Nash–Sutcliffe Efficiency between 0.91 and 0.95 for testing and between 0.98 and 0.99 for training during the baseline period. Future projections under the RCP 4.5 and RCP 8.5 scenarios generally differed significantly from baseline conditions across all flow regimes (p < 0.05). In contrast, SSP2 4.5 showed comparatively limited statistical significance, while SSP5 8.5 exhibited significant departures from baseline conditions across all flow regimes, reflecting the greater influence of high-emissions climate forcing on projected hydrologic alterations. Overall, the RCP scenarios projected more widespread statistically significant changes than the corresponding SSP scenarios at the same forcing level, particularly when comparing RCP4.5 with SSP2-4.5, while both RCP8.5 and SSP5-8.5 consistently indicated greater hydrologic alterations than their moderate-emissions counterparts. These findings highlight the importance of considering different flow regimes when assessing the impacts of climate variability on streamflow. This study contributes to the understanding of hydrologic alterations in the Mobile River and Perdido River Basins, providing insights for water resource management and ecological conservation efforts in the region. Full article

Rapid urban development has intensified conflicts between land development and ecological conservation, making spatially explicit land-use planning increasingly important for resource-depleted cities. This study develops a geoinformation-based decision-support framework for Huangshi, China, by integrating multi-scenario land-use modeling, production–living–ecological space analysis, landscape pattern assessment, and SDG 15 diagnostics. Four 2035 policy-oriented scenarios were compared: Business-as-Usual (BAU), Ecological Restoration Priority (ERP), Economic Development Priority (EDP), and Sustainable Development (SD). The results show that ERP delivers the strongest ecological performance, with ecological space reaching 46.47%, forest cover increasing from 35.40% to 36.80%, water area rising to 9.65%, net land degradation declining to −2.04%, and mean habitat quality reaching 0.484. SD provides a more balanced pathway, with ecological space of 44.80%, living space of 8.93%, a land-use stability rate of 96.36%, and a relatively low net degradation rate of 1.33%. BAU and EDP show higher ecological risks. The framework demonstrates how multi-source geospatial data and spatially explicit SDG diagnostics can support adaptive planning in resource-depleted cities. Full article

Urban climate risks, particularly extreme heat and flooding, increasingly threaten public infrastructure in rapidly urbanizing regions. Public schools represent critical community assets, yet their spatial planning often overlooks the role of natural capital in mitigating environmental risks. This study examines the intersection of natural capital and urban design through a case study of public schools in the Mount Vernon District of Fairfax County, Virginia. Using cartographic modeling and spatial analysis, the study assesses school-site exposure to urban heat island effects and localized flood risks by integrating geospatial data on land cover, surface temperature, and hydrological conditions. Results indicate that all analyzed school sites exhibit notable vulnerability to both heat exposure and flooding. The findings highlight the importance of incorporating natural capital—such as expanded tree canopy, green infrastructure, and permeable surfaces—into school site planning to enhance climate resilience and environmental quality. Full article

The Ifrane National Park (INP), situated in the Middle Atlas Mountains in Morocco, is renowned for its unique bioecological attributes and rich biodiversity. The park provides key ecosystem services, notably climate regulation, with its Atlas cedar forests serving as an important carbon reservoir. Despite its importance, the park faces challenges associated with Land Use Land Cover Changes (LULCCs). These dynamics, driven by population settlement in mountain areas, intensify pressure on forest resources and accelerate the conversion of rangelands into croplands. Our study aims to examine the carbon sequestration service (CSS) in INP and its changes in response to land use dynamics before and after the park’s creation. We first use Google Earth Engine platform to map Land Use Land Cover (LULC) over three decades (1992/2022). Then, we quantify and map CSS using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST 3.10.2) model. Finally, we assess the economic value of this service over time using the social cost of carbon, adjusted by an appropriate discount rate. Our findings show that CSS declined by −217,738.4 tC/year (1992–2002) and −177,903.2 tC/year (2002–2012) prior to the park’s establishment, before rising to +61,786.8 tC/year during 2012–2022, following the park’s establishment and subsequent forest restoration, increasing its economic value from a net loss to +12,340,544 USD/year. By addressing a significant knowledge gap regarding CSS dynamics and economic value within a protected area, this study highlights the effectiveness of conservation measures and offers a practical tool for managers to inform decision makers, promote conservation and restoration investments, and engage local communities in sustainable development initiatives. Full article

Land subsidence induced by excessive groundwater withdrawal has become one of the most significant geohazards affecting the Konya Closed Basin, Turkey. Although previous studies have successfully monitored ground deformation using geodetic and remote sensing techniques, limited attention has been devoted to transforming deformation measurements into quantitative, spatially classified hazard information. This study presents a severity-based framework for delineating land-subsidence hazard zones and critical hotspots in the Konya metropolitan area by integrating SBAS-InSAR observations and spatial statistical analyses. A total of 82 Sentinel-1 SAR acquisitions (41 ascending and 41 descending images) acquired between January 2023 and May 2026 were processed using the Small Baseline Subset (SBAS) technique. Ascending and descending line-of-sight deformation measurements were combined to derive vertical deformation rates, which were integrated with spatial statistical indicators and a composite severity index to quantify deformation clustering and classify subsidence severity. Hazard zones and critical hotspot areas were delineated through severity-based classification and spatial connectivity analyses. The results reveal a continuous north–south-oriented subsidence deformation belt extending across the eastern Konya. Maximum vertical subsidence rates exceeded 230 mm/yr, while spatial statistical analyses confirmed strongly clustered and statistically significant deformation patterns. Severity-based hazard zonation identified four hazard classes and a continuous high-hazard corridor. Clustering analysis further identified a dominant hotspot belt covering approximately 160 km², with mean subsidence rates of approximately 142 mm/yr. A sensitivity analysis of the composite severity index weighting scheme, the spatial statistical neighborhood distance, the DBSCAN clustering parameters, and the number of Jenks severity classes confirmed that the resulting hazard zones and critical hotspot belt are robust to reasonable parameter variations. The findings demonstrate that land subsidence in Konya is organized as a spatially continuous regional-scale deformation system rather than a collection of isolated subsidence centers. The proposed framework transforms InSAR-derived deformation measurements into quantitative, decision-support hazard information and provides a transferable methodology for land-subsidence hazard assessment in groundwater-stressed urban environments. Full article

High-resolution and regularly updatable land cover maps are essential for local-scale environmental monitoring, water resource management, and territorial governance, yet existing global and regional products fail to provide the spatial detail and thematic richness required for operational applications in complex Mediterranean landscapes. This study presents an operational workflow for high-resolution LULC mapping and its application to Sardinia for the reference year 2020, developed within the Sardinia Land Cover Mapping Project in collaboration with the Agenzia del Distretto Idrografico della Sardegna (ADIS). The workflow integrates multi-temporal SAR and multispectral satellite imagery with high-resolution ancillary geospatial vector datasets through a semi-automatic pipeline combining hierarchical cascade pixel-based classification, multi-resolution image segmentation, geometric overlay of infrastructure vector layers, and an iterative accuracy-driven reclassification cycle. The classification combines automated rule-based procedures, semi-automatic threshold-based methods, and expert photo-interpretation to address the high thematic and spatial complexity of the Sardinian landscape. The resulting map comprises 35 land cover classes at the third and selected fourth CORINE levels, with a minimum mapping unit of 400 m² and an overall weighted accuracy of 82.4%. Designed as a dynamic product updatable on an annual basis, it represents an operational tool for local environmental governance, spatial planning, and resource management. Full article

Within the framework of the dual carbon strategy, investigating the spatiotemporal characteristics and driving factors of carbon sequestration in metropolitan areas through land use analysis is important for mitigating climate change and promoting regional ecological protection and sustainable development. On the basis of land use time points for five phases from the Jinan metropolitan area (JMA) covering the period from 2000 to 2024, the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model was coupled with the extreme gradient boosting (XGBoost)–Shapley Additive exPlanations (SHAP) and multiscale geographically weighted regression (MGWR) models to explore the spatiotemporal variations in carbon storage and its driving factors. In the last 24 years, cropland has been the predominant land use category in the JMA, representing almost 62% of the overall area. Throughout the five periods, the transition from cropland to construction land predominated, resulting in an 11.78% reduction in farmland and a 49.73% expansion in construction land. Between 2000 and 2024, carbon storage in the JMA decreased overall, with a total reduction of 3.70 Tg. The occupation of farmland for construction purposes was the primary cause of the decrease in carbon storage. The spatial pattern of carbon storage was similar to that of land use in the JMA, characterized by a distribution pattern with elevated values in the southeast and reduced values in the northwest. The SHAP analysis results demonstrated that the contributions of driving factors such as elevation, vegetation coverage, human footprint, and population density were generally high, making them the main drivers affecting carbon storage, with a significantly greater contribution of natural factors than human activity factors. The MGWR model results revealed that the digital elevation model and fractional vegetation cover positively influenced carbon storage in the JMA, whereas the population density imposed a negative effect. These results could guide the judicious allocation and utilisation of resources in urban regions, the establishment of ecological conservation areas, and the advancement of regional sustainability. Full article

Desertification represents a critical environmental challenge in arid and semi-arid regions, driven by the combined impacts of climate change and unsustainable land-use practices. This review provides a comparative assessment of desertification dynamics, mitigation strategies, and ecological restoration approaches in the Ningxia Hui Autonomous Region (China) and Egypt. Both regions are characterized by severe water scarcity, increasing climatic variability, and fragile ecosystems; however, they differ in ecological conditions, institutional frameworks, and dominant land degradation processes. The study synthesizes major drivers of desertification, including rising temperatures, precipitation variability, recurrent droughts, soil salinization, overgrazing, wind erosion, and unsustainable agricultural expansion. It further evaluates key control measures implemented in both regions, such as afforestation and ecological engineering, sand dune stabilization, water-efficient irrigation systems, soil rehabilitation practices, and the integration of remote sensing and GIS-based monitoring technologies. The analysis highlights China’s large-scale, long-term ecological restoration programs, which have significantly improved vegetation cover and reduced land degradation, compared to Egypt’s emphasis on irrigation efficiency, land reclamation, and salinity management under extreme aridity constraints. Importantly, the comparison underscores institutional differences, with China’s state-led ecological engineering contrasting against Egypt’s multi-actor reclamation initiatives, offering novel insights into governance pathways for combating desertification. The comparative synthesis demonstrates that effective desertification control requires integrated strategies combining ecological restoration, sustainable water resource management, technological innovation, and strong policy support. Despite contextual differences, both regions offer complementary lessons for dryland management. The study emphasizes the potential for enhanced China–Egypt cooperation in climate-smart agriculture, digital environmental monitoring, and nature-based solutions, thereby advancing sustainable land restoration and contributing to global efforts toward land degradation neutrality under future climate change scenarios. Full article

Although conventionally quantified as an urban–rural thermal anomaly, urban heat islands are systematic expressions of development choices that shape unequal exposures and health risks across cities. This article develops an integrated urban development-health framework explaining how imperviousness, vegetation deficit, landscape configuration, urban morphology, thermally absorptive materials and nocturnal heat retention connect heat exposure with physical disease, mental well-being and climate-responsive planning. A critical integrative review reported using PRISMA 2020 and PRISMA-ScR principles, organised evidence from urban climate, public health, environmental epidemiology and planning. The synthesis covers the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Local Climate Zones (LCZs), sky-view factor (SVF), height-to-width ratio (H/W), land-surface temperature (LST), Universal Thermal Climate Index (UTCI), Physiological Equivalent Temperature (PET), wet-bulb globe temperature (WBGT) and nocturnal minimum temperature, together with cardiovascular, respiratory, psychiatric, sleep, mortality and well-being outcomes. Six recurrent amplification pathways were identified: imperviousness and low canopy cover; nocturnal heat retention; social vulnerability; blue-green and cool infrastructure; heat–pollution–humidity interaction; and sleep/mental-health disruption. The Urban Heat-Health Development Index (UHHDI) and Urban Heat-Health Amplification Pattern (UHHAP) are proposed as transparent, review-derived tools for urban diagnosis and policy prioritisation. A Spanish/Mediterranean climatic-zone transition analysis illustrates how future climatic severity can be translated into planning-relevant exposure potential. The findings support a shift from simply describing urban heat to diagnosing development-driven heat-health risk, with implications for urban regeneration, thermal justice, public-health adaptation and healthy-city governance. Full article

Rapid conversion of agricultural land into low-density housing reshapes local water balances at a time when climate change is intensifying hydrological extremes in Central Europe. This study evaluates the rooftop rainwater harvesting (RWH) potential of a newly urbanized housing estate in Rokietnica (Greater Poland Voivodeship, Poland) using a publicly available true orthophoto (5 cm ground sampling distance) as a reliable geometric data source. Roof footprints of 41 single-family buildings (5305 m² in total) were manually vectorized in QGIS and combined with monthly precipitation recorded at the nearest meteorological station (Złotniki) in a simplified volumetric model (V = A × R × C, with C = 0.95). To place the single reference year (2021) in its climatic context, the full 72-year precipitation record (1952–2023) was analyzed using the Mann–Kendall test and Sen’s slope estimator. The results indicate a harvestable volume of approximately 2642 m³ in 2021 (53–73 m³ per building; mean 64 m³), with May and August jointly accounting for almost one-third of the annual total. Annual precipitation at Złotniki exhibits a statistically significant increasing trend (+1.29 mm yr⁻¹; p = 0.028) concentrated in winter and early spring, while summer totals remain trendless but highly variable. A monthly storage simulation driven by the recent 30-year record (1994–2023) shows that summer garden irrigation, the dominant practical application of harvested rainwater in Polish households, is considerably harder to meet from the roof alone than year-round indoor non-potable uses: a 5 m³ tank covers approximately 73% of the seasonal demand of a 100 m² garden plot, while a 3–5 m³ tank would theoretically secure 91–98% of toilet-flushing demand for a four-person household. Under Poland’s national rainwater co-financing scheme (formerly “Moja Woda,” active 2020–2024, succeeded by “Mikroretencja” from June 2026), simple payback periods of roughly 3–7 years make household installations financially defensible. The findings suggest that true orthophotos enable rapid, low-cost RWH assessments in dynamically developing suburbs, and that storage sizing should anticipate the ongoing seasonal redistribution of precipitation under climate change. To our knowledge, this is the first study to combine free national true-orthophoto imagery, a 72-year Mann–Kendall trend analysis, and a subsidy-linked storage-reliability simulation within a single suburban rainwater-harvesting assessment. Full article

This study aims to spatially analyze the potential soil loss rates of the Özkonak Watershed, located within Nevşehir Province, using the Revised Universal Soil Loss Equation (RUSLE) integrated with Geographic Information Systems (GIS) and Remote Sensing (RS) technologies. In the 149.9 km² watershed, the main parameters triggering erosion—rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), land cover and management (C), and support practices (P)—were modeled in a GIS environment. According to the spatial analysis results, 85.8% of the watershed area falls within the “very low” and “low” erosion susceptibility classes. Nevertheless, erosion increases markedly in the northern areas with high slope gradients and in areas where agricultural activities are concentrated. The mean soil loss across the watershed was calculated as 2.75 t ha⁻¹ yr⁻¹. The eroded and transported material was determined to constitute a threat to the dam. The findings indicate that conservation plans, including afforestation in the upper watershed, adjustment of land use to natural land capability, and construction of check dams, should be implemented to ensure sustainable management of the watershed. From a soil and sediment remediation perspective, the identification of erosion-source areas and sediment-transport pathways provides a scientific basis for source-control measures aimed at reducing sediment delivery and associated water-quality deterioration in the Bayramhacılı Dam reservoir. Full article

Optical remote sensing images acquired in rugged mountains are affected by reflectance distortion caused by both topography and shadows. Most topographic correction (TC) methods normalize sunlit slopes but can become unstable in self shadow and cast shadow, where little or no direct solar radiation reaches the surface. This study builds on the topographic shadow effect correction (TSEC) model and develops TSEC+TC, a partitioned framework for horizontal equivalent normalization. Using a shadow mask extended to penumbra, the framework integrates TSEC for shadowed pixels with conventional TC for sunlit pixels. Both branches target horizontal equivalent reflectance, enabling simultaneous correction of topographic and shadow effects across the scene. We implemented TSEC+TC with path length correction (PLC) and SCS with C (SCSC) models and evaluated it using ten multi-temporal Landsat 8 OLI scenes under different illumination conditions. The results showed that TSEC+TC reduced terrain-related brightness variation and improved land cover classification in the auxiliary comparison relative to uncorrected and TC-only results. For TSEC+SCSC, the R² values between corrected reflectance and cosi were below 0.025 for both Red and SWIR1 bands, and the coefficient of variation of reflectance across aspects was consistently lower than the corresponding values for SE and SCSC, with a maximum of 36.00%. Shadow area analyses indicated that TSEC+TC compensated reflectance distortion in self shadow and cast shadow areas, reduced TC-induced outliers, and better preserved spectral patterns than TC-only correction. Tests using Sentinel-2 MSI and GF-1 WFV imagery provided preliminary evidence of applicability to other sensors. Accounting for the topographic shadow effect improved TC performance in complex mountainous areas. Full article