Search Results (1,240)

Urban heat islands pose increasing risks to human settlements, yet the differential mechanisms by which grey–green spaces regulate diurnal and nocturnal land surface temperature across local climate zones remain insufficiently understood. This study addresses this gap through a Hangzhou case study, integrating a ten-indicator grey–green space system with two local spatial identification approaches—K-means clustering and an LCZ-inspired simplified scheme—and a Random Forest-SHAP framework. The LCZ-inspired scheme outperformed K-means clustering, with a mean diurnal–nocturnal Test R² of 0.4344 across twelve models, compared to 0.2977 for K-means. Diurnal and nocturnal LST were driven by systematically different factors: building density dominated daytime LST in most LCZ types (22.0% to 27.8%), while canopy height dominated nighttime LST (22.7% to 30.2%), revealing a systematic shift from building-dominated daytime to vegetation-dominated nighttime. This shift did not occur in compact built-up areas, suggesting that built-up density may be a threshold condition for the shift. Key variables exhibited nonlinear threshold effects with saturation points varying by LCZ type: canopy height cooling saturated at approximately 4 m in LCZ2 but required 17–21 m in LCZ3 and LCZA. These SHAP-based patterns and turning points should be regarded as exploratory, sample-dependent associations evaluated within the training data; their spatial stability across held-out regions was not assessed. Factor interactions were interval-dependent rather than globally fixed. Spatial cross-validation confirmed that random splitting substantially overestimated model performance, highlighting the necessity of spatially explicit validation. The methodological framework provides a replicable approach for urban thermal environment research and offers LCZ-specific threshold hypotheses for thermal regulation planning in subtropical megacities, subject to further spatial and cross-city validation. Full article

Clarifying the relationships between built environment characteristics and urban fire risk is important for developing effective fire prevention and planning strategies. However, spatial dependence and nonlinear relationships between the built environment and fire risk remain insufficiently understood. Accordingly, this study presents a geographically enhanced machine learning (GE-ML) framework that incorporates spatial adjacency into machine learning models through spatially weighted feature construction. Specifically, contiguity-based spatial weight matrices were used to derive spatially weighted features from 2D and 3D built environment variables. The Optimal Parameter-based Geographical Detector (OPGD) was applied to assess scale sensitivity and compare the explanatory power and interactions of the original and spatially weighted features. Six candidate models, including Ordinary Least Squares (OLS), KNN, MLP, Random Forest (RF), LightGBM, and XGBoost, were then evaluated under different feature configurations, followed by SHapley Additive exPlanations (SHAP) analysis of the selected model. Results show that: (1) spatial weighting generally increased the explanatory power of major built environment factors and their interactions, with Queen contiguity yielding higher q-values than Rook contiguity; (2) spatially weighted features improved predictive performance across different models, and GE-XGBoost achieved the highest R² (0.7067) and lower residual spatial autocorrelation than GWR and GWRF; and (3) 2D and 3D built environment features accounted for 59.55% and 40.45% of the total SHAP importance, respectively, with Geo-TPD, Geo-BVD, Geo-PS, and Geo-LUI identified as the most important features. SHAP analysis further revealed nonlinear relationships and interactions between these features and predicted fire risk. These findings highlight the value of incorporating spatial adjacency information into fire risk modeling and support spatially differentiated fire risk management. Full article

Recent studies on users of urban parks call for the need for more insight into the importance of green spaces as catalysts of more inclusive spaces. This paper contributes to the ongoing debate by examining urban parks as age-inclusive infrastructures in a post-socialist context. Three parks in the City of Timișoara, Romania, are selected as a comparative case study. Our analysis combines systematic field observations and GIS-based mapping of park accessibility and facilities with 42 semi-structured interviews with young, mid-aged and older visitors to the three contrasting parks: a renovated historic central park, a peripheral forest-like park and a small neighbourhood park embedded in a communist-era housing estate. The findings suggest that inclusiveness for all generational categories, as well as attachment for neighbourhood parks, are important drivers for urban parks users. Inclusiveness is driven not only by amenities, but also by park-specific attachment, as well as the quality of maintenance and lighting. These factors shape perceived equity and convenience. We advocate for a differentiated management model tailored to each park, balancing conservation-oriented quiet zones with flexible, event-capable areas. This model prioritises lighting, seating ergonomics and safety measures as core components of age-inclusive planning. Our findings support the need for different management of urban green spaces that capitalises on the social and spatial specificities of each park. The comparison further shows that the smallest and least equipped park generates the strongest attachment and the most regular use across all generations: proximity and continuity of maintenance matter more than surface area or scale of investment. Full article

Wildland–urban interfaces (WUIs) are critical zones for wildfire prevention in Mediterranean and semi-arid landscapes, particularly where human settlements interact with continuous combustible vegetation. In Algeria, operational tools for delineating WUI configurations and spatially prioritizing preventive actions remain limited. This study develops a GIS-based structural wildfire-risk prioritization framework for the Djebel El Ouahch massif (Constantine, northeastern Algeria) by combining WUIMAP II settlement typologies with an Aggregation Index (AI) describing the horizontal continuity of arboreal and shrub vegetation derived from Sentinel−2 data. Built-up structures were classified as isolated housing, dispersed housing, main urbanized areas, or peripheral zones and combined with three AI classes to delineate relative structural-priority areas. Dispersed and isolated housing accounted for 55% and 37% of mapped housing structures, respectively, while the WUI contained an estimated 18,100 inhabitants distributed across 3620 housing units. The resulting WUI × AI structural-priority layer covered 0.863 km², of which high- and very-high-priority zones jointly represented 0.544 km² (63.04%). These priority areas identify locations where structurally exposed settlement configurations coincide with greater horizontal fuel continuity, providing a spatial basis for targeted fuel management, surveillance planning, emergency-access improvement, and wildfire-prevention actions. By improving the spatial targeting of preventive measures, the framework can contribute to sustainable forest and land management, disaster-risk reduction, ecosystem protection, and the resilience of WUI settlements. Full article

Remotely sensed surface temperature data are widely used by researchers and municipal practitioners to characterize urban thermal patterns and assess the impacts of increasing urban heat. While satellite-derived land surface temperature (LST) data effectively identify broad-scale patterns, their 30 m spatial resolution limits their ability to resolve fine-scale thermal variability. Downscaling approaches have been developed to generate higher-resolution LST products; however, in the absence of independent, high-resolution surface temperature observations, it remains difficult to determine whether the additional spatial variation introduced by downscaling reflects meaningful landscape-related thermal structure or model-generated variability. Our objective was to evaluate whether the additional spatial variation introduced by downscaling LST from 30 to 10 m was statistically consistent with independently derived land cover composition and landscape compositional heterogeneity. Using an open-source random forest model, we downscaled Landsat 8/9 LST to 10 m resolution using Sentinel-2-derived predictors over a summer season in Kelowna, BC, Canada. We quantified the additional thermal variation introduced through downscaling and used Generalized Additive Models (GAMs) to relate this variation to high-resolution land cover characteristics summarized within 100 m grid cells. Land cover composition and landscape compositional heterogeneity together explained 65.5% of the additional variation introduced by downscaling. Added thermal variation was most strongly associated with canopy and impervious cover, two landscape characteristics consistently identified as major controls on urban land surface temperature. These findings suggest that the additional spatial variation introduced through downscaling is consistent with independently derived landscape structure known to influence urban thermal patterns. Rather than providing direct validation of downscaled temperatures, this analysis offers an indirect means of evaluating whether downscaled LST produces interpretable, landscape-consistent thermal variability that may be useful for urban analysis and planning. Full article

Biotope mapping provides an essential spatial framework for biodiversity conservation, ecosystem management, and sustainable landscape planning. However, a standardized and transferable GIS-based methodological framework for biotope identification and mapping is still lacking in Türkiye, limiting the systematic integration of biodiversity considerations into spatial planning processes. This study develops an integrated GIS-based biotope mapping framework for Çınarcık District, Yalova Province, Türkiye, integrating Digital Elevation Model (DEM), CORINE Land Cover 2018, Forest Management Plans, stand characteristics, vegetation, floristic, and hydrological data through spatial analyses. Additionally, 30 national and 15 international studies were systematically reviewed to identify the common indicators, data sources, and methodological components used in biotope mapping and to establish the proposed GIS-based framework. The proposed approach identified four main biotope groups (forest, aquatic, agricultural, and urban). Based on ecological similarity and growing environment characteristics, 12 sub-biotope types were identified within the forest biotopes. Forest biotopes were the dominant ecological units, mainly characterized by broadleaved communities dominated by Fagus orientalis, Castanea sativa, Tilia tomentosa, and Quercus petraea. A total of 72 forest stand types were identified, with Fagus orientalis-dominated forests in plateau environments representing the largest sub-biotope type (36.40%). The proposed framework provides a repeatable and transferable, inventory-based methodology that can serve as a preliminary decision-support tool for biodiversity assessment, conservation planning, and sustainable landscape management in forested landscapes with similar ecological characteristics, pending future field-based validation. Full article

Nature-Based Solutions (NBSs) are increasingly recognized as strategic tools for climate adaptation and urban regeneration, yet their implementation requires balancing environmental performance, territorial identity, and governance feasibility. This article investigates the application of the euPOLIS H2020 methodology in Palermo, Italy, focusing on the proposed transformation of Villa Turrisi—a residual peri-urban agricultural area representing one of the last remnants of the historical Conca d’Oro landscape—into a new public green infrastructure within the framework of the new Municipal Development Plan (PRG/PUG). This study examines how a structured co-design process involving municipal actors, citizens, and local associations can be coupled with ecosystem service assessments. Rather than selecting a single optimal design, the analysis evaluates two alternative vision scenarios—a multifunctional framework aligned with euPOLIS principles and an intensive urban forest model—to generate quantitative and qualitative baseline data. Environmental indicators (carbon sequestration, microclimatic regulation, and hydrological resilience) are used not as deterministic selection criteria but as theoretical decision-support evidence within a predominantly qualitative, value-driven local planning process. The core contribution of this work lies in demonstrating the operational transferability of the euPOLIS framework from front-runner to follower cities within a Mediterranean planning context. The Palermo case shows how quantitative environmental simulations can effectively inform—rather than dictate—participatory governance, heritage preservation, and regulatory feasibility. Ultimately, this article offers a transferable planning and policy framework that bridges European NBS research with municipal decision-making, providing actionable insights for integrating climate-resilient green infrastructure into local urban plans. Full article

Land-use change affects regional carbon accounting through ecological conversion and the concentration of energy-intensive economic activity. We combined 30 m China Land Cover Dataset maps for 2010, 2015, 2020, and 2025 with provincial socioeconomic and energy statistics to quantify land-use transitions and carbon emissions across nine Yellow River Basin provinces. Construction-land-associated emissions were decomposed using the logarithmic mean Divisia index, factors associated with land expansion were examined using random-forest models, and three 2030 scenarios were evaluated. Construction land expanded by 38.87% from 2010 to 2025, with 71.33% of new construction land converted from cropland and 17.75% from grassland. Net land-use carbon emissions increased by 69.82%, from 1139.06 to 1934.33 million t C. Economic-output density contributed 1144.84 million t C to the increase in construction-land-associated emissions, compared with 576.11 million t C from land expansion, whereas declining energy intensity offset 922.72 million t C. Projected 2030 emissions ranged from 2124.72 million t C under ecological protection to 2866.55 million t C under urban expansion. Construction-land expansion was substantial, but economic-output density made the larger positive contribution to historical emission growth. The projected 2030 estimates depended on the combined trajectories of construction-land demand, economic growth, and energy intensity. These findings highlight the importance of coordinating land-use planning, economic development, and energy-efficiency improvement for sustainable low-carbon transitions. Full article

LULCC is a major driver of habitat fragmentation, biodiversity loss, and declining landscape connectivity, particularly in rapidly urbanizing regions. Although Islamabad has experienced substantial urban expansion and forest-cover change, long-term city-scale assessments linking land-cover dynamics with landscape connectivity remain limited. This study examined spatiotemporal LULCC in Islamabad from 1991 to 2021 and assessed whether recent forest recovery improved landscape structural connectivity. Landsat images acquired in 1991, 2001, 2011, and 2021 were classified into five land-cover categories: water, forest, built-up area, bare land, and agricultural land. Classification was performed using the Random Forest (RF) algorithm in Google Earth Engine (GEE). Landscape composition and spatial configuration were quantified using FRAGSTATS 4.3, while forest fragmentation was evaluated using the Landscape Fragmentation Tool v2.0 (LFT) with a 100 m edge threshold. The classifications achieved overall accuracies above 90%, with Kappa coefficients (K) greater than 0.85. Built-up area increased from 76.31 km², representing 7.55% of the study area, in 1991, to 258.62 km², or 25.60%, in 2021, demonstrating rapid urban expansion and associated habitat conversion. Forest cover increased to 340.86 km² in 2001, declined to 271.96 km² in 2011, and subsequently recovered to 409.22 km² in 2021. Despite this increase in forest extent, fragmentation metrics indicated persistent spatial subdivision and limited structural connectivity. High patch density (PD), reduced landscape aggregation, and changes in the largest patch index (LPI) indicated persistent spatial subdivision and limited habitat continuity. These findings highlight the value of integrating RF-based land-cover classification, multitemporal remote sensing, and landscape metrics for urban environmental monitoring. The findings suggest that future land-use planning should consider landscape connectivity, protection of existing forest patches, and spatially coordinated restoration alongside continued reforestation. Full article

Rapid urbanization and increasing land surface temperatures (LSTs) have intensified urban heat stress in rapidly growing tropical megacities such as Dhaka. However, the coarse spatial resolution of conventional thermal satellite imagery limits the identification of fine-scale urban thermal variability required for climate-sensitive urban planning. This study develops a multi-sensor LST downscaling framework by integrating Landsat 8 thermal imagery with Sentinel-2-derived spectral indices within the Google Earth Engine (GEE) platform. A random forest regression model was developed using the normalized difference vegetation index, normalized difference built-up index, and modified normalized difference water index as predictors for statistical downscaling from the 30 m Landsat grid to a nominal 10 m grid. To preserve localized thermal heterogeneity and improve radiometric consistency, a bicubic residual correction approach was incorporated into the downscaling workflow. The resulting statistically downscaled LST estimate on a nominal 10 m grid was subsequently used to classify Urban Thermal Zones (UTZs) across Dhaka City. The results showed that LST was negatively associated with vegetation and water-related indices and positively associated with the built-up index. The statistically downscaled product provided a more spatially detailed representation of the Landsat-derived thermal field and delineated relative surface-temperature hotspots and cooler zones across the study area. High-temperature zones were primarily concentrated within densely built-up commercial and industrial areas, whereas comparatively lower temperatures were observed in vegetated and water-dominated regions. The proposed framework demonstrates a computationally efficient approach to spatially refining Landsat-derived LST in a data-constrained tropical megacity. The findings provide valuable spatial information for urban climate adaptation, heat mitigation planning, and climate-resilient urban development. Full article

Land use and land cover change represent major anthropogenic carbon emission sources, yet most existing studies on landscape patterns and carbon emissions predominantly focus on plain urban agglomerations, with limited empirical evidence from terrain-restricted hilly regions. Taking Xuancheng, a typical hilly city in the Yangtze River Delta, as the study area, this paper utilized seven time-series of Landsat remote-sensing datasets spanning 1990–2020. Integrating the land use dynamic degree, transfer matrix, landscape pattern indexes, calibrated carbon coefficients, Spearman correlation analysis and grey relational analysis, this study explored the associations between land use patterns and carbon emissions. Results show that built-up areas expanded 4.08-fold in the past three decades and emerged as the dominant carbon source, while forests maintained persistent carbon sequestration. The largest patch index of built-up area exhibited the strongest correlation with carbon emissions; cultivated land and forest displayed temporally synchronous fluctuations with emissions rather than exerting independent causal effects. Terrain constraints drive axial urban sprawl along transport corridors, elevating correlations of edge-related indexes and generating carbon response patterns distinct from those observed in plain cities. The two-step correlation analysis framework proved suitable for small-sample long-term datasets. These findings suggest that curbing contiguous built-up area expansion, optimizing urban morphology, and strengthening ecological connectivity should be prioritized in low-carbon spatial planning for hilly cities. Full article

Urban forests play an important role in improving environmental quality and supporting the well-being of city residents. However, their effective planning and management require comprehensive assessment methods that integrate both spatial characteristics and users’ perceptions. This study evaluates the quality of the Piast Hills urban forest complex in Zielona Góra (Poland) using a methodology that combines field analysis, questionnaire surveys, expert assessment and Space Syntax analysis. Four assessment criteria were adopted: accessibility, safety, recreational appeal and user comfort. These components were integrated into the proposed Synthetic Assessment of Forest Environment Index (SAFEI), while Space Syntax analysis was incorporated as a complementary spatial analysis supporting the interpretation of accessibility-related results and the spatial configuration of the path network. The findings indicate that recreational appeal was the highest-rated aspect of the urban forest, whereas safety received the lowest assessment, highlighting the need to improve lighting, surveillance and wayfinding. The calculated SAFEI value (0.607) indicates a moderately high quality of the investigated urban forest complex. The Space Syntax analysis provided complementary spatial information that was consistent with the accessibility patterns identified through the questionnaire and expert assessment. Unlike most previous studies focusing on individual aspects of urban forests, the SAFEI framework provides an integrated approach that can support evidence-based planning, management and long-term monitoring of urban forests and other urban green spaces. Full article

Wetlands monitoring is essential for sustainable watershed biodiversity conservation, and climate resilience. This study performs a spatiotemporal wetland change analysis for the Sangamon River Watershed, Illinois between 2000 and 2025, using Landsat 5 Thematic Mapper (TM), Sentinel–2 Surface Reflectance (SR), Synthetic Aperture Radar (SAR), Gray–Level Co–occurrence Matrix (GLCM) and terrain data through cloud–based processing in Google Earth Engine (GEE), Google Colab and ArcGIS Pro 3.6.0. We conducted a comparative assessment of deep learning (Deep Neural Network [DNN], U-Net, Attention U-Net, and SegFormer), and machine learning models (Random Forest [RF], Gradient Tree Boosting [GTB], and Support Vector Machines [SVM]) through pixel–based and object–based methods. National Land Cover Database (NLCD) was used for training and validation using stratified random sampling for five categories namely wetlands, forest, agriculture/grassland/barren land, urban/developed and water. A proportion of 54.85% (860.68 km²) of wetlands extent was lost to other land uses, particularly agriculture, urban and forest, along with 46.51% (694.88 km²) forest and 36.80% (66.13 km²) water bodies loss. Agriculture/grassland/barren and urban/developed witnessed increases of 8.56% (820.62 km²) and 72.92% (799.52 km²), respectively. For Landsat–based and Sentinel–based classifications, SegFormer outperformed all ML and DL classifiers with (OA = 94%, Kappa = 0.89, mean F1 = 0.80, mean IoU = 0.70 and OA = 96%, Kappa = 0.92, mean F1 = 0.95, mean IoU = 0.73, respectively) with excellent wetland delineation (PA = 0.99, UA = 0.97, F1 = 0.98, IoU = 0.97 and PA = 0.99, UA = 0.99, F1 = 0.98, IoU = 0.99, respectively). Sentinel–based classifications had improved performance than Landsat, while object–based models consistently outperformed pixel–based methods. The Digital Elevation Model (DEM) and slope were the most influential predictors for RF models, while GLCM and SAR produced negligible influence. The integrated and comparative GeoAI framework provides a robust methodology for watershed–scale wetland monitoring and supports evidence–based conservation, restoration prioritization, climate resilience, and sustainable land–use planning, while offering strong potential for application in other agricultural watersheds following regional validation. Full article

To address the uncertainties in emergency shelter siting under compound disaster scenarios, based on the connotative characteristics and formation mechanisms of urban natural hazards, a hazard assessment system for geological and flood disasters was constructed using the Random Forest (RF) algorithm. On this basis, considering the triggering relationships between disasters, the hazard intensity of disaster chains was adjusted by using a multi-hazard coupling incentive model, and the comprehensive hazard index of geological-flood compound disasters was calculated. Then, from the perspectives of accessibility and safety, a comprehensive analysis of the suitability of candidate emergency shelter sites was conducted via the Gaussian Two-step Floating Catchment Area (G2SFCA) method, where the comprehensive hazard assessment coefficient of geological-flood compound disasters was incorporated as a weighting factor affecting the suitability evaluation. Furthermore, an urban emergency shelter siting model was established by using the Multi-Objective Gray Wolf Optimizer (MOGWO). Taking Sanya City as a case study, the results show that: (1) The estimation of area under the curve (AUC) of the single hazard assessment models for geological and flood disasters constructed by the RF were 0.904 and 0.899, respectively. The high-hazard zones of geological-flood compound disasters were mainly concentrated in the mountain-valley transition zones of Tianya District and Jiyang District, as well as the potential storm surge-affected zones along the southern coast. (2) The model constructed based on the MOGWO under the influence of compound disasters could effectively make up for the deficiencies in the spatial layout of current shelter siting schemes and achieve effective connection between hazard assessment and spatial planning. The methods mentioned provide a scientific basis for risk zoning control in urban territorial spatial planning and disaster prevention and mitigation in emergency management. Full article

Peri-urban forest ecosystems play a crucial role in sustaining biodiversity, regulating climate, and providing essential ecosystem services; however, they are increasingly threatened by rapid urban expansion. Despite advances in remote sensing-based forest monitoring, long-term reconstruction of continuous forest canopy cover dynamics remains challenging in highly fragmented peri-urban landscapes. This study developed a machine-learning workflow to reconstruct forest canopy cover dynamics within the “Suelo de Conservación” of Mexico City between 1994 and 2024 using Landsat imagery and forest canopy cover information derived from the Hansen Global Forest Change dataset. A balanced training dataset comprising 5000 samples distributed across five forest canopy cover classes was used to compare four regression algorithms (Multiple Linear Regression, Random Forest, Gradient Boosting, and Multilayer Perceptron) under five-fold spatial cross-validation. Model performance was evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), bias, Pearson’s correlation coefficient (r), coefficient of determination (R²), and Lin’s Concordance Correlation Coefficient (CCC). The best-performing model was applied to generate forest canopy cover maps for 1994, 2003, 2014, and 2024, and forest-cover change was quantified using propagated uncertainty and threshold sensitivity analysis. The reconstructed forest canopy cover maps revealed an initial decline between 1994 and 2003, followed by partial recovery during 2003–2014 and relatively stable forest canopy cover conditions through 2024. Independent comparison with the National Forest and Soils Inventory (INFyS) and Global Forest Watch forest canopy cover products indicated moderate agreement in the spatial distribution of canopy cover while highlighting uncertainties associated with differences in reference datasets and acquisition periods. The proposed workflow provides a transparent and reproducible framework for long-term forest canopy cover reconstruction using freely available satellite imagery and supports forest monitoring and conservation planning in peri-urban landscapes. Full article