Search Results (1,577)

Due to climate change and rapid urbanization, cities worldwide face the dual challenge of improving flood resilience and providing accessible green space within limited land resources. Sponge City parks offer a landscape-based approach for integrating stormwater management with park services. However, how park morphology structures this combined performance remains insufficiently understood. This study examines 26 Sponge City parks in Shanghai and evaluates how node-, line-, and patch-type morphologies are linked to stormwater storage and service provision. Using geospatial analysis, DEM-derived catchment delineation, land-cover interpretation, and statistical analysis, this study compares estimated stormwater storage, storage efficiency, local park availability, and land-cover composition across different park morphologies. The results show that estimated performance of stormwater management and park service provision vary across morphological types, but these differences do not follow a simple node–line–patch hierarchy. Rather, the observed patterns are jointly shaped by park morphology, catchment setting, land-cover allocation, and surrounding urban context. These findings suggest that Sponge City parks should not only be evaluated by total stormwater storage. Their contribution depends on morphology, scale, catchment setting, land-cover allocation, and urban context. The study provides a morphology–performance perspective to support more differentiated planning of multifunctional green infrastructure. Full article

Environmental datasets are often characterized by complex spatial structures and the presence of atypical observations that may influence the interpretation of landscape patterns. This study proposes a comparative framework for identifying spatial outliers in landscape structure using two complementary approaches: K-means clustering and multivariate visual exploration based on Chernoff faces. The analysis is conducted on two temporal snapshots (1956 and 2019) representing long-term changes in land use and land cover in the Zemplínska Šírava region, Eastern Slovakia. Outlier detection results from both approaches are systematically compared to assess their consistency and robustness. The two methods show substantial correspondence in the identification of anomalous landscape units. The number of land-cover classes increases from 19 in 1956 to 25 in 2019, reflecting increased landscape heterogeneity over time. Persistent spatial outliers across both methods and time periods include road networks and associated land and broad-leaved forest with continuous canopy, indicating the structural stability of these landscape elements despite long-term transformation. The results demonstrate that combining clustering-based approaches with multivariate visual analytics can improve the interpretation of complex spatial patterns in environmental data. However, the study is exploratory in nature, and the interpretation of Chernoff faces involves inherent visual subjectivity, which should be considered when evaluating the results. The proposed framework should therefore be regarded as a complementary exploratory tool rather than standalone analytical evidence. Future research may extend this framework by integrating identified spatial outliers into environmental assessment models focused on biodiversity patterns, ecological connectivity, and sustainable landscape planning. Full article

Mountain stream ecosystems are often considered among the least disturbed freshwater environments; however, increasing land-use pressures may affect their ecological integrity even under apparently high-water quality conditions. This study aimed to assess the relative influence of landscape, physicochemical, and hydromorphological factors on benthic macroinvertebrate communities in three sub-catchments (Ambroz, Jerte, and Tiétar) of the Sierra de Gredos (Central Spain). A total of 33 sampling sites were surveyed, and macroinvertebrate assemblages were analyzed in relation to environmental variables using partial Redundancy Analysis (pRDA) and variance partitioning. All sites were classified as having “Excellent” ecological status based on the Iberian Biological Monitoring Working Party (IBMWP) index. However, multivariate analyses revealed clear spatial patterns and responses to environmental gradients. Results indicated that catchment-scale landscape characteristics defined the pool of potential colonizers, while local physicochemical and hydromorphological conditions acted as secondary filters structuring macroinvertebrate assemblages. Landscape variables explained the largest fraction of variance in community structure (30.6%), followed by physicochemical parameters (29.0%) and hydromorphological indices (24.9%), with a significant shared component (16.5%) indicating interactions among drivers. Agricultural land use, particularly in the Jerte sub-catchment, was associated with shifts in community composition, favoring tolerant taxa such as Diptera, while sub-catchments dominated by natural vegetation supported higher richness of sensitive groups, including Ephemeroptera and Plecoptera. These findings highlight the importance of multi-scale processes in structuring mountain stream communities and reveal limitations of traditional biotic indices in detecting early ecological changes. The results support the integration of catchment-scale variables into ecological assessment frameworks and emphasize the need for preventive, basin-scale management strategies to maintain ecological integrity under increasing anthropogenic pressure. Full article

Soil wind erosion (SWE) remains a significant challenge to improving ecological environmental quality and achieving sustainable socioeconomic development in drylands of northern China. An in-depth understanding of the spatio-temporal variations and underlying mechanisms of regional SWE is a prerequisite for the scientific prevention and mitigation of erosion-related hazards. However, in regions with high variability in intra-annual climate, quantitative studies on the spatial heterogeneity and intra-annual variability of drivers of SWE are scarce. This knowledge gap poses challenges for policymakers in developing effective landscape management strategies that are spatially and temporally specific. Here, the dynamics of SWE in the Xilingol typical steppe of China were simulated using the Revised Wind Erosion Equation (RWEQ) at seasonal and annual scales during 2000–2020. Stepwise regression and geographically weighted regression (GWR) were employed to examine the spatial heterogeneity in the relationships between SWE and environmental variables. The results revealed that RWEQ simulations were significantly correlated with the frequency of dust storm events at the seasonal scale (R² = 0.807, p < 0.01). SWE in spring accounted for approximately two-thirds of the annual total, indicating that spring was the critical period for SWE control. High SWE intensity was concentrated in sandy soil regions, with the Otindag Sandy Land and Gahai Elesu Sandy Land being identified as priority areas for desertification prevention and control. Over the study period, SWE exhibited an overall decreasing trend at both seasonal and annual scales, suggesting an enhancement in the ecosystem’s capacity for windbreak and sand stabilization. The stepwise regression results indicated that climatic factors generally had greater explanatory power than topographic and landscape pattern variables. Wind speed showed the strongest association with SWE across different time scales, whereas the relationships of normalized difference vegetation index (NDVI) and precipitation with SWE exhibited clear seasonal dependence. The GWR results further revealed pronounced spatial heterogeneity and seasonal variability in both the direction and magnitude of the associations between SWE and climatic and landscape pattern variables. These findings provide scientific support for identifying priority areas for desertification prevention and for developing spatio-temporally targeted landscape management strategies in dryland sandy regions. Full article

Investigating the spatiotemporal dynamics of urban heat islands and their responses to urban forest (UF) landscape patterns is crucial for mitigating urban thermal stress. However, the nonlinear influence and conditional constraints of UF landscape composition and configuration on the warming effects across varying urbanization gradients remain inadequately understood. By integrating land use/cover data, MODIS-derived land surface temperature (LST), and meteorological datasets, this study employed the XGBoost-SHAP model to quantify the nonlinear and interaction effects of UF landscape patterns on developed and developing urban regions of the Pearl River Delta. The results indicate that (1) spatial clustering patterns of warming varied significantly between the two regions, with substantial seasonal heterogeneities (p < 0.05). Summer exhibited the most intense warming, characterized by more rapid temperature increase in developed areas than in developing regions. (2) Relative to UF landscape metrics, the proportion of impervious surfaces, precipitation, and temperature exerted greater influence on regional warming. Coverage area, fragmentation, and connectivity of UFs emerged as the primary landscape drivers modulating warming. In developed areas, spatial configuration metrics exerted greater influence on LST than compositional metrics. (3) The responses of LST to diverse UF landscape patterns are characterized by nonlinearity and pronounced threshold effects. These landscape thresholds vary by season, revealing critical tipping points for warming suppression; however, this regulatory effect is highly context-dependent. Specifically, under high percentages of impervious surface, the warming-suppression capacity of UFs intensifies with increasing percentage of UF area or core. Our findings highlight the necessity of strategic UF planning and forest fragmentation mitigation for developing effective climate resilience strategies. These results provide a foundation for adaptive planning tailored to specific urbanization stages and the implementation of targeted UF cooling strategies. Full article

Landscape Ecological Risk (LER) reflects the potential adverse effects of landscape change on ecological structure, function, and stability. In rapidly urbanizing megacities such as Dhaka, vegetation loss and built-up expansion have intensified environmental pressure over recent decades. This study examines the spatiotemporal dynamics of LER in Dhaka from 2004 to 2024 under the combined influence of vegetation change and urban expansion. Multi-temporal remote sensing data were used to generate land cover maps, derive Fractional Vegetation Cover (FVC), and quantify urbanization intensity using Nighttime Light (NTL) data. The Landscape Ecological Risk Index (LERI) was calculated using landscape pattern metrics, while bivariate spatial autocorrelation and geographically weighted regression (GWR) were applied to examine spatial associations and local spatial heterogeneity. The results show that vegetation degradation affected 34.39% of the study area during 2004–2024, while high-risk zones increased from 24.36% in 2004 to 42.95% in 2024. Land cover analysis further indicates a substantial expansion of built-up areas, accompanied by the contraction and fragmentation of vegetation, agricultural land, and lowland classes. Spatial analyses reveal that the relationships among vegetation cover, urbanization intensity, and ecological risk vary across the city and became increasingly spatially differentiated over time. These findings suggest that vegetation loss and urban expansion are spatially associated with increasing ecological risk in Dhaka. However, the results should be interpreted with caution because of uncertainties related to remotely sensed data, unsupervised land cover classification, resampling procedures, and limited ground validation. Despite these limitations, the study provides a spatially explicit framework for understanding ecological risk dynamics and offers useful evidence for green-space conservation, ecological restoration, and sustainable urban planning in rapidly urbanizing regions. Full article

In biodiversity-rich borderlands, some humanitarian settlements are rapidly expanding. This creates a profound conflict: refugees need a place to live, and ecosystems need protection. However, how settlement growth spatially affects the ecology surrounding protected areas remains understudied. This study takes as an example the city of Teknaf in Bangladesh, one of the world’s largest refugee gathering areas, to explore how settlement expansion changes the ecological structure and function of protected area boundaries, with a focus on two questions: Are there critical spatial thresholds? What is the role of climate feedback mechanisms? We build an analysis framework that integrates several types of data: multitemporal remote sensing images, land-use changes, ecological indicators (NDVI, LST, HQ), landscape pattern indices, gradient analysis, and 2036 simulations based on the business-as-usual scenario. Through this framework, we identify the ecological threshold at the junction of settlements and forests within the Teknaf Wildlife Sanctuary. The expansion of settlements has turned the landscape, which was originally dominated by vegetation, into fragmented hard patches. At the same time, the habitat is severely degraded, and heat stress intensifies. Notably, a critical transition zone emerges at approximately 300–500 m from the protected area boundary, where landscape fragmentation intensifies, habitat quality declines, and heat stress reaches its peak, highlighting a spatial hotspot of ecological vulnerability. If there are no intervention measures, future scenario simulations show that the continued expansion of settlements will only isolate protected areas and accelerate ecological degradation. On the basis of gradient analysis for spatial diagnosis, we propose a zoning management framework and regeneration landscape strategy with the direct goal of coordinating ecological protection and humanitarian needs in crisis-prone border areas. Full article

Coastal wetlands, as sensitive ecological interfaces of land–sea interactions, provide regulating functions and ecosystem service values for maintaining regional ecological security. To achieve systematic restoration of ecological functions and intelligent management of resources in coastal wetlands, it is critical to deconstruct the evolution patterns of their landscape configurations across multiple spatiotemporal scales and precisely identify driving factors and ecological risk transmission mechanisms. This study constructs a trinity framework of “pattern evolution-driver analysis-risk assessment” for landscape ecological risk (LER) evaluation, integrating spatial statistical analyses (Standard Deviational Ellipse, Land Use Transition Matrix) and Geographically Weighted Regression (GWR) models to systematically analyze the spatiotemporal evolution characteristics and multidimensional driving mechanisms of landscape patterns in the Yellow River Delta (YRD), a typical coastal wetland, from 2000 to 2023. The results are as follows: (1) total wetland area initially declines followed by partial recovery, with natural wetlands decreasing persistently and artificial wetlands expanding; (2) Gross domestic product (GDP) and temperature (TMP) are identified as the primary drivers of wetland evolution; (3) Wetland LER levels significantly increase from 2015 to 2020, with the proportion of high-risk areas rising from 10% in 2015 to 23% in 2020; (4) LER is predominantly characterized by High-High (H-H) clustering, with Moran’s I values ranging from 0.493 to 0.672 (all p < 0.001), indicating significant positive spatial autocorrelation. The wetland LER assessment framework developed in this study, grounded in a land–sea integrated perspective, provides decision-making support and theoretical foundations for formulating differentiated wetland restoration strategies and optimizing coastal ecological security patterns. Full article

Urban heat mitigation in megacities depends not only on cooling sources, but also on the connectivity through which cooling effects are transmitted across heterogeneous landscapes. However, existing studies have mainly focused on the static patterns of urban cold islands (UCIs), while the connectivity and disturbance response of urban cooling systems remain poorly understood. Taking Landsat-based summer thermal observations in Beijing, this study developed an integrated framework to assess the structure and resilience of the urban cold island network (CIN) by combining thermal source identification, resistance-surface construction, connectivity modeling, and disturbance simulations. Land surface temperature (LST) was extracted from Landsat 8 OLI/TIRS Collection 2 Level-2 surface temperature products acquired in July–August 2022, and cold island core sources (CICS) were subsequently identified by integrating thermal conditions with land-use characteristics. GeoDetector was used to quantify the explanatory power and interaction effects of natural, land-use, and socio-economic factors on LST spatial heterogeneity, serving as an attribution tool for interpreting thermal-environment drivers. These factors were then integrated into a resistance surface for circuit-theory-based connectivity analysis. Under the summer heat-stress scenario, 202 CICS covering 6416.95 km² were identified, mainly concentrated in peripheral mountainous areas. A total of 401 corridors were identified, including 70 primary corridors forming the structural backbone of the CIN. This spatial distribution reveals a mountain–plain cooling structure in Beijing, in which mountainous CICS constitute the regional cooling-supply base, while potential cooling transmission toward the urban core mainly depends on a limited number of backbone corridors. LULC was the dominant driver of LST, and its interactions with PD, NTL, and vegetation-related factors substantially enhanced explanatory power. Compared with random disturbance, targeted node removal led to an earlier and sharper decline in network resilience, with substantial deterioration already evident after approximately 20–30% of critical nodes were removed. These summer-based findings provide spatially explicit evidence for prioritizing cooling corridors, critical nodes, and restoration areas in connectivity-oriented urban heat mitigation and climate-responsive planning, thereby supporting hierarchical maintenance and restoration strategies based on their relative importance within the cooling network. Full article

Land-use change is a major driver of ecosystem service reconfiguration, yet the character and intensity of resulting trade-offs remain highly variable across studies. This review synthesizes English-language research retrieved primarily from the Web of Science Core Collection and supplemented by Scopus and Google Scholar, with particular attention to the multi-scale characteristics of trade-offs, the analytical consequences of different assessment approaches, and their relevance for sustainability governance. The reviewed literature reveals several recurrent patterns. Intensive land conversion commonly produces short-term gains in provisioning or construction-related benefits while reducing regulating and supporting services. Trade-offs are strongly scale dependent, reflecting differences in ecological processes, land-use decisions, and governance units rather than analytical sensitivity alone. The landscape configuration further shapes ecosystem service interactions in ways that cannot be inferred from land-use area alone. However, evidence on restoration co-benefits, spatial-optimization gains, and governance claims based on scenario results remains context-dependent. These findings should be interpreted as conditional support for comparing land-use options, identifying potential trade-off displacement, and clarifying planning constraints, rather than as proof that restoration or optimization will automatically improve governance outcomes. The current evidence base is geographically uneven and strongly concentrated in Chinese case studies, which enriches planning-oriented research but limits straightforward generalization across institutional and environmental settings. Further progress may depend on stronger cross-scale and dynamic analysis, closer integration of the ecosystem service supply, demand, and flow, and more explicit treatment of uncertainty. More importantly, the value of future research will lie not simply in producing additional maps or indicators, but in establishing a clearer correspondence between the type of evidence generated and the governance decisions it is expected to inform. Full article

Fire is an essential component of species, ecosystems, and atmospheric dynamics. However, human activity has caused changes in fire regimes over the past two decades. In many cases, the spatial patterns of vegetation change after fire at the landscape scale remain unknown. The aim of this study was to evaluate spatial vegetation trends in burned areas across the Americas (2001–2024), using non-parametric tests and analyzing Normalized Difference Vegetation Index (NDVI) remote sensing products. Over a period of 24 years, fire activity burned a total area of 429.7 million hectares in 44 countries or territories and 269 ecoregions in the Americas. Regarding fire recurrence, the data indicates that 244.7 Mha (56.9%) burned only once (≤1), while 185.0 Mha (43.1%) burned multiple times (≥2), with certain regions experiencing up to 39 fires. The NDVI trend analysis showed that burned areas with increasing trends (p < 0.05) represented a total of 149.6 Mha (34.8%), primarily in Brazil (54.6 Mha, 12.7%), Argentina (17.8 Mha, 4.2%), the United States (14.4 Mha, 3.4%). In terms of decreasing NDVI trends (p < 0.05), these represented a total of 91.8 Mha (21.37%), primarily in Brazil (29.1 Mha, 6.8%), Canada (23.4 Mha, 5.4%), and the United States (14.2 Mha, 3.3%). The ecoregions with the largest areas showing increasing NDVI trends (p < 0.05) were the Cerrado (33.8 Mha, 7.8%), the Llanos (13.3 Mha, 3.1%) and the Humid Chaco (7 Mha, 1.6%). In contrast, the ecoregions with the largest areas showing decreasing NDVI trends (p < 0.05) were the Dry Chaco (9.2 Mha, 2.1%), the Cerrado (8.6 Mha, 2.0%), and the Boreal Shield (8.3 Mha, 1.9%). In terms of land cover types, savannas (37.2%) exhibited the highest proportions of increasing NDVI trends (p < 0.05), while decreasing trends were also present in savannas (28.0%) and grasslands (22.1%). Identifying spatiotemporal trends in vegetation change after fires is a fundamental step in implementing strategies and public policies to ensure ecological restoration. Moreover, given the high costs of restoration efforts, governments must work together to prevent these ecosystems from burning repeatedly. Full article

Urban expansion is a major anthropogenic driver of ecosystem service degradation, and its effects differ significantly among expansion patterns and city types. This study selects Jinan, a megacity in Shandong Province, and Dongying, a resource-based city, as study areas. Based on 2000–2020 land cover data, we identified the key urban expansion patterns that lead to ecosystem service losses. We used a built-up land source matrix to analyze the land composition of newly developed built-up areas and adopted the Landscape Expansion Index (LEI) to classify urban expansion into three types: edge-expansion, infilling, and leapfrog expansion. We quantified losses of five core ecosystem services—carbon sequestration, water yield, food production, habitat quality, and soil retention—to identify which expansion pattern exerted the most significant impact on ecosystem service degradation. We further compared loss differences and underlying mechanisms to propose differentiated urban strategies. The results indicate that cultivated land was the primary source in Jinan, while Dongying’s sources were more diverse. Edge-expansion dominated both cities, with a higher proportion in Dongying. Jinan showed a greater increase in leapfrog expansion, and infilling expansion was limited. Leapfrog expansion caused the most severe losses for most services, while edge-expansion dominated food production loss via farmland occupation. This study provides a scientific basis for optimizing spatial development and coordinating urban expansion with ecological conservation. Full article

Karst critical zones in Southwest China are highly vulnerable to soil–water loss because thin soils, exposed carbonate bedrock, well-developed epikarst, and strong surface–subsurface connectivity promote both surface erosion and subsurface leakage. Although soil erosion, subsurface leakage, karst rocky desertification, and ecological restoration have been widely studied, the coupling between landscape structural patterns and soil–water loss remains insufficiently synthesized. This semi-systematic critical review synthesizes evidence from karst hydrology, soil erosion, karst rocky desertification, landscape structure, and critical zone studies, with a primary focus on Southwest China. The reviewed evidence indicates that geomorphic setting, land use vegetation structure, bare-rock exposure, and epikarst development jointly regulate runoff generation, infiltration, sediment detachment, subsurface leakage, and sediment connectivity. Peak–cluster depressions commonly favor internal sediment storage and vertical leakage, whereas valley and canyon systems tend to enhance surface runoff connectivity and channelized sediment export. However, pathway dominance varies with rainfall intensity, soil moisture, soil thickness, land use, karst rocky desertification degree, and fracture–conduit connectivity. Long-term soil–water loss may further reshape landscape structure through soil thinning, vegetation degradation, bedrock exposure, and karst rocky desertification feedbacks. Current research is limited by insufficient quantification of subsurface soil loss, weak integration between landscape metrics and hydrological models, and scarce long-term monitoring data. Future studies should integrate field monitoring, tracers, remote sensing, landscape metrics, and coupled surface–subsurface models to support geomorphic-setting-specific karst rocky desertification control. Full article

Low-altitude logistics is increasingly considered a promising solution for urban last-mile delivery, yet how urban morphology is associated with the simulated cost of drone logistics networks across cities remains unclear. This study examines model-based relationships between urban spatial form and the cost performance of drone logistics networks under unified simulation assumptions. A multi-tier facility location model is developed and applied to 101 Chinese cities, with simulated annealing used to obtain cost-minimizing configurations of drone take-off and landing facilities. An XGBoost model with SHAP analysis is employed to interpret nonlinear associations and interaction patterns between urban morphology indicators and simulated network cost, while K-means clustering is used to identify representative morphology–cost patterns. The results show that built-up area and landscape shape index are the most influential predictors in the adopted modeling setting, both exhibiting threshold-like sensitivity ranges. Simulated network costs increase more rapidly when built-up area exceeds approximately 1000 km² and when landscape shape index falls within 5–15, with a notable interaction between them. Three morphology–cost types are further identified, reflecting systematic differences in simulated network organization. These findings provide simulation-derived evidence for morphology-sensitive planning of low-altitude logistics infrastructure, while actual deployment decisions still require calibration with local demand, operational, regulatory, and airspace conditions. Full article

Vegetation phenology refers to the cyclical growth patterns of vegetation in nature, which are influenced by climatic conditions, human activities, and genetic factors. It plays an irreplaceable role in regulating carbon cycling and energy flow within natural ecosystems. However, the combination of a cloudy and rainy climate with a landscape characterized by the interplay of land and water and fragmented patches has long posed challenges for remote sensing phenological monitoring data, including a scarcity of valid observations, frequent temporal gaps, and spectral distortion in mixed pixels. These issues make it difficult to reliably support the needs of wetland phenological inversion and mapping. To address this issue, this study uses vegetation inversion in the Poyang Lake wetlands as a case study and reconstructs high-spatiotemporal-resolution time-series kNDVI data based on multi-source remote sensing data. Methodologically, we propose an improved and enhanced spatiotemporal adaptive reflectance fusion model, IESTARFM. This model enhances the homogeneity of similar pixel selection through adaptive matching windows and land cover constraints. Additionally, it explicitly incorporates cloud probability and time-lag factors into the weighting structure to systematically downweight unreliable observations, and further employs quadratic term corrections to account for the nonlinear growth response of kNDVI. Using the reconstructed dataset, key phenological information is extracted by combining third-order harmonic analysis with a dynamic thresholding method, thereby enhancing the robust characterization of seasonal trajectories under conditions of missing data and noise. Accuracy evaluation results show that the 10m/8d high-frequency kNDVI dataset reconstructed by IESTARFM achieves at least a 12.61% improvement in fusion accuracy compared to classical methods such as ESTARFM, STARFM, and FSDAF, with a maximum reduction in RMSE of 0.026, and effectively restores details in areas with thin cloud cover. The reconstructed kNDVI series achieved a coefficient of determination $R^2$ = 0.875 and RMSE = 0.066 relative to Sentinel-2 observations, indicating that the reconstructed series closely reproduces the reference imagery in both amplitude and spatial structure. The phenological parameters derived from kNDVI exhibit an RMSE of 4.81 days compared to field observations, demonstrating that the reconstructed time series reliably captures the timing of key phenological events. It should be noted that the proposed approach is designed for post-event time-series reconstruction and is not intended for real-time forecasting. In summary, this study collaboratively enhanced the reliability of high-resolution index time-series reconstruction and phenological identification in cloudy and rainy wetlands through three key aspects: cloud noise suppression, heterogeneous boundary preservation, and nonlinear growth characterization. It provides a generalizable technical foundation for dynamic monitoring of wetland vegetation, ecological restoration assessment, and refined management in regions with frequent cloud and rainfall. Full article