Search Results (57)

Urbanization has profoundly transformed land surface morphology and amplified thermal environmental modifications, culminating in intensified urban heat island (UHI) phenomena. Local climate zones (LCZs) provide a robust methodological framework for quantifying thermal heterogeneity and dynamics at local scales. Our study investigated the Changsha–Zhuzhou–Xiangtan urban agglomeration (CZXA) as a case study and systematically examined spatiotemporal patterns of LCZs and land surface temperature (LST) from 2002 to 2019, while elucidating mechanisms influencing urban thermal environments and proposing optimized cooling strategies. Key findings demonstrated that through multi-source remote sensing data integration, long-term LCZ classification was achieved with 1,592 training samples, maintaining an overall accuracy exceeding 70%. Landscape pattern analysis revealed that increased fragmentation, configurational complexity, and diversity indices coupled with diminished spatial connectivity significantly elevate LST. Rapid development of the city in the vertical direction also led to an increase in LST. Among seven urban morphological parameters, impervious surface fraction (ISF) and pervious surface fraction (PSF) demonstrated the strongest correlations with LST, showing Pearson coefficients of 0.82 and −0.82, respectively. Pearson coefficients of mean building height (BH), building surface fraction (BSF), and mean street width (SW) also reached 0.50, 0.55, and 0.66. Redundancy analysis (RDA) results revealed that the connectivity and fragmentation degree of LCZ_8 (COHESION8) was the most critical parameter affecting urban thermal environment, explaining 58.5% of LST. Based on these findings and materiality assessment, the regional cooling model of “cooling resistance surface–cooling source–cooling corridor–cooling node” of CZXA was constructed. In the future, particular attention should be paid to the shape and distribution of buildings, especially large, openly arranged buildings with one to three stories, as well as to controlling building height and density. Moreover, tailored protection strategies should be formulated and implemented for cooling sources, corridors, and nodes based on their hierarchical significance within urban thermal regulation systems. These research outcomes offer a robust scientific foundation for evidence-based decision-making in mitigating UHI effects and promoting sustainable urban ecosystem development across urban agglomerations. Full article

(1) Background: Urban parks play a critical role in conserving biodiversity within city landscapes, yet the effects of fine-scale microhabitat heterogeneity remain poorly understood. This study examines how land cover and vegetation unit type within parks influence butterfly diversity. (2) Methods: From July to September 2019 and June to September 2020, adult butterflies were surveyed in 27 urban parks across Beijing. We classified vegetation into units based on vertical structure and management intensity, and then applied the patch–matrix framework and landscape metrics to quantify fine-scale heterogeneity in vegetation unit composition and configuration. Generalized linear models (GLM), generalized additive models (GAM), and random forest (RF) models were applied to identify factors influencing butterfly richness (Chao1 index) and abundance. (3) Results: In total, 10,462 individuals representing 37 species, 28 genera, and five families were recorded. Model results revealed that the proportion of park area covered by spontaneous herbaceous areas (SHA), wooded spontaneous meadows (WSM), and the Shannon diversity index (SHDI) of vegetation units were positively associated with butterfly species richness. In contrast, butterfly abundance was primarily influenced by the proportion of park area covered by cultivated meadows (CM) and overall green-space coverage. (4) Conclusions: Fine-scale vegetation patch composition within urban parks significantly influences butterfly diversity. Our findings support applying the patch–matrix framework at intra-park scales and suggest that integrating spontaneous herbaceous zones—especially wooded spontaneous meadows—with managed flower-rich meadows will enhance butterfly diversity in urban parks. Full article

Valuation of ecosystem services (ESs) is crucial for understanding the benefits provided by ecosystems and informing sustainable management and policy decisions related to ecosystem protection. This study explores the disagreements in ecosystem service value (ESV) at the county level across China in 2020 by comparing ten land cover datasets of varying resolutions from 500 to 10 m, using the equivalent factor method. Significant disagreements in ESV estimates are identified, revealing spatial heterogeneity and large inconsistencies among estimates from different datasets, even with high spatial resolution (10 m). Across all counties, the typical discrepancy in ESV estimates between any two datasets reaches 3503 CNY/ha, and the ESV estimates for each county show an average coefficient of variation (CV) of 0.186 across the ten datasets, indicating considerable inconsistency attributable to dataset selection. The results highlight that ESV evaluations based on the CLCD, Globeland30, and GLC-FCS30 datasets demonstrate higher consistency and reliability, making them suitable for regional ecosystem service valuation. Both the landscape configurations and the area disparities of different land types have significant impacts on ESV disagreement. This study provides valuable insights into the applicability of different datasets for ESV evaluation, thereby enhancing the reliability of ESV assessments and supporting informed decision making in ecosystem management. Full article

Ecological network construction has been widely accepted and applied to guide regional ecological conservation and restoration. For arid regions, ecological networks proposed based on ecological risk assessments are better aligned with the sensitive and fragile characteristics of local ecosystems. This study assesses landscape ecological risk in Wensu County, located on the southern slope of the Tianshan Mountains in the arid region of northwestern China, and it further proposes an optimized ecological network. A multidimensional framework composed of the natural environment, human society, and landscape patterns was employed to construct an ecological risk assessment system. Spatial principal component analysis (SPCA) was applied to identify the spatial pattern of ecological risk. Morphological spatial pattern analysis (MSPA) and a minimum cumulative resistance (MCR) model integrated with circuit theory were used to extract the ecological sources and delineate the ecological corridors. The results reveal significant spatial heterogeneity in terms of ecological risk: Low-risk zones (16.26%) are concentrated in the southwestern forest and water areas. In comparison, high-risk zones (28.27%) are mainly distributed in the northern mountainous mining region. A total of 24 ecological source patches (4105.24 km²), 44 ecological corridors (313.6 km), 39 ecological pinch points, and 38 ecological barriers were identified. Following optimization, the Integral Index of Connectivity (IIC) increased by 89.04%, and the Landscape Coherence Probability (LCP) rose by 105.23%, indicating markedly enhanced ecological connectivity. The current ecological network exhibits weak connectivity in the south and fragmentation in the central region. Targeted restoration of critical nodes, optimization of corridor configurations, and expansion of ecological sources are recommended to improve landscape connectivity and promote biodiversity conservation. Full article

Multi-scale thermal regulation of urban green spaces is critical for climate-adaptive planning. Addressing the limited research on key indicators and cross-scale synergies in high-density areas, this study developed an integrated framework combining multi-granularity grids and boosted regression tree (BRT) modeling to investigate nonlinear scale-dependent relationships between landscape parameters and land surface temperature (LST) in the central urban area of Shijiazhuang. Key findings: (1) Spatial heterogeneity and scale divergence: Vegetation coverage (FVC) and green space area (AREA) showed decreasing contributions at larger scales, while configuration metrics (e.g., aggregation index (AI), edge density (ED)) exhibited positive scale responses, confirming a dual mechanism with micro-scale quality dominance and macro-scale pattern regulation. (2) Threshold effects quantification: The BRT model revealed peak marginal cooling efficiency (0.8–1.2 °C per 10% FVC increment) within 30–70% FVC ranges, with minimum effective green patch area thresholds increasing from 0.6 ha (micro-scale) to 3.5 ha (macro-scale). (3) Based on multi-scale cooling mechanism analysis, a three-tier matrix optimization framework for green space strategies is established, integrating “micro-level regulation, meso-level connectivity, and macro-level anchoring”. This study develops a green space optimization paradigm integrating machine learning-driven analysis, multi-scale coupling, and threshold-based management, providing methodological tools for mitigating urban heat islands and enhancing climate resilience in high-density cities. Full article

Surface mining activities cause severe disruption to ecosystems, resulting in the substantial destruction of surface vegetation, the loss of soil organic carbon stocks, and a decrease in the ecosystem’s ability to sequester carbon. The ecological restoration of mining areas has been found to significantly enhance the carbon storage capacity of ecosystems. This study evaluated ecological restoration strategies in Chongqing’s Tongluo Mountain mining area by integrating GF-6 satellite multispectral data (2 m panchromatic/8 m multispectral resolution) with ground surveys across 45 quadrats to develop a quadratic regression model based on vegetation indices and the field-measured biomass. The methodology quantified carbon storage variations among engineered restoration (ER), natural recovery (NR), and unmanaged sites (CWR) while identifying optimal vegetation configurations for karst ecosystems. The methodology combined the high-spatial-resolution satellite imagery for large-scale vegetation mapping with field-measured biomass calibration to enhance the quantitative accuracy, enabling an efficient carbon storage assessment across heterogeneous landscapes. This hybrid approach overcame the limitations of traditional plot-based methods by providing spatially explicit, cost-effective monitoring solutions for mining ecosystems. The results demonstrate that engineered restoration significantly enhances carbon sequestration, with the aboveground vegetation biomass reaching 5.07 ± 1.05 tC/ha, a value 21% higher than in natural recovery areas (4.18 ± 0.23 tC/ha) and 189% greater than at unmanaged sites (1.75 ± 1.03 tC/ha). In areas subjected to engineered restoration, both the vegetation and soil carbon storage showed an upward trend, with soil carbon sequestration being the primary form, contributing to 81% of the total carbon storage, and with engineered restoration areas exceeding natural recovery and unmanaged zones by 17.6% and 106%, respectively, in terms of their soil carbon density (40.41 ± 9.99 tC/ha). Significant variations in the carbon sequestration capacity were observed across vegetation types. Bamboo forests exhibited the highest carbon density (25.8 tC/ha), followed by tree forests (2.54 ± 0.53 tC/ha), while grasslands showed the lowest values (0.88 ± 0.52 tC/ha). For future restoration initiatives, it is advisable to select suitable vegetation types based on the local dominant species for a comprehensive approach. Full article

In recent years, rapid urbanization has transformed the man–land relationship in rural areas, highlighting issues such as the homogenization of vernacular landscapes. This study uses the Liuxi River in Guangzhou as a case and applies a hierarchical interpretation system for vernacular landscapes, utilizing methods from landscape character assessment (LCA) and Historic Landscape Characterization (HLC). Focusing on two scales, “basin” and “vernacular unit”, this study proposes a framework for identifying vernacular landscape patterns. This framework includes scale definition, pattern identification, feature description, and factor analysis. At the basin scale, the investigation concentrates on spatial configurations of vernacular landscapes in 1985, whereas the unit-scale analysis delineates temporal evolutionary trajectories spanning 1974–2020. The results indicate significant differences in landscape fragmentation, dominance, and diversity between upstream and downstream at the basin scale. At the unit scale, the landscape connectivity in the Shaxi River unit remains relatively stable, while landscape heterogeneity increases, resulting in greater diversity. This study provides valuable insights into the continuity and development of diversity in analogous vernacular landscape regions globally, particularly those comparable to the Liuxi River basin. Full article

Understanding the spatial differentiation of ecosystem service (ES) interactions and their underlying driving mechanisms is crucial for effective ecosystem management and enhancing regional landscape sustainability. However, comprehensive analyses of the effects of key influencing factors on ES interactions remains limited, especially regarding the nonlinear driving mechanisms of factors and their regional heterogeneity. We assessed and validated five key ES in the National Key Ecological Function Zones (NKEFZs) of China—net primary productivity (NPP), soil conservation (SC), sandstorm prevention (SP), water retention (WR), and biodiversity maintenance (BM). By integrating the optimal parameter geographical detector with constraint line methods, we further explored the complex responses of ES interactions to driving factors across different functional zones. The results showed that most ES exhibited significant spatial synergistic clustering. In contrast, widespread spatial trade-off clustering was detected in ES pairs related to WR, mainly distributed in the Tibetan Plateau, northeast China, and the Southern Hills region. Due to the improvement in ES, the overall synergies of ES enhanced from 2000 to 2020. The dominant factors in different functional zones influenced ES interactions in a non-stationary manner, with the same factors potentially showing diverse effect types in different sub-regions. Additionally, we detected the dominant role of landscape configuration factors in sub-regions for specific interaction types (e.g., WR-NPP interaction in the SP zones), suggesting the potential for achieving multi-ES synergies through landscape planning without altering landscape composition. This research provides valuable insights into understanding ES interactions and offers a scientific foundation for the implementation of ecological protection and restoration plans. Full article

The expansion of anthropogenic activities drives changes in the composition, structure, and spatial configuration of natural landscapes, influencing both the taxonomic and functional diversity of bird communities. This pattern is evident in the Colombian Amazon, where agricultural and livestock expansion has altered ecological dynamics, avifaunal assemblages, and the provision of regulating ecosystem services. This study analyzed the influence of agroforestry (cocoa-based agroforestry systems—SAFc) and silvopastoral systems (SSP) on the functional diversity of birds and their potential impact on ecosystem services in eight productive landscape mosaics within the Colombian Amazon. Each mosaic consisted of a 1 km² grid, within which seven types of vegetation cover were classified, and seven landscape metrics were calculated. Bird communities were surveyed through visual observations and mist-net captures, during which functional traits were measured. Additionally, functional guilds were assigned to each species based on a literature review. Five multidimensional indices of functional diversity were computed, along with community-weighted means per guild. A total of 218 bird species were recorded across both land-use systems. Bird richness, abundance, and functional diversity—as well as the composition of functional guilds—varied according to vegetation cover. Functional diversity increased in mosaics containing closed vegetation patches with symmetrical configurations. Variations in functional guilds were linked to low functional redundancy, which may also lead to differences in the provision of regulating ecosystem services such as biological pest control and seed dispersal—both of which are critical for the regeneration and connectivity of productive rural landscapes. In conclusion, functional diversity contributes to the resilience of bird communities in landscapes with Amazonian agroforestry and silvopastoral systems, highlighting the need for landscape management that promotes structural heterogeneity to sustain regulating ecosystem services and ecological connectivity. Full article

In the rapidly evolving landscape of telecommunications, the integration of commercial 5G solutions and the rise of edge computing have reshaped service delivery, emphasizing the customization of requirements through network slices. However, the heterogeneity of devices and technologies in 5G and beyond networks poses significant challenges, particularly in terms of security management. Addressing this complexity, our work adopts the Zero-touch network and Service Management (ZSM) reference architecture to enable end-to-end automation of security and service management in Beyond 5G networks. This paper introduces the ZSM-based framework, which harnesses software-defined networking, network function virtualization, end-to-end slicing, and orchestration paradigms to autonomously enforce and preserve security service level agreements (SSLAs) across multiple domains that make up a 5G network. The framework autonomously manages end-to-end security slices through intent-driven closed loops at various logical levels, ensuring compliance with ETSI end-to-end network slice management standards for 5G communication services. The paper elaborates with an SSLA-triggered use case comprising two phases: proactive, wherein the framework deploys and configures an end-to-end security slice tailored to the security service level agreement specifications, and reactive, where machine learning-trained security mechanisms autonomously detect and mitigate novel beyond 5G attacks exploiting open-sourced 5G core threat vectors. Finally, the results of the implementation and validation are presented, demonstrating the practical application of this research. Interestingly, these research results have been integrated into the ETSI ZSM Proof of Concept #6: ’Security SLA Assurance in 5G Network Slices’, highlighting the relevance and impact of the study in the real world. Full article

Crop mapping using remote sensing is a reliable and efficient approach to obtaining timely and accurate crop information. Previous studies predominantly focused on large-scale regions characterized by simple cropping structures. However, in complex agricultural regions, such as China’s Huang-Huai-Hai region, the high crop diversity and fragmented cropland in localized areas present significant challenges for accurate crop mapping. To address these challenges, this study introduces a landscape-clustering zoning strategy utilizing multi-temporal Sentinel-1 and Sentinel-2 imagery. First, crop heterogeneity zones (CHZs) are delineated using landscape metrics that capture crop diversity and cropland fragmentation. Subsequently, four types of features (spectral, phenological, textural and radar features) are combined in various configurations to create different classification schemes. These schemes are then optimized for each CHZ using a random forest classifier. The results demonstrate that the landscape-clustering zoning strategy achieves an overall accuracy of 93.52% and a kappa coefficient of 92.67%, outperforming the no-zoning method by 2.9% and 3.82%, respectively. Furthermore, the crop mapping results from this strategy closely align with agricultural statistics at the county level, with an R² value of 0.9006. In comparison with other traditional zoning strategies, such as topographic zoning and administrative unit zoning, the proposed strategy proves to be superior. These findings suggest that the landscape-clustering zoning strategy offers a robust reference method for crop mapping in complex agricultural landscapes. Full article

Urbanization transforms landscapes from natural ecosystems to configurations of impervious surfaces and green spaces, leading to urban heat island effects that impact health and ecosystem sustainability. This study in Xiamen City, China, categorizes urban areas into functional zones, employs Random Forest and Stepwise Regression models to assess thermal differences, and proposes optimization measures for the building–green space landscape. The optimization involves altering the characterization of the building–green space landscape pattern. Results indicate: (1) due to the spatial heterogeneity of the building–green space landscape pattern in different functional zones, the surface temperature also shows strong spatial heterogeneity in different functional zones; (2) different optimization measures for the building–green space pattern are needed for different functional zones; taking the urban residential zone as an example, the Normalized Difference Vegetation Index (NDVI) in the hot spot area can be adjusted according to the value range of the cold spot area; (3) considering the solar radiation process, Sun View Factor (S_unVF) plays an important role in indicating the change in surface temperature in the commercial service area, and as S_unVF increases, the surface temperature of the functional zone tends to rise. This research offers insights into urban thermal environment improvement and landscape pattern optimization. Full article

Despite significant progress in recent decades, air pollution remains the leading environmental cause of premature death in Europe. Urban populations are particularly exposed to high concentrations of air pollutants, such as particulate matter smaller than 10 µm (PM₁₀). Understanding the spatiotemporal variations of PM₁₀ is essential for developing effective control strategies. This study aimed to enhance PM₁₀ prediction models by integrating landscape metrics as ecological indicators into our previous models, assessing their significance in monthly average PM₁₀ concentrations, and analyzing their correlations with PM₁₀ air pollution across European urban landscapes during heating (cold) and non-heating (warm) seasons. In our previous research, we only calculated the proportion of land uses (PLANDs), but according to our current research hypothesis, landscape metrics have a significant impact on PM₁₀ air quality. Therefore, we expanded our independent variables by incorporating landscape metrics that capture compositional heterogeneity, including the Shannon diversity index (SHDI), as well as metrics that reflect configurational heterogeneity in urban landscapes, such as the Mean Patch Area (MPA) and Shape Index (SHI). Considering data from 1216 European air quality (AQ) stations, we applied the Random Forest model using cross-validation to discover patterns and complex relationships. Climatological factors, such as monthly average temperature, wind speed, precipitation, and mean sea level air pressure, emerged as key predictors, particularly during the heating season when the impact of temperature on PM₁₀ prediction increased from 5.80% to 22.46% at 3 km. Landscape metrics, including the SHDI, MPA, and SHI, were significantly related to the monthly average PM₁₀ concentration. The SHDI was negatively correlated with PM₁₀ levels, suggesting that heterogeneous landscapes could help mitigate pollution. Our enhanced model achieved an R² of 0.58 in the 1000 m buffer zone and 0.66 in the 3000 m buffer zone, underscoring the utility of these variables in improving PM₁₀ predictions. Our findings suggest that increased urban landscape complexity, smaller patch sizes, and more fragmented land uses associated with PM₁₀ sources such as built-up areas, along with larger and more evenly distributed green spaces, can contribute to the control and reduction of PM₁₀ pollution. Full article

A comprehensive understanding of the pattern of genetic variation among populations and adaptations to environmental heterogeneity is very important for conservation and genetic improvement. Forest tree species are ideal resources for understanding population genetic differentiation and detecting signatures of selection due to their adaptations to heterogeneous landscapes. Ulmus lamellosa is a tree species that is endemic to northern China. In this study, using restriction-site-associated DNA sequencing (RAD-seq) data, 12,179 single-nucleotide polymorphisms were identified across 51 individuals from seven populations. There was a high level of genetic diversity and population differentiation in U. lamellosa. Population genetic structure analyses revealed a significant genetic structure related to the configuration of the mountains. Additionally, we found that the isolation-by-distance pattern explained the genetic differentiation best, and environmental heterogeneity also played a role in shaping the landscape genetics of this species inhabiting mountain ecosystems. The F_ST-based outlier and genotype–environment association approaches were used to explore the genomic signatures of selection and local adaptation and detected 317 candidate outlier loci. Precipitation seasonality (coefficient of variation), precipitation in the driest month, and enhanced vegetation index were important determinants of adaptive genetic variation. This study provides abundant genetic resources for U. lamellosa and insights into the genetic variation patterns among populations. Full article

Urban forests are expected to mitigate the urban heat island (UHI) effect in megacities. The mechanism and factors influencing the cooling effect of urban forest have been extensively discussed; however, the spatial scale effect of cooling heterogeneity of the urban forest is still uncertain. Based on Landsat 8/9 OLI/TIRS imagery, the relationship between land surface temperature (LST) and the spatial patterns of forest vegetation in Beijing, Shanghai, and Tianjin was investigated at different spatial scales, including patch level, rural–urban gradient, and multiple spatial extents. The results indicated that the cooling effect of forest vegetation is stronger than that of grassland. The combination of the three indicators—Area, Normalized Difference Vegetation Index (NDVI), and the percentage of neighboring greenspace (NGP)—can largely explain the differences in cooling intensity between forest vegetation patches. The results suggest that the cooling effect of forest vegetation was affected by air humidity, and the cooling intensity of forest vegetation is stronger in coastal cities than in inland cities. In dry cities, the impact of the patch area on the cooling intensity of forest patches is greater than the NDVI, while the opposite is true in humid coastal cities. The LST variations in the urban–rural gradient can largely be explained by the landscape composition. This study proposes to apply larger spatial extents (e.g., 450 m × 450 m grid in this study) to investigate the relationship between landscape configuration metrics (e.g., Aggregation and Cohesion in this study) and the LST; and to use smaller spatial extents (e.g., 90 m × 90 m grid in this study) to reveal the relationship between area and shape related metrics. This study extends our scientific understanding of scaling effects to the relationship between landscape metrics and the LST. Full article