Search Results (1,530)

Depopulation and urban–rural population redistribution are challenges that reshape settlement patterns, landscapes, and local economies in many regions, from Europe to China and from Japan to North America. This study examines spatial and demographic transformations in the Baltic States (Europe), using Lithuania as a detailed case study. The analysis is based on high-resolution GIS population data derived from official population registers and linked to georeferenced settlement polygons for the years 2011 and 2021, combined with a linear projection of population change to 2026 (five-year period). The results reveal that population decline, which appears modest at the aggregated statistical level (approximately −1.1% to −1.5% per year), is territorially concentrated and reaches 45–48% in the most affected areas, which can only be identified through fine-scale spatial analysis. The most pronounced decline (−46%) is observed in the population of detached rural dwellings between 2011 and 2021, with trend-based estimation indicating that vacant rural houses may exceed 50% by 2026. At the same time, peri-urban zones surrounding the largest cities show clear population growth, largely driven by internal migration from ageing urban districts, smaller towns, and peripheral rural areas, compensating aggregated values and masking underlying processes. The findings reveal a dual process of rural shrinkage and suburban expansion, increasing pressures on territorial cohesion, service provision, infrastructure planning, and the preservation of cultural landscapes. The application of high-resolution spatial data allows the detection of localized demographic processes that remain insufficiently captured in conventional municipality-level statistics and that have rarely been analyzed at this level of spatial detail. Based on these results, this study emphasizes policy approaches such as adaptive rural regeneration and managed shrinkage. Although the empirical analysis is focused on Lithuania, the identified trends are relevant to many shrinking regions worldwide and may be reproduced using local population register data in other countries to support evidence-based regional planning. Full article

As a form of living heritage, Historic Urban Landscapes (HULs) have long been limited by the static perspectives and reductionist tendencies of conventional conservation and research approaches. Although the geological and archaeological concept of “stratification” offers a methodological basis for understanding the diachronic evolution of heritage, its unidimensional temporal lens fails to capture the inherent complexity and systemic nature of historic urban landscapes. To address this gap, this study proposes a “multidimensional stratification” theoretical framework through theoretical critique and paradigm reconstruction. The framework introduces innovations at the ontological, epistemological, and methodological levels, positing that the evolution of historic urban landscapes emerges from the nonlinear interaction and dynamic interweaving of four core dimensions: time, space, society, and value. It further systematizes five intrinsic attributes of such landscapes: authenticity, integrity, continuity, adaptability, and dynamism. Building on this foundation, the paper constructs a systematic analytical pathway—elements–processes–patterns–modes–drivers–characteristics—that enables dynamic analysis from micro-level identification to macro-level generalization, offering a scalable tool for HUL conservation and regeneration. To demonstrate the framework’s applicability, the historic urban area of Shenyang—a nationally designated historical and cultural city—is selected as a case study. Its urban landscape comprises four core districts: the Shengjing City District, the South Manchuria Railway Concession District, the Commercial Port District, and the Tiexi Industrial District, representing historical strata from the Qing dynasty capital, modern colonial planning, commercial opening, to industrial heritage. Using the multidimensional stratification approach, this study elucidates the spatial complexity, temporal nonlinearity, social dynamism, and value pluralism embedded in Shenyang’s historic urban area. Corresponding conservation strategies grounded in holism, dynamism, and differentiation are proposed. The research not only advances the theoretical understanding of HUL but also provides a novel paradigm—integrating holistic, dynamic, and operational perspectives—for the conservation, renewal, and regenerative practice of historic urban landscapes worldwide. Full article

Administrative fragmentation, whereby political boundaries are used as analytical extents, can disrupt ecological flows and weaken ecological network planning by creating a mismatch between governance units and ecological processes. However, the pathways through which such fragmentation alters network structure and function remain insufficiently quantified. This study quantifies these effects and identifies the landscape conditions that shape the effectiveness of cross-boundary integration. Using a multi-scale buffer experiment (1–32 km) across 30 representative counties in China, we constructed ecological networks based on Morphological Spatial Pattern Analysis and on the minimum cumulative resistance model. Results show that relaxing administrative boundaries reduced structural distortions and lowered total ecological flow cost, indicating that fragmentation increases connectivity costs. Mechanistically, reducing redundant internal links and forced detours improved network efficiency mainly by shortening corridors and lowering flow costs, whereas mean corridor resistance changed little. This suggests that functional degradation is driven primarily by topological disruption rather than by declines in corridor quality. The benefits of cross-boundary integration were greater in counties with regular shapes, high grassland cover, humid climates, and rugged terrain, but weaker under strong human pressure and warmer temperatures. Improvements leveled off beyond 32 km, suggesting a 32 km buffer (study-specific) for integration and supporting context-specific strategies for ecological network planning. Full article

This study aims to investigate the short-term dynamics of agricultural landscapes using AI-supported multi-temporal land use/land cover (LULC) data. The Finike district, located within the Mediterranean climate zone, was selected as the study area, and 10 m spatial resolution ArcGIS Living Atlas LULC raster datasets for the years 2017 and 2024 were used. Spatial dynamics of agricultural areas were analyzed using Fragstats by quantifying changes in area and dominance (CA, PLAND), fragmentation and patch density (NP, PD), spatial integrity and largest patch structure (LPI), shape complexity (PARA_MN), and aggregation–connectivity patterns (CLUMPY, AI), thereby providing a comprehensive assessment of fragmentation, dispersion, clustering, and landscape cohesion over time. The analyses were conducted specifically for the agricultural class for both class-level and landscape-level metrics. The findings indicate that agricultural areas, which covered approximately 3128 hectares in 2017, decreased to 2643 hectares by 2024, as shown in the quantitative results of landscape metrics, accompanied by a pronounced increase in fragmentation. The increase in the number of patches, the decrease in mean patch size, and the rise in patch density demonstrate that the agricultural landscape has transformed into a more fragmented and irregular structure. The results further reveal a weakening of spatial integrity in agricultural areas, suggesting increased pressure from land use change processes, particularly urban expansion, in the study area, and highlighting potential risks for land management, agricultural sustainability, and ecological functions. Overall, the study highlights that the integrated use of high-resolution, AI-supported LULC data and landscape metrics provides a robust and effective framework for monitoring short-term dynamics in agricultural landscapes and supporting evidence-based planning processes. Full article

Scientifically assessing ecosystem health and optimizing ecological source areas (ESAs) are essential for effective environmental management, particularly in ecologically strategic mountain barrier regions. However, existing studies face challenges in identifying and optimizing ESAs. To address these limitations, this study integrated the contribution–vigor–organization–resilience (CVOR)-based ecosystem health framework, a genetic algorithm (GA), and circuit theory to assess ecosystem health, optimize ESAs, and identify ecological corridors (EC) and restoration priorities in the Daiyun Mountain Rim. The results demonstrate the following: (1) a significant ecosystem health decline from 2012 to 2022, evidenced by a 38.97% to 21.09% reduction in high-priority ecological zones accompanied by increased landscape fragmentation; (2) delineation of 90 GA-optimized ESA and 248 EC (2164.71 km), forming an interconnected ecological network; (3) enhanced connectivity metrics through GA optimization, showing α-index improvements of 0.15–0.23 and β-index gains of 0.05–0.08 compared to the traditional large-patch and morphological spatial pattern analysis (MSPA)-based ESA selection methods; (4) development of a tiered spatial strategy featuring primary/secondary restoration clusters and a “three-belt–one area–multiple clusters” framework for adaptive landscape governance. Although uncertainties remain due to the selected study period, parameter settings, and lack of field-based validation, this framework provides a useful reference for ecological planning, restoration prioritization, and ecosystem management in similar mountainous ecological barrier regions. Full article

Landscape pattern is closely associated with pollution in rapidly urbanizing watersheds, but most studies still focus on single pollutants or single environmental media. This study developed a watershed-based framework to compare coupled water and air pollution in the Liaohe River Basin, China. A total of 156 hydrologically connected sub-basins were used as common spatial units. Landscape metrics were calculated for 2000, 2010, and 2020. Total nitrogen and total phosphorus loads were simulated using the Soil and Water Assessment Tool, while annual mean PM_2.5 and O₃ concentrations were aggregated from gridded products to the same sub-basin scale. Coupling coordination degree was used to identify relative co-pollution patterns within the aquatic and atmospheric systems. GeoXGBoost with spatial block cross-validation was used to evaluate predictive performance, and GeoSHAP was used to interpret model-based predictor contributions. The aquatic coupled pollution index was predicted more accurately than the atmospheric index, indicating a stronger landscape association with nutrient coupling. Cropland proportion was the most stable predictor of aquatic coupling, whereas forest proportion was the most stable predictor of atmospheric coupling. These results suggest that water-oriented management should focus on cropland structure and ecological buffering, while air-oriented management should emphasize forest continuity and fragmentation control. The framework provides a spatially explicit basis for differentiated watershed management and territorial spatial planning. Full article

The rapid expansion of ski tourism in Northeast China has triggered extensive land use and land cover change (LULCC), yet the micro-scale spatial mechanisms linking historical land conversion to the accessibility of tourist services remain largely unquantified. This study addresses this gap by integrating annual 30 m CLCD land cover data with GIS network analysis of Points of Interest (POIs) around 30 major ski resorts (2018–2023). Specifically, it makes a novel distinction between the accessibility outcomes of construction-oriented and agriculture-oriented land transitions. Results indicate that while forest-to-construction conversion significantly predicts reduced travel distances to services (e.g., hotels: r = −0.532, p < 0.01), a distinct and previously unreported agri-tourism synergy emerges: forest-to-cropland conversion is positively associated with higher per capita tourist spending (r = 0.366, p < 0.05). This finding challenges the conventional zero-sum view of land use competition and suggests that cultivated landscapes can function as complementary tourism assets. These empirical patterns provide an evidence-based framework for integrated land-transport planning in emerging winter sports destinations. Full article

This study examines the preservation of cultural landscapes in Asian heritage villages, using the Qiaonan Village in China as a case study. The study proposes an integrated model that combines macro-level planning, meso-level governance and micro-level community participation. Key findings show that only 32% of residents perceive the distribution of tourism benefits as fair, while a GIS analysis revealed a 28% increase in commercial land use within the heritage core between 2019 and 2022, indicating rising commercialisation pressures. The study explores the tensions between heritage conservation and tourism-driven development, with a focus on spatial integrity and local identity. It suggests that co-management and equitable benefit-sharing could strike a balance between economic growth, preservation, and community well-being. Rather than offering validated solutions, the research provides a diagnostic lens and generates hypotheses for other heritage villages. The transferability of these findings depends on local governance capacity, regulatory clarity, and the stage of tourism development, factors that will require systematic assessment in future comparative research. Full article

Existing research on the landscape patterns of urban parks and green spaces demonstrates a disproportionate focus across tiers within China’s urban hierarchy. Numerous studies have concentrated on economically developed first-tier cities, such as Beijing, Shanghai, and Guangzhou. In contrast, medium-to-large non-first-tier cities, especially provincial capitals and emerging cities within the first- and second tiers, have been relatively understudied, although they have received increasing attention in recent years. This bias extends regionally, with studies predominantly examining cities in the more developed central and eastern regions, while less-developed areas and lower-tier cities receive significantly less attention. This study tracks changes in park quantity, spatial concentration, patch structure and driver associations at three planning-related time points. Shenyang provides a distinct cold-region and old industrial city case, shaped by long winters, industrial renewal and outward urban growth. Furthermore, to inform park and green-space planning in Northeast China’s cold-climate cities, exemplified here by Shenyang, a major metropolis with a monsoon-influenced humid continental climate (Köppen Dwa), long cold winters, and relatively short warm summers, we document a shift in park distribution from the urban core to peripheral areas. Based on park vector layers reconstructed from planning documents, remote sensing interpretation and field verification, this study combined spatial analysis, landscape metric calculation and driver-association modeling. ArcGIS Pro was used to identify changes in distribution centers, directional extension and local clustering; FRAGSTATS 4.2 was used to calculate park landscape metrics; and SIMCA-P 14.1 was used to examine the statistical associations between selected landscape indicators and potential driving variables. The results show that the number and total area of parks in central Shenyang increased substantially from 2000 to 2024. Spatially, park distribution became less concentrated in the traditional inner city, while new clusters gradually appeared in peripheral districts and newly developed urban areas. The old urban core remained important, but its dominance weakened as park provision expanded outward. The landscape metric results further indicate that park expansion was accompanied by more irregular patch forms, stronger fragmentation and declining structural continuity. The driver association analysis suggests that climate conditions, population change, industrial restructuring, real estate investment, road construction and urban greening policies were related to different aspects of park landscape change. These associations should be interpreted as statistical relationships rather than direct causal effects. Overall, this study clarifies the spatial restructuring of park green spaces in a cold-region old industrial city and provides planning evidence for improving park connectivity, coordinating green space expansion with urban construction and supporting sustainable park system development in Northeast China. Full article

StoryMapping has emerged as an accessible geotechnological approach that combines spatial analysis, interactive cartography and digital storytelling to communicate urban landscape transformations. This study aims to demonstrate the methodological potential of StoryMaps for integrating historical cartography, GIS-based analysis and narrative visualisation to explain long-term urban landscape change in an accessible and scientifically rigorous way. Using a case study of Madrid, the research integrates more than 150 years of historical maps, georeferenced images and thematic GIS layers to visualise shifts in blue–green infrastructures, land-use patterns and morphological configurations. The methodology includes the compilation of historical cartographic sources, GIS processing of contemporary datasets, georeferencing of archival materials and the construction of an interactive narrative using ArcGIS Pro 3.6 StoryMaps. Results show that StoryMapping enhances public understanding of complex urban processes, supports participatory planning, and provides a bridge between technical analyses and community engagement. The study concludes that StoryMapping is not only a powerful communication tool but also a valuable geotechnological solution for sustainable landscape planning, complementing traditional GIS approaches and promoting interdisciplinary perspectives in urban studies. Full article

In arid inland watersheds, the compounding impacts of climate change and intensive human activities have severely altered hydrological regimes and accelerated landscape degradation. However, conventional spatial planning often overlooks the critical coupling between subsurface hydrological processes and surface landscape dynamics. Taking the Manas River Watershed in northwestern China as a representative case, this research investigates the multi-scale dynamics of landscape patterns and their underlying spatial determinants. Integrating multi-period land-use data (2000–2020), landscape metrics, and the GeoDetector model, we diverge from conventional uniform buffer approaches by redefining riparian boundaries utilizing four distinct River–Groundwater Transformation (RGT) patterns. This methodological shift reveals critical eco-hydrological heterogeneities previously masked by fixed-width approaches. Our multi-scale analyses demonstrate that watershed-level landscapes exhibited a trajectory of declining diversity, transient recovery, and ultimately, intensified fragmentation, while riparian patches concurrently expanded and became increasingly homogenized. GeoDetector assessments indicate a fundamental shift in driving forces: early-stage variations were constrained by natural factors, whereas post-2010 dynamics became overwhelmingly dominated by socio-economic determinants, particularly agricultural expansion and GDP growth. Crucially, our RGT-coupled spatial analysis reveals a strong spatial association between agricultural sprawl and landscape risk hotspots concentrated within groundwater overflow zones—a pattern consistent with, but not directly demonstrating, disrupted vertical hydrological connectivity. Direct verification of subsurface mechanisms would require continuous piezometric monitoring beyond the scope of this study. Consequently, rather than generic zoning, we propose a multi-scale “hydro-spatial” governance framework featuring targeted interventions. By establishing strict agricultural redlines in vulnerable overflow zones and implementing eco-hydrological restoration tailored to specific RGT regimes, this paradigm delivers robust methodological insights for advancing precision spatial planning in fragile arid ecosystems. Full article

Remote sensing technologies provide valuable geospatial data for analyzing environmental conditions and for supporting spatial ecological modeling across large, heterogeneous landscapes. In this study, multi-source remote sensing–derived environmental variables were integrated with ensemble machine learning techniques to model the habitat suitability of the common leopard (Panthera pardus) in Azad Jammu and Kashmir (AJ&K), Pakistan. Environmental predictors derived from satellite observations included land cover, vegetation condition, terrain attributes, and climate-related indicators. To ensure model reliability, multicollinearity among predictors was evaluated, and spatial clustering patterns of leopard occurrence records were examined using global spatial autocorrelation analysis. Two complementary machine learning algorithms, Maximum Entropy (MaxEnt) and Random Forest (RF), were implemented and integrated through a weighted ensemble approach to improve predictive accuracy and robustness. The ensemble model achieved high predictive performance with an area under the curve (AUC) value of 0.942, outperforming individual algorithms. The resulting habitat suitability map indicates that approximately 30% of the study region is highly suitable habitat, primarily in the northern and central districts, including Muzaffarabad, Neelum, Hattian, Poonch, and Sudhnutti. Variable importance analysis identified remotely sensed land cover, elevation, vegetation cover, slope, and temperature seasonality as the dominant predictors of habitat suitability, whereas anthropogenic indicators such as proximity to roads and population density had secondary effects in fragmented areas. The results demonstrate the potential of integrating remote sensing data and ensemble machine learning for spatial habitat modeling and wildlife conservation planning in mountainous ecosystems. Full article

Blue–green spaces serve as the core carriers of urban ecosystems, and their conservation and optimization have emerged as pivotal issues in territorial spatial planning and ecological governance. Taking Guilin, a national innovation demonstration zone for China’s Sustainable Development Agenda, as the study area, a deep learning-based DBDTAF-Net classification model is constructed using 2020 Sentinel-2 remote sensing imagery and AW3D30 Digital Surface Model (DSM) data. The model achieves a mean Intersection-over-Union (mIoU) of 86.05% on the test set and an IoU of 94.67% for rocky desertification areas. Based on the classification results, 21 derived indicators (including landscape patterns of BGSs) and six meteorological and topographic factors, alongside three core ecosystem service indicators—Aboveground Biomass (AGB), Net Primary Productivity (NPP), and soil conservation—are extracted to characterize their spatial patterns. The XGBoost-SHAP framework is employed to quantify the driving effects and threshold responses of BGS patterns on ecosystem services. The results indicate that (1) BGSs in Guilin display a spatial pattern of “green-dominated, blue-supplemented, generally contiguous yet locally fragmented,” and all three ecosystem services exhibit significant spatial clustering. (2) Landscape pattern factors of green spaces constitute the dominant influencing factors, with contribution rates ranging from 22.3% to 28.6%. Specifically, green space_COHESION demonstrates a stable linear positive effect. A green space ratio below 45% suppresses AGB, whereas exceeding 45% shifts to a positive effect and represents an efficient enhancement interval for NPP while exerting a continuously positive influence on soil conservation. A cultivated land proportion below 30% leads to a strongly increasing inhibitory effect on AGB and soil conservation, whereas its inhibition on NPP weakens beyond 20%. A construction land proportion exceeding 10% significantly suppresses NPP, and the inhibitory effect stabilizes above 20%. Green space patch density below 0.8 shows a pronounced negative effect, which diminishes above 0.8. Blue space factors exert relatively weak effects. (3) The ecosystem service supply capacity varies across functional zones in Guilin, with the ecological barrier zone performing the best, the modern agricultural zone performing moderately, and the six central urban districts of the Shanshui Metropolis Area exhibiting the lowest levels. This study provides a technical framework for high-precision extraction of urban BGSs and quantitative analysis of factors influencing ecosystem services, offers decision support for ecological conservation and restoration in Guilin, and furthermore proposes insights for the coordinated development of rational land resource utilization and ecosystem service enhancement in other karst cities. Full article

Flood-prone riverfront zones face increasing challenges due to climate change, urbanisation, and legacy industrial development. Riverfront regeneration presents a unique opportunity not only to restore ecological function and public amenity but also to integrate adaptive architectural strategies that enhance flood resilience. This study aims to investigate the interplay between riverfront regeneration and adaptive architectural planning in flood-prone areas. This study provides a framework for understanding how built form, landscape infrastructure, and socio-spatial systems were developed to mitigate flood risk while reactivating riverfronts. Through a literature review and a methodology that integrates comparative case study analysis with generative scenario modelling, key design typologies were identified, including amphibious buildings, multifunctional embankments, and dynamic land-use zoning, and their performance was evaluated in terms of flood risk reduction, amenity provision, and community resilience. Based on the results, recommendations are proposed for practitioners and policymakers on advancing integrated riverfront regeneration in flood-prone regions, emphasising the necessity of multi-stakeholder governance, adaptable architectural strategies, and nature-based infrastructure. Full article

The morphological structure and connectivity of river systems are critical to ecological functions, water resource allocation, and disaster prevention in watersheds. This study applies an integrated approach combining morphological analysis, graph theory, landscape ecology, and complex network theory to analyze and optimize the river network structure of the Baiyangdian basin. The results showed significant structural improvements from 2014 to 2025: river network density (Dr) increased from 0.0335 to 0.1443 km/km², and river frequency (Fr) rose from 0.0015 to 0.0132 rivers/km². Connectivity indices also exhibited an overall increasing trend: circuitry (α) increased from −0.05 to 0.03, the edge–node ratio (β) from 0.90 to 1.06, and network connectivity (γ) from 0.30 to 0.35. Spatial analysis further identified a clear gradient of node importance: peripheral nodes showed low centrality, while critical nodes were predominantly concentrated along the South-to-North Water Diversion Project (SNWD) corridor. Optimization based on the 2025 baseline further improved network connectivity, with corresponding increases in connectivity metrics. These findings provide scientific support for integrated water management and climate-adaptive planning in the Baiyangdian basin. Full article