Search Results (1,439)

Rapid urbanization and industrialization have significantly reshaped rural landscapes in metropolitan fringe areas, resulting in “hybridized” characteristics. This study establishes an analytical framework to systematically characterize hybrid rural landscapes, diagnose specific local issues, reveal their spatial differentiation patterns and driving mechanisms, and propose targeted governance strategies. Taking 124 rural units in Baoshan District, Shanghai as a case, multi-source data from the latest available years (2020–2023) were compiled as a cross-sectional snapshot, and a comprehensive indicator system integrating landscape pattern (P), social function (F), and spatial vitality (V) was developed. Utilizing multi-source geospatial data—including land-use maps, points of interest, and mobile signaling data—Gaussian Mixture Models were applied to classify typical hybrid landscape types. Spatial evolution processes and underlying driving forces were further interpreted through remote sensing imagery analysis, field investigations, and policy document reviews. Eleven distinctive hybrid rural landscape types (HTs) were characterized, forming a spatial gradient from urban to rural, encompassing “high-density urbanized” → “ecologically embedded” → “production–living integrated” → “traditional rural landscapes”. Additionally, five representative evolutionary patterns—“urban restructuring”, “ecological orientation”, “industrial-driven transition”, “transitional hybridization”, and “traditional preservation”—were identified, shaped by spatial configuration, planning policies, industrial investments, and demographic dynamics. The framework enhances understanding of the complexity and evolutionary dynamics of rural landscapes, providing theoretical insights and practical guidance for effective typological governance and targeted policy interventions. Full article

Under the fast development of urbanization, PM_2.5 pollution has become a prominent issue affecting the urban ecological environment and residents’ health. To investigate the impact of urban landscape patterns on PM_2.5 concentrations, this study applies the Local Climate Zone (LCZ) classification to Shanghai using the World Urban Database and Access Portal Tools (WUDAPT). LCZ-derived landscape metrics are adopted as predictor variables to focus on how urban form and spatial configuration affect PM_2.5 distribution and to identify the key landscape categories and types influencing PM_2.5 levels. The results reveal notable seasonal and spatial differences in the effects of different LCZ types and landscape metrics on PM_2.5 concentrations; on average, over 69% of the spatial variation in PM_2.5 across the four seasons can be explained by the Multi-scale Geographically Weighted Regression (MGWR) model. This research demonstrates that the LCZ framework effectively uncovers the seasonal and spatial mechanisms by which urban landscape patterns influence PM_2.5 concentrations in Shanghai. It offers a novel perspective for understanding the interplay between urban landscape and atmospheric pollution, and provides scientific guidance for sustainable urban planning and precise air pollution control strategies in other cities. Full article

Understanding how birds adjust their flight in response to biomechanical characteristics and environmental conditions can be useful for interpreting homing behavior. This study investigates homing pigeons’ (Columba livia) flight behavior using multi-sensor biologgers, integrating GPS, tri-axial accelerometer, pressure, and temperature sensors. Flight biomechanics were assessed by extracting: wingbeat frequency from the Short-Time Fourier Transform of the total acceleration signal and peak-to-peak acceleration from the dorso-ventral component. Landscape characteristics were provided by classifying land cover along the route using a geographic atlas and by computing flight altitude above ground level through the combination of pressure-derived altitude and a digital elevation model. The results reveal a progressive decrease in wingbeat frequency along the homing route, showing a linear relationship with traveled distance. To assess whether this pattern can be interpreted in terms of flight regulation, flight altitude was modeled as a function of biomechanical and environmental variables using a linear mixed-effect approach. The analysis indicates that flight altitude is significantly affected by wingbeat frequency as well as by temperature, ground speed, and land cover, with wingbeat frequency and temperature showing the strongest negative association. Full article

Since land use considerably affects the spatial variation of PM_2.5 levels, it is crucial to predict PM_2.5 concentrations under future land use changes. However, prior research has primarily concentrated on meteorological factors influencing PM_2.5 predictions, while neglecting the effect of land use configurations. Consequently, in our study, a novel Patch-generating Land Use Simulation–Land Use Regression (PLUS-LUR) method was developed by integrating the PLUS model’s dynamic prediction capability with the LUR model’s spatial interpretation strength. The incorporation of landscape indices as key variables was essential for predicting PM_2.5 concentrations. First, the random forest-optimized LUR method was trained with PM_2.5 datasets from the Pearl River Delta (PRD) monitoring stations and multi-source spatial datasets. We assessed the modeling accuracy with and without considering landscape indices using the test dataset. Subsequently, the PLUS approach was applied to forecast land use as well as associated landscape indices in 2028. Based on these projections, grid-scale influencing factors were input into the previously constructed LUR model to forecast future PM_2.5 distributions at a grid scale. The results reveal a spatial pattern with higher PM_2.5 levels in central areas and lower levels in peripheral regions. Furthermore, the PM_2.5 concentrations in the PRD are all below the Grade II threshold of the China Ambient Air Quality Benchmark in 2028. Notably, the predictions incorporating landscape indices demonstrate higher accuracy and reliability compared to those excluding them. These results provide methodological support for future PM_2.5 assessment and land use management. Full article

The complex topography and harsh natural environment of the Loess Plateau in Longzhong have been suffering from an undefined living circle structure, which has hindered rural planning and development. A rural community living circle is a spatial unit centered on meeting the needs of villagers, within which various service facilities are rationally allocated within a specific spatial scope. To refine its spatial patterns, the concept of living circles was introduced to address travel challenges. The extent of these living circles is affected by the accessibility of public service facilities and barriers to travel. Using land use data, DEM, population density, and road networks, this study employed the MCR model, gravity model, and ArcGIS spatial analysis to examine the patterns of rural community living circles. The focus was on analyzing the living circle structure of rural communities on the Loess Plateau in Longzhong, considering both natural and artificial environmental constraints. The results show: (1) Rural community living circles present multi-scale spatial features. The basic living circle covers a 15 min slow-travel area. The central living circle corresponds to village-level needs, accessible within 35 min by both slow and motorized travel. The town living circle covers a 10 km radius, reachable within 60 min by a mix of transport modes. The county living circle, dominated by motorized travel, represents the top tier of public service configuration. (2) Quantitatively, the delineation identified 2753 basic, 444 central, 19 township, and 1 county-level living circles in the Anding District of Dingxi City. The Northern, Eastern, and Southwest Zones suffer from fragmented mountainous landscapes, limiting mobility and accessibility. The Central Zone, however, benefits from a combination of mountainous terrain and river valley plains, offering superior service accessibility. (3) The analysis results based on the MCR model and gravity model aligned more closely with reality, reflecting the scale patterns of rural community living circles. The results of this study can provide theoretical guidance for rural planning, construction, and management in the hilly and gully areas of the Loess Plateau. Full article

Seasonal dynamics in wetland landscapes are closely associated with habitat availability and are likely to influence the spatial organization and diversity of waterbird communities. However, most existing studies rely on static land-cover representations or single spatial scales, limiting our ability to characterize how waterbirds respond to seasonally shifting habitats across scales. Focusing on the Qilihai Wetland in Tianjin, China, we combined high-frequency waterbird surveys from 2019–2021 with multi-temporal, season-matched Sentinel-2 imagery and the Dynamic World dataset. Partial least squares regression (PLSR) was applied across a continuous spatial gradient (100–3000 m) to quantify scale-dependent statistical associations between landscape composition and configuration derived from satellite-mapped habitat mosaics on different functional groups. Waterbird diversity exhibited pronounced seasonal contrasts. During the breeding and post-fledging period, high-diversity assemblages were stably concentrated within core wetland areas, showing limited spatial variability. In contrast, during the wintering and stopover period, community distributions became increasingly dispersed, with elevated spatial heterogeneity and interannual variability associated with habitat reorganization. The scale of effect shifted systematically between seasons. In the breeding and post-fledging period, both waterfowl and waders responded predominantly to local-scale landscape factors (<800 m), consistent with nesting requirements and microhabitat conditions. During the wintering and stopover period, however, the characteristic response scale of waterfowl expanded to 1500–2000 m, suggesting stronger associations with broader landscape context, whereas waders remained closely linked to local-scale shallow-water and mudflat connectivity (~200 m). Functional traits played a key role in structuring these scale-dependent responses, with diving behavior and tarsus length being associated with strong constraints on habitat use. Overall, our results suggest that waterbird diversity patterns emerge from the interaction between seasonal habitat dynamics, landscape structure, and functional trait filtering, underscoring the need for phenology-informed, multi-scale conservation strategies that move beyond static spatial boundaries. Full article

Globally, grasslands, savannas, and wetlands are degrading rapidly and increasingly being replaced by woody vegetation. Woody Plant Encroachment (WPE) disrupts natural landscapes and has significant consequences for biodiversity, ecosystem functioning, and key ecosystem services. This review synthesizes findings from 159 peer-reviewed studies identified through a PRISMA-guided systematic literature review to evaluate the drivers of WPE, its ecological impacts, and the remote sensing (RS) approaches used to monitor it. The drivers of WPE are multifaceted, involving interactions among climate variability, topographic and edaphic conditions, hydrological change, land use transitions, and altered fire and grazing regimes, while its impacts are similarly diverse, influencing land cover structure, water and nutrient cycles, carbon and nitrogen dynamics, and broader implications for ecosystem resilience. Over the past two decades, RS has become central to WPE monitoring, with studies employing classification techniques, spectral mixture analysis, object-based image analysis, change detection, thresholding, landscape pattern and fragmentation metrics, and increasingly, machine learning and deep learning methods. Looking forward, emerging advances such as multi-sensor fusion (optical– synthetic aperture radar (SAR), Light Detection and Ranging (LiDAR)–hyperspectral), cloud-based platforms including Google Earth Engine, Microsoft Planetary Computer, and Digital Earth, and geospatial foundation models offer new opportunities for scalable, automated, and long-term monitoring. Despite these innovations, challenges remain in detecting early-stage encroachment, subcanopy woody growth, and species-specific patterns across heterogeneous landscapes. Key knowledge gaps highlighted in this review include the need for long-term monitoring frameworks, improved socio-ecological integration, species- and ecosystem-specific RS approaches, better utilization of SAR, and broader adoption of analysis-ready data and open-source platforms. Addressing these gaps will enable more effective, context-specific strategies to monitor, manage, and mitigate WPE in rapidly changing environments. Full article

This study is essential for medium- and long-term land-use management, as land-use patterns directly influence local economic and social development. Geographic Information System (GIS) techniques are fundamental tools for analyzing a wide range of geomorphological processes, including relief fragmentation density, relief energy, soil texture, slope gradient, and slope orientation. The present research focuses on the Pesceana river basin in the Southern Carpathians, Romania. It addresses three main objectives: (1) to analyze land-use dynamics derived from CORINE Land Cover (CLC) data between 1990 and 2018, along with the long-term distribution of the Normalized Difference Vegetation Index (NDVI) for the period 2000–2025; (2) to evaluate the basin’s natural potential byintegrating topographic data (contour lines and profiles) with relief fragmentation density, relief energy, vegetation cover, soil texture, slope gradient, aspect, the Stream Power Index (SPI), and the Topographic Wetness Index (TWI); and (3) to assess the spatial distribution of habitat types, characteristic plant associations, and soil properties obtained through field investigations. For the first two research objectives, ArcGIS v. 10.7.2 served as the main tool for geospatial processing. For the third, field data were essential for geolocating soil samples and defining vegetation types across the entire 247 km² area. The spatiotemporal analysis from 1990 to 2018 reveals a landscape in which deciduous forests clearly dominate; they expanded from an initial area of 80 km² in 1990 to over 90 km² in 2012–2018. This increase, together with agricultural expansion, is reflected in the NDVI values after 2000, which show a sharp increase in vegetation density. Interestingly, other categories—such as water bodies, natural grasslands, and industrial areas—barely changed, each consistently representing less than 1 km² throughout the study period. These findings emphasize the importance of land-use/land-cover (LULC) data within the applied GIS model, which enhances the spatial characterization of geomorphological processes—such as vegetation distribution, soil texture, slope morphology, and relief fragmentation density. This integration allows a realistic assessment of the physical–geographic, landscape, and pedological conditions of the river basin. Full article

Although extensive evidence notes a nonlinear relationship between urban greenspace and wellbeing, the conditional role of spatial patterns in this relationship has rarely been examined. To address this gap, this study investigates whether and how landscape metrics moderate the nonlinear association between greenspace coverage and life satisfaction (LS) in urban China. Using nationally representative data from the 2015 wave of the Chinese Social Survey (N = 4319 across 321 subdistricts), this study combines individual-level LS scores with high-resolution GlobeLand30 land use data. Moderated quadratic regression models and formal endpoint slope and turning point tests are applied to identify both the shape and dynamics of the greenspace–wellbeing relationship. The analysis reveals a robust U-shaped curve: LS is lowest at moderate greenspace levels and higher at both low and high extremes. Critically, spatial pattern features, including aggregation index, Euclidean nearest neighbor distance, patch density, and patch richness, significantly moderate this relationship. The turning point of the U-shape moves rightward with greater aggregation and leftward with higher fragmentation or richness. While visual presentation indicates that the curve flips at low patch isolation, further statistical analyses indicate insufficient curve steepness. These findings support that the “more is better” argument should be extended to consider both greenspace quantity and spatial configuration in urban planning for optimal wellbeing outcomes. Full article

The West Liaohe River Basin, a core arid region in Northeast China, faces a significant evaporation–precipitation imbalance and exhibits fragmented land systems, epitomized by the Horqin Sandy Land. Integrating three decades of land use/land cover (LULC) data with meteorological, ecological, and socioeconomic variables, we employed obstacle diagnosis and structural equation modeling (SEM) to elucidate the spatiotemporal dynamics and drivers of LULC transformations. The results demonstrate the following: (1) Land use exhibited a spatially heterogeneous pattern, with forests, shrubs, and grasslands predominantly concentrated in the northwest and southwest. (2) Vegetation coverage significantly increased from 53.15% in 1990 to 61.32% in 2020, whereas cropland and sandy land areas declined. While the overall basin landscape underwent a marked increase in fragmentation. (3) Human activities were the dominant contributor of LULC changes, particularly for cropland conversion, with key determinants such as population and GDP showing negative path coefficients of −0.59 and −0.77, respectively. Climate change was a secondary contributor, with precipitation exerting a strong positive path coefficient (0.63) that was particularly pronounced during the conversion of grassland to forest. These findings offer a scientific basis for land management, ecological restoration strategies, and water resource utilization in the basin. Full article

The genus Agave L. is a key component of Mediterranean alien flora, yet its inland distribution in the Iberian Peninsula remains poorly understood. This research integrates exhaustive field surveys with Species Distribution Models (SDMs) to characterize the genus diversity and, specifically, the spatial patterns and environmental niche of Agave americana in the southern Iberian Peninsula (Andalusia). Our results reveal a diversity of 23 taxa, yet crucially, the widespread occurrence of A. americana demonstrated that its actual inland distribution is significantly more extensive than previously recorded. Spatial Point Pattern Analysis (SPPA) revealed a strong aggregated distribution pattern (Clark & Evans R = 0.277; p < 0.001). The MaxEnt Spatial Distribution Model demonstrated robust predictive performance (Mean AUC = 0.770 ± 0.007; Mean TSS = 0.420 ± 0.009). The distribution was primarily driven by elevation range (26.9%) and land use (23.1%), with maximum suitability peaking in anthropized, low-to-intermediate elevation areas. Projections to the broader Andalusian region confirmed high suitability in the Guadalquivir valley and coastal zones, validated by low spatial uncertainty (SD < 0.05 in optimal areas). These findings provide new insights into the biogeography of Agave in the region, emphasizing the significance of anthropogenic drivers within a cultural landscape context. Full article

Landscape ecology is the study of how different land uses and natural areas are arranged across a region, and how these spatial patterns affect biodiversity, ecosystem health, and human impacts. To measure and track these patterns, ecologists are using a range of tools and metrics that capture features such as connectivity, fragmentation, and the balance between natural and developed land. One such method is the Landscape Mosaic (LM) approach which classifies land into categories based on the mix of agriculture, natural habitats, and developed areas (e.g., urban), providing an integrated view of how humans are influencing ecosystems. Until recently, LM was only available through a specialized software package (i.e., GuidosToolbox), which limits its flexibility, interaction with other tools, and integration in scientific workflows. To address this, we present PyLM, a Python-based implementation of the LM model, making it easier for researchers, planners, and conservationists to analyze land use/cover (LUC) maps, generate statistics, and embed results into broader environmental workflows. The applicability of PyLM is demonstrated through a use case based on a LUC dataset for Switzerland. This new implementation enhances accessibility, supports sustainability assessments, and strengthens the ability to monitor landscapes over time. Full article

Repeated sowing of winter cereals represents one of the adaptive dryland approaches to make more sustainable the rainfed agriculture activities in southern Kazakhstan. This study conducted a multi-year reconstruction of crop transitions using Sentinel-2 imagery for 2018–2025, based on the combined analysis of Normalized Difference Vegetation Index (NDVI) temporal profiles and the Plowed Land Index (PLI), enabling the creation of a field-level harmonized classification set. The transition “spring crop → winter crop” was used as a formal indicator of repeated winter sowing, from which annual repeat layers and an integrated metric, the R-index, were derived. The results revealed a pronounced spatial concentration of repeated sowing in foothill landscapes, where terrain heterogeneity and locally elevated moisture availability promote the recurrent return of winter cereals. Comparison of NDVI composites for the peak spring biomass period (1–20 May) showed a systematic decline in NDVI with increasing R-index, indicating the cumulative effect of repeated soil exploitation and the sensitivity of winter crops to climatic constraints. Precipitation analysis for 2017–2024 confirmed the strong influence of autumn moisture conditions on repetition phases, particularly in years with extreme rainfall anomalies. These findings demonstrate the importance of integrating multi-year satellite observations with climatic indicators for monitoring the resilience of agricultural systems. The identified patterns highlight the necessity of implementing nature-based solutions, including contour–strip land management and the development of protective shelterbelts, to enhance soil moisture retention and improve the stability of regional agricultural landscapes. Full article

Optimizing the “production–living–ecological” space (PLES) is critical for achieving the UN Sustainable Development Goals (SDGs), particularly in ecologically sensitive mountainous border regions. This study investigates the spatial patterns and dynamic evolution of PLES in Lincang City (2010–2020) to reveal the trade-offs between development and conservation. Methodologically, we proposed a coupling-coordination-based grid-level PLES identification framework. This framework integrates the coupling coordination degree model (CCDM) directly into the functional classification process at a 600 m grid scale—a resolution selected to balance the capture of spatial heterogeneity with the maintenance of functional integrity in complex terrains. Spatiotemporal dynamics were further quantified using transition matrices and a dimension-based landscape metric system. The results reveal that (a) ecological space and production–living–ecological space represent the predominant categories in the study area. During the study period, ecological space continued to decrease, while production–living space increased steadily, and other PLES categories showed only marginal variations. (b) Mutual transitions among PLES types primarily occurred among ecological space, production–ecological space, and production–living–ecological space. These transitions intensified markedly between 2015 and 2020 compared to the 2010–2015 period. (c) From 2010 to 2020, the landscape in Lincang evolved towards lower ecological risk yet higher fragmentation. High fragmentation values, often associated with grassland, cropland, and forested areas, were evenly distributed across northeastern and northwestern regions. Likewise, high landscape dominance and isolation appeared in these regions as well as in the southeast. Conversely, landscape disturbance remained relatively uniform throughout the city, with lower values detected in forested land. Full article

Accurate and efficient surface water quality monitoring is essential for ecological protection and sustainable development. However, conventional monitoring methods, such as fixed-site observations, often suffer from spatial limitations and overlook crucial auxiliary variables. This study proposes an innovative modeling framework for large-scale river water quality inversion that integrates multi-source data—including Sentinel-2 imagery, meteorological conditions, land use classification, and landscape pattern indices. To improve predictive accuracy, three tree-based machine learning models (Random Forest, XGBoost, and LightGBM) were constructed and further optimized using the Whale Optimization Algorithm (WOA), a nature-inspired metaheuristic technique. Additionally, model interpretability was enhanced using SHAP (Shapley Additive Explanations), enabling a transparent understanding of each variable’s contribution. The framework was applied to the Red River Basin (RRB) to predict six key water quality parameters: dissolved oxygen (DO), ammonia nitrogen (NH₃-N), total phosphorus (TP), total nitrogen (TN), pH, and permanganate index (COD_Mn). Results demonstrate that integrating landscape and meteorological variables significantly improves model performance compared to remote sensing alone. The best-performing models achieved R² values exceeding 0.45 for all parameters (DO: 0.70, NH₃-N: 0.46, TP: 0.59, TN: 0.71, pH: 0.83, COD_Mn: 0.57). Among them, WOA-optimized LightGBM consistently delivered superior performance. The study also confirms the feasibility of applying the models across the entire basin, offering a transferable and interpretable approach to spatiotemporal water quality prediction in other large-scale or data-scarce regions. Full article