Search Results (180)

Urban fragmentation has emerged as a central issue in the study of socio-spatial dynamics in contemporary cities, reflecting processes of inequality, segregation, and spatial discontinuities. This article introduces a new methodological approach to measure fragmentation by focusing on discontinuities at limits rather than on the content of statistical units alone. The method applies robust standardization of selected socioeconomic variables—higher education, foreign-born population, and low-income population—at the census tract scale in the city of Lleida, Spain. Rupture intensity is measured through a Rupture Intensity Index, which integrates standardized differences across 217 limits. Principal component analysis identifies the most influential variables, while cluster analysis characterizes the multidimensional nature of limits. Results show that fragmentation in Lleida does not follow a simple center–periphery model but a tessellated pattern of fracture lines and gradient zones. Intense fractures emerge at borders between advantaged and disadvantaged neighborhoods, whereas gradients mark gradual transitions. The study demonstrates that limits are critical sites for observing and quantifying urban fragmentation and proposes a transferable methodology for comparative research and urban policy design in diverse urban contexts. This approach provides a replicable tool for urban analysis and the design of cohesion-oriented policies. Full article

A core challenge in urban green space (UGS) management lies in precisely identifying public demand heterogeneity toward landscape elements. Grounded in Expectation Confirmation Theory (ECT), this study aims to systematically identify the key landscape elements shaping public satisfaction and elucidate their driving mechanisms to inform UGS planning. Using 107 UGS in central Beijing as case studies, this study first retrieved 712,969 social media data (SMD) from multiple online platforms. A landscape element lexicon derived from these data was then integrated with the Bidirectional Encoder Representations from Transformers (BERT) model to assess public attention and satisfaction toward the natural, cultural, and artificial attributes of UGS, achieving an accuracy of 84.4%. Finally, spatial variations and the effects of different landscape elements on public satisfaction were analyzed using GIS-based visualization, K-means clustering, and multiple linear regression. Key findings reveal the following: (1) satisfaction follows a “core-periphery” gradient, peaking in heritage-rich City Wall Parks (>0.63) and plunging in green belts due to imbalanced element configurations (~0.04); (2) naturally dominant green spaces contribute most to satisfaction, while a nonlinear relationship exists between element dominance and satisfaction: strong features enhance perception, balanced patterns mask issues; (3) regression analysis confirms natural elements (vegetation β = 0.280, water β = 0.173) as core satisfaction drivers, whereas artificial facilities (e.g., service infrastructure β = 0.112, p > 0.05) exhibit a high frequency but low satisfaction paradox. These insights culminate in a practical implementation framework for policymakers: first, establish a data-driven monitoring system to flag high-frequency, low-satisfaction facilities; second, prioritize budgeting for enhancing natural elements and contextualizing cultural elements; and finally, implement site-specific optimization based on primary UGS functions to counteract green space homogenization in high-density cities. Full article

To enhance ecosystem services (ESs) benefits and promote ecological–economic–sociologic sustainability in highly urbanized regions such as the Beijing–Tianjin–Hebei (BTH) region, it is essential to assess the dynamic changes in ESs within these regions from a functional zoning perspective and to explore the interactions between ESs. This research delved into how ESs change over space and time, using land-use projections for 2035 based on Natural Development (ND), Ecological Protection (EP), Economic Construction (EC) scenarios. This study also took a close look at the interplay of these ESs across BTH and its five distinct functional zones: the Bashang Plateau Ecological Protection Zone (BS), the Northwestern Ecological Conservation Zone (ST), the Central Core Functional Zone (HX), the Southern Functional Expansion Zone (TZ), and the Eastern Coastal Development Zone (BH). We utilize the Multiple Ecosystem Service Landscape Index (MESLI) to assess the capacity to supply multiple ESs. Key results include the following: (1) Projected land-use changes for 2035 scenarios consistently show cropland and grassland declining, while forest and urbanland expand, though the magnitude of change varies by scenario. (2) Habitat quality, carbon storage, and soil conservation displayed a “high northwest–low southeast” gradient, opposite to water yield. The average MESLI value declined in all scenarios relative to 2020, with the highest value under the EP scenario. (3) Synergies prevailed between habitat quality, carbon storage, and soil conservation, while trade-offs occurred with water yield. These relationships varied spatially—for instance, habitat quality and soil conservation were weakly synergistic in the BS but showed weak trade-offs in the HX. These insights can inform management strategies in other rapidly urbanizing regions. Full article

Urbanization imposes complex environmental gradients that threaten plant diversity and urban ecosystem integrity. Understanding the multifactorial drivers that govern species distribution in urban contexts is essential for biodiversity conservation and sustainable landscape planning. This study addresses this challenge by examining the environmental determinants of urban flora in a rapidly developing city. We integrated data from 397 floristic sampling sites and 13 environmental monitoring locations across Düzce, Türkiye. A multidimensional suite of environmental predictors—including microclimatic variables (soil temperature, moisture, light), edaphic properties (pH, EC (Electrical Conductivity), texture, carbonate content), precipitation chemistry (pH and major ions), macroclimatic parameters (CHELSA bioclimatic variables), and spatial metrics (elevation, proximity to urban and natural features)—was analyzed using nonlinear regression models and machine learning algorithms (RF (Random Forest), XGBoost, and SVR (Support Vector Regression)). Shannon diversity exhibited strong variation across land cover types, with the highest values in broad-leaved forests and pastures (>3.0) and lowest in construction and mining zones (<2.3). Species richness and evenness followed similar spatial trends. Evenness peaked in semi-natural habitats such as agricultural and riparian areas (~0.85). Random Forest outperformed other models in predictive accuracy. Elevation was the most influential predictor of Shannon diversity, while proximity to riparian zones best explained richness and evenness. Chloride concentrations in rainfall were also linked to species composition. When the models were recalibrated using only native species, they exhibited consistent patterns and maintained high predictive performance (Shannon R² ≈ 0.937474; Richness R² ≈ 0.855305; Evenness R² ≈ 0.631796). Full article

Exploring the impact of industrial land misallocation (ILM) on sustainable urban development (SUD) helps provide strong empirical support for SUD from the perspective of land factor allocation. Based on panel data from 283 cities between 2009 and 2021, this paper systematically analyzes the impact mechanism and spatial spillover effects of ILM on SUD from the perspective of factor misallocation. The results show that most Chinese cities face a surplus-type misallocation of industrial land, and resource allocation urgently needs optimization. During the study period, the overall level of SUD increased and exhibited a spatial gradient distribution characterized by high levels in the east and low levels in the west. ILM significantly inhibited the improvement of SUD, with the negative impact being particularly pronounced in central-western regions and non-resource-based cities. ILM also showed a significant negative spatial spillover effect. Mechanism analysis found that ILM mainly negatively affected SUD by hindering industrial transformation and upgrading as well as the progress of urban technological innovation. Further research found that the implementation of the policy for exit audits of natural resource assets alleviated the problem of ILM to a certain extent and weakened its adverse effects on SUD. Therefore, deepening efforts to correct ILM is a key measure to break resource allocation barriers and promote SUD. Full article

Lakes account for approximately 87% of the Earth’s surface water resources and serve as sensitive indicators of climate and environmental change. Understanding how lake areas respond to climate change across different elevation gradients is crucial for guiding sustainable water resource management in Xinjiang. We utilized Landsat series remote sensing imagery (1990–2023) on the Google Earth Engine (GEE) platform to extract the temporal dynamics of natural lakes larger than 10 km² in Xinjiang, China (excluding reservoirs). We analyzed the relationships between lake area dynamics, climatic factors, and human activities to assess the sensitivity of lakes at different altitudinal zones to environmental change. The results showed that (1) the total area of Xinjiang lakes increased by 1188.36 km² over the past 34 years, with an average annual area of 5998.54 km²; (2) plain lakes experienced fluctuations, reaching their maximum in 2000 and their minimum in 2015, alpine lakes peaked in 2016, and plateau lakes continued to expand, with the maximum recorded in 2020 and the minimum in 1995; and (3) human activities such as urban and agricultural water use were the primary causes of shrinking plain lakes, while an increased PET accelerates evaporation, alpine lakes were influenced by both climate variability and human disturbance, and plateau lakes were highly sensitive to climate change, with rising temperatures increasing snowmelt and glacial runoff into lakes, which were the main drivers of their expansion. These findings highlight the importance of incorporating elevation-specific lake responses into climate adaptation strategies and sustainable water management policies in arid regions. Full article

In recent years, under the dual pressures of climate change and human activities, wetlands in Liaoning Province, China, are increasingly threatened, raising concerns about regional ecological security. To better understand these changes, we developed a vulnerability assessment framework integrating a 30 m wetland dataset (2000–2020) with multi-source environmental and socio-economic data. Using the XGBoost–SHAP model, we analyzed wetland spatiotemporal evolution, driving mechanisms, and ecological vulnerability. Results show the following: (1) ecosystem service functions exhibited significant spatiotemporal differentiation; carbon storage has generally increased, water conservation capacity has significantly improved in the northern region, while wind erosion control and soil retention functions have declined due to urban expansion and agricultural development; (2) driving factors had evolved dynamically, shifting from population density in the early period to increasing influences of precipitation, vegetation index, GDP, and wetland area in later years; (3) ecologically vulnerable areas demonstrated a pattern of fragmented patches coexisting with zonal distribution, forming a three-level spatial gradient of ecological vulnerability—high in the north, moderate in the central region, and low in the southeast. These findings demonstrate the cascading effects of natural and human drivers on wetland ecosystems, and provide a sound scientific basis for targeted conservation, ecological restoration, and adaptive management in Liaoning Province. Full article

Accurate forecasting of waste is essential for effective management and allocation of resources. As urban populations grow, the demand for municipal waste systems increases, creating the need for reliable forecasting methods to support planning and decision making. This study compares statistical models Error Trend Seasonality (ETS) and Auto Regressive Integrated Moving Average (ARIMA) with advanced machine learning approaches, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) networks. Five waste categories were analyzed: dead animal, building, commercial, domestic, and liquid waste. Historical datasets were used for model training and validation, with accuracy assessed through mean absolute error and root mean squared error. Results indicate that ARIMA generally outperforms ETS in forecasting building, commercial, and domestic waste streams, especially in capturing long-term domestic waste patterns. Both statistical models, however, show limitations in predicting liquid waste due to its irregular and highly variable nature, where even baseline models sometimes perform competitively. In contrast, machine learning methods consistently achieve the lowest forecasting errors across all categories. Their capacity to capture nonlinear relationships and adapt to complex datasets highlights their reliability for real-world waste management. The findings underline the importance of selecting forecasting techniques tailored to the characteristics of each waste type rather than applying a uniform method. By improving forecasting accuracy, municipalities and policymakers can design more effective waste management strategies that align with Sustainable Development Goal 11 on sustainable cities and communities, Sustainable Development Goal 12 on responsible consumption and production, and Sustainable Development Goal 13 on climate action. Full article

Urban blue-green infrastructure (UBGI) has been recognized as an effective nature-based solution (NbS) for mitigating urban overheating through temperature reduction. However, there is a paucity of research examining whether UBGI spatial configurations align with the geographical distribution of the heat exposure risks of urban residents. This study focuses on this research gap, employing a population-weighted algorithm to conduct a refined assessment of the blue-green spaces exposure and heat exposure risks of urban residents. Then, the heat exposure risk was conceptualized as the demand for cooling services, with exposure to blue-green spaces serving as the supply. A comprehensive assessment was finally conducted of the supply–demand relationship and coupling coordination level for cooling services in central Wuhan. The following findings were revealed: (1) Both heat exposure risks and blue-green exposure demonstrate distinct “west high–east low” spatial gradients. It is evident that extreme high/high-risk zones, which encompass 17.1% of the study area, house 74.49% of the permanent population; (2) A substantial and pervasive positive correlation exists between UGBI exposure and the heat exposure risk. “High-demand–high-supply” areas (14.90% coverage) concentrate in urban cores, overlapping with 61.25% high-risk populations, while 0.29% of zones show “high-demand–low-supply” mismatches, revealing concentrated but ineffective UGBI distribution; (3) A pervasive supply–demand imbalance is evident, with 90.64% of regions exhibiting an unacceptable coupling type range (0 < D ≤ 0.4) and a mere 1.39% attaining an acceptable range (0.6 < D ≤ 1). These findings underscore the inadequacy of prevailing urban blue-green infrastructure configurations in addressing heat exposure risks. The construction of cities with greater heat resilience necessitates the implementation of multidimensional strategies aimed at risk mitigation. Full article

Urban agglomerations underpin regional economic growth and sustainability transitions, yet the spatial heterogeneity and drivers of land use intensity (LUI) remain insufficiently resolved in inland settings. This study develops a high-resolution framework—combining a 1 km hexagonal grid with XGBoost-SHAP—to (i) map subsystem-specific LUI evolution, (ii) identify dominant drivers and nonlinear thresholds, and (iii) inform differentiated, sustainable land governance in the Guanzhong Plain Urban Agglomeration (GPUA) over 2000–2020. Composite LUI indices were constructed for human settlement (HS), cropland (CS), and forest (FS) subsystems; eleven natural, socioeconomic, urban–rural, and locational variables served as candidate drivers. The results show marked redistributions across subsystems. In HS, the share of low-intensity cells declined (86.54% to 83.18%) as that of medium- (12.10% to 14.26%) and high-intensity ones (1.22% to 2.56%) increased, forming a continuous high-intensity corridor between Xi’an and Xianyang by 2020. CS shifted toward medium-intensity (32.53% to 50.57%) with the contraction of high-intensity cells (26.62% to 14.53%), evidencing strong dynamism (55.1% net intensification; 38.5% net decline). FS transitioned to low-intensity dominance by 2020 (59.12%), with stability and delayed growth concentrated in conserved mountainous zones. Urban–rural gradients were distinct: HS rose by >20% (relative to 2000) in cores but remained low and stable in rural areas (mean < 0.20); CS peaked and stayed stable at fringes (mean ≈ 0.60); FS shifted from an inverse gradient (2000–2010) to core-area recovery by 2020. Explainable machine learning revealed inverted U-shaped relationships for HS (per capita GDP) and CS (population density) and a unimodal peak for FS with respect to distance to urban centers; model performance was strong (HS R² up to 0.82) with robust validation. Policy recommendations are subsystem-specific: enforce growth boundaries and prioritize infill/polycentric networks (HS); pair farmland redlines with precision agriculture (CS); and maintain ecological redlines with differentiated conservation and afforestation (FS). The framework offers transferable, data-driven evidence for calibrating thresholds and sequencing interventions to reconcile land use intensification with ecological integrity in rapidly urbanizing contexts. Full article

When considering an urban agglomeration as a unit, promoting the coupling and coordination of the land price gradient and industrial gradient is crucial for achieving regional integrated development. We selected the Beijing–Tianjin–Hebei Urban Agglomeration (BTHUA) as a case study; constructed a three-dimensional analytical framework involving static coupling, dynamic coupling, and spatial matching; theoretically clarified the coupling mechanism between the land price gradient and industrial gradient; and systematically assessed their spatial-temporal patterns and coupling characteristics. The results indicate that from 2012 to 2022, both the land price gradient and industrial gradient within the BTHUA exhibited a “core-periphery” spatial distribution, gradually forming an over-all pattern of “one core, multiple nodes, and multi-level rings.” For the Beijing–Tianjin–Hebei urban agglomeration, overall static coupling and spatial matching exhibit an evolutionary trajectory of “first rising, then declining.” By contrast, dynamic coupling remains relatively weak, exhibiting a corridor-shaped distribution along core and sub-core cities. All three indicators consistently show that core cities outperform peripheral cities. Nonlinear mechanism analysis based on the gradient boosting decision tree method showed that “second-nature” factors like economic development and public utilities significantly promote multidimensional coupling. Conversely, “first-nature” factors, such as geographic conditions, have limited impacts with threshold effects; surpassing these thresholds results in inhibitory effects. Based on the research findings, this study proposes that regional integration should serve as the guiding principle, emphasizing the cultivation of regional development corridors, the implementation of flexible and functionally aligned land supply policies, the strengthening of land use performance audits, and the reorientation of fiscal and financial policies toward structural and qualitative improvements. These measures can provide valuable references for promoting coordinated industrial development and balanced land allocation in urban agglomerations. Full article

Accurate classification of tree species is crucial for forest management and biodiversity conservation. Remote sensing technology offers a unique capability for classifying and mapping trees across large areas; however, the accuracy of extracting and identifying individual trees remains challenging due to the limitations of available imagery and phenological variations. This study presents a novel integrated machine learning (ML) and Geographic Object-Based Image Analysis (GEOBIA) framework to enhance tree species classification in a botanical garden using multi-temporal unmanned aerial vehicle (UAV) imagery. High-resolution UAV imagery (2.3 cm/pixel) was acquired across four different seasons (summer, autumn, winter, and early spring) to incorporate the phenological changes. Spectral, textural, geometrical, and canopy height features were extracted using GEOBIA and then evaluated with four ML models (Random Forest (RF), Extra Trees (ET), eXtreme gradient boost (XGBoost), and Support Vector Machine (SVM)). Multi-temporal data significantly outperformed single-date imagery, with RF achieving the highest overall accuracy (86%, F1-score 0.85, kappa 0.83) compared to 57–75% for single-date classifications. Canopy height and textural features were dominant for species identification, indicating the importance of structural variations. Despite the limitations of moderate sample size and a controlled botanical garden setting, this approach offers a robust framework for forest and urban landscape managers as well as remote sensing professionals, by optimizing UAV-based strategies for precise tree species identification and mapping to support urban and natural forest conservation. Full article

Flood risk assessment is an effective tool for disaster prevention and mitigation. As land use is a key factor influencing flood disasters, studying the impact of different land use patterns on flood risk is crucial. This study evaluates flood risk in the Chang-Zhu-Tan (CZT) urban agglomeration by selecting 17 socioeconomic and natural environmental factors within a risk assessment framework encompassing hazard, exposure, vulnerability, and resilience. Additionally, the Patch-Generating Land Use Simulation (PLUS) and multilayer perceptron (MLP)/Bayesian network (BN) models were coupled to predict flood risks under three future land use scenarios: natural development, urban construction, and ecological protection. This integrated modeling framework combines MLP’s high-precision nonlinear fitting with BN’s probabilistic inference, effectively mitigating prediction uncertainty in traditional single-model approaches while preserving predictive accuracy and enhancing causal interpretability. The results indicate that high-risk flood zones are predominantly concentrated along the Xiang River, while medium-high- and medium-risk areas are mainly distributed on the periphery of high-risk zones, exhibiting a gradient decline. Low-risk areas are scattered in mountainous regions far from socioeconomic activities. Simulating future land use using the PLUS model with a Kappa coefficient of 0.78 and an overall accuracy of 0.87. Under all future scenarios, cropland decreases while construction land increases. Forestland decreases in all scenarios except for ecological protection, where it expands. In future risk predictions, the MLP model achieved a high accuracy of 97.83%, while the BN model reached 87.14%. Both models consistently indicated that the flood risk was minimized under the ecological protection scenario and maximized under the urban construction scenario. Therefore, adopting ecological protection measures can effectively mitigate flood risks, offering valuable guidance for future disaster prevention and mitigation strategies. Full article

In rapidly urbanizing regions, escalating land use conflicts have raised concerns over sustainable development and ecological security. This study focuses on the Chang-Ji-Tu Development and Opening Pilot Zone in Jilin Province, aiming to reveal the spatiotemporal evolution of land use conflicts and identify their driving factors, based on land use data from 2000 to 2023. The study employs land use data, the PLUS model, SCCI, and the geographic detector to analyze conflict dynamics and influencing factors. Cropland and forest land have steadily declined, while construction land has expanded. Conflicts exhibit a spatial gradient of “western pressure, central alleviation, and eastern stability,” with hotspots in Changchun, Jilin, and Yanji. Conflict evolution is categorized into three phases: intensification (2000–2010), peak (2010–2015), and mitigation (2015–2023), as shaped by industrialization and later policy interventions. Among four simulated scenarios, the Sustainable Development (SD) scenario most effectively postpones conflict escalation. Population density and DEM emerged as dominant driving factors. Natural factors have greater explanatory power for land use conflicts than do socio-economic or locational factors. Strengthening spatial planning coordination and refining conflict governance are key to balancing human–environment interactions in the region. Full article

Urban vegetation shows significant spatial differences due to the combined effects of natural and human factors, yet fine-scale evolutionary patterns and their cross-scale feedback mechanisms remain limited. This study focuses on the Yangtze River Delta (YRD), the top economic area in China. By integrating data from multiple Landsat sensors, we built a high—resolution framework to track vegetation dynamics from 1990 to 2020. It generates annual 30-m Enhanced Vegetation Index (EVI) data and uses a new Vegetation Green—Brown Balance Index (VBI) to measure changes between greening and browning. We combined Mann-Kendall trend analysis with machine—learning based attribution analysis to look into vegetation changes across different city types and urban—rural gradients. Over 30 years, the YRD’s annual EVI increased by 0.015/10 a, with greening areas 3.07 times larger than browning. Spatially, urban centers show strong greening, while peri—urban areas experience remarkable browning. Vegetation changes showed a city-size effect: larger cities had higher browning proportions but stronger urban cores’ greening trends. Cluster analysis finds four main evolution types, showing imbalances in grey—green infrastructure allocation. Vegetation baseline in 1990 is the main factor driving the long-term trend of vegetation greenness, while socioeconomic and climate drivers have different impacts depending on city size and position on the urban—rural continuum. In areas with low urbanization levels, climate factors matter more than human factors. These multi-scale patterns challenge traditional urban greening ideas, highlighting the need for vegetation governance that adapts to specific spatial conditions and city—unique evolution paths. Full article