Search Results (2,265)

Non-grain production of cultivated land has increasingly affected regional food security and the stability of agricultural ecosystems. In traditional rice-producing regions, changes associated with non-rice cultivation, fallow land, rice-fishery integrated farming, and intensive agricultural management are reshaping the spatial structure of paddy landscapes. To identify the long-term spatiotemporal evolution of paddy systems, this study investigated Northern Jiangxi, China, using Landsat surface reflectance imagery from 2000, 2005, 2010, 2015, and 2020 on the Google Earth Engine (GEE) platform. The Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) were used to construct a phenology-based Flooding Frequency (FF) indicator. Based on the annual frequency with which pixels satisfied the condition LSWI > EVI, cultivated land was classified into three categories: non-flooded cropland, standard rice paddy, and high-frequency flooded cropland. In this study, non-flooded cropland was used as an indicator of potential non-rice cultivation rather than as direct evidence of confirmed non-grain production. Landscape metrics, transition matrices, gravity center migration, standard deviation ellipses, and geographically weighted regression (GWR) were then used to examine paddy landscape dynamics, fragmentation patterns, and county-level spatial associations with socioeconomic factors. The results suggest that the paddy system in Northern Jiangxi experienced marked stage-based fluctuations between 2000 and 2020. Standard rice paddy recovered during 2005–2010, whereas non-flooded cropland expanded considerably during 2010–2015, accompanied by intensified paddy landscape fragmentation. Non-flooded cropland was mainly distributed around urban fringes, transport corridors, and some hilly margins. Standard rice paddy was concentrated in traditional grain-producing areas, including the Poyang Lake Plain and the Gan-Fu Plain. High-frequency flooded cropland was primarily located in low-lying lake areas, where its dynamics were likely associated with rice-fishery integrated farming, continuous irrigation, and hydrological fluctuations. Landscape metrics showed that the largest patch index and mean patch size of standard rice paddy declined after 2010, indicating reduced spatial continuity of core paddy fields. The GWR analysis provided auxiliary evidence that total population, per capita gross domestic product (GDP), and urbanization rate were spatially associated with changes in non-flooded cropland at the county level; however, the results should be interpreted as exploratory associations rather than causal mechanisms. Overall, paddy landscape change in Northern Jiangxi was expressed not only through changes in cultivated land area, but also through the reorganization of paddy function, spatial continuity, and land-use intensity. Future cropland protection should therefore move beyond area-based control toward integrated management of quantity, quality, function, and spatial configuration. Future research should further verify these findings using dynamic cropland boundaries, higher-resolution imagery, and more detailed socioeconomic data. Full article

Terrestrial water storage in semi-arid basins is increasingly affected by the combined pressures of climate variability, land use change and intensive human activities. However, how landscape composition and configuration interact with climatic forcing to shape basin-scale terrestrial water storage anomalies (TWSA) remains insufficiently understood, particularly in rapidly urbanising tributary basins of the Yellow River. This study integrates GRACE/GRACE-FO-derived TWSA, meteorological observations and multi-period land use data to examine the coupled relationships among climate variability, landscape patterns and water storage in the human-dominated Yiluo River Basin. The results show that basin-averaged TWSA experienced a significant long-term decline during the GRACE/GRACE-FO period (−4.47 mm yr⁻¹), with a statistically detectable transition around 2012 and stronger depletion in the northern and eastern parts of the basin. Precipitation exhibited a cumulative and delayed relationship with TWSA, with the strongest raw association occurring under a six-month accumulation and one-month lag (Pearson’s r = 0.44, p < 0.001, n = 134). After removing the seasonal cycle, the relationship remained significant but weaker (r = 0.30, p = 0.001, n = 129), indicating that precipitation explains part, but not all, of the interannual variability in water storage. Landscape composition showed stronger associations with TWSA than landscape configuration. Construction land was negatively associated with water storage, whereas cultivated land and grassland showed positive associations, suggesting that impervious surface expansion and the loss of permeable land may weaken the basin’s water retention capacity. These findings indicate that water storage change in the Yiluo River Basin is shaped by both climatic forcing and human-induced land surface transformation. Basin management should therefore prioritise the control of urban impervious surface expansion, the protection of permeable agricultural and ecological land, and the integration of land use planning with adaptive water resource regulation. Full article

The sustainable management of land resources and the formulation of land policies are closely linked to the stability and health of terrestrial ecological systems, which in turn underpin sustainable regional economic, social, and environmental development. However, land use change has a time effect on the environment and requires long-term observation to discover its impact on landscape patterns. The Yellow River Basin functions as a critical ecological barrier in northern China, where land use changes are particularly intense in the transitional zone between its middle and lower reaches. Using Landsat imagery as the data source, this study adopts the Random Forest (RF) algorithm to classify eight sets of sequential data covering a 35-year period from 1990 to 2025 in the study area. Landscape pattern metrics and transfer matrices are employed to conduct qualitative and quantitative analyses of the spatiotemporal dynamics of land use changes. Additionally, land expansion analysis strategies and the RF algorithm are applied to identify the relative importance of different driving factors. The results show that: (1) The classification accuracy based on the Google Earth Engine (GEE) cloud platform remains consistently high, exceeding 90% across all phases. (2) Patch density decreases significantly, while the largest patch index continues to decline; the Shannon diversity index shows a fluctuating upward trend, and the aggregation index exhibits a slight increase. (3) Mutual conversions among farmland, forest, and grassland are the dominant processes driving land use changes in the region. (4) The Digital Elevation Model (DEM), construction land area distribution, and distance to primary roads are the key factors influencing land use patterns, with human activities acting as the primary driver of land use type transformations in the area. Full article

The Mississippi River Delta region is of national and international relevance in terms of agriculture, energy, river navigation, and fisheries. Being one of the most engineered rivers in the world, the Mississippi River has been intensively altered over the past 150 years. The river alterations included the construction of dams, levees, diversions, channelization, spillway flood control systems, and many others. These engineering practices have significantly modified the natural hydrology and sediment dynamics of the river and its deltaic region. While these interventions have provided critical benefits such as flood protection, improved navigation, and economic development, they have also led to profound environmental and ecological consequences. The reduction in sediment delivery to the Mississippi River Delta has accelerated land loss, contributing to the disappearance of coastal wetlands at an alarming rate. The land loss has diminished critical habitats for wildlife, reduced storm surge protection for coastal communities, and disrupted the delta’s natural ability to adapt to fast subsidence. The long-term sustainability of the delta is further threatened by the compounding effects of climate change, including rising sea levels, increased storm intensity, and extreme precipitation and drought conditions. This paper examines the effects, consequences, and future risks of the major river engineering practices on the Mississippi River Delta and provides strategic recommendations that balance human needs with changing natural conditions to ensure sustainability. Specific recommendations include river diversion upstream of New Orleans, better strategies to deal with floods and droughts, strategic maintenance or removal of portions of levees, hybrid coastal-inland human migration, improved transportation connections between coast and inland, and better preparation for future ecosystem shifts. This review is needed because river engineering has made the Mississippi River Delta economically vital yet increasingly vulnerable to sediment loss, wetland collapse, saltwater intrusion, flooding, and population decline. By synthesizing these linked natural and human consequences, it provides a timely framework for rethinking delta sustainability under climate change. Full article

Accelerated urbanisation and associated land-use conversion are reshaping the composition and functions of terrestrial ecosystems globally. In Southeast Asia, ecological change is increasingly mediated not only by demographic urbanisation but also by urban expansion, peri-urban development, and the conversion of agricultural, coastal, and forest land into built-up surfaces. This study integrates multi-source geographical information from 2014 to 2024 to examine 351 provincial-level units in 11 Southeast Asian nations. To describe the spatial-material dimension of urbanisation and ecological conditions, two indices were created: the Composite Nighttime Light Index (CNLI), used as a proxy for urban expansion and built-up development intensity, and the Improved Remote Sensing Ecological Index (IRSEI), which is tailored to tropical coastal locations. The development of human-environment interactions was measured using the Coupling Coordination Degree (CCD) model. Pathways of synergy and trade-off were found using an incremental four-quadrant framework, and nonlinear causes of spatial differentiation were investigated using Spearman correlation and the Optimal Parameter-based Geographical Detector (OPGD). Uncertainty was addressed through data-quality masking, annual compositing, consistent index-construction rules, and cautious interpretation of CCD and driver results as relative provincial-scale patterns. The regional mean CCD rose from 0.250 to 0.314 during the decade, showing a slow improvement; nevertheless, most places still have low to moderate levels of coordination. There is clear pathway divergence, with 38.7% of locations enduring trade-offs where built-up development happens at the price of ecological quality and 58.4% of regions seeing synergistic improvement. The coupling pattern is primarily driven by built-up area expansion, with multiple factors jointly producing strong nonlinear enhancement effects. Climate conditions and forest disturbance further strengthen these effects. This study extends beyond single-country analyses by situating remote-sensing coupling results within land-use transition, peri-urbanisation, urban–rural linkage, and regional-governance perspectives. It provides quantitative evidence to support differentiated policy strategies in rapidly urbanising places. Full article

Agricultural expansion is usually viewed as ecological pressure in dryland agro-pastoral ecotones, yet regional greening can also emerge alongside agricultural restructuring. This paradox is particularly important where cropland expansion, ecological restoration, and water-limited landscapes overlap. Using the Mu Us Sandy Land of northern China, we examined whether greening from 2000 to 2020 represented genuine non-cropland recovery and whether its spatial organization was associated with agricultural restructuring. Annual fractional vegetation cover (FVC), land use/cover, and environmental data were used to decompose regional greening and construct an Agricultural Opportunity Space Index (AOSI) based on cropland boundary proximity, local cropland density, interface intensity, and agricultural core-area influence. Ecological non-cropland improvement accounted for 84.30% of the total FVC increase, whereas the change in cropland share contributed only 2.30%. Recovery was broad-based, with FVC rising across low, median, and high quantiles and 68.97% of valid ecological non-cropland pixels showing significant greening. Restoration occurrence and mean FVC change (ΔFVC) generally increased along the AOSI gradient, while high-opportunity zones hosted stronger, more recurrent, and more stable restoration hotspots. These findings suggest that agricultural restructuring shaped non-cropland recovery not primarily through land occupation, but through the spatial redistribution of land-use pressure and resource management conditions beyond cropland boundaries. Full article

Against the backdrop of global climate change, enhancing the carbon sink capacity of ecosystems has become one of the key pathways for implementing climate action. This study takes the Western Hills–Yongding River Cultural Belt in Beijing as its study area to systematically investigate the coupling mechanisms between multidimensional spatial pattern factors and the carbon sink capacity of vegetation net primary productivity (NPP). The study employed the morphological spatial pattern analysis (MSPA) method to structurally identify and quantify regional green space landscape elements. By integrating vertical vector data such as building height and canopy height, and incorporating terrain background and human activity intensity indicators, a comprehensive system of driving factors was established, encompassing two-dimensional landscape patterns, three-dimensional spatial structures and multi-dimensional attributes. Based on the Local Climate Zone (LCZ) classification system, a detailed classification of the study area’s land cover was carried out, and eight typical units—encompassing built-up settlements, brownfield sites, forested green spaces and riparian wetlands—were selected as the core objects of analysis. Building on this, multiple non-linear machine learning regression models were constructed to conduct fitting analyses and accuracy validation; the LightGBM model was identified as the optimal fitting model based on the test set coefficient of determination (R²) and root mean square error (RMSE) as core indicators; Furthermore, the SHAP interpretability analysis framework was introduced to systematically reveal the relative importance of each of the driving factors, their positive and negative effects, non-linear response characteristics, and two-factor interaction mechanisms. Based on these findings, category-specific, differentiated spatial regulation strategies for enhancing carbon sinks were proposed, providing scientific support for the optimisation of ecological spaces, the ecological restoration of brownfield sites, and the realisation of carbon sink potential within the study area. Full article

To evaluate the application potential of convolutional neural network (CNN)-based Local Climate Zone (LCZ) mapping in urban thermal environment studies, this study employed a lightweight convolutional neural network model (Light-model) to classify LCZs within the area enclosed by the Fourth Ring Road of Xuzhou City. ECOSTRESS data obtained from summer (June to September) at different times were integrated to analyze the temporal and spatial changes of surface temperature (LST) and surface urban heat island intensity (SUHII). The classification results demonstrate that the Light-model achieved an overall accuracy of 84.46%, which is markedly higher than that of the random forest model (72.07%). It also outperformed random forest in built-up area identification (built-up overall accuracy: 69.41% vs. 46.91%) and non-built-up area identification (natural overall accuracy: 91.85% vs. 84.43%), as well as in Kappa coefficient and mean F1-score. Time-series analysis based on ECOSTRESS observations revealed a typical diurnal LST pattern characterized by the lowest temperatures before dawn, a peak in the afternoon, and a decline at night. High-density built-up zones (LCZ1–LCZ3) and large impervious areas (LCZ8) exhibited the highest daytime temperatures and the slowest nocturnal cooling, whereas bare soil areas (LCZF) showed the largest diurnal temperature range and the greatest fluctuations. Vegetation-covered and bare land zones (LCZA and LCZD) generally maintained lower temperatures, while water bodies (LCZG) functioned as persistent cooling sources throughout the day due to their high specific heat capacity. Overall, the findings suggest that CNN-based LCZ classification, when integrated with high-temporal-resolution LST observations, provides a reliable technical framework for urban thermal environment monitoring and regulation at the regional scale. Full article

Human activity intensity has become an increasingly significant driver of changes in ecosystem services, and understanding their spatiotemporal relationships is essential for watershed ecological governance. Taking the Taihu Lake Basin as the study area, this research utilizes multi-source geospatial data from 2003 to 2023. The InVEST model is employed to assess four key ecosystem services: water yield, soil conservation, carbon storage, and habitat quality. A human activity intensity (HAI) index is constructed using the entropy-weight method, while bivariate spatial autocorrelation and geographically weighted regression are applied to disentangle the spatiotemporal evolution, spatial associations, and local variations in the associations between HAI and ecosystem services. The results indicate that (1) HAI has continuously intensified, with high-intensity areas undergoing an approximately 9.5-fold expansion and exhibiting a distinct “high in the east, low in the west” pattern; (2) carbon storage and habitat quality show persistent declines, whereas water yield and soil conservation exhibit fluctuating upward trends, with an overall “high in the southwest, low in the northeast” distribution; (3) HAI is significantly positively correlated with water yield and negatively correlated with carbon storage, soil conservation, and habitat quality, with 2018 identified as a critical turning point; and (4) the associations exhibit notable spatial non-stationarity, with the proportion of significant grid cells following a hierarchical order of habitat quality > water yield > carbon storage > soil conservation. Zones with strong regression coefficients occur only as localized patches, while the majority of the basin is characterized by low-to-moderate driving intensity. This study further reveals three core mechanisms—land-use path dependence, policy-induced threshold responses, and spatial spillover associations—providing a scientific basis for differentiated ecological management and sustainable development in the Taihu Lake Basin. Full article

Wetland area and quality have continued to decline worldwide due to the combined impacts of global climate change and human activities, so there is a need for a scientific and standardized framework to assess wetland degradation risk and support conservation and restoration efforts. In this study, a wetland degradation risk assessment framework integrating pressure level and degradation level was developed and applied to the western Songnen Plain, one of China’s major marsh wetland regions, using data from 2000, 2010, and 2020. The results showed that: (1) anthropogenic activity intensity and wetland area change were the most influential indicators. The degradation risk index ranged from 0.20 to 0.68, with mean values of 0.52, 0.46, and 0.46 in 2000, 2010, and 2020, respectively, indicating a slight overall decline. (2) The pressure level increased from 0.56 to 0.59 from 2000 to 2020, suggesting a growing influence of external pressures. In the same period, the degradation level decreased from 0.34 to 0.30, reflecting a reduced effect of internal environmental conditions. (3) Spatially, medium-risk areas have expanded from 47.5% to 64.1% and became the dominant area, while high-risk areas remained relatively stable at 14–16%, whereas relatively high-risk areas decreased sharply from 33.87% to 6.29%. These results provide useful guidance for regional land management for sustaining ecological systems, and the proposed framework provides a transferable assessment logic for wetland degradation risk, but its application to other regions require local calibration of indicators, weights, thresholds, and validation datasets. Full article

Understanding the coordination between ecosystem service value (ESV) and landscape ecological risk (ERI) is essential for ecological management in arid inland river basins. However, their spatiotemporal coupling and nonlinear driving mechanisms remain poorly understood. Using the main stem of the Tarim River as a case study, this study integrated multi-source data from 1990 to 2020 with a coupling coordination degree (CCD) model and the XGBoost-SHAP interpretable machine learning framework to investigate the evolution and drivers of ESV–ERI coordination. The results revealed a significant and intensifying spatial clustering of CCD, with Global Moran’s I increasing from 0.741 to 0.793. The basin showed a distinct spatial gradient, with high-value clustering in the upper reaches, transitional differentiation in the middle reaches, and low-value clustering in the lower reaches. Land use intensity (LUI) was identified as the dominant driver, explaining 68.9% of CCD variation. The driving mechanisms varied by river section: the upper reaches were mainly influenced by socioeconomic disturbances, the middle reaches were jointly controlled by LUI and deep soil moisture, and the lower reaches were more sensitive to climate variability and land-use change. Nonlinear analysis further showed an inverted U-shaped relationship between LUI and CCD, with a threshold near 1.53. These findings provide new insights into the spatially heterogeneous and nonlinear mechanisms of ESV–ERI coordination and offer scientific support for ecological risk control and land–water resource management in arid inland river basins. Full article

Brownfield redevelopment increasingly requires sustainability-oriented frameworks integrating Life Cycle Assessment (LCA), Life Cycle Costing (LCC), Net Present Value (NPV), Social Life Cycle Assessment (S-LCA), and Social Return on Investment (SROI) to evaluate conventional remediation benchmarks and nature-based solutions (NBS) for the restoration of the Vieux-Charmont brownfield (France) into a public ecological park. Eight remediation scenarios were assessed, including combinations of excavation, soil treatment, landfill disposal, soil reuse, and phyto-management. The results demonstrated substantial differences among restoration pathways. The conventional landfill-oriented benchmark generated the highest environmental burdens, whereas the best-performing phyto-management scenario achieved the lowest impacts, reducing climate change and land use impacts by 91.6% and 75.6%, respectively. Scenarios integrating reduced excavation intensity and treated soil reuse consistently improved environmental performance and long-term economic viability. The best-performing phyto-management configuration also achieved the highest NPV after 20 years (1.38 million Euros, 2026). The social assessment results indicated improved socio-economic performance for phyto-management systems within the adopted S-LCA and SROI framework. Overall, the findings demonstrated that remediation strategies should not be evaluated solely according to contaminant removal efficiency or direct operational costs. Instead, integrated sustainability frameworks combining environmental, economic, and social dimensions provide a more robust basis for supporting sustainable brownfield restoration and circular land management strategies. Full article

This study evaluates the spatial variability of hydric stress in the State of Sinaloa, northwestern Mexico, through the integrated analysis of hydroclimatic variables, multispectral remote sensing indicators, and environmental factors. Historical hydroclimatic conditions were analyzed using meteorological records from 1961 to 2020, whereas Landsat 8 imagery acquired on 7 July 2025, was used to evaluate the spatial expression of hydric stress. Reference evapotranspiration (ETo) was estimated using the FAO-56 Penman–Monteith methodology, and hydrological deficit conditions were determined from the relationship between precipitation (P) and ETo. Spectral indicators including land surface temperature ($\overline{T}$_a), the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), and the NDWI/MNDWI relationship were used to evaluate vegetation response, surface moisture conditions, and thermal anomalies associated with hydric stress. The results revealed persistent conditions where ETo systematically exceeded P, with hydrological deficit values ranging from approximately −1600 mm·year⁻¹ to localized positive values near 50 mm·year⁻¹. The most severe deficits were concentrated within the northwestern and north-central agricultural valleys of Sinaloa. Statistical validation revealed significant negative relationships between hydrological deficit and all evaluated spectral indicators. The strongest association was observed for MNDWI (R² = 0.387), followed by NDWI/MNDWI (R² = 0.277), NDWI (R² = 0.220), and NDVI (R² = 0.134), confirming the sensitivity of vegetation and moisture-related indicators to long-term hydrological stress conditions. Spatial analyses revealed a strong correspondence among low NDVI, negative NDWI and MNDWI responses, elevated $\overline{T}$_a, and regions characterized by high atmospheric evaporative demand. Additional spatial validation integrating land-use and vegetation-cover changes (1993–2011), regional geology, topography, and the distribution of highly productive agricultural valleys demonstrated that the most severe hydrological deficits coincided with areas affected by vegetation-cover loss, agricultural expansion, and intensive land use. Although these datasets correspond to different observation periods, they collectively reflect the cumulative environmental effects associated with persistent hydrological stress across the region. The combined effects of hydrological imbalance, forest-cover reduction, and agricultural intensification have progressively reduced ecosystem resilience and increased environmental vulnerability throughout one of the most productive agricultural regions of northwestern Mexico. These findings provide a scientific basis for water-resource management, territorial planning, ecosystem restoration, and climate-adaptation strategies under increasing water-scarcity conditions. Full article

Against the backdrop of global climate change and carbon neutrality strategies, land use carbon emissions have become a prominent topic amid regional efforts toward low-carbon transformation. However, existing studies on land-use carbon emissions have predominantly focused on humid and economically developed regions, while the unique carbon metabolism pathways of arid oasis–desert ecosystems, which are characterized by extremely low environmental carrying capacity and high sensitivity to land-use disturbance, remain largely unexplored. This study takes the oasis urban cluster in the Tarim Basin in southern Xinjiang Uygur Autonomous Region as the research object. This region belongs to a typical oasis–desert composite ecosystem, with a simple structure and low environmental carrying capacity (reflected by sparse vegetation cover < 20%, annual precipitation < 100 mm, extremely limited water resources, and high sensitivity to land disturbance). Its carbon metabolism pathway (i.e., the dynamic balance between carbon sources and sinks induced by land-use change) is fundamentally different from that in humid areas, and thus merits dedicated investigation. This study selects the period from 2000 to 2020 as the research period, which completely covers the acceleration period of urbanization and agricultural expansion in the Tarim Basin oasis urban cluster since the advancement of China’s Western Development Initiative. The data have a temporal resolution of 5 years (samples in 2000, 2005, 2010, 2015, 2020) and a spatial resolution of 30 m for land use and prefecture level for socio-economic indicators. Based on this, to fill the above-mentioned research gap, a research framework integrating the carbon emission coefficient accounting method, landscape pattern index, spatial autocorrelation analysis and geographic detector is adopted. Specifically, this study aims to systematically quantify the spatio-temporal evolution of land use carbon emissions and identify the most robust driving factors in the Tarim Basin oasis urban cluster by integrating multiple models, an approach that has not been previously applied to arid oasis regions. The research results show: (1) Based on the carbon emission coefficient method, total carbon emissions increased from 1.4455 million tons to 22.364 million tons, following a ‘slow-then-fast’ trajectory. In terms of temporal evolution, the study period can be further divided into three sub-stages: 2000–2005 (slow diffusion, with emission center skewed toward the northern energy-intensive zone), 2005–2015 (rapid restructuring, characterized by a ‘unipolar surge’ in Aksu and spread to the central oasis belt), and 2015–2020 (high-intensity stabilization, forming a cross-regional emission belt). Meanwhile, the land use structure has undergone a significant transformation. Construction land and cultivated land have continued to expand, while ecological land has significantly shrunk, resulting in a complex transformation pattern of oasis–desert ecotone. (2) The overall landscape became increasingly fragmented and diversified, the integrity of ecological space was damaged, and the regional carbon sink function was weakened. (3) The spatial autocorrelation analysis indicates that the spatial distribution of carbon emissions shows a heterogeneous pattern, forming a high-emission concentration area centered around Aksu-Bayingol. However, the global Moran’s I index is negative (such as −0.171 in 2020, p > 0.05), suggesting that carbon emissions have not formed a significant spatial clustering. (4) Carbon emissions are dominated by human and economic factors, and the interaction of factors is significant. The geographic detector identifies population density (average q value 0.904) and the proportion of construction land (average q value 0.858) as the key determinants of spatial variation in carbon emissions, reflecting the sensitive response of the human-nature system of arid zones to the urbanization process. These findings not only clarify the spatio-temporal features and driving forces of land use carbon emissions in the Tarim Basin oasis urban cluster, but also provide a replicable analytical framework for carbon-emission research in other arid and semi-arid regions worldwide. Based on these findings, we discuss the unique driving mechanisms of carbon emissions in arid regions, conclude that construction land expansion and population density are the dominant factors, and recommend a three-tier zoning governance system (carbon source control zone, carbon sink enhancement zone, coordinated development zone) for low-carbon spatial planning in arid areas. Full article

The coastal wetlands of Yancheng, Jiangsu Province, serve as a crucial overwintering and stopover site for rare waterbirds such as red-crowned cranes. The changes in their landscape pattern are directly related to the protection of regional wetland ecological functions and biodiversity. Using land use data from 2010 to 2022 to extract landscape pattern indicators for each period, this study adopts the Covariance–Analytic Hierarchy Process (Cov-AHP) to construct a comprehensive Landscape Fragmentation Index (LFI) for coastal wetlands, considering aspects including patch density, boundary complexity, spatial connectivity, and landscape diversity. Combined with multi-source indicators of Ecology–Economy–Society (EES), the Generalized Additive Model (GAM) and Geographically Weighted Regression (GWR) are adopted to systematically analyze the nonlinear response relationships and spatially heterogeneous driving mechanisms of landscape fragmentation. GWR is employed to reveal the spatial heterogeneity of the influence of each driving factor on fragmentation by mapping local regression coefficients. The results show that: (1) During the study period, the overall landscape fragmentation of the coastal wetlands in Yancheng, Jiangsu Province, exhibited a slow increase trend, with a spatial gradient pattern of “higher in the north and lower in the south, and higher in coastal areas than in inland areas,” reflecting the combined effects of varying levels of economic development and human activity intensity across different administrative regions and along the coast-inland gradient. (2) Based on the deviance explained by the GAMs, social factors generally had higher explanatory power for LFI than ecological and economic factors. Specifically, human population density and the proportion of construction land showed a significant positive correlation with LFI, while NDVI and the proportion of farmland exhibited obvious nonlinear effects under different fragmentation levels. (3) The GWR results indicated that the regression coefficients of the main driving factors were highly spatially non-stationary, and regions with high coastal development intensity had the most significant promoting effect on landscape fragmentation. The local coefficient maps further reveal that GDP and NDVI exhibit the strongest spatial heterogeneity, with their effects shifting from positive to negative across different sub-regions. The study demonstrates that the integrated framework of Cov-AHP combined with GAM and GWR can effectively characterize the spatiotemporal dynamics and multi-dimensional driving mechanisms of coastal wetland landscape fragmentation, providing a reference for the protection of coastal wetlands in Yancheng, Jiangsu Province, and the conservation of waterbird habitats represented by red-crowned cranes. Full article