Search Results (231)

Understanding the spatiotemporal evolution of forest biomass energy potential is essential for supporting low-carbon land-use planning and regional energy transitions. China, characterized by pronounced spatial heterogeneity in forest resources and ecological conditions, provides an ideal case for examining how biophysical endowments and management factors shape biomass energy potential. This study constructs a province-level panel dataset for China covering the period from 1998 to 2018 and investigates long-term spatial patterns, regional disparities, and driving mechanisms using spatial visualization, Dagum Gini decomposition, and fixed-effects estimation. The results reveal a gradual spatial reorganization of forest biomass energy potential, with the national center of gravity shifting westward and northwestward, alongside a moderate dispersion of high-potential clusters from coastal areas toward the interior. Interregional transvariation is identified as the dominant source of regional inequality, indicating persistent structural differences among major regions. To explore future dynamics, a random forest model is employed to project provincial forest biomass energy potential from 2018 to 2028. The projections suggest moderate overall growth, smoother distributional structures, and a partial reduction in extreme provincial disparities. Central, southwestern, and northwestern provinces are expected to emerge as important contributors to future growth, reflecting ecological restoration efforts, expanding plantation forests, and improved forest management. The findings highlight a continued upward trend in national forest biomass energy potential, accompanied by a spatial shift toward inland regions and evolving regional disparities. This study provides empirical evidence to support region-specific development strategies, optimized spatial allocation of forest biomass resources, and integrated policies linking ecological sustainability with renewable energy development. Full article

Understanding the driving forces of land use and land cover change (LUCC) is crucial for revealing the coupled dynamics of human–land systems and supporting optimized spatial planning and resource allocation. To overcome the limitations of conventional Geodetector applications in mountainous regions with complex terrain, this study proposes a terrain–population dual-factor adaptive grid designed for use with the Geodetector model. This adaptive grid refines cells in steep and densely populated areas while merging cells in flatter and sparsely populated regions, thus capturing both natural and socioeconomic heterogeneity. Coupled with the Geodetector model, this framework improves the accuracy and computational efficiency of identifying LUCC drivers. Using the Chongqing Metropolitan Area (CMA) as a case study, LUCC dynamics and their driving mechanisms were systematically examined based on five annual land cover datasets (from 2000 to 2020 at five-year intervals.). The results show the following: (1) From 2000 to 2020, cropland, forest land, and built-up land were the dominant land use types. During this period, cropland and forest land declined, whereas built-up land expanded continuously, with the most pronounced changes occurring between 2000 and 2010. (2) The dominant drivers of LUCC shifted over time: socioeconomic factors such as population density and GDP were primary drivers from 2000 to 2010, while both natural and socioeconomic factors exerted strong influence between 2010 and 2020. (3) The proposed terrain–population dual-factor irregular grid performed better than traditional regular grids in detecting socioeconomic drivers while retaining comparable explanatory power for natural factors. Compared with traditional regular grids, with an average q-value improvement of 18.7% and a 55.52% reduction in sampling points, resulting in substantially improved computational efficiency. Overall, the proposed method enhances the applicability of Geodetector in complex mountainous cities and provides practical implications for urban land use regulation and refined spatial management. Full article

Rapid urbanization has intensified resource and population agglomeration while exacerbating urban-rural disparities. To address the long-standing dual structure, China advocates urban-rural integrated development (URID) to achieve common prosperity. However, the long-term evolutionary patterns and explanatory mechanisms of URID remain insufficiently explored, particularly at the county (district)-level in western China. This study constructed an entropy-weighted TOPSIS evaluation system combined with kernel density estimation and an optimal parameters-based geographical detector (OPGD) model to analyze the spatiotemporal evolution and explanatory mechanisms of URID in 26 counties (districts) of the Xi’an metropolitan area from 2010 to 2022. The results showed that: (1) URID levels increased steadily over the study period, forming a pronounced core-periphery gradient with faster improvement in national URID pilot counties. (2) Factor associations evolved from being dominated by a few dimensions to multidimensional coupling. Socioeconomic and geographical factors remained dominant and relatively stable, demographic influences were clearly stage specific, and the interaction between forest coverage and economic variables weakened over time. (3) Enhancing regional transport accessibility, optimizing land use efficiency, and fostering positive population-industry interaction are key pathways for promoting URID in the study area. Methodologically, this study introduces a “significance testing followed by threshold verification” logic into the OPGD model, refining the parameter-setting process and improving the robustness and q-value of factor detection. The findings enrich URID theory, provide county (district)-scale evidence for western China, and offer policy implications for optimizing factor allocation and promoting coordinated regional development. Full article

Land use systems are closely coupled with socioeconomic activities. To explore the interactions between land use and socioeconomic activities in Bazhong City, clarify the characteristics, drivers, and future trends of land use change, and provide scientific support for optimizing regional land resource allocation, ecological conservation, and food security, this study analyzes land cover data from 2004 to 2024, identifies economic drivers via principal component analysis, and predicts future land use trends for 2025, 2030, and 2035 using the GM(1,1) model. The results indicate the following: (1) Cropland decreased by 1338.69 km², while forest increased by 1304.88 km², with the largest area of mutual conversion occurring between these two types. (2) The comprehensive index of land use exhibited a fluctuating decline. The quality and continuity of cropland decreased, while the expansion of forest increased ecosystem services. (3) Principal component analysis identified the Comprehensive Economic Development and Urbanization Factor (e.g., GDP, urbanization rate, etc.) as the long-term core driver, with the land use driving system evolving through three stages. (4) Projections indicate that forest will increase, while cropland will decrease by 263.83 km². While the cropland is projected to remain above the planned target by 2035, the persistent downward trend will nonetheless pose a threat to food security. This study provides insights for harmonizing land use planning with socioeconomic progress and ecological conservation with cropland protection and may also serve as a reference for related decision-making in similar regions. Full article

The spatiotemporal evolution of ecosystem services has a profound influence on the fragile eco-environment in Inner Mongolia and the arid/semi-arid and the ecological barrier regions of Northern China; in particular, the small-scale and high-value land variables may lead to large eco-environment effects through altering the ecosystem services, which is still unclear in this vulnerable area. The differential driving mechanism of both human activities and natural factors on ecosystem services also needs to be revealed. To solve this scientific issue, the synergistic methodology of spatial analysis technology, the improved ecosystem service assessment method, flow gain/loss model, global/local Moran’s I approach, and the Geographically and Temporally Weighted Regression (GTWR) model were applied. Our main results are as follows: remote sensing monitoring showed that the land changes featured a persistent expansion of cropland and built-up areas, with a decline in grassland and wetland, along the east–west gradient from forests, grasslands, and unused-lands, to become the dominant cover type. According to our improved model, the ecosystem services considering the internal structure of build-up lands were first investigated in this ecologically fragile area of China, and the evaluated ecosystem service value (ESV) reduced from CNY 5515.316 billion to CNY 5425.188 billion, with an average annual decrease of CNY 3.004 billion from 1990 to 2020. Another finding was that the small-scale land variables with large ecological service impacts were quantified; namely, the proportion of grassland, woodland, wetland, and water body decreased from 62.71% to 61.34%, with only a relatively minor fluctuation of −1.37%, but this decline resulted in a large ESV loss of CNY 116.141 billion from 1990 to 2020. From the driving perspective, the temperature, digital elevation model (DEM), and slope exhibited negative effects on ESV changes, whereas a positive association was analyzed in terms of the precipitation and human footprint during the studied period. This study provides important support for optimizing land resource allocation, guiding the development of agriculture and animal husbandry, and protecting the ecological environment in arid/semi-arid and ecological barrier regions. Full article

Wildfires are a recurring dry-season hazard in northern Thailand, contributing to severe air pollution and trans-boundary haze. However, the region lacks the ground-based measurements necessary for monitoring Live Fuel Moisture Content (LFMC), a key variable influencing vegetation flammability. This study presents a preliminary framework for near-real-time (NRT) LFMC estimation using Sentinel-2 multispectral imagery. The system integrates normalized vegetation and moisture-related indices, including the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Infrared Index (NDII), and the Moisture Stress Index (MSI) with an NDVI-derived evapotranspiration fraction (ETf) within a heuristic modeling approach. The workflow includes cloud and shadow masking, weekly to biweekly compositing, and pixel-wise normalization to address the persistent cloud cover and heterogeneous land surfaces. Although currently unvalidated, the LFMC estimates capture the relative spatial and temporal variations in vegetation moisture across northern Thailand during the 2024 dry season (January–April). Evergreen forests maintained higher moisture levels, whereas deciduous forests and agricultural landscapes exhibited pronounced drying from January to March. Short-lag responses to rainfall suggest modest moisture recovery following precipitation, although the relationship is influenced by additional climatic and ecological factors not represented in the heuristic model. LFMC-derived moisture classes reflect broad seasonal dryness patterns but should not be interpreted as direct fire danger indicators. This study demonstrates the feasibility of generating regional LFMC indicators in a data-scarce tropical environment and outlines a clear pathway for future calibration and validation, including field sampling, statistical optimization, and benchmarking against global LFMC products. Until validated, the proposed NRT LFMC estimation product should be used to assess relative vegetation dryness and to support the refinement and development of future operational fire management tools, including early warnings, burn-permit regulation, and resource allocation. Full article

With the intensification of global warming, surface thermal environment issues have become increasingly prominent, particularly in the ecologically fragile Yellow River Basin (YRB). However, most studies neglect the synergistic effects of underlying surface composition and geomorphological context, limiting the understanding of regional thermal contribution patterns. Based on MODIS-derived land surface temperature and Landsat 8-based land use and Fathom DEM-derived geomorphological datasets, this study constructs an integrated assessment framework combining a dual “quality–quantity” perspective with land use–geomorphology coupling, systematically analyzing the comprehensive thermal contributions of different underlying surfaces. Results show that (1) the YRB features diverse underlying surfaces, transitioning from natural (forest, grassland) to human-dominated (cropland, construction land) land uses, and from high-altitude, large undulating mountains to low-altitude, small undulating plains along the source-to-downstream gradient. (2) The average LST is 17.97 °C, displaying a south–north and east–west gradient. Human disturbance intensity drives thermal responses at the land use level, with natural surfaces contributing to cooling regulation, while artificial and desert surfaces generate heat accumulation. Geomorphology jointly shapes the thermal distribution, with high mountains acting as cold sources and plains/hills as heat sources. (3) Dual “quality–quantity” dimensional evaluation reveals that temperature-based assessments alone overestimate localized extremes (e.g., towns, extremely high mountains) and underestimate broad, moderate surfaces (e.g., drylands, large and medium undulating high mountains). This “area-neglect effect” may lead to biased regional thermal assessments and unbalanced resource allocation. (4) Coupled land use–geomorphology analysis uncovers the multi-scale composite mechanisms of thermal formation and mitigates single-factor assessment biases. Geomorphology defines macro-scale energy exchange, while land use regulates local heat responses. The results provide scientific support for large-scale thermal assessment and refined management. Full article

Mangrove forests are critical coastal ecosystems that store carbon, support marine life, and serve as natural barriers, protecting shorelines from erosion and reducing the impact of storms by absorbing wave energy. However, the rise of human activities and sea levels has led to their destruction over the past decades. It is important to know how the areas of mangrove forests change and adapt every year to plan for their restoration and protection and to support future trends like using carbon credits to help developing countries generate income. This study aims to map and monitor mangrove forest area changes over four decades in the Mekong region, comprising Myanmar, Thailand, Cambodia, and Vietnam, from 1984 to 2023 using a time series of Landsat data together with random forest (RF) classification. This analysis implemented multiple approaches, including creating stabilized Landsat imagery composites from the LandTrendr algorithm, Otsu edge detection, Minimum Mapping Unit (MMU), and RF classifier. The study found the map accuracy based on the RF model classifier achieved an overall accuracy between 86.2% and 88.8%, providing reliable data for analysis. Country-level analysis revealed increasing mangrove forest cover in Thailand (12.9%) and Vietnam (28.4%) since 1984. Conversely, mangrove areas in Cambodia and Myanmar have decreased significantly from 1984 to 2023 by about 14.6% and 22.7%, respectively. These findings have significant implications for resource allocation, investment strategies, and the development of carbon credits to support mangrove conservation efforts. This comprehensive dataset offers valuable insights for stakeholders involved in mangrove management and restoration in the Mekong region. By understanding the spatial-temporal distribution patterns of mangrove forest change, decision-makers can make informed decisions to safeguard these critical ecosystems for future generations. Full article

The three northeastern provinces of China are the country’s most important grain-producing region, particularly for maize, soybean, and rice, and form its largest commercial grain base. Over the past two decades, cropping structures in this region have undergone notable shifts driven by both climate change and human activities. Generating long-term, high-resolution maps of multi-crop distribution and evaluating their suitability is essential for understanding cropping dynamics, optimizing land use, and promoting sustainable agriculture. In this study, we integrated multi-source satellite imagery from Landsat and Sentinel-2 to map the distribution of rice, maize, and soybean from 2000 to 2023 using a Random Forest classifier. A crop suitability assessment framework was developed by combining a multi-criteria evaluation model with the MaxEnt model. Reliable training samples were derived by overlaying suitability evaluation results with stable crop growth areas, and environmental variables—including climate, topography, soil, hydrology, and anthropogenic factors—were incorporated into MaxEnt to assess suitability. Furthermore, the spatial consistency between actual cultivation and suitability was evaluated to identify areas of misallocated land use. The results show that: (1) the six classification maps achieved an average overall accuracy of 91.05% and a Kappa coefficient of 0.857; (2) the cultivation area of all three crops expanded, with maize showing the largest increase, followed by soybean and rice, and the dominant conversion being from soybean to maize; (3) suitability areas ranked as soybean (376,692 km²) > maize (329,056 km²) > rice (311,869 km²), with substantial spatial overlap, particularly between maize and soybean, suggesting strong competition; and (4) in 2023, highly suitable zones accounted for 57.39% of rice, 39.69% of maize, and 28.89% of soybean cultivation, indicating a closer alignment between actual distribution and suitability for rice, weaker for maize, and weakest for soybean, whose suitable zones were often displaced by rice and maize. These findings provide insights to guide farmers in optimizing crop allocation and offer a scientific basis for policymakers in designing cultivated land protection strategies in Northeast China. Full article

Adaptive forest and fire management in parks and protected areas is becoming increasingly complex as climate change alters the frequency and intensity of disturbances (wildfires, pest and disease outbreaks, etc.), while park visitation and the number of people living adjacent to publicly managed lands continues to increase. Evidence-based, climate-adaptive forest and fire management practices are critical for the responsible stewardship of public resources and require the continued availability of long-term ecological monitoring data. The US National Park Service has been collecting long-term fire monitoring plot data since 1998, and has continued to add monitoring plots, but these data are housed in databases with limited access and minimal analytic capabilities. To improve the availability and decision support capabilities of this monitoring dataset, we created the Trends in Forest Fuels Dashboard (TFFD), which provides an implementation framework from data collection to web visualization. This easy-to-use and updatable tool incorporates data from multiple years, plot types, and locations. We demonstrate our approach at Rocky Mountain National Park using the ArcGIS Online (AGOL) software platform, which hosts TFFD and allows for efficient data visualizations and analyses customized for the end user. Adopting interactive, web-hosted tools such as TFFD allows the National Park Service to more readily leverage insights from long-term forest monitoring data to support decision making and resource allocation in the context of environmental change. Our approach translates to other data-to-decision workflows where customized visualizations are often the final steps in a pipeline designed to increase the utility and value of collected data and allow easier integration into reporting and decision making. This work provides a template for similar efforts by offering a roadmap for addressing data availability, cleaning, storage, and interactivity that may be adapted or scaled to meet a variety of organizational and management use cases. Full article

Conflicting priorities between policymakers and local communities often compromise conservation outcomes in landscapes reliant on natural resources. Understanding how diverse stakeholders value ecosystem services (ESs) across coexisting land uses is essential; however, empirical evidence from rural Southeast Asia remains limited. This study examined ES perceptions and priorities among community members (n = 500) and experts (n = 30) within a bamboo forest, rice paddy, and teak plantation in Sangthong District, Lao PDR. A two-step survey methodology was employed: initially assessing ES perceptions to filter locally relevant services using a ≥50% recognition threshold, followed by quantifying priorities for this subset through a 100-point allocation task. The results revealed a systematic divergence in priorities rooted in differing knowledge systems. Communities, grounded in traditional ecological knowledge (TEK), prioritized tangible provisioning and cultural services (e.g., food and raw materials). In contrast, experts emphasized regulating services (e.g., carbon sequestration and hazard regulation) and habitat services (e.g., biodiversity and habitat provision). Distinct “ES bundles” also emerged by land use: bamboo (raw materials and freshwater), rice (food and medicine), and teak (timber/bioenergy and regulating services). Our findings suggest a policy transition from single-objective management toward optimizing landscape-level ES portfolios, alongside institutionalizing participatory co-management that formally integrates local knowledge and enhances ES literacy. Full article

With the rapid development of China’s tourism industry, the homogenization of regional tourism images has become a growing concern. To address this, this study quantifies the similarities and differences in tourism image perception across China’s 31 provinces, focusing on 350 5A-level destinations, analyzing 757,046 tourist reviews collected from Ctrip.com in 2024. Using a three-dimensional framework (cognitive, affective, and overall image), we analyze social media data through natural language processing, random forest regression, and social network analysis. Key findings include the following: (1) most comments are positive, with Jiangsu and Chongqing showing high cognitive image similarity but low overall similarity; (2) cognitive image significantly impacts affective image, especially through unique tourism resources; (3) an inter-provincial similarity–difference matrix reveals significant perceptual differences among provinces. This study provides a novel methodological approach for multidimensional image evaluation and offers crucial empirical insights for regional policy-making aimed at optimizing land and tourism resource allocation, balancing regional disparities, and promoting sustainable land use and development across China. Full article

Under the accelerating demographic aging trend, the rational allocation of elderly care facilities has emerged as a critical challenge. Although existing studies have investigated elderly care facilities planning using conventional methods, they frequently overlook the nonlinear interactions between built environment factors and heterogeneous demands across different elderly care facility types. This study addresses these gaps by proposing a data-driven framework that integrates machine learning with spatial analysis to optimize elderly care facility distribution in Xi’an City central area, Shaanxi Province, China. Leveraging multi-source datasets encompassing points of interest (POIs), road networks, and demographic statistics, we classify facilities into three categories (service-oriented, activity-oriented, and care-oriented) and employ an XGBoost model with SHAP interpretability to evaluate spatial distributions and influencing factors. The results demonstrate that the XGBoost model outperforms comparative algorithms (Random Forest, CatBoost, LightGBM) with superior performance metrics (accuracy rate of 97%, precision of 95%, and F1-score of 90%), effectively capturing nonlinear thresholds effects. Key findings reveal the following: (1) Accessibility and road density exert threshold effects on care-oriented facilities, with facility attractiveness saturating when these values exceed 6; (2) Land use intensity and medical resources positively correlate with activity-oriented facilities, while excessive retail density inhibits their distribution; (3) Service-oriented facilities thrive in areas with balanced accessibility and moderate commercial diversity. Spatial analysis identifies clustered distribution patterns in urban core areas contrasted with peripheral deficiencies, indicating need for targeted interventions. This research contributes a scalable methodology for equitable facility planning, emphasizing the integration of dynamic built environment variations with model interpretability. The framework provides significant implications for formulating age-friendly urban policies applicable to global cities undergoing rapid urbanization and population aging. Full article

Fodder maize constitutes a key economic crop in Thailand, particularly in the northeastern region, where it significantly contributes to livestock feed production and local economic development. Nevertheless, the planning and management of cultivation areas remain a major challenge, especially in urban and peri-urban agricultural zones, due to the limited availability of spatial data and suitable analytical frameworks. These difficulties are exacerbated in urban settings, where the complexity of land use patterns and high spectral similarity among land cover types hinder accurate classification. The Google Earth Engine (GEE) platform provides an efficient and scalable solution for geospatial data processing, enabling rapid land use classification and spatiotemporal analysis. This study aims to enhance the classification accuracy of fodder maize cultivation areas in Mueang District, Nakhon Ratchasima Province, Thailand—an area characterized by a heterogeneous mix of urban development and agricultural land use. The research integrates GEE with four machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes (NB), and Classification and Regression Trees (CART). Eleven datasets were developed using Sentinel-2 imagery and a combination of biophysical variables, including elevation, slope, Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Modified Normalized Difference Water Index (MNDWI), to classify land use into six categories: fodder maize cultivation, urban and built-up areas, forest, water bodies, paddy fields, and other field crops. Among the 44 classification scenarios evaluated, the highest performance was achieved using Dataset 11—which integrated all spectral and biophysical variables—with the SVM classifier. This model attained an overall accuracy of 97.41% and a Kappa coefficient of 96.97%. Specifically, fodder maize was classified with 100% accuracy in both Producer’s and User’s metrics, as well as a Conditional Kappa of 100%. These findings demonstrate the effectiveness of integrating GEE with machine learning techniques for precise agricultural land classification. This approach also facilitates timely monitoring of land use changes and supports sustainable land management through informed planning, optimized resource allocation, and mitigation of land degradation in urban and peri-urban agricultural landscapes. Full article

Understanding the spatio-temporal evolution of land consolidation is essential for optimizing regional land resource allocation and mitigating human–land conflicts during socio-economic development. This study introduced a novel framework for analyzing such patterns in China. Utilizing a two-decade (2002–2022) prefecture-level city dataset of land consolidation projects in Lianyungang, Jiangsu Province, we developed a “land consolidation intensity” metric and applied quantitative techniques—including land use transfer matrices, landscape pattern indices, Sankey diagrams, and standard deviation ellipses—to assess spatio-temporal dynamics and centroid shifts. Key findings included: (1) Land consolidation intensity exhibited distinct stages, evolving from initial development to rapid growth and eventual stabilization, closely aligning with national policy shifts. (2) The primary sources for supplemented cultivated land were ponds, rivers, and tidal flats, followed by grassland, construction land, and forest land, with cultivated land consistently dominating the consolidated landscape. (3) Land consolidation projects distribution concentrated in economic and political centers, with a spatial shift from inland western region towards the eastern coastal region. (4) Gray relational analysis identified economic development as the predominant driver, with policy and social factors providing secondary guidance. This research elucidates the spatio-temporal evolution characteristics of land consolidation at the prefecture-level city and demonstrates the utility of the proposed framework for similar analyses, offering insights relevant to national land use planning and policy formulation. Full article