Search Results (424)

We developed a microscale airflow modeling system with detailed building and terrain data to better understand the urban microclimate. Building shapes and heights, and terrain elevation data were integrated to construct a high-resolution urban surface geometry. The system, based on computational fluid dynamics using OpenFOAM, can resolve complex flow structures around built environments. Inflow boundary conditions were generated using logarithmic wind profiles derived from Automatic Weather System (AWS) observations under neutral stability. After validation with wind-tunnel data for a single block, the system was applied to airflow modeling around a university campus in Seoul using AWS data from four nearby stations. The results demonstrated that the system captured key flow characteristics such as channeling, wake, and recirculation induced by complex terrain and building configurations. In particular, easterly inflow cases with high-rise buildings on the leeward side of a mountain exhibited intensified wakes and internal recirculations, with elevated centers influenced by tall structures. This modeling framework, with further development, could support diverse urban applications for microclimate and air quality, facilitating urban resilience. Full article

Urban and regional ecosystems are increasingly challenged by the compounded effects of climate change and intensive land use. In this study, a predictive assessment framework for ecosystem resilience in the Beijing–Tianjin–Hebei region was developed by integrating multi-source remote sensing data, with the aim of quantitatively evaluating the coupled effects of climate change and land use change on future ecosystem resilience. In the first stage of the study, the SD-PLUS coupled modeling framework was employed to simulate land use patterns for the years 2030 and 2060 under three representative combinations of Shared Socioeconomic Pathways and Representative Concentration Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Building upon these simulations, ecosystem resilience was comprehensively evaluated and predicted on the basis of three key attributes: resistance, adaptability, and recovery. This enabled a quantitative investigation of the spatio-temporal dynamics of ecosystem resilience under each scenario. The results reveal the following: (1) Temporally, ecosystem resilience exhibited a staged pattern of change. From 2020 to 2030, an increasing trend was observed only under the SSP1-2.6 scenario, whereas, from 2030 to 2060, resilience generally increased in all scenarios. (2) In terms of scenario comparison, ecosystem resilience typically followed a gradient pattern of SSP1-2.6 > SSP2-4.5 > SSP5-8.5. However, in 2060, a notable reversal occurred, with the highest resilience recorded under the SSP5-8.5 scenario. (3) Spatially, areas with high ecosystem resilience were primarily distributed in mountainous regions, while the southeastern plains and coastal zones consistently exhibited lower resilience levels. The results indicate that climate and land use changes jointly influence ecosystem resilience. Rainfall and temperature, as key climate drivers, not only affect land use dynamics but also play a crucial role in regulating ecosystem services and ecological processes. Under extreme scenarios such as SSP5-8.5, these factors may trigger nonlinear responses in ecosystem resilience. Meanwhile, land use restructuring further shapes resilience patterns by altering landscape configurations and recovery mechanisms. Our findings highlight the role of climate and land use in reshaping ecological structure, function, and services. This study offers scientific support for assessing and managing regional ecosystem resilience and informs adaptive urban governance in the face of future climate and land use uncertainty, promotes the sustainable development of ecosystems, and expands the applicability of remote sensing in dynamic ecological monitoring and predictive analysis. Full article

The spatial genes of rural settlements show a lot of different traditional settlement traits, which makes them a great starting point for studying rural spatial morphology. However, qualitative and macro-regional statistical indicators are usually used to find and extract rural settlement spatial genes. Taking Yubei Village in the Nanxi River Basin as an example, this study combined remote sensing images, real-time drone mapping, GIS (geographic information system), and space syntax, extracted 12 key indicators from five dimensions (landform and water features (environment), boundary morphology, spatial structure, street scale, and building scale), and quantitatively “decoded” the spatial genes of the settlement. The results showed that (1) the settlement is a “three mountains and one water” pattern, with cultivated land accounting for 37.4% and forest land accounting for 34.3% of the area within the 500 m buffer zone, while the landscape spatial diversity index (LSDI) is 0.708. (2) The boundary morphology is compact and agglomerated, and locally complex but overall orderly, with an aspect ratio of 1.04, a comprehensive morphological index of 1.53, and a comprehensive fractal dimension of 1.31. (3) The settlement is a “clan core–radial lane” network: the global integration degree of the axis to the holy hall is the highest (0.707), and the local integration degree R3 peak of the six-room ancestral hall reaches 2.255. Most lane widths are concentrated between 1.2 and 2.8 m, and the eaves are mostly higher than 4 m, forming a typical “narrow lanes and high houses” water town streetscape. (4) The architectural style is a combination of black bricks and gray tiles, gable roofs and horsehead walls, and “I”-shaped planes (63.95%). This study ultimately constructed a settlement space gene map and digital library, providing a replicable quantitative process for the diagnosis of Jiangnan water town settlements and heritage protection planning. Full article

Numerous rural residential buildings exhibit inadequate seismic performance when subjected to blast-induced vibrations, which poses potential threats to their overall stability and structural integrity when in proximity to blasting project sites. The investigation conducted in conjunction with the Qianshi Mountain blasting operations along the Wenzhou segment of the Hangzhou–Wenzhou High-Speed Railway integrates household field surveys and empirical measurements to perform modal analysis of rural residential buildings through finite element simulation. Adhering to the principle of stratified arrangement and composite measurement point configuration, an effective and reasonable experimental observation framework was established. In this investigation, the seven-story rural residential building in adjacent villages was selected as the research object. Strong-motion seismographs were strategically positioned adjacent to frame columns on critical stories (ground, fourth, seventh, and top floors) within the observational system to acquire test data. Methodical signal processing techniques, including effective signal extraction, baseline correction, and schedule conversion, were employed to derive temporal dynamic characteristics for each story. Combined with the Fourier transform, the frequency–domain distribution patterns of different floors are subsequently obtained. Leveraging the structural dynamic theory, time–domain records were mathematically converted to establish the structure’s maximum response spectra under blast-induced loading conditions. Through the analysis of characteristic curves, including floor acceleration response spectra, dynamic amplification coefficients, and spectral ratios, the dynamic response patterns of rural residential buildings subjected to blast-induced vibrations have been elucidated. Following the normalization of peak acceleration and velocity parameters, the mechanisms underlying differential floor-specific dynamic responses were examined, and the layout principles of measurement points were subsequently formulated and summarized. These findings offer valuable insights for enhancing the seismic resilience and structural safety of rural residential buildings exposed to blast-induced vibrations, with implications for both theoretical advancements and practical engineering applications. Full article

Precise extraction of rural settlements in alpine regions is critical for geographic data production, rural development, and spatial optimization. However, existing deep learning models are hindered by insufficient datasets and suboptimal algorithm structures, resulting in blurred boundaries and inadequate extraction accuracy. Therefore, this study uses high-resolution unmanned aerial vehicle (UAV) remote sensing images to construct a specialized dataset for the extraction of rural settlements in alpine mountainous areas, while introducing an innovative shadow mitigation technique that integrates multiple spectral characteristics. This methodology effectively addresses the challenges posed by intense shadows in settlements and environmental occlusions common in mountainous terrain analysis. Based on the comparative experiments with existing deep learning models, the Swin Transformer was selected as the baseline model. Building upon this, the Feature Fusion Swin Transformer (FF-SwinT) model was constructed by optimizing the data processing, loss function, and multi-view feature fusion. Finally, we rigorously evaluated it through ablation studies, generalization tests and large-scale image application experiments. The results show that the FF-SwinT has improved in many indicators compared with the traditional Swin Transformer, and the recognition results have clear edges and strong integrity. These results suggest that the FF-SwinT establishes a novel framework for rural settlement extraction in alpine mountain regions, which is of great significance for regional spatial optimization and development policy formulation. Full article

Ancient temple complexes in China’s mountainous landscapes exemplify a profound synthesis of environmental adaptation and cultural expression. This research investigates the spatial logic underlying the Wudang Mountain temple complex—a UNESCO World Heritage site—through integrated geospatial analysis of environmental factors. Using GIS-based modeling, GeoDetector, and regression analysis, we systematically assess how terrain, hydrology, climate, vegetation, and soil conditions collectively influenced site selection. The results reveal a clear hierarchical clustering pattern, with dense temple cores in the southwestern highlands, ridge-aligned belts, and a dominant southwest–northeast orientation that reflects intentional alignment with mountain ridgelines. Temples consistently occupy zones with moderate thermal, hydrological, and vegetative stability while avoiding geotechnical extremes such as lowland humidity or unstable slopes. Regression analysis confirms that site preferences vary across temple types, with soil pH, porosity, and bulk density emerging as significant influencing factors, particularly for cliffside temples. These findings suggest that ancient temple planning was not merely a passive response to sacred geography but a deliberate process that actively considered terrain, climate, soil, and other environmental factors. While environmental constraints strongly shaped spatial decisions, cultural and symbolic considerations also played an important role. This research deepens our understanding of how environmental factors influenced the formation of historical landscapes and offers theoretical insights and ecologically informed guidance for the conservation of mountain cultural heritage sites. Full article

Karst mountain areas, as complex geological systems formed by carbonate rock development, possess unique three-dimensional spatial structures and hydrogeological processes that fundamentally influence regional ecosystem evolution, land resource assessment, and sustainable development strategy formulation. In recent years, through the implementation of systematic ecological restoration projects, the ecological degradation of karst mountain areas in Southwest China has been significantly curbed. However, the research on the fine-grained land use mapping and quantitative characterization of spatial heterogeneity in karst mountain areas is still insufficient. This knowledge gap impedes scientific decision-making and precise policy formulation for regional ecological environment management. Hence, this paper proposes a novel methodology for land use mapping in karst mountain areas using very high resolution (VHR) remote sensing (RS) images. The innovation of this method lies in the introduction of strategies of geographical zoning and stratified object extraction. The former divides the complex mountain areas into manageable subregions to provide computational units and introduces a priori data for providing constraint boundaries, while the latter implements a processing mechanism with a deep learning (DL) of hierarchical semantic boundary-guided network (HBGNet) for different geographic objects of building, water, cropland, orchard, forest-grassland, and other land use features. Guanling and Zhenfeng counties in the Huajiang section of the Beipanjiang River Basin, China, are selected to conduct the experimental validation. The proposed method achieved notable accuracy metrics with an overall accuracy (OA) of 0.815 and a mean intersection over union (mIoU) of 0.688. Comparative analysis demonstrated the superior performance of advanced DL networks when augmented with priori knowledge in geographical zoning and stratified object extraction. The approach provides a robust mapping framework for generating fine-grained land use data in karst landscapes, which is beneficial for supporting academic research, governmental analysis, and related applications. Full article

With the widespread application of deep learning in Earth observation, remote sensing image-based building change detection has achieved numerous groundbreaking advancements. However, differences across time periods caused by temporal variations in land cover, as well as the complex spatial structures in remote sensing scenes, significantly constrain the performance of change detection. To address these challenges, a change detection algorithm based on spatio-spectral information aggregation is proposed, which consists of two key modules: the Cross-Scale Heterogeneous Convolution module (CSHConv) and the Spatio-Spectral Information Fusion module (SSIF). CSHConv mitigates information loss caused by scale heterogeneity, thereby enhancing the effective utilization of multi-scale features. Meanwhile, SSIF models spatial and spectral information jointly, capturing interactions across different spatial scales and spectral domains. This investigation is illustrated with a case study conducted with the real-world dataset QL-CD (Qinling change detection), acquired in the Qinling region of China. The work includes the construction of QL-CD, which includes 12,724 pairs of images captured by the Gaofen-1 satellite. Experimental results demonstrate that the proposed approach outperforms a wide range of state-of-the-art algorithms. Full article

From the mid-Ming to early Qing dynasties, Mount Jizu 雞足山 in Yunnan achieved unexpected prominence within China’s Buddhist sacred landscape—an event of regional, national, and transnational significance. Employing an explicit comparative lens that juxtaposes Jizu with China’s core-region sacred sites like Mount Wutai and Emei, this study investigates the timing, regional dynamics, institutional mechanisms, and causal drivers behind the rapid ascent. Rejecting teleological narratives, it traces the mountain’s trajectory through four developmental phases to address critical historiographical questions: how did a peripheral Yunnan site achieve national prominence within a remarkably compressed timeframe? By what mechanisms could its sacred authority be constructed to inspire pilgrimages even across vast distances? Which historical agents and processes orchestrated these transformations, and how did the mountain’s symbolic meaning shift dynamically over time? Departing from earlier scholarship that privileges regional and secular frameworks, this work not only rebalances the emphasis on religious dimensions but also expands the analytical scope beyond regional confines to situate Mount Jizu within national and transnational frameworks. Eventually, by analyzing the structural, institutional, and agential dynamics—spanning local, imperial, and transnational dimensions—this study reveals how the mountain’s sacralization emerged from the convergence of local agency, acculturative pressures, state-building imperatives, late-Ming Buddhist revival, literati networks, and the strategic mobilization of symbolic capital. It also reveals that Mount Jizu was not a static sacred site but a dynamic arena of contestation and negotiation, where competing claims to spiritual authority and cultural identity were perpetually redefined. Full article

Biodiversity conservation plays a pivotal role in achieving sustainable development and fostering harmonious coexistence between humans and nature. This study identifies avian conservation priority areas across China by analyzing multi-dimensional biodiversity, incorporating species diversity, functional diversity, and phylogenetic diversity. Through systematic conservation planning using Zonation version 4 software, we delineated priority areas across these diversity dimensions. Our results demonstrate a distinct south-to-north diversity gradient in China’s avifauna, with functional and phylogenetic diversity hotspots concentrated in Yunnan Province, the Hengduan Mountains, Hainan Island, Taiwan Island, and southeastern coastal regions. The identified priority conservation areas cover 14.6% of China’s terrestrial territory, protecting 89.8% of the country’s bird species—including 93.5% of endemic species and 88.9% of critically endangered species. Notably, existing nature reserves encompass merely 8.1% of these priority areas, revealing substantial conservation gaps within the current protection framework. Building upon China’s 3C Zoning Framework (Cities and farms, Shared landscapes, and Large wild areas), we propose zone-specific conservation strategies, with particular emphasis on strengthening protected area networks in the eastern coastal regions and the middle-lower Yangtze River basin, where urbanization pressures are most acute. These findings highlight the critical importance of incorporating multi-dimensional diversity in conservation planning and offer novel perspectives for optimizing China’s protected area system. Full article

Urban regulations in Amman, Jordan, enforce uniform building setbacks irrespective of topography, exacerbating shading effects and compromising daylight access in residential areas—a critical factor for occupant health and psychological well-being. This study evaluates the interplay between standardized setbacks, slope variations (0–30%), and shadow patterns in Amman’s dense, mountainous urban fabric. Focusing on the Al Jubayhah district, a mixed-methods approach was used, combining field surveys, 3D modeling (Revit), and seasonal shadow simulations (March, September, December) to quantify daylight deprivation. The results reveal severe shading in winter (78.3% site coverage in December) and identify slope-dependent setbacks as a key determinant: for instance, a 15 m building on a 30% slope requires a 26.4 m rear setback to mitigate shadows, compared to 13.8 m on flat terrain. Over 39% of basements in the study area remain permanently shaded due to retaining walls, correlating with poor living conditions. The findings challenge Amman’s one-size-fits-all regulatory framework (Building Code No. 67, 1979), and we propose adaptive guidelines, including slope-adjusted setbacks, restricted basement usage, and optimized street orientation. This research underscores the urgency of context-sensitive urban policies in mountainous cities to balance developmental density with daylight equity, offering a replicable methodology for similar Mediterranean climates. Full article

As a pivotal node in urban spatial restructuring, the evolution of three-dimensional (3D) compactness in high-speed rail station areas is crucial for sustainable development. However, the existing research predominantly focuses on two-dimensional forms and lacks dynamic analysis and models that are adaptable to complex terrains. This study develops an enhanced 3D gravitational model that integrates satellite imagery and Gaode building data to quantify the spatiotemporal heterogeneity and carry out multidimensional classification of the compactness across 16 stations in the Chengdu-Chongqing urban agglomeration (2015–2025), with driving factors being identified through correlation and regression analyses. The key findings reveal the following: (1) The mean compactness increased by 22.41%, exhibiting nonlinear heterogeneity characterized by high initial values with low growth rates versus low initial values with high growth rates. Spatially, the southern line evolved from a dual-core pattern at the terminals to multigradient development, while the northern line maintained stable growth despite gradient discontinuities. These spatial differentiations resulted from synergistic effects of urban sizes (station hierarchy), terrain features, administrative divisions, and the line affiliation. (2) The built-up land area (under equal study conditions) and vertical development emerged as key drivers, with the building height diversity demonstrating dual spatial effects (enhancing both compactness and aesthetic richness). Complex terrain characteristics were found to promote clustered urban land use and compact efficiency during initial development phases. This study proposes a planning framework that integrates morphology-adaptive zoning control, ecology-responsive compactness principles, and urban–rural integrated settlement patterns, providing quantitative tools for mountainous station development. These findings offer theoretical and practical support for achieving urban sustainability goals and meeting the 3D compactness and transit-oriented development requirements in territorial spatial planning. Full article

Traditional villages contain rich natural and humanistic information, and exploring the spatial distribution characteristics and cultural landscape zoning of traditional villages can provide scientific support for their centralized and continuous protection and renewal and sustainable development. In this study, 326 traditional villages in the northern Henan region were taken as the research object, followed by analyzing their spatial distribution characteristics by using geostatistical methods, such as nearest-neighbor index, imbalance index, geographic concentration index, etc., combining the theory of cultural landscape to construct the traditional villages’ cultural factor index system, extracting the cultural factors of the traditional villages to form a database, and adopting the K-means clustering method to divide the region. The results show that the spatial distribution of traditional villages in northern Henan tends to be concentrated overall, with an uneven distribution throughout the region. The density is highest in the northwestern part of Hebi City and lower in the central and southern parts of Xinxiang City, Neihuang County, and Puyang City. Based on the cultural factor index system, the K-means algorithm divides the traditional villages in northern Henan into six clusters. Among them, the five cultural factors of topography and geomorphology, building materials, courtyard form, structural system, and altitude and elevation are the most significant, and they are the cultural factors that dominate the landscape of the villages. There is a significant correlation between topography, altitude, and other cultural factors, while the correlation between the street layout and other factors is the lowest. Based on the similarity between the clustering results and the landscape characteristics, the traditional villages in northern Henan can be divided into the stone masonry building culture area along the Taihang Mountains, the brick and stone mixed building culture area in the low hills of the Taihang Mountains, the brick and wood building culture area in the North China Plain, and the raw soil building culture area in the transition zone of the Loess Plateau. Full article

Facing severe depopulation and aging, rural Japanese communities—particularly marginal settlements (genkai shūraku)—increasingly require revitalization strategies that integrate local culture and elder well-being. This study examines the Ōsawa Engawa Café, a community-led initiative in a mountainous tea-growing village, as a site of ikigai-zukuri—the active creation of life purpose among elderly residents. With the use of a mixed-methods approach, including spatial analysis, household surveys, and interviews, Chi-square Automatic Interaction Detection (CHAID) decision tree analysis was applied to identify factors shaping distinct household café operational states: Operating, Discontinued, and Never Operated. Qualitative findings reveal that support from local leaders, experts, and the government enabled the Ōsawa Engawa café’s launch. Broad household participation, often guided by elderly women, sustained the initiative by sharing local culture—such as engawa (verandas), Zairai tea (native variety), and omotenashi (hospitality)—thereby nurturing residents’ ikigai through daily engagement. Complementing these insights, the CHAID analysis revealed a hierarchy of influential factors: high-frequency support from out-migrated family members was the strongest predictor of continued operation; in the absence of such support, co-resident family cooperation proved essential; where both were lacking, agricultural engagement distinguished households that discontinued from those that never operated. Practically, the Ōsawa model offers a replicable, bottom-up strategy that activates the Rural Cultural Landscape (landscapes shaped by traditional rural life and culture, RCL) through community engagement grounded in cultural practices and elderly ikigai-zukuri, contributing to sustainable rural livelihoods. Theoretically, this study reframes ikigai-zukuri as a key socio-cultural pillar of community resilience in aging rural areas. Fostering such culturally embedded, purpose-driven initiatives is essential for building vibrant, adaptive rural communities in the face of demographic decline. However, the study acknowledges that the Ōsawa model’s success is rooted in its specific socio-cultural context, and its replication in other cultural settings may be limited without contextual adaptation. Full article

The large-scale detection of greenhouses and rural buildings is important for natural resource surveys and farmland protection. However, in rural and mountainous areas, the resolution and accessibility of remote sensing satellite images from a single source are poor, making it difficult to detect greenhouses and rural buildings effectively and automatically. In this paper, a wide-area greenhouse and rural building (GH-RB) detection dataset is constructed as a benchmark by using high-resolution remote sensing images of Hebei Province, China, collected from the image platform. Then, Efficient Weighted YOLOv8 (EW-YOLOv8) is proposed by using the dataset with unbalanced and small samples of greenhouse and rural buildings, in which the improvement measures are introduced. These include the following: (1) replacing the traditional up-sampler with DySample in the up-sampling part of the neck of the model to recover the lost details after multiple down-sampling operations; (2) replacing the calculation loss function with NWD loss to compensate for the sensitivity of the IoU to the position deviation of small objects; and (3) introducing a weight function named Slide to resolve the data imbalance between easy and difficult samples. The experimental results show that the proposed method can achieve excellent object detection performance on the RSOD dataset compared with state-of-the-art methods, proving the effectiveness of the proposed EW-YOLOv8. The results on the constructed GH-RB dataset show that the proposed method can detect greenhouse and rural buildings quickly and accurately, which could help improve the efficiency of investigating farmland usage and performing natural resource surveys. Full article