Search Results (305)

Metropolitan fringe villages are important interfaces where urban–rural factor flows, urban functional spillovers, and spatial restructuring converge. However, how spatial governance shapes the evolution of their territorial spatial patterns remains insufficiently explained. Taking Donglin Village in Chengdu, China, as a case study, this paper integrates field investigation, in-depth interviews, and remote-sensing image interpretation to examine the mechanisms and governance logic underlying the evolution of territorial spatial patterns in metropolitan fringe villages. The findings show that the spatial evolution of Donglin Village is not merely a process of land-use change, but a dynamic process characterized by the coordinated restructuring of material, functional, and social spatial patterns. Spatial governance operates through three interrelated mechanisms: element integration promotes the reorganization of spatial resources and the reshaping of material space; functional synergy facilitates rural multifunctional transformation and spatial value enhancement; and benefit sharing helps stabilize actor relationships and institutionalize the distribution of development gains. Policy and institutional arrangements do not constitute an independent mechanism, but instead provide boundary constraints, rule support, and implementation guarantees for the above mechanisms. The case of Donglin Village further demonstrates that spatial governance connects spatial restructuring, functional reorganization, and benefit coordination into a continuous process of territorial spatial optimization. This study clarifies the mechanisms through which spatial governance shapes the evolution of territorial spatial patterns in metropolitan fringe villages and provides implications for spatial optimization in similar villages under the context of urban–rural integrated development. Full article

The spatial distribution and productivity of subsistence resources are central to food security, nutrition, and cultural vitality in circumpolar Indigenous communities. Yet few studies incorporate Indigenous Knowledge in methodology to monitor subsistence plant species. Here, we apply participatory action research to develop a monitoring system for the culturally and nutritionally important Rubus chamaemorus (atsalugpiaq, salmonberry) near the Yup’ik village of Quinhagak in southwest Alaska. With support from community members, two ground-truth surveys assessed berry productivity at nine sites within Quinhagak’s Traditional Land Use Area. Seventeen interviews identified key themes related to subsistence harvest and highlighted winter meteorological factors important for analysis. We compiled a multi-year dataset including PlanetScope eight-band SuperDove imagery (3 m GSD); airborne LiDAR and satellite-derived DEMs; and four meteorological parameters. Linear regression and multiple adaptive regression splines were tested to evaluate relationships among vegetation health, climate, landscape features, and berry productivity. Model outputs identified chlorophyll-related vegetation indices, particularly MTCI, as strong predictors of harvest outcomes, with higher flowering-season MTCI values associated with greater berry abundance. This work establishes a foundational, scalable approach for the long-term monitoring of Arctic subsistence plants in conjunction with Arctic communities and demonstrates the value of multi-layer data integration in regions historically challenging for remote sensing and ground surveys improving outcomes for regional harvest predictions and increased understanding of possible mechanisms controlling berry productivity in Arctic regions. Full article

Against the backdrop of China’s vigorous promotion of rural revitalization, traditional villages have become important carriers of rural tourism; however, their tourism development potential varies significantly. Using 182 traditional villages in Shexian County, Anhui Province, as the study area, this paper integrates multi-source data, including remote sensing, socio-economic, and online data. It constructs an evaluation index system from three dimensions: resource endowment, socio-economic conditions, and natural environment. Three machine learning models, namely, Random Forest (RF), XGBoost, and LightGBM, are employed to measure tourism development potential, and the optimal model is selected through comparative analysis. On this basis, the SHAP method is introduced to interpret the influencing factors and reveal the direction and mechanisms of their effects. The results show that (1) the LightGBM model performs best and is more suitable for evaluating tourism development potential of traditional villages; (2) service facilities, land resources, and transportation conditions are the most important influencing factors, while cultural resources and online attention also play significant roles; (3) the effects of different factors exhibit obvious nonlinear characteristics with interaction effects; and (4) the spatial pattern of tourism development potential presents a structure of “core agglomeration–transitional distribution–peripheral dispersion”. From the perspective of multi-source data and explainable machine learning, this study provides a systematic analysis of tourism development potential in traditional villages and offers a scientific reference for their differentiated development and conservation. Full article

Satellite-derived vegetation indices and climate data from 2018 to 2024 were analysed to quantify smallholder agricultural responses to climate variability in two rural villages in the Eastern Cape, South Africa. Using Google Earth Engine, R programming 4.4.0, and ArcGIS Pro 3.6, the study assessed spatiotemporal trends in vegetation condition in relation to bioclimatic variables and plot-scale land ownership. The results showed an overall accuracy of 96%, with producer and user accuracies at 79% and 85%, respectively, and a kappa coefficient of 0.95. Time-series analysis revealed a trend of decreasing rainfall and increasing temperatures across the study area, accompanied by elevated Plant Senescence Reflectance Index (PSRI > 0.294) values indicative of advanced vegetation stress. Spatial analysis showed that valley areas exhibited higher moisture accumulation potential and aligned with drainage networks, reflecting enhanced soil moisture retention relative to surrounding terrain. These findings demonstrate the strong influence of topography-mediated water availability on vegetation health in rain-fed smallholder systems. In accordance with the Sustainable Development Goals, the study stresses the importance of gender equity in combating climate change and achieving food security, highlighting the value of integrating multi-scale remote sensing and climate data to identify localised agricultural vulnerability, and underscores the importance of gender-responsive, climate-aware land management strategies to support food security under changing environmental conditions. By situating smallholder agriculture within a land system science framework, the study advances understanding of how topography-mediated soil moisture retention, climate variability, and gendered land governance jointly shape land system trajectories in communal tenure settings. Full article

Asbestos dust exposure remains a significant public health concern, particularly in areas with unrehabilitated asbestos mines. This study aims to evaluate the spatial distribution of asbestos and community awareness and perceptions of the risk of asbestosis in Ga-Mathabatha, a rural settlement in Limpopo Province, South Africa. Using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery, remote sensing techniques, and GIS mapping, we predicted areas containing different types of minerals associated with asbestos, validated by field observations at Makapeng, Moleke, Maseleseleng, Success, Masioneng, Olifants River, Mphogodima River, and Tongwane River sites. A mixed-methods research approach, including 18 in-depth interviews and 250 survey questionnaires, assessed community awareness and perceptions of potential asbestosis risk. Remote sensing analysis results indicated high concentrations of chrysotile asbestos in the eastern part of the study area, tremolite asbestos in the southern part, and minor serpentine deposits in the east. Field observations confirmed asbestos deposits along riverbanks and in the surrounding villages. Survey results revealed that 45.6% of participants were not aware of areas of high asbestos concentration in Ga-Mathabatha, while 28% (15% + 13%) did not perceive passing near asbestos dumps with or without herds as another source of exposure. These findings underscore the need for targeted education and awareness programs for communities living near asbestos deposits and those whose day-to-day activities increase their risk of exposure. Full article

Background: Malaria remains hyperendemic in the Democratic Republic of the Congo, while arboviral infections are increasingly reported but remain under-surveilled, particularly in remote regions. Overlapping ecological niches and non-specific clinical presentations complicate case management and surveillance. Methods: A cross-sectional door-to-door survey was conducted in December 2023 in Inkanamongo village (Lokolia Health Area, Boende Health Zone, Tshuapa Province). Blood samples were collected from 379 adults; malaria infection was assessed by using HRP2-based rapid diagnostic tests, and arboviral IgG antibodies were measured on dried blood spots using Luminex^® multiplex immunoassay. Sociodemographic data were collected via standardized questionnaires. Results: Malaria prevalence was 51.7% (95%CI: 46.7–56.7). Overall arboviral seroprevalence reached 78.4% (95%CI: 73.1–81.5), dominated by O’nyong-nyong virus, 42.8% (95%CI: 37.6–47.5), Rift Valley fever virus, 32.0% (95%CI: 26.9–36.2), and chikungunya virus, 23.4% (95%CI: 19.0–27.4). Concurrent malaria infection and arboviral exposure were observed in 40.4% (95%CI: 35.6–45.4) of participants. No sociodemographic factors were significantly associated with co-exposure in the multivariable analysis. Conclusions: Substantial co-exposure of malaria and multiple arboviruses occurs in this remote Congo Basin setting. Integrated surveillance and improved diagnostics are urgently needed to guide febrile illness management and preparedness in under-resourced regions. Full article

Green communities play a critical role in advancing sustainable development; however, evaluating their performance and identifying appropriate improvement strategies remain challenging due to uncertain, incomplete, and multidimensional information. This study formalizes three key processes essential to green community governance—sustainability evaluation, attribute reduction, and decision-rule generation—and proposes a rough set-based decision framework that integrates quantitative indicators, expert knowledge, and rule-based reasoning. Using empirical assessment data from Nantou County, the framework identifies the most influential determinants of community performance, including accessibility-related facilities, remote-area status, and socioeconomic conditions. The results reveal clear drivers of sustainable community performance. Remote villages lacking community hubs face structural barriers to participation. Communities without facilities supporting vulnerable groups tend to stall at the registration stage, while bronze-level villages require equity-focused engagement despite possessing stronger resource endowments. Notably, silver-level performance is consistently associated with moderate income levels and moderate income disparity, underscoring socioeconomic balance—rather than economic extremes—as a key precondition for stable sustainability advancement. Together, these findings provide interpretable, evidence-based guidance for policymakers and community managers to identify performance gaps and allocate resources more effectively. Full article

This study introduces a novel Quantum-Inspired Hybrid Bald Eagle-Ukari Algorithm with Reinforcement Learning (QI-HBEUA-RL) for comprehensive optimization of conical solar distillers equipped with sand-filled copper conical fins. The proposed algorithm synergistically combines quantum computing principles (superposition and entanglement), bio-inspired metaheuristics (Bald Eagle Search and Ukari Algorithm), and reinforcement learning mechanisms to achieve unprecedented optimization performance in complex thermal-hydraulic systems. The QI-HBEUA-RL framework employs quantum-encoded population representation, enabling simultaneous exploration of multiple solution states, while reinforcement learning dynamically adjusts algorithmic parameters based on search landscape characteristics and historical performance data. Experimental validation tested seven distiller configurations in El-Oued, Algeria, under controlled conditions (7.85 kWh/m²/day solar radiation, 42.2 °C ambient temperature). The optimal configuration of copper conical fins with 14 g sand at 0 cm spacing achieved: daily productivity of 7.75 L/m²/day (+61.46% improvement over conventional design), thermal efficiency of 61.9%, exergy efficiency of 4.02%, and economic payback period of 5.8 days. Comprehensive algorithm comparison against six state-of-the-art multi-objective optimizers (NSGA-II, MOEA/D, MOPSO, MOGWO, MOHHO) across 30 independent runs demonstrated statistically significant superiority (p < 0.001, Wilcoxon test). QI-HBEUA-RL achieved 7.42% improvement in hypervolume indicator, 29.35% reduction in inverted generational distance, and 19.49% better solution spacing. Generalization validation on seven benchmark problems (ZDT1-6, DTLZ2, DTLZ7) and three renewable energy applications confirmed algorithm robustness across diverse problem types. Three real-world case studies, remote village water supply (238:1 benefit–cost), industrial facility (100% energy reduction), and emergency relief (740× cost savings) validate practical implementation viability. This research advances solar thermal desalination technology and multi-objective optimization methodologies, providing validated solutions for sustainable freshwater production in water-scarce regions. Full article

Urban sustainability depends on rural hinterlands, yet national-scale evaluation and AI screening often rely on urban-centric proxies, which can under-recognize remote villages where the evidence base is sparse. Using China’s national honored-village programme (N = 24,450) as a case, we examine how recognition patterns change when data availability and observability are unequal across regions, with a focus on the Qinghai–Tibetan Plateau (QTP), where 923 honored villages account for only 3.78% of the national total. We interpret urban-centric proxy reliance as the tendency for recognition patterns to correlate with urban-linked observability signals (e.g., nighttime lights). In this study, discovery lag refers to situations where villages exhibit characteristics similar to historically recognized villages but remain unrecognized under the current honor regime due to uneven data availability and observability. Methodologically, we build a scene-aware predictive framework that integrates multi-source geospatial indicators and explicitly handles extreme imbalance and environmental heterogeneity to estimate recognition likelihood under the current honor regime, treating national honor lists as administratively produced recognition outcomes rather than objective measures of village value. The model highlights four high-probability nomination belts on the QTP and reveals a pronounced DEM–NTL decoupling: the median NTL of currently honored QTP villages is 0, suggesting that NTL-based urban proxies can fail in high-altitude, data-scarce contexts. Overall, the observed under-representation is consistent with uneven observability and institutional constraints within the current honor system, and the proposed framework provides a scalable diagnostic and screening tool for identifying villages with high predicted recognition likelihood and supporting more evidence-aware rural data collection. Full article

As a unique cultural heritage of Chinese ethnic minorities, Dong architecture provides rich historical and cultural information. Rapid and accurate extraction of ethnic building information from remote sensing images in complex terrain and high-density settlement environments is highly important for the protection of architectural heritage and the management of rural space. Huanggang Dong Village in Liping County, Guizhou Province, China, is taken as a case study. This paper develops a multifeature fusion machine learning framework for the automatic recognition of Dong ethnic architecture based on centimeter-level visible images captured by UAV. First, the vegetation index, HSI color features and texture features based on the gray level co-occurrence matrix are extracted from the UAV visible light orthophoto image. Through the random forest feature importance ranking and correlation test, six key features, namely, the VDVI, HSI-S, HSI-I, mean, variance and contrast, are selected to construct a multifeature space. This step constitutes the feature construction stage of the proposed methodology and provides the basis for subsequent classification. Second, on the basis of a support vector machine (SVM) and random forest (RF), classification models are constructed. The effects of different feature combinations and different algorithms on classification accuracy are systematically compared, and the results are evaluated in terms of overall accuracy (OA), the kappa coefficient, user accuracy (UA) and producer accuracy (PA). This second part highlights the classification phase of the methodology, which tests the feature space using different algorithms and evaluates the performance of the models. The experimental data fully show that under the condition of a single feature, the SVM model dominated by texture features performs best, with an OA of 85.33% and a kappa of 0.799; under the condition of multifeature fusion, the RF algorithm has a stronger ability to integrate multisource features. The accuracy of building category recognition based on the total feature and dimensionality reduction feature space is particularly prominent. The total feature and overall accuracy reach 89.00%, and the kappa coefficient is 0.850. The UA and PA reached 89.66% and 94.55%, respectively. Through in-depth comparative analysis, the vegetation index–color–texture multifeature fusion and machine learning classification framework based on UAV visible light images can achieve high-precision extraction of Dong architecture without relying on high-cost sensors. It can effectively alleviate the confusion between water bodies and shadows and between dark roofs and vegetation and effectively separate traditional Dong architecture from roads, vegetation and other elements. It provides a low-cost and feasible way for digital archiving, dynamic monitoring and protection management of the traditional village architectural heritage of ethnic minorities. Full article

In the digital era, even the most remote communities are increasingly connected to global networks. However, a critical question persists: how can such connectivity translate into tangible economic growth and sustainable development for isolated mountainous villages? Guided by the sustainable livelihood framework, this study investigates how digital capital—specifically the use of social media to showcase a village’s natural and cultural assets—drives tourism development and improves local livelihoods. Focusing on Dazhai Village in China, a rural community that gained substantial online attention and tourism inflow through social media promotion, this research employs qualitative methods, including 17 semi-structured interviews. Data were analysed using thematic analysis and matrix coding techniques via NVivo 12 Plus. Findings reveal that the introduction of digital capital enhances village visibility, stimulates tourist interest, and initiates a development trajectory describe as “going live.” In contrast, “going alive” refers to the process of revitalizing a once abandoned, impoverished mountain village, enabling it to survive and thrive once more. However, the sustainability of this trajectory is fragile as the departure of influential digital promoters can deplete digital capital, undermining diminishing online engagement and risking renewed marginalization. To transform “going live” into “going alive,” remote communities must continuously adapt and reinforce their online presence to secure long-term stakeholders’ engagement and resilient tourism flows. An interesting finding of this study is that the village achieved regenerative tourism, whereby its environmental conditions improved as a result of tourism development. This unexpected outcome was facilitated by sustained visibility, both online and offline, which prompted residents to place greater emphasis on environmental protection. This study enriches the sustainable livelihoods framework by integrating digital capital and regenerative tourism into the understanding of livelihood assets and outcomes in remote settings. Ultimately, it underscores the transformative potential of digital capital in revitalizing “hollowed-out” villages, offering a strategic pathway for remote communities to reclaim their developmental agency and achieve sustainable rural revitalization. Full article

Effective management zone delineation is key to implementing site-specific strategies that address spatiotemporal heterogeneity in agriculture. Although time-series remote sensing offers a dynamic perspective, most current methods lack the framework to integrate it with soil properties, thereby hindering accurate characterization of crop growth variability. This study bridges the gap by developing a spatiotemporal framework that synthesizes remote sensing-derived phenology and soil attributes for daylily management zoning. Through a sequential approach—phenological metric extraction, SNIC-based segmentation, and STSF classification—we produce refined phenological time-series stacks. These outputs are designed to elucidate the drivers of field heterogeneity and directly inform precision management strategies. Compared to pixel-based and SNIC-based random forest, the STSF–SNIC framework increased spatial overlap rates by 5.4–8.0% (reaching 88.6%), despite comparable overall accuracy and kappa coefficients (OA/kappa: 92–94%). Geographical detector analysis identified village boundaries, soil type, total nitrogen, and organic carbon as key drivers of spatial patterns. A spatial generalized fuzzy c-means model, incorporating crop growth dynamics and soil gradients, reduced management zone fragmentation by 27.8% compared to conventional methods, with spatial autocorrelation analysis confirming enhanced spatial consistency (Moran’s I = 0.600 vs. 0.433, p < 0.001). In conclusion, by integrating time-series remote sensing phenology with soil attribute analysis within a spatially constrained clustering scheme, this study (1) provides a novel method for delineating coherent management zones, (2) reveals key drivers of crop growth heterogeneity, and (3) demonstrates a transferable pathway for translating satellite data into precision management actions. It thereby exemplifies the value of applied remote sensing in addressing practical challenges in sustainable agriculture. Full article

Tourism-driven villages in metropolitan suburbs have become crucial spaces for the interaction of urban and rural factors; however, the spatiotemporal patterns and underlying mechanisms of land use change in such contexts remain inadequately explored. This study takes Huangshandian Village in Beijing, China, as a case study, utilizing remote sensing interpretation, ArcGIS 10.8 spatial analysis, and land use transition matrix methodology to examine tourism-driven land use change from 2008 to 2021. The findings reveal three development stages: initial development, rapid expansion, and integration and upgrading. Tourism development has greatly increased the proportion of tourism-related land, diversified land use structure, and shifted land functions from agriculture to tourism and services. Under urban–rural interaction, power-driven (local governments), resource-driven (village collectives and villagers), and capital-driven (enterprises, entrepreneurs, tourists) actors have jointly reshaped land use through the circulation and integration of key resources. This study reveals the mechanisms of tourism-driven rural land use transformation and provides theoretical and practical insights for land planning and sustainable rural tourism development in metropolitan suburban areas. Full article

The 2023 Al Haouz earthquake (Mw 6.7–6.9) is the strongest quake ever recorded in modern Morocco and ranks among North Africa’s most significant seismic events of the century. It struck the High Atlas region, causing widespread land changes, thousands of landslides, destruction in remote mountain villages, and heavy losses of life and cultural heritage. The earthquake not only had immediate humanitarian and economic effects but also dramatically transformed the landscape, uncovered new geological features, and reshaped the region—providing a unique opportunity to study seismic activity as geoheritage. Researchers have begun systematically documenting how this earthquake affected perceptions of seismic hazards in the High Atlas area. Although often considered a dark geoheritage, the event holds valuable lessons that can inform programs to strengthen resilience to geohazards. This research places the 2023 Al Haouz earthquake in a geoheritage context, underscoring its scientific, educational, and cultural importance. By analyzing how the earthquake altered the terrain, exposed tectonic activity, and left lasting geological marks, this work aims to bridge the gap between the high scientific interest in seismic events and their limited roles in geoheritage, conservation, tourism, and education. Full article

Urban villages (UVs) are widespread in rapidly urbanizing regions, but their fine-grained delineation from high-resolution remote sensing imagery remains a challenge due to complex spatial textures and ambiguous boundaries. To address this issue, this paper proposes TransUV, a TransNeXt-based encoder–decoder segmentation framework tailored to UV extraction. At the encoder front end, a Multi-level Feature Enhancement Module (MFEM) injects boundary- and texture-aware inductive bias by combining Laplacian-of-Gaussian (LoG) filtering with Gaussian smoothing, which strengthens edge responses while suppressing noise. At the decoder stage, we design a lightweight SegUV decoder equipped with an Advanced Attention Fusion Module (AAFM) that adaptively fuses multi-scale features using complementary channel, spatial, and directional attention. Experiments on 0.5 m imagery from two Chinese cities demonstrate that TransUV achieves an mIoU of 86.67% and an overall accuracy of 92.98%, significantly outperforming other mainstream models. Full article