Search Results (64)

Watershed ecosystem resilience (RES) plays a vital role in supporting ecosystem sustainability. However, comprehensive assessments and investigations into the complex mechanisms driving RES remain limited, particularly in ecologically sensitive basins. To address this gap, this study proposes a multidimensional RES evaluation framework tailored to watershed-specific natural characteristics. The framework integrates five core dimensions: ecosystem resistance, ecosystem recovery capacity, ecosystem adaptability, ecosystem services, and ecosystem vitality. RES patterns under 2030 different future scenarios were simulated using the PLUS model combined with CMIP6 climate projections. Spatial and temporal dynamics of RES from 2010 to 2020 were quantified using Geodetector and Partial Least Squares Path Modeling, offering insights into the interactions among natural and anthropogenic drivers. The results reveal that RES in the Upper Yangtze River Basin exhibits a spatial gradient of “high in the east and west, low in the middle” with an overall 2.80% decline during the study period. Vegetation coverage and temperature emerged as dominant natural drivers, while land use change exerted significant indirect effects by altering ecological processes. This study emphasizes the importance of integrated land-climate strategies and offers valuable guidance for enhancing RES and supporting sustainable watershed management in the context of global environmental change. Full article

The spatial distribution of ephemeral and perennial dryland plant species is increasingly modified and restricted by ever-changing climates and development expansion. At the interface of biodiversity conservation and developmental planning in desert landscapes is the growing need for adaptable tools in identifying and monitoring these ecologically fragile plant assemblages, habitats, and, often, heritage sites. This study evaluates usage of Sentinel-2 time series composite imagery to discriminate vegetation assemblages in a hyper-arid landscape. Spatial predictor spaces were compared to classify different vegetation communities: spectral components (PCs), vegetation indices (VIs), and their combination. Further, the uncertainty in discriminating field-verified vegetation assemblages is assessed using Shannon entropy and intensity analysis. Lastly, the intensity analysis helped to decipher and quantify class transitions between maps from different spatial predictors. We mapped plant assemblages in 2022 from combined PCs and VIs at an overall accuracy of 82.71% (95% CI: 81.08, 84.28). A high overall accuracy did not directly translate to high class prediction probabilities. Prediction by spectral components, with comparably lower accuracy (80.32, 95% CI: 78.60, 81.96), showed lower class uncertainty. Class disagreement or transition between classification models was mainly contributed by class exchange (a component of spatial allocation) and less so from quantity disagreement. Different artefacts of vegetation classes are associated with the predictor space—spectral components versus vegetation indices. This study contributes insights into using feature extraction (VIs) versus feature selection (PCs) for pixel-based classification of plant assemblages. Emphasising the ecologically sensitive vegetation in desert landscapes, the study contributes uncertainty considerations in translating optical satellite imagery to vegetation maps of arid landscapes. These are perceived to inform and support vegetation map creation and interpretation for operational management and conservation of plant biodiversity and habitats in such landscapes. Full article

Stand structural configuration dictates ecosystem functional performance. Mangrove ecosystems, located in ecologically sensitive coastal ecotones, require efficient acquisition of stand structure parameters and health assessments based on these parameters for practical applications. Effective assessment of mangrove ecosystem health, crucial for their functional performance in ecologically sensitive coastal ecotones, relies on efficient acquisition of stand structure parameters. This study developed a UAV (Unmanned Aerial Vehicle)-based framework for mangrove health evaluation integrating stand structure parameters, utilizing UAV visible-light imagery, field plot surveys, and computer vision techniques, and applied it to the assessment of a national nature reserve. We obtained the following results: (1) A deep neural network, combining UAV visible-light data with tree height constraints, achieved 88.29% overall accuracy in simultaneously identifying six dominant mangrove species; (2) Stand structure parameters were derived based on individual tree extraction results in seedling zones along forest edges (with canopy individual tree segmentation accuracy ≥ 78.57%), and a stand health evaluation model was constructed; (3) Health assessment revealed that the core zone exhibited significantly superior stand health compared to non-core zones. This method demonstrates high efficiency, significantly reducing the time and effort for monitoring, and offers robust support for future mangrove forest health assessments and adaptive conservation strategies. Full article

Nature-Based Solutions (NBSs) offer promising pathways to enhance ecological resilience and address urban water challenges, particularly in heritage cities where conventional gray infrastructure often fails to balance environmental needs with cultural preservation. This study proposes a strategic framework for the integration of NBSs into historic urban landscapes by employing Internal–External (IE) matrix modeling and an impact–uncertainty assessment, grounded in a structured evaluation of key internal strengths and weaknesses, as well as external opportunities and threats. The Internal Factor Evaluation (IFE) score of 2.900 indicates a favorable internal environment, characterized by the multifunctionality of NBS and their ability to reconnect urban populations with nature. Meanwhile, the External Factor Evaluation (EFE) score of 2.797 highlights moderate support from policy and public awareness but identifies barriers such as funding shortages and interdisciplinary coordination. Based on these findings, two strategies are developed: an SO (Strength–Opportunity) strategy, promoting community-centered and policy-driven NBS design, and a WO (Weakness–Opportunity) strategy, targeting resource optimization through legal support and cross-sectoral collaboration. This study breaks new ground by transforming theoretical NBS concepts into actionable, culturally sensitive planning tools that enable decision-makers to navigate the unique challenges of implementing adaptive stormwater and environmental management in historically constrained urban environments. Full article

Wildfires are becoming an increasingly severe threat to European forests, driven by climate change, land use changes, and socio-economic factors. Integrated solutions for wildfire prevention, early detection, emergency management, and ecological restoration are urgently needed to enhance forest resilience. The Horizon 2020 SILVANUS project developed a comprehensive multi-sectoral platform combining technological innovation, stakeholder engagement, and sustainable forest management strategies. This report analyses the Slovak Republic’s participation in SILVANUS, applying a seven-criterion fit–gap framework (governance, legal, interoperability, staff capacity, ecological suitability, financial feasibility, and stakeholder acceptance) to evaluate the platform’s alignment with national conditions. Notable contributions include stakeholder-supported functional requirements for wildfire prevention, climate-sensitive forest models for long-term adaptation planning, IoT- and UAV-based early fire detection technologies, and decision support systems (DSS) for emergency response and forest-restoration activities. The Slovak pilot sites, particularly in the Podpoľanie region, served as important testbeds for the validation of these tools under real-world conditions. All SILVANUS modules scored ≥12/14 in the fit–gap assessment; early deployment reduced high-risk fuel polygons by 23%, increased stand-level structural diversity by 12%, and raised the national Sustainable Forest Management index by four points. Integrating SILVANUS outcomes into national forestry practices would enable better wildfire risk assessment, improved resilience planning, and more effective public engagement in wildfire management. Opportunities for adoption include capacity-building initiatives, technological deployments in fire-prone areas, and the incorporation of DSS outputs into strategic forest planning. Potential challenges, such as technological investment costs, inter-agency coordination, and public acceptance, are also discussed. Overall, the Slovak Republic’s engagement with SILVANUS demonstrates the value of participatory, technology-driven approaches to sustainable wildfire management and offers a replicable model for other European regions facing similar challenges. Full article

Regulating lamprey populations is crucial for maintaining ecological equilibrium. However, the unique sex determination process of lampreys is constrained by multiple factors, complicating intuitive analysis of population dynamics and their impact on the natural environment. This study employed a two-species competition mechanism to elucidate the factors influencing sex ratios and their mechanistic effects on lamprey population size. Using the Lotka–Volterra equations, we investigated how sex ratios affect trophic levels both upstream and downstream of lampreys in the food web. A logistic population growth model was applied to assess the impact of sex ratio variations on symbiotic parasitic species, while the Analytic Hierarchy Process (AHP) was utilized to explore the dynamic relationship between sex ratio changes and ecosystem stability. To validate model efficacy, we manipulated temperature and food availability under controlled disturbance conditions, analyzing temporal variations in lamprey population size across different disturbance intensities to evaluate model sensitivity. The findings indicate that the variable sex ratio’s benefit is in facilitating the lampreys’ population’s enhanced adaptation to environmental shifts. The coexisting species exhibit a similar pattern of population alteration as the lampreys, albeit with a minor delay. A definitive link between the quantity of lampreys and the parasitic species is absent. A male ratio of 0.6 optimally contributes to the ecosystem’s equilibrium. Over time, the configuration of our model’s parameters proves to be sensible. This research provides robust theoretical support for developing scientific strategies to regulate lamprey populations. Full article

In recent years, the increasing frequency and intensity of climate-related disasters have underscored the urgent need for resilient urban development. In cold-region cities, low temperatures pose a distinct and underexplored threat, with serious implications for human well-being, infrastructure performance, and ecological stability. Despite growing attention to climate resilience, existing urban risk assessments have largely focused on heatwaves and flooding, leaving a notable gap in research on cold-weather vulnerability. To address this gap, this study develops a fine-scale cold-climate vulnerability assessment framework grounded in the widely recognized “Exposure–Sensitivity–Adaptive Capacity” (ESA) model. Using subdistricts as the basic units of analysis, we integrate multi-source spatial data—including demographics, built environment, services, and ecological indicators—to construct a comprehensive evaluation system tailored to low-temperature conditions. The model is applied to the central urban area of Harbin, China, a representative cold-region city. The results reveal distinct spatial disparities in vulnerability: older urban districts exhibit higher vulnerability due to high population density and inadequate public services, while newly developed areas show relatively greater adaptive capacity. Further analysis identifies key drivers of vulnerability in different zones. Based on these insights, the study proposes differentiated, subdistrict-level planning strategies aimed at reducing exposure, mitigating sensitivity, and enhancing adaptive capacity. By extending the ESA model to cold-climate scenarios and operationalizing it at the subdistrict scale, this research contributes both methodologically and practically to the field of urban climate resilience. The findings offer actionable strategies for policymakers and provide a replicable framework applicable to other cold-region cities facing similar challenges. Full article

Mine ecological restoration is a critical process for promoting the sustainable development of resource-dependent regions, yet existing monitoring methods remain limited in accuracy and adaptability. To address challenges such as small-object recognition, insufficient multi-scale feature fusion, and blurred boundaries in UAV-based remote sensing imagery, this paper proposes an enhanced semantic segmentation model based on Segformer. Specifically, a multi-scale feature-enhanced feature pyramid network (MSFE-FPN) is introduced between the encoder and decoder to strengthen cross-level feature interaction. Additionally, a selective feature aggregation pyramid pooling module (SFA-PPM) is integrated into the deepest feature layer to improve global semantic perception, while an efficient local attention (ELA) module is embedded into lateral connections to enhance sensitivity to edge structures and small-scale targets. A high-resolution UAV image dataset, named the HUNAN Mine UAV Dataset (HNMUD), is constructed to evaluate model performance, and further validation is conducted on the public Aeroscapes dataset. Experimental results demonstrated that the proposed method exhibited strong performance in terms of segmentation accuracy and generalization ability, effectively supporting the image analysis needs of mine restoration scenes. Full article

Surface urban heat island (SUHI) effects are intensifying in arid desert cities due to rapid urban expansion, limited vegetation, and increasing impervious and barren land surfaces. This leads to serious ecological and socio-environmental challenges in cities. This study investigates the relationship between landscape composition and land surface temperature (LST) in Phoenix and Tucson, two rapidly growing cities located in the Sonoran Desert of the southwestern United States. Landsat-9 OLI-2/TIRS-2 satellite imagery was used to derive the LST value and calculate spectral indices. A multi-resolution grid-based approach was applied to assess spatial correlations between land cover and mean LST across varying spatial scales. The strongest positive correlations were observed with barren land, followed by impervious surfaces, while green space showed a negative correlation. Furthermore, the Urban Thermal Field Variation Index (UTFVI) and the Ecological Evaluation Index (EEI) assessments indicated that over one-third of both cities are exposed to strong SUHI effects and poor ecological quality. The findings highlight the critical need for ecologically sensitive urban planning, emphasizing the importance of the morphological structure of cities, the necessity of planning holistic blue–green infrastructure systems, and the importance of reducing impervious surfaces to decrease LST, mitigate SUHI and SUHI impacts, and increase urban resilience in desert environments. These results provide evidence-based guidance for landscape planning and climate adaptation in hyper-arid urban environments. Full article

Background/Objectives: Traditional nutritional knowledge, shaped by centuries of cultural and ecological adaptation, offers holistic and sustainable dietary frameworks that remain highly relevant to modern health challenges. However, current digital nutrition platforms often fail to reflect this diversity, relying instead on standardized models with limited cultural sensitivity. This paper aims to explore how traditional nutritional wisdom can be integrated into digital health platforms to promote more inclusive and effective approaches to personalized nutrition. Methods: This perspective paper employs a cultural adaptation framework to analyze the integration of traditional food knowledge into digital contexts. Drawing from interdisciplinary research across nutrition science, anthropology, digital health and implementation science, we utilize the Knowledge-to-Action (KTA) Framework and the PEN-3 Cultural Model to structure our analysis. A systematic scoping review of literature published between 2010 and 2025 was conducted to identify integration challenges and opportunities. Additionally, we analyzed case studies of three traditional dietary systems (Argentina, Italy and Japan) and evaluated five leading digital nutrition platforms for their degree of cultural inclusivity, using qualitative comparative methods. Results: The analysis highlights significant challenges in adapting traditional knowledge to digital formats, including standardization barriers, contextual loss and technological limitations. However, successful integration initiatives demonstrate that through participatory design, flexible data architectures and culturally-informed algorithms, traditional food systems can be meaningfully represented. Our proposed four-phase integration framework—documentation, digital adaptation, implementation and evaluation—provides a structured approach for developers and researchers. Conclusions: Bridging traditional nutrition with digital platforms represents a vital opportunity to enhance personalization and preserve food heritage while improving health outcomes for diverse populations. This integration requires interdisciplinary collaboration, user-centered design processes and ethical approaches that respect cultural ownership and context. Full article

Accurate prediction of individual tree mortality in Pinus yunnanensis Franch. is essential for sustainable forest management and ecological monitoring in southwest China. The aim of this study is to develop a tree mortality prediction model for Pinus yunnanensis based on resurvey data from the Cangshan area in Dali, Yunnan Province, using a stacked ensemble learning algorithm. After an initial evaluation of model performance, the classification thresholds were optimized using the Minimum Classification Error method, the Maximum Sensitivity and Specificity method, the Kappa coefficient method, and the Precision-Recall (PR) curve method to enhance classification results. The findings show that, compared to traditional statistical methods and individual machine learning models, the stacked ensemble learning model (Stacked-RSX) outperforms others in tree mortality classification tasks, which achieved an accuracy of 0.8947, recall of 0.9431, true negative rate of 0.9490, misclassification rate of 0.2289, and an area under the curve of 0.953. Through an exhaustive search for the best classification thresholds, the PR curve method demonstrated good adaptability across all models. All optimal thresholds, relative to the default threshold, significantly improved overall classification performance. Furthermore, feature importance analysis revealed that tree height, diameter at breast height (DBH), Hegyi competition index, and the ratio of DBH to stand basal area are key variables influencing mortality risk. These results indicate that the stacking ensemble learning algorithm effectively analyzes the complex relationships among different factors, significantly improving the prediction accuracy of tree mortality, and providing scientific insights for the management and health monitoring of Pinus yunnanensis forests. Full article

Oak forests are an important part of temperate European ecosystems, where they are actively improving biodiversity, carbon storage, and ecological stability. However, current concerns such as climatic changes, and especially rising temperatures and changing precipitation patterns, are impacting their resilience. In this context, our study intends to evaluate the impact of climatic variability on temperate oak forests, focusing on the influence of temperature and precipitation. This covers different sites that have different environmental conditions. By using both a bibliometric approach and a systematic analysis of publications that have studied the influence of climate change on oak forests, our study has identified specific species and site responses to climate stressors. Furthermore, we have also evaluated trends in drought sensitivity. All these aspects have allowed us to understand and suggest improvements for the impact of climate change on the resilience and productivity of oak ecosystems. We have analyzed a total number of 346 publications that target the impact of climate change on oak forests. The articles were published between 1976 and 2024, with the majority originating from the USA, Spain, Germany, and France. These studies were published in leading journals from Forestry, Environmental Sciences, and Plant Sciences, among which the most cited journals were Forest Ecology and Management, the Journal of Biogeography, and Global Change Biology. As for the keywords, the most frequent ones were climate change, drought, growth, forest, and oak. However, we have observed a trend towards drought sensitivity, which indicates the intensification of climate changes on oak ecosystems. Moreover, this trend was more present in central and southern regions, which further highlights the impact of regional conditions. As such, certain local factors (soil properties, microclimate) were also taken into account in our study. Our literature review focused on the following aspects: Oak species affected by climate change; Impact of drought on oak forests; Influence of climate change on mixed forests containing oaks; Effects of climate change on other components of oak ecosystems; Radial growth of oaks in response to climate change; Decline of oak forests due to climate change. Our results indicate that oak forests decline in a process caused by multiple factors, with climate change being both a stressor and a catalyst. Across the globe, increasing temperatures and declining precipitation affect these ecosystems in their growth, functions, and resistance to pathogens. This can only lead to an increased forest decline. As such, our results indicate the need to implement forest management plans that take into account local conditions, species, and climate sensitivity. This approach is crucial in improving the adaptivity of oak forests and mitigating the impact of future climate extremes. Full article

Balancing ecological conservation with development pressures remains a critical challenge in regions hosting mega-events like the Winter Olympics. This study evaluates the ecological impacts of pre-Olympic construction in Chongli, China (2016–2021), through the integrated analysis of ecosystem service value (ESV) and landscape ecological risk (LERI). Using Sentinel-2 imagery and spatial statistics, we quantified land-use changes, applied benefit transfer methods for ESV assessment, and calculated the LERI using landscape pattern indices. The results revealed a 4.6% increase in the total ESV (266.4 to 278.7 million CNY), which was driven by afforestation initiatives that expanded the area of shrub-grassland and forests. Concurrently, the proportion of high/moderate LERI areas decreased by 12.3%, indicating reduced ecological vulnerability. Spatial correlation analysis demonstrated significant negative relationships between the ESV and LERI, particularly in zones that were undergoing ecological restoration. However, urban expansion weakened these synergies locally. The findings of this study highlight that strategic greening effectively enhanced ecosystem services while mitigating landscape risks during preparations for the Olympics. We propose an adaptive zoning framework that emphasizes dynamic ESV-LERI monitoring, tourism carrying capacity regulation, and payment for ecosystem service mechanisms to optimize post-event management. This integrated approach provides a transferable model for ecological governance in ecologically sensitive areas facing rapid development pressures, demonstrating the value of dual assessment methodologies in sustainable spatial planning. Full article

Intelligent algorithms and decision models are key tools for improving the efficiency and adaptability of multi-objective optimization and allocation, and for achieving sustainable utilization of water resources. This study takes Hancheng City as a case study to develop a water resource optimization allocation model based on economic, social, and ecological benefits, analyzing and predicting the supply and demand of conventional and unconventional water resources in the study area. The model is solved using the NSGA algorithm, and solutions are screened from the Pareto front using the TOPSIS-CCDM two-level decision model, with the RSR method used for comparative verification. The results show that the schemes II-2022-21 (water shortage of 17,802.35 m³/d, economic benefits of 21,019,556.17 yuan, pollutant emissions of 745.92 tons), II-2027-ACS (shortage of 14,098.76 m³/d, economic benefits of 29,401,252.75 yuan, emissions of 712.07 tons), and II-2032-ACS (shortage of 12,709.33 m³/d, economic benefits of 36,660,367.83 yuan, emissions of 700.96 tons) are in line with the water resource allocation planning for Hancheng City before 2035. These schemes not only meet the regional planning requirements but also maximize economic benefits while minimizing water shortages and pollutant emissions. The study finds that NSGA-II has an advantage in selecting more coordinated schemes, while NSGA-III focuses more on the selectivity of specific targets. Although the TOPSIS-CCDM model performs well in comprehensive evaluation, it also exposes limitations such as sensitivity to data fluctuations and high computational complexity. By developing and applying advanced optimization and decision models, this study provides a scientific water resource allocation scheme for Hancheng City, supporting the sustainable management of regional water resources, and offering a reference for future research in addressing data uncertainties and improving computational efficiency. Full article

Rapid urbanisation in Sub-Saharan Africa demands innovative land management strategies that promote sustainable, inclusive, and resilient urban development. This study investigates the potential of land readjustment (LR) to foster neighbourhood-scale social–ecological urban resilience (SEUR) through a case study of the Nunga LR project in Kigali, Rwanda. Grounded in the social–ecological system (SES) theory and operationalised through the social–ecological land readjustment model for resilient neighbourhoods, the research evaluates LR practices against an integrated benchmark framework combining LR aspects, neighbourhood design standards, and resilience attributes. The study uses secondary data, project shapefiles, and key informant interviews to assess how Rwanda’s emerging LR model contributes to resilient neighbourhood development. Findings demonstrate strong community mobilisation and adaptive governance capacity. However, critical resilience dimensions—including modularity, green infrastructure integration, land-use diversity, and adaptive feedback mechanisms—were only partially operationalised. Consequently, while LR improved spatial formalisation and basic infrastructure provision, it fell short of creating a truly resilient, multifunctional neighbourhood ecosystem. These findings highlight the need to reframe LR from a purely technical land management tool into a systemic resilience-building mechanism. Policy recommendations include mandating green/blue infrastructure in LR plans, establishing innovative financing mechanisms, institutionalising adaptive monitoring, strengthening affordability safeguards, and promoting multifunctional spatial layouts. The study contributes to urban resilience and land governance scholarship by offering a context-sensitive, empirically tested model for integrating SEUR principles into LR practice in rapidly urbanising African cities. Future research should pursue longitudinal analyses and dynamics modelling of land readjustment impacts to deepen understanding of urban resilience pathways in the Global South. Full article