Search Results (182)

Accurate estimation of actual evapotranspiration (ET) is critical for understanding hydrothermal cycles and ecosystem functioning in arid regions, where water scarcity governs ecological resilience. To address persistent gaps in ET quantification, this study integrates multi-source remote sensing data, energy balance modeling, and ground-based validation that significantly enhances spatiotemporal ET accuracy in the vulnerable desert steppe ecosystems. The study utilized meteorological data from several national stations and Landsat-8 imagery to process monthly remote sensing images in 2019. The Surface Energy Balance System (SEBS) model, chosen for its ability to estimate ET over large areas, was applied to derive modeled daily ET values, which were validated by a large-weighted lysimeter. It was shown that ET varied seasonally, peaking in July at 6.40 mm/day, and reaching a minimum value in winter with 1.83 mm/day in December. ET was significantly higher in southern regions compared to central and northern areas. SEBS-derived ET showed strong agreement with lysimeter measurements, with a mean relative error of 4.30%, which also consistently outperformed MOD16A2 ET products in accuracy. This spatial heterogeneity was driven by greater vegetation coverage and enhanced precipitation in the southeast. The steppe ET showed a strong positive correlation with surface temperatures and vegetation density. Moreover, the precipitation gradients and land use were primary controllers of spatial ET patterns. The process-based SEBS frameworks demonstrate dual functionality as resource-optimized computational platforms while enabling multi-scale quantification of ET spatiotemporal heterogeneity; it was therefore a reliable tool for ecohydrological assessments in an arid steppe, providing critical insights for water resource management and drought monitoring. Full article

Agriculture is the foundational industry for human survival, profoundly impacting economic, ecological, and social dimensions. In the face of global challenges such as rapid population growth, resource scarcity, and climate change, achieving technological innovation in agriculture and advancing smart farming have become increasingly critical. In recent years, deep learning and computer vision have developed rapidly. Key areas in computer vision—such as deep learning-based image processing, object detection, and multimodal fusion—are rapidly transforming traditional agricultural practices. Processes in agriculture, including planting planning, growth management, harvesting, and post-harvest handling, are shifting from experience-driven methods to digital and intelligent approaches. This paper systematically reviews applications of deep learning and computer vision in agricultural growth management over the past decade, categorizing them into four key areas: crop identification, grading and classification, disease monitoring, and weed detection. Additionally, we introduce classic methods and models in computer vision and deep learning, discussing approaches that utilize different types of visual information. Finally, we summarize current challenges and limitations of existing methods, providing insights for future research and promoting technological innovation in agriculture. Full article

The ecological drivers of wildlife crop-foraging behavior—whether as a compensatory response to natural resource scarcity or as opportunistic exploitation of anthropogenic food sources—remain poorly understood in human–wildlife conflict research. Traditional methodologies, which primarily rely on direct observation and morphological identification, have limitations in comprehensively quantifying wildlife dietary composition, particularly in accurately distinguishing between morphologically similar plant species and conducting precise quantitative analyses. This study utilized DNA metabarcoding technology (rbcL gene markers) to identify and quantify plant dietary components through fecal sample analysis, systematically investigating the dietary composition and patterns of agricultural resource utilization of wild rhesus macaques (Macaca mulatta) in human–wildlife interface zones of southwestern China. A total of 29 rhesus macaque fecal samples were analyzed (15 from spring and 14 from winter), identifying 142 plant genera, comprising 124 wild plant genera, and 18 crop genera. The results revealed distinct seasonal foraging patterns: crops accounted for 32.11% of the diet in winter compared to 7.66% in spring. Notably, rhesus macaques continued to consume crops even during spring when wild resources were relatively abundant, challenging the traditional hypothesis driven by resource scarcity and suggesting that crop-foraging behavior may reflect an opportunistic, facultative resource selection strategy. This study demonstrates the significant value of DNA metabarcoding technology in wildlife foraging behavior research, providing scientific evidence for understanding human–primate conflict ecology and developing effective management strategies. Full article

This work studies and analyzes the transition from a linear to a circular economy through wastewater treatment and resource recovery. As wastewater volumes grow, sustainable management becomes critical. This study highlights the reuse of treated effluent, beneficial sludge utilization, and energy generation via anaerobic digestion. Wastewater treatment plants should be envisioned as hubs for recovering water, materials, and energy, rather than disposal facilities. Emphasizing resource efficiency, the circular economy approach offers viable solutions to challenges related to resource scarcity, climate change, and ecological impact. Full article

In an era marked by accelerating ecological degradation and widening global inequalities, prevailing patterns of resource extraction and consumption are proving increasingly unsustainable. Driven by hyper-consumption and entrenched linear production models, the global economy continues to exert immense pressure on planetary systems. This communication article calls for a fundamental paradigm shift from the long-standing assumption of resource abundance to a framework of responsible scarcity. Drawing from recent data on material throughput, on the transgression of planetary boundaries, and on the structural and geopolitical disparities underlying global resource use, this article highlights the urgent need to realign natural resource governance with ecological limits and social justice. A conceptual framework is proposed to support this transition, grounded in principles of ecological constraint, functional sufficiency, equity, and long-term resilience. The article concludes by outlining a forward-thinking research and policy agenda aimed at fostering sustainable and just modes of resource utilization in the face of growing environmental and socio-economic challenges. Full article

Climate change, resource scarcity, and ecological degradation have become critical bottlenecks constraining socio-economic development. Basin cities serve as key nodes in China’s ecological security pattern, playing indispensable roles in ecological civilization construction. This study established an evaluation index system spanning five dimensions to assess the effectiveness of ecological civilization construction. This study employs the entropy-weighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Back-Propagation (BP) neural network methods to evaluate the level of ecological civilization construction in the Yellow River Basin from 2010 to 2022, to analyze its indicator weights, and to explore the spatio-temporal evolution characteristics of each city. The results demonstrate the following: (1) Although the ecological civilization construction level of cities in the Yellow River Basin shows a steady improvement, significant regional development disparities persist. (2) The upper reaches are primarily constrained by ecological fragility and economic underdevelopment. The middle reaches exhibit significant internal divergence, with provincial capitals leading yet demonstrating limited spillover effects on neighboring areas. The lower reaches face intense anthropogenic pressures, necessitating greater economic–ecological coordination. (3) Among the dimensions considered, Territorial Space and Eco-environmental Protection emerged as the two most influential dimensions contributing to performance differences. According to the ecological civilization construction performance and changing characteristics of the 48 cities, this study proposes differentiated optimization measures and coordinated development pathways to advance the implementation of the national strategy for ecological protection and high-quality development in the Yellow River Basin. Full article

Understanding the relationship between regional vegetation dynamics and water resources is essential for improving integrated vegetation–water management, enhancing ecosystem services, and advancing the sustainable development of ecological–economic–social systems. As China’s second largest river basin, the Yellow River Basin (YRB) is ecologically fragile and experiences severe water scarcity. Vegetation changes further intensify conflicts between water supply and demand. To investigate the evolution and interaction mechanisms between vegetation and water resources in the YRB, this study uses the InVEST model to simulate annual water yield (Wyield) from 1982 to 2020 and applies the Dimidiate Pixel Model (DPM) to estimate fractional vegetation cover (FVC). The Theil–Sen method is applied to quantify the spatiotemporal trends of Wyield and FVC. A pixel-based second-order partial correlation analysis is performed to clarify the intrinsic relationship between FVC and Wyield at the grid scale. The main conclusions are as follows: (1) During the statistical period (1982–2020), the multi-year average annual Wyield in the YRB was 73.15 mm. Interannual Wyield showed a clear fluctuating trend, with an initial decline followed by a subsequent increase. Wyield showed marked spatial heterogeneity, with high values in the southern upper reaches and low values in the Longzhong Loess Plateau and Hetao Plain. During the same period, about 68.74% of the basin experienced increasing Wyield, while declines were concentrated in the upper reaches. (2) The average FVC across the basin was 0.51, showing a significant increasing trend during the statistical period. The long-term average FVC showed significant spatial heterogeneity, with high values in the Fenwei Plain, Shanxi Basin, and Taihang Mountains, and low values in the Loess Plateau and Hetao Plain. Spatially, 68.74% of the basin exhibited significant increases in FVC, mainly in the middle and lower reaches, while decreases were mostly in the upper reaches. (3) Areas with significant FVC–Wyield correlations covered a small portion of the basin: trade-off regions made up 10.35% (mainly in the southern upper reaches), and synergistic areas accounted for 5.26% (mostly in the Hetao Plain and central Loess Plateau), both dominated by grasslands and croplands. Mechanistic analysis revealed spatiotemporal heterogeneity in FVC–Wyield relationships across the basin, influenced by both natural drivers and anthropogenic activities. This study systematically explores the patterns and interaction mechanisms of FVC and Wyield in the YRB, offering a theoretical basis for regional water management, ecological protection, and sustainable development. Full article

The transformative trajectory of urban development in the contemporary era has engendered a substantial escalation in construction waste generation, particularly in China, where it constitutes approximately 40% of the total solid waste stream. Traditional landfill disposal methodologies pose formidable ecological challenges, encompassing soil contamination, groundwater pollution, and significant greenhouse gas emissions. Furthermore, the unsustainable exploitation of natural sandstone resources undermines energy security and disrupts ecological balance. In response to these pressing issues, an array of scholars and researchers have embarked on an exploratory endeavor to devise innovative strategies for the valorization of construction waste. Among these strategies, the conversion of waste into recycled aggregates has emerged as a particularly promising pathway. However, the practical deployment of recycled aggregates within the construction industry is impeded by their inherent physico-mechanical properties, such as heightened water absorption capacity and diminished compressive strength. To surmount these obstacles, a multitude of enhancement techniques, spanning physical, chemical, and thermal treatments, have been devised and refined. This paper undertakes a comprehensive examination of the historical evolution, recycling methodologies, and enhancement strategies pertinent to recycled aggregates. It critically evaluates the efficacy, cost–benefit analyses, and environmental ramifications of these techniques, while elucidating the microstructural and physicochemical disparities between recycled and natural aggregates. Furthermore, it identifies pivotal research gaps and prospective avenues for future inquiry, underscoring the imperative for collaborative endeavors aimed at developing cost-effective and environmentally benign enhancement techniques that adhere to the stringent standards of contemporary construction practices, thereby addressing the intertwined challenges of waste management and resource scarcity. Full article

Understanding the spatiotemporal dynamics of vegetation net primary productivity (NPP) and its drivers is critical to sustainable land -carbon management, carbon-neutral development, and ecological restoration in fragile karst landscapes. This study proposes a Pearson Correlation—Deep Transformer (PCADT) model that integrates attention mechanisms and geospatial covariates to enhance NPP estimation accuracy in Guangxi, China—a global karst hotspot. Leveraging multisource remote sensing data (2015–2020), PCADT achieves 10.7% higher predictive accuracy (R² = 0.83 vs. conventional models) at 500 m resolution, thereby capturing the fine-scale heterogeneity essential for sustainability planning. The results reveal a significant annual NPP increase (4.14 gC·m⁻²·a⁻¹, p < 0.05), with eastern areas exhibiting higher baseline productivity (1129 gC·m⁻² in Wuzhou) but western regions showing steeper growth rates (5.2% vs. 2.1%). Vegetation carbon sequestration capacity, validated against MOD17A3HGF data (R² = 0.998), demonstrates spatial consistency (east > west), with forest-dominated Wuzhou contributing 6.5 TgC annually. Mechanistic analyses identify precipitation as the dominant climatic driver (partial r = 0.62, p < 0.01), surpassing temperature sensitivity, while bimodal NPP-altitude peaks (300 m and 900 m) and land -use transitions (e.g., forest-to-cropland conversions) explain 18.5% of NPP variability and reduce regional carbon stocks by 4593 tC. The PCADT framework offers a scalable solution for precision carbon management by emphasizing the role of anthropogenic interventions—such as afforestation—in alleviating climatic constraints. It advocates for spatially adaptive strategies to optimize water resource utilization, enhance forest conservation, and promote sustainable land -use transitions. By identifying areas where water -scarcity relief and targeted afforestation would yield the highest carbon returns, the PCADT framework directly supports SDG 13 (Climate Action) and SDG 15 (Life on Land), providing a strategic blueprint for sustainable development in water-limited karst regions globally. Full article

The arid and semi-arid regions of Northwest China, as major agricultural production zones, have long faced dual challenges: increasing water resource pressure and severe supply–demand imbalances caused by the expansion of cultivated land. The crop water footprint, an effective indicator for quantifying agricultural water use, plays a crucial role in supporting sustainable development in the region. This study adopted a multi-scale spatiotemporal analysis framework, combining the CROPWAT model with Geographic Information System (GIS) techniques to investigate the spatiotemporal evolution of crop water footprints in Northwest China from 2000 to 2020. The Logarithmic Mean Divisia Index (LMDI) model was used to analyze spatial variations in the driving forces. A multidimensional evaluation system—encompassing structural, economic, ecological, and sustainability dimensions—was established to comprehensively assess agricultural water resource utilization in the region. Results indicated that the crop water footprint in Northwest China followed a “decline-increase-decline” trend, it increased from 90.97 billion m³ in 2000 to a peak of 133.49 billion m³ in 2017, before declining to 129.30 billion m³ in 2020. The center of the crop water footprint gradually shifted northward—from northern Qinghai to southern Inner Mongolia—mainly due to rapid farmland expansion and increasing water consumption in northern areas. Policy and institutional effect, together with economic development effect, were identified as the primary drivers, contributing 49% in total. Although reliance on blue water has decreased, the region continues to experience moderate water pressure, with sustainable use achieved in only half of the study years. Water scarcity remains a pressing concern. This study offers a theoretical basis and policy recommendations to enhance water use efficiency, develop effective management strategies, and promote sustainable water resource utilization in Northwest China. Full article

Forest carbon sink (FCS) is essential for achieving carbon neutrality and supporting sustainable development in ecologically fragile, resource-based cities such as those on the Loess Plateau. Despite the success of national afforestation programs, economic valuations of FCS at the city level remain limited. This study develops an integrated framework combining carbon stock estimation, regional carbon pricing, and net present value (NPV)-based valuation. Using Shenmu City in Shaanxi Province as a case study, forest carbon stocks from 2010 to 2023 are estimated based on the 2006 IPCC Guidelines. Future stocks (2024–2060) are projected using the GM (1,1) model. A dynamic pricing mechanism with a government-guaranteed floor price is applied under three offset scenarios (5%, 10%, 15%). The results show that Shenmu’s forest carbon stock could reach 20.67 million tonnes of CO₂ by 2060, and under a 15% offset scenario, the peak NPV reaches CNY 4.02 billion. Higher offset ratios increase FCS value by 18–22%, reflecting the growing scarcity of carbon credits. The pricing model improves market stability and investor confidence. This study provides a replicable approach for carbon sink valuation in semi-arid areas and offers policy insights aligned with SDG 13 (Climate Action) and SDG 15 (Life on Land). Full article

Against the backdrop of global climate change and increasing ecological pressure, the refined monitoring of forest resources and accurate tree species identification have become essential tasks for sustainable forest management. Hyperspectral remote sensing, with its high spectral resolution, shows great promise in tree species classification. However, traditional methods face limitations in extracting joint spatial–spectral features, particularly in complex forest environments, due to the “curse of dimensionality” and the scarcity of labeled samples. To address these challenges, this study proposes a synergistic classification approach that combines the spatial feature extraction capabilities of deep learning with the generalization advantages of machine learning. Specifically, a 2D convolutional neural network (2DCNN) is integrated with a support vector machine (SVM) classifier to enhance classification accuracy and model robustness under limited sample conditions. Using UAV-based hyperspectral imagery collected from a typical plantation area in Fuzhou City, Jiangxi Province, and ground-truth data for labeling, a highly imbalanced sample split strategy (1:99) is adopted. The 2DCNN is further evaluated in conjunction with six classifiers—CatBoost, decision tree (DT), k-nearest neighbors (KNN), LightGBM, random forest (RF), and SVM—for comparison. The 2DCNN-SVM combination is identified as the optimal model. In the classification of Masson pine, Chinese fir, and eucalyptus, this method achieves an overall accuracy (OA) of 97.56%, average accuracy (AA) of 97.47%, and a Kappa coefficient of 0.9665, significantly outperforming traditional approaches. The results demonstrate that the 2DCNN-SVM model offers superior feature representation and generalization capabilities in high-dimensional, small-sample scenarios, markedly improving tree species classification accuracy in complex forest settings. This study validates the model’s potential for application in small-sample forest remote sensing and provides theoretical support and technical guidance for high-precision tree species identification and dynamic forest monitoring. Full article

Complex feedback mechanisms and interdependencies exist among the water–energy–food–ecosystems (WEFE) nexus. In water-scarce regions, fluctuations in the supply or demand of any single subsystem can destabilize the others, with water shortages intensifying conflicts among food production, energy consumption, and ecological sustainability. Balancing the synergies and trade-offs within the WEFE system is therefore essential for achieving sustainable development. This study adopts the natural–social water cycle as the core process and develops a coupled simulation model of the WEFE (CSM-WEFE) system, integrating food production, ecological water replenishment, and energy consumption associated with water supply and use. Based on three performance indices—reliability, coupling coordination degree, and equilibrium—a coordinated sustainable development index (CSD) is constructed to quantify the performance of WEFE system under different scenarios. An integrated evaluation framework combining the CSM-WEFE and the CSD index is then proposed to assess the sustainability of WEFE systems. The framework is applied to the Beijing–Tianjin–Hebei (BTH) region, a representative water-scarce area in China. Results reveal that the current balance between water supply and socio-economic demand in the BTH region relies heavily on excessive groundwater extraction and the appropriation of ecological water resources. Pursuing food security goals further exacerbates groundwater overexploitation and ecological degradation, thereby undermining system coordination. In contrast, limiting groundwater use improves ecological conditions but increases regional water scarcity and reduces food self-sufficiency. Even with the full operation of the South-to-North Water Diversion Project (Middle Route), the region still experiences a 16.4% water shortage. By integrating the CSM-WEFE model with the CSD evaluation approach, the proposed framework not only provides a robust tool for assessing WEFE system sustainability but also offers practical guidance for alleviating water shortages, enhancing food security, and improving ecological health in water-scarce regions. Full article

The scarcity of phosphorus resources and the excessive accumulation of phosphates in aquatic environments pose significant threats to ecological systems and human health, while traditional treatment methods often fail to achieve effective resource recovery and reuse. This study aims to develop an efficient method for phosphate removal and resource recycling through the modification of reed straw (MRS) by introducing amine groups. Key operational parameters such as packed bed height, flow velocity, and initial solute concentration were systematically investigated to optimize MRS’s adsorption efficiency. Experimental results demonstrated that under optimized conditions, MRS achieved a maximum phosphate adsorption capacity of 8.337 mg/g and maintained over 80% efficiency after nine adsorption–desorption cycles. Utilizing the desorbed solution as a nutrient solution significantly enhanced maize seedling growth, increasing stem height by 23.8%, fresh weight by 51.3%, and phosphorus content by 80.7%. These findings highlight MRS’s potential, not only as an effective phosphate adsorbent, but also as a means of successful phosphorus resource recovery and recycling, indicating promising applications in environmental remediation and resource management. Full article

Water scarcity has become a critical global challenge, particularly in rapidly developing regions where water demand often exceeds sustainable supply capacities. Traditional “demand-driven” water management approaches have proven inadequate to address this imbalance, necessitating the development of more sophisticated “supply-driven” solutions. This study presents a groundbreaking System Dynamics (SD)-Feedback-Hierarchical Water Demand (SD-F-HWD) model that advances water resources management through three contributions. First, the model substantially extends conventional water demand hierarchy methods by developing a comprehensive classification framework with enhanced sector-specific criteria for industrial, agricultural, and ecological needs. Second, the innovative feedback regulation mechanism resolves persistent challenges from previous studies, including ambiguous control parameters and system instability. Third, the model establishes a unified analytical platform that effectively integrates these components for robust supply–demand equilibrium analysis. Validation in Wu’an City, Hebei Province—a representative water-stressed industrial region in northern China—demonstrated the model’s effectiveness. Under low-flow conditions (P = 75%), total water demand decreased by 11.24% while rigid demand was reduced by 8.50%. For normal flow conditions (P = 50%), corresponding reductions reached 9.88% and 6.99%, respectively. Crucially, all adjustments remained within practical policy implementation boundaries, demonstrating the model’s real-world applicability. The SD-F-HWD model offers a practical and scalable solution for sustainable water allocation in water-stressed regions through its integrated methodological framework. Full article