Search Results (84)

Willow stands (Salix spp.) are an essential part of riparian ecosystems, as they sustain biodiversity and provide bioenergy solutions. The present review synthesizes the global scientific literature about the management of willow stands. In order to achieve this goal, we used a dual approach combining bibliometric analysis with traditional literature review. As such, we consulted 416 publications published between 1978 and 2024. This allowed us to identify key species, ecosystem services, conservation strategies, and management issues. The results we have obtained show a diversity of approaches, with an increase in short-rotation coppice (SRC) systems and the multiple roles covered by willow stands (carbon sequestration, biomass production, riparian restoration, and habitat provision). The key trends we have identified show a shift toward topics such as climate resilience, ecological restoration, and precision forestry. This trend has become especially pronounced over the past decade (2014–2024), as reflected in the increasing use of these keywords in the literature. However, as willow systems expand in scale and function—from biomass production to ecological restoration—they also raise complex challenges, including invasive tendencies in non-native regions and uncertainties surrounding biodiversity impacts and soil carbon dynamics over the long term. The present review is a guide for forest policies and, more specifically, for future research, linking the need to integrate and use adaptive strategies in order to maintain the willow stands. Full article

Mining activities in a quarry significantly interfere with the landscape, weaken its ecological functions, disrupt the continuity of habitats and change its natural character. The aim of this study is to present a robust, transparent, and participatory methodological framework centered on the Reclamation Quality Index, which enables a comprehensive and repeatable assessment of reclamation quality. At a time when the restoration of functional, ecologically stable and long-term sustainable landscapes is increasingly important, there is a need for reliable tools to assess the quality of restoration. This article presents an original methodology for the evaluation of quarry reclamation, which combines scientific precision with practical applicability. The proposed Reclamation Quality Index is built on multidisciplinary foundations and uses the Delphi methodology, through which expert knowledge and weighted preferences enter the evaluation process. A tool designed in this way makes it possible to quantify the quality of land restoration, identify the benefits of individual interventions, support effective planning, and strengthen the strategic management of post-mining transformation. At the same time, the Reclamation Quality Index creates space for the application of the principles of ecological stability and integration of the landscape as a living, dynamic system in the process of restoration. With its structure and philosophy, the methodology represents a prospective approach to the evaluation and planning of the post-extraction landscape. Its application goes beyond academia, as it can serve as a support for environmental policymaking, landscape planning, and assessing the quality of restoration in practice. Full article

Machine learning (ML) has established itself as an innovative tool in sustainable forest management, essential for tackling critical challenges such as deforestation, biodiversity loss, and climate change. Through the analysis of large volumes of data from satellites, drones, and sensors, machine learning facilitates everything from precise forest health assessments and real-time deforestation detection to wildfire prevention and habitat mapping. Other significant advancements include species identification via computer vision and predictive modeling to optimize reforestation and carbon sequestration. Projects like SILVANUS serve as practical examples of this approach’s success in combating wildfires and restoring ecosystems. However, for these technologies to reach their full potential, obstacles like data quality, ethical issues, and a lack of collaboration between different fields must be overcome. The solution lies in integrating the power of machine learning with ecological expertise and local community engagement. This partnership is the path forward to preserve biodiversity, combat climate change, and ensure a sustainable future for our forests. Full article

Ectomycorrhizal symbiosis is a cornerstone of ecosystem health, facilitating nutrient uptake, stress tolerance, and biodiversity maintenance in trees. Optimizing Pinus armandii–Tuber indicum mycorrhizal synthesis enhances the ecological stability of coniferous forests while supporting high-value truffle cultivation. This study conducted a pot experiment to compare the effects of three root nutrient regulations—Aolu 318S (containing N-P₂O₅-K₂O in a ratio of 15-9-11 (w/w%)), Aolu 328S (11-11-18), and Youguduo (19-19-19)—on the mycorrhizal synthesis of P. armandii–T. indicum. The results showed that root nutrient supplementation significantly improved the seedling crown, plant height, ground diameter, biomass dry weight, and mycorrhizal infection rate of both the control and mycorrhizal seedlings, with the slow-release fertilizers Aolu 318S and 328S outperforming the quick-release fertilizer Youguduo. The suitable substrate composition in this experiment was as follows: pH 6.53–6.86, organic matter content 43.25–43.49 g/kg, alkali-hydrolyzable nitrogen 89.25–90.3 mg/kg, available phosphorus 83.69–87.32 mg/kg, available potassium 361.5–364.65 mg/kg, exchangeable magnesium 1.17–1.57 mg/kg, and available iron 33.06–37.3 mg/kg. It is recommended to mix the Aolu 318S and 328S solid fertilizers evenly into the substrate, with a recommended dosage of 2 g per plant. These results shed light on the pivotal role of a precise N-P-K ratio regulation in fostering sustainable ectomycorrhizal symbiosis, offering a novel paradigm for integrating nutrient management with mycorrhizal biotechnology to enhance forest restoration efficiency in arid ecosystems. Full article

Ningxia has emerged as a strategic hub for China’s photovoltaic (PV) industry by leveraging abundant solar energy resources and geoclimatic advantages. This study analyzed the spatiotemporal expansion trends and microclimatic impacts of PV installations (2015–2024) using Gaofen-1 (GF-1) and Landsat8 satellite imagery with deep learning algorithms and multidimensional environmental metrics. Among semantic segmentation models, DeepLabV3+ had the best performance in PV extraction, and the Mean Intersection over Union, precision, and F1-score were 91.97%, 89.02%, 89.2%, and 89.11%, respectively, with accuracies close to 100% after manual correction. Subsequent land surface temperature inversion and spatial buffer analysis quantified the thermal environmental effects of PV installation. Localized cooling patterns may be influenced by albedo and vegetation dynamics, though further validation is needed. The total PV site area in Ningxia expanded from 59.62 km² to 410.06 km² between 2015 and 2024. Yinchuan and Wuzhong cities were primary growth hubs; Yinchuan alone added 99.98 km² (2022–2023) through localized policy incentives. PV installations induced significant daytime cooling effects within 0–100 m buffers, reducing ambient temperatures by 0.19–1.35 °C on average. The most pronounced cooling occurred in western desert regions during winter (maximum temperature differential = 1.97 °C). Agricultural zones in central Ningxia exhibited weaker thermal modulation due to coupled vegetation–PV interactions. Policy-driven land use optimization was the dominant catalyst for PV proliferation. This study validates “remote sensing + deep learning” framework efficacy in renewable energy monitoring and provides empirical insights into eco-environmental impacts under “PV + ecological restoration” paradigms, offering critical data support for energy–ecology synergy planning in arid regions. Full article

In ecological restoration of arid/semi-arid sandy lands, micro-topographic variations and artificial shrub arrangement synergistically drive vegetation recovery and soil quality improvement. As a typical fragile ecosystem in northern China, the Mu Us Sandy Land has long suffered wind erosion, desertification, soil infertility, and vegetation degradation, demanding precise vegetation configuration for ecological rehabilitation. This study analyzed soil nutrients, plant diversity, and their correlations under various micro-topographic conditions across different types of artificial shrub plantations in the Mu Us Sandy Land. Employing one-way and two-way ANOVA, we compared the significant differences in soil nutrients and plant diversity indices among different micro-topographic conditions and shrub species. Additionally, redundancy analysis (RDA) was conducted to explore the direct and indirect relationships between micro-topography, shrub species, soil nutrients, and plant diversity. The results show the following: 1. The interdune depressions have the highest plant diversity and optimal soil nutrients, with relatively suitable pH values; the windward slopes and slope tops, due to severe wind erosion, have poor soil nutrients, high pH values, and the lowest plant diversity. Both micro-topography and vegetation can significantly affect soil nutrients and plant diversity (p < 0.05), and vegetation has a greater impact on soil nutrients. 2. The correlation between surface soil nutrients and plant diversity is the strongest, and the correlation weakens with increasing soil depth; under different micro-topographic conditions, the influence of soil nutrients on plant diversity varies. 3. In sandy land ecological restoration, a “vegetation type + terrain matching” strategy should be implemented, combining the characteristics of micro-topography and the ecological functions of shrubs for precise configuration, such as planting Corethrodendron fruticosum on windward slopes and slope tops to rapidly replenish nutrients, promoting Salix psammophila and mixed plantation in interdune depressions and leeward slopes to accumulate organic matter, and prioritizing Amorpha fruticosa in areas requiring soil pH adjustment. This study provides a scientific basis and management insights for the ecological restoration and vegetation configuration of the Mu Us Sandy Land. Full article

This study used Jilin-1 satellite data and unmanned aerial vehicle (UAV)-collected visible-thermal infrared imagery to calculate twelve spectral indices and evaluate their effectiveness in distinguishing post-fire forest areas and identifying human-altered land-cover changes in Jinyun Mountain, Chongqing. The research goals included mapping wildfire impacts with M-statistic separability, measuring land-cover distinguishability through Jeffries–Matusita (JM) distance analysis, classifying land-cover types using the random forest (RF) algorithm, and verifying classification accuracy. Cumulative human disturbances—such as land clearing, replanting, and road construction—significantly blocked the natural recovery of burn scars, and during long-term human-assisted recovery periods over one year, the Red Green Blue Index (RGBI), Green Leaf Index (GLI), and Excess Green Index (EXG) showed high classification accuracy for six land-cover types: road, bare soil, deadwood, bamboo, broadleaf, and grass. Key accuracy measures showed producer accuracy (PA) > 0.8, user accuracy (UA) > 0.8, overall accuracy (OA) > 90%, and a kappa coefficient > 0.85. Validation results confirmed that visible-spectrum indices are good at distinguishing photosynthetic vegetation, thermal bands help identify artificial surfaces, and combined thermal-visible indices solve spectral confusion in deadwood recognition. Spectral indices provide high-precision quantitative evidence for monitoring post-fire land-cover changes, especially under human intervention, thus offering important data support for time-based modeling of post-fire forest recovery and improvement of ecological restoration plans. Full article

As a crucial ecological barrier in China, the Greater Khingan Mountains play a vital role in global ecological security. Investigating the spatiotemporal variations in fractional vegetation cover (FVC) and the driving mechanisms behind its spatial differentiation is essential. This study introduced a KNDVI-XGeoML framework integrating the Kernel NDVI and explainable geospatial machine learning to analyze the FVC dynamics and the mechanisms driving their spatial differentiation in China’s Greater Khingan Mountains, based on which targeted ecological management strategies were proposed. The key findings reveal that (1) from 2001 to 2022, FVC showed an increasing trend, confirming the effectiveness of ecological restoration. (2) The XGeoML model successfully revealed nonlinear relationships and threshold effects between driving factors and FVC. In addition, both single-factor importance and inter-factor interaction analyses consistently showed that landform factors dominated the spatial distribution of FVC. (3) Regional heterogeneity emerged—human activities drove the northern alpine zones, while landform factors governed other areas. (4) The natural-environment-dominated zones and human-activity-dominated zones were established, and management strategies were proposed: restricting tourism in low-altitude zones, optimizing the cold-resistant vegetation at high elevations, and improving the southern soil conditions to support ecological barrier construction. The innovation lies in merging nonlinear vegetation indices with interpretable machine learning, overcoming the traditional limitations in terms of saturation effects and analyses of spatial heterogeneity. This approach enhances our understanding of high-latitude vegetation dynamics, offering a methodological advancement for precision ecological management. The spatial zoning strategy based on dominant drivers provides actionable insights for maintaining this critical ecological barrier, particularly under climate change pressures. The framework demonstrates strong potential for extrapolation to other ecologically sensitive regions requiring data-driven conservation planning. 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

Research on the supply–demand relationships of ecosystem services (ESs) in alpine pastoral regions remains relatively scarce, yet it is crucial for regional ecological management and sustainable development. This study focuses on the Sanjiangyuan Region, a typical alpine pastoral area and significant ecological barrier, to quantitatively assess the supply–demand dynamics of key ESs and their spatial heterogeneity from 2000 to 2020. It further aims to elucidate the underlying driving mechanisms, thereby providing a scientific basis for optimizing regional ecological management. Four key ES indicators were selected: water yield (WY), grass yield (GY), soil conservation (SC), and habitat quality (HQ). ES supply and demand were quantified using an integrated approach incorporating the InVEST model, the Revised Universal Soil Loss Equation (RUSLE), and spatial analysis techniques. Building on this, the spatial patterns and temporal evolution characteristics of ES supply–demand relationships were analyzed. Subsequently, the Geographic Detector Model (GDM) and Geographically and Temporally Weighted Regression (GTWR) model were employed to identify key drivers influencing changes in the comprehensive ES supply–demand ratio. The results revealed the following: (1) Spatial Patterns: Overall ES supply capacity exhibited a spatial differentiation characterized by “higher values in the southeast and lower values in the northwest.” Areas of high ES demand were primarily concentrated in the densely populated eastern region. WY, SC, and HQ generally exhibited a surplus state, whereas GY showed supply falling short of demand in the densely populated eastern areas. (2) Temporal Dynamics: Between 2000 and 2020, the supply–demand ratios of WY and SC displayed a fluctuating downward trend. The HQ ratio remained relatively stable, while the GY ratio showed a significant and continuous upward trend, indicating positive outcomes from regional grass–livestock balance policies. (3) Driving Mechanisms: Climate and natural factors were the dominant drivers of changes in the ES supply–demand ratio. Analysis using the Geographical Detector’s q-statistic identified fractional vegetation cover (FVC, q = 0.72), annual precipitation (PR, q = 0.63), and human disturbance intensity (HD, q = 0.38) as the top three most influential factors. This study systematically reveals the spatial heterogeneity characteristics, dynamic evolution patterns, and core driving mechanisms of ES supply and demand in an alpine pastoral region, addressing a significant research gap. The findings not only provide a reference for ES supply–demand assessment in similar regions regarding indicator selection and methodology but also offer direct scientific support for precisely identifying priority areas for ecological conservation and restoration, optimizing grass–livestock balance management, and enhancing ecosystem sustainability within the Sanjiangyuan Region. Full article

River restoration and nature-based solutions pertaining to the riverscape are measures frequently confused, but indeed they are not identical; they present both differences and common points, and only in some cases and following precise criteria, interventions can be considered both restoration and Nature-based Solution (NbS) projects. In other words, there is an intersection between the two concepts, both in a theoretical framework and in practical applications. The understanding of their distinctions and common points is important because it affects the objectives and implementation of measures, complying with a wide spectrum of relative importance of ecological goals and ecosystem services delivery, different critical issues for effective implementation, and different spatial scales. We provide a theoretical analysis of some simple criteria to identify interventions as riverscape NbS, river restoration measures, or both. We illustrate these ideas by means of three case studies of projects carried out in different European riverine environments: the real-world cases exemplify, respectively, pure river restoration projects, mere riverscape NbS, and finally, interventions representing both NbS and ecosystem restoration. These examples allow us to clearly show measures with a small number of goals, even a single one, and, on the other hand, multipurpose measures. We also illustrate the prioritization of objectives and their implications in planning and design, implementation phases, and stakeholders’ involvement. Particular attention is devoted to effective monitoring and assessment, considering that the quantitative evaluation of measures’ impacts is a difficult and resource-demanding task. Full article

Market-oriented ecological restoration is vital for advancing ecological civilization and promoting harmonious human–nature relationships. However, the precise implementation pathway remains unclear. Few studies specifically address challenges that arise during ecological restoration implementation. Ensuring the smooth and effective implementation and landing of ecological restoration projects harmonizes ecological and economic objectives at the regional scale and fosters sustainable development in the region. Based on the policies of market-oriented ecological restoration collected from various Chinese provinces, and through multi-level institutional analysis, the policy measures are categorized into three phases: early, middle, and late. For each phase, we summarize the challenges encountered in implementing market-oriented ecological restoration projects. Finally, by the method of constructing theoretical models, we propose sustainable countermeasures based on multiple theoretical models. The results show (1) China’s ecological restoration sector is experiencing rapid growth, and market-oriented policies in China, multiple Chinese provinces, and municipalities have enacted successive market-oriented ecological restoration policies, and the outlook for ecological restoration marketization in China remains highly promising. (2) The implementation process of current market-oriented ecological restoration projects confronts and encounters several challenges. These include the absence of project screening and evaluation mechanisms, limited investment and financing channels, ill-defined approval processes, ambiguous delineation of departmental responsibilities, insufficient industry incentives, and the absence of effective operational and management mechanisms. (3) To address the identified challenges, taking forest ecological restoration as an example, theoretical models should be developed encompassing six critical dimensions: the aspects of the mechanism, mode, approval process, management system, industrial chain, and platform. This aims to provide sustainable pathways for the effective implementation of market-oriented forest ecological restoration projects. Full article

The Urban Agglomeration in the Middle Reaches of the Yangtze River (UAMRYR), serving as a pivotal hub for coordinated economic and ecological development in central China, is characterized by marked ecological fragility and climate sensitivity. Investigating the land use dynamics and ecological benefit changes within this region holds critical strategic significance for balancing regional development with the construction of ecological security barriers. This study systematically analyzed the spatiotemporal variations in land use/land cover (LULC) across the UAMRYR, using multi-source remote sensing data, climatic factors, land conditions, and anthropogenic influences. By integrating the four-quadrant model and the coupling degree model, we developed a remote sensing ecological index (RSEI)–ecological service index (ESI) coupling evaluation framework to assess the spatiotemporal evolution patterns of changes in ecological benefits in the region. Furthermore, we employed Geodetector analysis to identify the key influencing factors driving the RSEI–ESI coupling relationship and their interactive mechanisms. The research findings are as follows: (1) The ecological regional pattern has changed. The area of Quadrant I (RSEI > 0.5 and ESI > 0.5) decreased by 13,800 km², whereas Quadrants II (RSEI < 0.5 and ESI > 0.5) and IV (RSEI > 0.5 and ESI < 0.5) increased by 14,900 km² and 3500 km², respectively. Quadrant III (RSEI < 0.5 and ESI < 0.5) remained relatively stable. This indicates that the imbalance in ecological functional spaces has intensified, affecting key ecological processes. (2) The quantitative analysis of the spatiotemporal evolution characteristics of the RSEI and ESI revealed contrasting trends: the RSEI decreased by 0.006, whereas the ESI showed a slight increase of 0.001. (3) The ranking of the driving factors indicated that the Normalized Difference Vegetation Index (NDVI) and the mean annual rainfall (MAP) were the primary factors driving ecological evolution, while the influence of economic driving factors was relatively weak. This study establishes a three-pillar framework (quadrant-based diagnosis, Geodetector-driven analysis, and RSEI–ESI coupled interventions) to guide precision-based ecological restoration and spatial governance. 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

An accurate forest soil organic carbon (SOC) assessment aids in the ecological restoration of forest mining areas, enabling dynamic monitoring of carbon sink accounting and informed land reclamation decisions. Digital soil mapping (DSM) has enhanced soil monitoring, with machine learning and environmental covariates becoming the keys to improving accuracy. This study utilized 32 environmental variables from multispectral, topographic, and soil data, along with 142 soil samples and six machine learning methods to construct a forest SOC model for the Huodong mining district. The performance of Boruta and SHAP (SHapley Additive exPlanations) in optimizing feature selection was evaluated. Ultimately, the optimal machine learning model and feature selection method were applied to map the SOC distribution, with variable contributions quantified using SHAP. The results showed that CatBoost performed best among the six algorithms in predicting the SOC content (R² = 0.70). Both Boruta and SHAP improved the prediction accuracy, with Boruta achieving the highest precision. Introducing the Boruta model increased R² by 8.57% (from 0.70 to 0.76) compared to models without feature selection. The spatial distribution mapping revealed higher SOC concentrations in the southern and northern regions and lower levels in the central area, indicating strong spatial heterogeneity. Key factors influencing the SOC distribution included pH, the nitrogen content, sand content, DEM, and B3 band. Full article