Search Results (113)

Accurate mapping of landslide-affected land surfaces from very-high-resolution optical imagery is essential for mountainous land monitoring and hazard-related land management, yet it remains difficult in complex terrain because landslide bodies are fragmented, elongated, shadowed, and easily confused with bare soil, terraces, roads, and erosion features. This study presents TM-Net as a task-oriented decoder-centric architecture for landslide-affected land surface mapping. The Inception Token Mixer is adopted from InceptionNeXt as an encoder-side component, whereas TM-Block is newly designed as a decoder-side twin-path refinement module for boundary recovery, slender target preservation, and complex-background suppression. On the fused public benchmark, TM-Net achieved the highest IoU (73.56%) and F1 (84.76%) under the identical evaluation protocol. On XBLD, TM-Net achieved the highest IoU (44.94%) and F1 (62.01%). These results indicate that TM-Net provides a favorable balance between missed detections and false-positive predictions for mapping visually identifiable landslide-affected land surfaces in heterogeneous mountainous landscapes. Full article

Accurate assessment of carbon storage dynamics and their driving factors is important for ecological sustainability and land management on the Loess Plateau under China’s dual carbon goals. In this study, the InVEST and PLUS models were integrated to evaluate carbon storage changes from 2000 to 2020 and simulate future carbon storage patterns for 2030 under four development scenarios, including natural development (ND), rapid development (RD), cropland protection (CP), and ecological protection (EP). In addition, the XGBoost-SHAP framework was employed to identify the dominant drivers and nonlinear response relationships controlling spatial variation in carbon storage. During 2000–2020, ecosystem carbon storage across the Loess Plateau generally increased, rising from 5.780 Pg to 5.893 Pg. Spatially, carbon storage displayed a pronounced pattern characterized by higher levels in the southeast and lower levels in the northwest, aligning with forest–grassland restoration belts. Scenario simulations showed that EP produced the largest carbon storage gain, with total carbon storage projected to reach 5.962 Pg in 2030. In contrast, RD reduced carbon storage to 5.858 Pg because of intensive construction land expansion. XGBoost-SHAP results identified net primary productivity (NPP) as the most influential factor controlling spatial variation in carbon storage, accounting for 57.3% of the total explanatory importance, whereas soil erosion (SE) exhibited a strong negative effect on carbon storage. Population density (POPD) also exerted a negative effect, whereas gross domestic product (GDP) showed positive contributions in economically developed counties. These findings enhance understanding of the spatial response characteristics of carbon storage under environmental gradients and human disturbance across the Loess Plateau. They further provide scientific support for differentiated ecological management and regionally adapted carbon mitigation planning. Full article

Crop residue management is an important factor in sustainable agriculture as it impacts soil erosion, water retention, soil organic matter, and crop yield. Accurately measuring the crop residue cover helps in the strategic planning, control, and monitoring of crop residue. While advancements in machine learning have allowed for significant progress in crop residue classification work, a major challenge still exists in the creation of an accurately annotated dataset for crop residue and the application of segmentation-based models to accurately segment crop residues. This study aims to develop an efficient image annotation framework and evaluate deep learning models for crop residue cover estimation. For this, the Residue Segmentation Tool, a standalone graphical user interface, was designed to facilitate accurate and efficient image annotation that enables flexible and high-throughput annotation of residue images. The tool is publicly available and supports multiple segmentation modes, which include classical and modern computer vision algorithms such as Otsu, Canny, and manual thresholding, as well as the Segment Anything Model and user-guided mask refinement through manual editing options. This tool was also utilized to create annotated datasets for machine learning training and testing of crop residue cover estimation. Three different sizes of datasets (100, 250, and 500 images) were utilized for machine learning training and testing to evaluate the performance of the models trained using U-Net and DeepLabV3. U-Net consistently outperformed DeepLabV3 across most metrics, particularly on smaller datasets, showing better Dice, IoU, and Recall scores. The best-performing model had Dice, IoU, and Accuracy scores of 0.748, 0.627, and 0.864, respectively. The findings demonstrate that the Residue Segmentation Tool enables scalable and reproducible dataset creation and supports effective segmentation for crop residue cover estimation. Full article

Soil erosion is a global ecological and environmental issue that severely degrades terrestrial ecosystems. A range of soil and water conservation measures, notably the construction of check dams in gullies, have been widely implemented to mitigate soil erosion and sustain agricultural productivity. In this study, Ningxia province in China was selected as the study area. High-resolution Google Earth imagery and digital elevation model (DEM) data were integrated with three representative deep learning semantic segmentation models—FCN, U-Net, and DeepLab v3+—to achieve automatic extraction and spatial distribution analysis of engineered check dams. Model performance was quantified using overall accuracy (OA), F1-score, and mean intersection over union (mIoU), among other metrics. The results demonstrated that U-Net outperformed FCN and DeepLab v3+ across all evaluation metrics. On the test dataset, U-Net’s F1-score exceeded those of FCN and DeepLab v3+ by 3.89% and 7.08%, while mIoU increased by 2.17% and 6.57%, demonstrating superior boundary delineation. Based on the precise area extraction by U-Net, a piecewise empirical equation was subsequently developed to relate predicted silted land area to actual sediment volume, achieving R² values of 0.92 for small dams and 0.96 for large dams. Spatial distribution analysis revealed that check dams are predominantly concentrated in the southern mountainous and hilly-gully regions, moderately distributed in the central areas, and relatively sparse in the northern plains. Overall, this study demonstrates the feasibility and effectiveness of deep learning-based semantic segmentation for automated check dam mapping and sediment volume estimation. Full article

The ongoing warming–wetting trend is profoundly reshaping water and land resources (WLR) in alpine regions, challenging their ecological functions. Focusing on the Yellow River source region above Huangheyan Station, we developed a synergistic WLR allocation framework explicitly oriented towards ecological function enhancement. We systematically assessed the spatiotemporal evolution of WLR and key ecological functions from 2000 to 2020, and projected future dynamics for 2030–2060 under four SSP scenarios. A multi-objective optimization model was established to minimize water shortage, maximize water conservation capacity (WCC), maximize vegetation water use efficiency (WUE), and minimize soil erosion amount (SEA), solved using the Non-dominated Sorting Genetic Algorithm II algorithm (NSGA-II). The results indicate significant ecological improvements over the past two decades (Net Primary Production (NPP) +14.3%, WCC +67.9%, SEA −34.1%). Critically, the optimized allocation schemes demonstrated substantial benefits across all future scenarios, enhancing WCC by 4.6–20.2%, improving WUE by 0.6–10.7%, and reducing SEA by 3.9–9.1%. This study offers a useful reference for coordinating ecological conservation and resource management in climate-sensitive and ecologically fragile alpine regions. Full article

Benggang (Benggang), a typical landform characterized by severe erosion and a geohazard in the red-soil hilly regions of southern China, is characterized by a fragmented texture, irregular boundaries, and high similarity to background objects such as bare soil and roads, which poses a dual challenge of “multiscale variability + strong noise” for automated identification at regional scales. To address insufficient information from a single modality and the limited representation of cross-scale features, this study proposes a dual-stream feature-fusion network (DF-Net) for multisource data consisting of a digital orthophoto map (DOM) and a digital elevation model (DEM). The method adopts ResNeSt50d as the backbone of the two branches: on the DOM side, a Canny-edge channel is stacked to enhance high-frequency boundary information; on the DEM side, derived terrain factors, including slope, aspect, curvature, and hillshade, are introduced to provide morphological constraints. In the cross-modal fusion stage, a multiscale sparse attention fusion module is designed, which acquires contextual information via multiwindow average pooling and suppresses noise interference through top-K sparsification. In the decision stage, a multibranch ensemble is employed to improve classification stability. Taking Anxi County, Fujian Province, as the study area, a coregistered dataset of GF-2 (1 m) DOM and ALOS (12.5 m) DEMs is constructed, and a zonal partitioning strategy is adopted to evaluate the model’s generalization ability. The experimental results show that DF-Net achieves 97.44% accuracy, 85.71% recall, and an 82.98% F1 score in the independent test zone, outperforming multiple mainstream CNN/transformer classification models. This study indicates that the strategy of “multimodal feature enhancement + sparse attention fusion” tailored to Benggang erosional landforms can significantly improve recognition performance under complex backgrounds, providing technical support for rapid Benggang surveys and governance-effectiveness assessments. Full article

This study provides a comprehensive economic valuation of ecosystem services and environmental impacts across four major agroecosystems—wheat, barley, sugar beet, and coriander—under intensive management in the semi-arid Nahavand County, Iran. Soil properties, ecosystem service provision, and environmental disservices such as greenhouse gas emissions, soil erosion, and nutrient leaching were systematically assessed using field surveys, farmer questionnaires, and established ecological models. Coriander exhibited the highest net ecosystem service value, ranging from $115,840 to $154,750 ha⁻¹, driven by superior provisioning services (39.77% of total value) and the lowest environmental costs. In contrast, sugar beet presented the greatest ecological burden, with environmental costs exceeding $22,000 ha⁻¹, leading to the lowest net benefits ($51,940–$79,300 ha⁻¹). Nonlinear Gaussian regression models demonstrated strong predictive capacity (R = 0.91 to 0.99) for marketable value based on yield metrics, highlighting the importance of biomass productivity in economic valuation. These findings underscore the multifunctionality of coriander and emphasize the pivotal role of crop selection in optimizing agroecosystem sustainability, balancing food security, ecosystem health, and environmental conservation in semi-arid agricultural landscapes. Full article

Sloping cropland on the Loess Plateau faces severe challenges from soil organic carbon (SOC) depletion and structural instability due to erosion and intensive tillage. Although mulching can enhance SOC sequestration, its long-term effects on the spatial distribution of SOC and aggregates across slopes remain unclear. A 15-year field experiment evaluated five practices—conventional tillage (T), no tillage (NT), straw mulching (SM), plastic film mulching (PM), and ridge–furrow plastic film mulching (RPM)—on SOC storage, aggregate stability, and their variation with different slope positions. Compared to T, all mulching treatments significantly increased SOC concentration by 4.19% to 83.48% in the 0–30 cm layer. SM and RPM notably increased macro-aggregates (>2 mm) and their associated SOC (24.04–56.49% higher than T) by adding organic matter and optimizing micro-topography. Different slope positions strongly influenced SOC redistribution: lower slopes accumulated more SOC than upper slopes due to erosion–deposition processes. Mulching reduced SOC spatial variability and minimized differences between slope positions. Although mulching increased cumulative SOC mineralization compared to T, the long-term net SOC gain was positive, driven by improved aggregate protection and reduced erosion. SM and RPM are recommended for sustainable slope farmland management due to their dual benefits in enhancing carbon sinks and soil stability. This study offers practical strategies for improving soil health and SOC sequestration in vulnerable sloping landscapes. Full article

This study takes the source region of the Yellow River from 2000 to 2024 as the research area, and integrates multi-source remote sensing, long-term meteorological observation, and land use data from 2000 to 2024. Using GIS spatial analysis, the standard ellipse model, and a geographic detector, this study systematically depicts the spatio-temporal heterogeneity and multi-scale evolution trend of soil and water conservation services, and then quantifies the spatial differentiation of the contribution rate of climate fluctuation, land use transformation, and human activity intensity to service change. The results showed the following: (1) The land use pattern in the source region of the Yellow River showed a one-way transformation of “grassland dominated, forest land increased alone, and the rest decreased”. The net increase in forest land 204.3 km² was all from the transformation of grassland. The vegetation coverage increased by 9.9%, and the low-value area of soil and water conservation services in the northwest continued to expand. (2) The overall moving distance of the center of gravity of soil and water conservation service capacity is not significant compared with the spatial scale of the source area of the Yellow River. The standard deviation ellipse of each year also did not show systematic and large changes in area, shape, or direction. (3) Annual mean temperature (Q = 0.590) and vegetation coverage (Q = 0.527) are the most influential single factors, while the interaction between annual mean temperature and precipitation (bidirectional enhancement) is the most stable synergistic driving combination. The single-factor Q values of topography and human activities were <0.10. (4) Climate and economic factors are the key factors driving the spatial differentiation of soil and water conservation service capacity, and the role of each driving factor has an optimal range to reduce the risk of soil erosion. The optimal range of population density is 7~9 person/km², the optimal range of average GDP is 11,900~14,100 yuan/km², the optimal range of annual average temperature is 1.71~3.47 °C, the optimal range of annual precipitation is 682~730 mm, the optimal range of vegetation coverage is 81.7~100%, and the optimal range of altitude is 3390~3740 m. The optimal range of slope is 18.3~24.3°. The optimal range of soil moisture is 26.7~29.4%. The optimal range of grazing intensity is 0.352~0.652. The study proposes countermeasures such as strict control of development in high-value areas of soil and water conservation services and key ecological restoration in low-value areas, the establishment of breeding bases and catchment areas in low-precipitation areas to cope with climate change, the optimization of grazing strategies, so as to provide scientific support for the stability of alpine grassland ecosystem services, and the high-quality development of the Yellow River Basin. Full article

Polyacrylamide (PAM), a water-soluble polymer, is revolutionizing horticulture by improving water use efficiency and soil health, particularly in Pakistan’s water-scarce regions, offering a transformative solution. It reduces irrigation frequency by 30–40%, saving up to 50% of water while boosting crop yield by 20–50%. This results in a net profit increase of 30–60%, depending on the crop and soil type. Global studies show that PAM reduces soil erosion by 90–95% in furrow irrigation systems and increases water infiltration by 15–30%. Its hydrophilic properties enhance soil water-holding capacity by up to 400% compared to untreated soil, enabling plants to thrive in arid and semi-arid regions. Economically, the adoption of PAM is cost-effective. PAM also supports sustainable agriculture by mitigating the effects of water scarcity. These characteristics are in line with the objective of Pakistan to achieve agricultural sustainability and productivity. In conclusion, polyacrylamide is a feasible solution to address the water shortage in Pakistan and soil erosion, as well as to provide a significant amount of economic and environmental gains to the horticulture industry. The wide adoption of the technology could be triggered by pilot projects, farmer training, and government subsidies, which would change the agricultural landscape in the country. Full article

Steep olive orchards in northwest Syria are experiencing severe land degradation as a result of unsustainable uphill–downhill tillage, which accelerates erosion and reduces productivity. To address this problem, three tillage systems, no-till natural vegetation strips (NVSs), contour tillage, and uphill–downhill tillage, were evaluated at two research sites, Yakhour and Tel-Hadya, NW Syria. The adoption of no-till NVSs significantly increased soil organic matter (SOM) at both sites, outperforming uphill–downhill tillage. While contour tillage resulted in lower SOM levels than NVSs, it still performed better than the conventional uphill–downhill practice. Contour soil flux (CSF) was lower in Yakhour, where mule-drawn tillage on steep slopes (31–35%) was practiced, compared to higher CSF values in Tel-Hadya, where tractor tillage was applied on gentler slopes (11–13%), which highlights the influence of slope steepness on soil fluxes. Over four years, net soil flux (NSF) indicated greater soil loss under tractor tillage, confirming that mule-drawn tillage is less disruptive. Olive trees with no-till NVSs benefited from protected root systems, improved soil structure through SOM accumulation, reduced erosion risk, and improved surface runoff buffering, which resulted in increased water infiltration and soil water retention. This study was carried out using a participatory technology development (PTD) framework, which guided the entire research process, from diagnosing problems to co-designing, field testing, and refining soil conservation practices. In Yakhour, farmers actively identified the challenges of degradation. They collaboratively chose no-till natural vegetation strips (NVSs) and contour tillage as key interventions, valuing NVSs for their ability to conserve moisture, suppress weeds and pests, and increase olive productivity. The farmer–scientist co-learning network positioned PTD not only as an outreach tool but also as a core research method, enabling locally relevant and scalable strategies to restore soil functions and combat land degradation in northwest Syria’s hilly olive orchards. Full article

The Qinba Mountains in China span six provinces, characterized by a large population, rugged terrain, steep peaks, deep valleys, and scarce flat land, making large-scale agricultural development challenging. Terraced fields serve as the core cropland type in this region, playing a vital role in preventing soil erosion on sloping farmland and expanding agricultural production space. They also function as a crucial medium for sustaining the ecosystem services of mountainous areas. As a transitional zone between China’s northern and southern climates and a vital ecological barrier, the Qinba Mountains’ terraced ecosystems have undergone significant spatial changes over the past two decades due to compound factors including the Grain-for-Green Program, urban expansion, and population outflow. However, current large-scale, long-term, high-resolution monitoring studies of terraced fields in this region still face technical bottlenecks. On one hand, traditional remote sensing interpretation methods rely on manually designed features, making them ill-suited for the complex scenarios of fragmented, multi-scale distribution, and terrain shadow interference in Qinba terraced fields. On the other hand, the lack of high-resolution historical imagery means that low-resolution data suffers from insufficient accuracy and spatial detail for capturing dynamic changes in terraced fields. This study aims to fill the technical gap in detailed dynamic monitoring of terraced fields in the Qinba Mountains. By creating image tiles from Landsat-8 satellite imagery collected between 2017 and 2020, it employs three deep learning semantic segmentation models—DeepLabV3 based on ResNet-34, U-Net, and PSPNet deep learning semantic segmentation models. Through optimization strategies such as data augmentation and transfer learning, the study achieves 15-m-resolution remote sensing interpretation of terraced field information in the Qinba Mountains from 2000 to 2020. Comparative results revealed DeepLabV3 demonstrated significant advantages in identifying terraced field types: Mean Pixel Accuracy (MPA) reached 79.42%, Intersection over Union (IoU) was 77.26%, F1 score attained 80.98, and Kappa coefficient reached 0.7148—all outperforming U-Net and PSPNet models. The model’s accuracy is not uniform but is instead highly contingent on the topographic context. The model excels in environments that are archetypal for mid-altitudes with moderately steep slopes. Based on it we create a set of tiles integrating multi-source data from RBG and DEM. The fusion model, which incorporates DEM-derived topographic data, demonstrates improvement across these aspects. Dynamic monitoring based on the optimal model indicates that terraced fields in the Qinba Mountains expanded between 2000 and 2020: the total area was 57.834 km² in 2000, and by 2020, this had increased to 63,742 km², representing an approximate growth rate of 8.36%. Sichuan, Gansu, and Shaanxi provinces contributed the majority of this expansion, accounting for 71% of the newly added terraced fields. Over the 20-year period, the center of gravity of terraced fields shifted upward. The area of terraced fields above 500 m in elevation increased, while that below 500 m decreased. Terraced fields surrounding urban areas declined, and mountainous slopes at higher elevations became the primary source of newly constructed terraces. This study not only establishes a technical paradigm for the refined monitoring of terraced field resources in mountainous regions but also provides critical data support and theoretical foundations for implementing sustainable land development in the Qinba Mountains. It holds significant practical value for advancing regional sustainable development. Full article

Gully erosion poses a serious threat to soil fertility and agricultural sustainability in Northeast China’s black soil region. Accurate and efficient mapping of erosion gullies is critical for enabling targeted soil conservation and precision land management. In this study, we developed a texture-enhanced deep learning framework for automated gully extraction using high-resolution GF-1 and GF-2 satellite imagery. Key texture parameters—specifically mean and contrast features derived from the gray-level co-occurrence matrix (GLCM) under a 5 × 5 window and 32 gray levels—were systematically optimized and fused with multispectral bands. We trained and evaluated three convolutional neural network architectures—U-Net, U-Net++, and DeepLabv3+—under consistent data and evaluation protocols. Results demonstrate that the integration of texture features significantly enhanced extraction performance, with U-Net achieving the highest overall accuracy (90.27%) and average precision (90.87%), surpassing DeepLabv3+ and U-Net++ by margins of 6.06% and 9.33%, respectively. Visualization via Class Activation Mapping (CAM) further confirmed improved boundary discrimination and reduced misclassification of spectrally similar non-gully features, such as field roads and farmland edges. The proposed GLCM–CNN integrated approach offers an interpretable and transferable solution for gully identification and provides a technical foundation for large-scale monitoring of soil and water conservation in black soil landscapes. Full article

Global ecosystems have undergone significant degradation and deterioration, making the identification of ecosystem changes essential for promoting sustainable development and enhancing quality of life. Hami City, a representative region characterized by the complex “desert–oasis–mountain” ecosystem in Xinjiang, China, provides a critical context for examining ecosystem changes in extremely arid environments. This study utilizes remote sensing data alongside the Revised Wind Erosion Equation and Revised Universal Soil Loss Equation models to analyze the transformations within the desert–oasis ecosystems of Hami City and their driving forces. The findings reveal that (1) over the past 24 years, there have been substantial alterations in the ecosystem patterns of Hami City, primarily marked by an expansion of cropland and grassland ecosystems and a reduction in desert ecosystems. (2) Between 2000 and 2023, there has been an upward trend in Fractional Vegetation Cover, Net Primary Productivity, and windbreak and sand fixation amount in Hami City, whereas soil retention has shown a declining trend. (3) The overall ecosystem change in Hami City is moderate, encompassing 61.85% of the area, with regions exhibiting positive change comprising 16.79% and those with negative change comprising 21.33%. (4) Temperature, precipitation, and evapotranspiration are the primary drivers of ecosystem change in Hami City. Although the overall changes in ecosystems in Hami City have shown an improving trend, significant spatial heterogeneity still exists. The natural climatic conditions of Hami City constrain the potential for further ecological improvement. This study enhances the understanding of ecosystem change processes in extremely arid regions and demonstrates that strategies for mitigating or adapting to climate change need to be implemented as soon as possible to ensure the sustainable development of ecosystems in arid areas. Full article

Ecosystem services play a crucial role in maintaining ecological balance and supporting socio-economic development. However, long-term human activities and climate change have led to severe ecosystem degradation and exacerbated soil erosion on the Loess Plateau. This study takes the Loess Plateau as a case study and using key models such as Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) to analyze the spatiotemporal variations of five ecosystem services—water yield, habitat quality, windbreak and sand fixation, soil conservation, and net primary productivity (NPP)—from 2000 to 2020. Based on the land use types projected by the Patch-generating Land Use Simulation (PLUS) model for 2025 and 2030 under natural development, ecological protection, and cropland protection scenarios, the study simulates these five ecosystem services and the comprehensive ecosystem service index for the Loess Plateau in 2025 and 2030. Finally, an ecological risk assessment model based on the inverse transformation of ecosystem services is constructed to identify key ecological restoration areas on the Loess Plateau by 2030. The results indicate: (1) From 2000 to 2020, water yield, soil conservation services, and NPP on the Loess Plateau showed a significant increasing trend, The unit area sand fixation capacity displayed a spatial pattern of higher values in the Northwest and lower values in the Southeast, while soil conservation and NPP exhibited the opposite trend, with higher values in the Southeast and lower values in the Northwest. Water yield decreased from the Southeast to the Northwest. During this period, the comprehensive ecosystem service index of the Loess Plateau generally declines, but the balance and synchronicity of ecosystem services improve, with a reduction in regional disparities. (2) Different future scenarios have different effects on the regional pattern of ecosystem services and restoration. Among future scenarios, the ecological protection scenario is most conducive to enhancing comprehensive ecosystem services, reducing the proportion of medium- and high-priority restoration zones. The cropland protection scenario has the lowest proportion of general restoration zones, but local ecological risks increase. The findings of this study can provide a scientific basis for ecological restoration and land-use planning on the Loess Plateau, promoting the long-term stability and sustainable development of ecosystem services. Full article