Search Results (142)

The Taklimakan Desert Highway shelterbelt is the world’s largest ecological protection system established along a highway in a shifting desert environment and plays a critical role in mitigating wind-blown sand hazards and ensuring transportation safety. However, its long-term stability and protective capacity after more than two decades of operation remain insufficiently understood. In this study, Landsat imagery from 2005 to 2025 was used to monitor the long-term evolution of the shelterbelt along the Middle Section (~180 km) of the Taklimakan Desert Highway. A Random Forest classifier was employed to extract shelterbelt distribution, and classification results were validated using high-resolution Google Earth imagery and unmanned aerial vehicle observations. To quantify shelterbelt condition, a Shelterbelt Stability Index (SSI) was developed by integrating fractional vegetation cover (FVC), connectivity index (CI), percentage of landscape (PLAND), and perimeter-area fractal dimension (FRAC). The shelterbelt experienced initial seedling decline from 2005 to 2011, followed by progressive restoration during 2011–2020 and finally entered a stable saturated stage after 2020. Affected by saline water drip irrigation, wind-sand erosion and pipeline clogging, the overall vegetation condition deteriorated continuously before 2011. After targeted irrigation regulation, optimization of planting patterns and replanting measures were implemented; the degradation trend was reversed, contributing to the sustained improvement of vegetation thereafter. Significant spatial heterogeneity was observed along the highway, with certain sections maintaining high continuity and vegetation coverage, while others exhibited fragmentation, local discontinuities, area shrinkage, and increasing structural complexity. The proposed SSI effectively captured long-term structural dynamics and identified vulnerable sections subject to degradation. This study provides new insights into the life-cycle evolution of desert highway shelterbelts and offers scientific support for the sustainable management of ecological protection systems in arid environments. Full article

Coastal dune ecosystems play an essential role in shoreline stabilization, biodiversity conservation, and carbon storage, but they are increasingly threatened by human activities and climate-related disturbances. This study assessed long-term forest vegetation dynamics in restored coastal dune ecosystems in northwestern Tunisia, integrating a 30-year Landsat dataset (1994–2024) with Random Forest classification. We quantified changes in forest and shrubland cover, evaluated the effectiveness of dune stabilization reforestation (Pinus pinea and Acacia spp.), and assessed the impact of the 2023 wildfire on forest biomass and carbon stocks. The findings demonstrate that reforestation efforts reduced mobile sand areas by 42.9% over three decades, with 78% of the sand loss attributable to vegetation stabilization. When infrastructure-affected areas were excluded, sand decreased by 64.7%, confirming genuine reforestation effectiveness. The 2023 wildfire caused substantial forest biomass losses in the reforested pine stands (264.19 t·ha⁻¹) and carbon reductions (124.17 t·ha⁻¹). These losses reflect the high vulnerability of Mediterranean coastal forests to wildfire disturbances under recurrent summer drought and increasing temperatures. The study emphasizes the importance of long-term remote sensing time-series for coastal forest management and restoration planning in Mediterranean ecosystems. Full article

Accurate characterization of riverbed substrate from remote sensing imagery is essential for applications in fluvial geomorphology, habitat modeling, and river management. While recent advances in computer vision, particularly deep learning, have improved sediment mapping capabilities, their reliance on large annotated datasets and computational resources limits their broader applicability. This study presents a scalable workflow for categorical substrate classification using ultra-high-resolution aerial RGB orthoimagery in clear-water river environments. The approach integrates spectral information with statistical and structural texture descriptors derived from Gray-Level Co-occurrence Matrices (GLCM) and Local Binary Patterns (LBP), combined within a Random Forest classification framework. The methodology is structured as a semi-automated, five-stage workflow: (1) expert-based ground-truth substrate annotation; (2) feature set generation; (3) spatially aware model optimization; (4) full-domain classification; and (5) design-based validation for independent accuracy assessment. Model performance is evaluated using spatially aware cross-validation and design-based probability sampling to account for spatial autocorrelation and provide unbiased accuracy estimates. The method was applied in four geomorphologically distinct alpine river reaches, achieving design-based overall accuracy ranging from 70% to 88%. These results demonstrate that RGB-based approaches can achieve reliable reach-scale categorical substrate classification when combined with appropriate feature representation and rigorous validation strategies. However, limitations remain for visually similar or transitional substrate classes, particularly fine sediments such as sand and clay, which are difficult to distinguish consistently even during manual annotation. The workflow is implemented using open-source tools and is applicable to clear-water conditions where the riverbed remains optically visible. Full article

In dryland ecosystems, the restoration of Salix psammophila shrubs plays a vital role in wind erosion control and sand stabilization. However, the temporal dynamics of soil particle-size distribution and erodibility during S. psammophila restoration remain poorly understood. To address this gap, we established a chronosequence of S. psammophila plantations in the Hobq Desert—a temperate desert in northern China—that had been restored for 6, 12, 15, 25, and 35 years, with adjacent shifting sand dunes serving as the control (CK). At each of the six sites, ten replicate plots were established, and soil samples were collected from the 0–20 cm layer, yielding a total of 60 samples. Multifractal parameters and the soil erodibility K factor were calculated to quantify the effects of stand age on particle-size distribution and erodibility. Principal component analysis (PCA) and a Random Forest model were then applied to factors associated for the observed changes. Compared with CK, soil nutrient and fine particle contents increased significantly with increasing shrub age, whereas pH and sand content declined continuously. Specifically, under S. psammophila plantations, organic carbon (OC), total nitrogen (TN), total phosphorus (TP), available phosphorus (AP), and alkali-hydrolysable nitrogen (AHN) contents increased continuously with stand age, while the soil texture became progressively finer. During long-term S. psammophila restoration, the ranges of the multifractal parameters D₀, D₁, D₂ and D₁/D₀ were 0.82–0.91, 0.59–0.71, 0.50–0.58, and 0.70–0.78, respectively. S. psammophila restoration exhibited pronounced multifractal characteristics, which reduced the heterogeneity of the soil particle-size distribution and made the distribution more uniform, thereby resulting in a more stable soil structure and a more balanced ratio of fine to coarse particles. The soil erodibility K factor indicated that soil erosion resistance gradually increased with stand age, with a 23.71% reduction at 35 years compared with CK. Random Forest analysis identified organic carbon (OC), total nutrients (TN, TP), pH, soil particle-size fractions (clay, silt, sand), D₁, D₂, and vegetation characteristics (aboveground biomass, AGB; plant density, PD) as important predictor variables for soil erodibility (p = 0.01, R² = 0.961). These findings provide new insights into the mechanisms by which long-term S. psammophila restoration improves soil structural stability and erosion resistance, offering a scientific basis for optimizing vegetation restoration and sustainable desert ecosystem management in arid regions. Full article

The Dongping Lake Basin (DLB) serves as a critical water regulation and supply zone for the South-to-North Water Diversion Project in China. Understanding the coupling effects and influence mechanisms between ecosystem services is essential for regional ecological restoration and sustainable development. This study employed the Coupling Coordination Degree (CCD) model, Random Forest, and Geodetector. We analyzed the spatiotemporal characteristics and driving factors of the relationship between carbon storage and water yield in the DLB from 1990 to 2020. The results showed that: (1) Carbon storage and water yield exhibited a pronounced spatial mismatch. This was generally characterized by a pattern of high in the eastern/northeastern regions and low in the west/southwest. (2) The overall coordination between carbon storage and water yield remained at a medium-to-low level. Temporally, the CCD followed a trajectory of initial stability, abrupt decline post-2000, and subsequent low-level stagnation. Spatially, the CCD presented an agglomeration gradient of “high in the northeast and low in the southwest”. It also exhibited a significant positive correlation with rising elevation, peaking in mid-to-high altitude zones. Furthermore, the overall coupling relationship showed a continuous degradation trend, heavily concentrated in the southwestern region. (3) Land use type and topographic slope were the primary driving factors shaping the CCD pattern. However, the synergistic interaction between precipitation and soil sand content demonstrated the strongest spatial explanatory power. This underscores the necessity of adapting localized management to specific environmental conditions. This study provides scientific support for carbon sink enhancement and water resource management in lake basins, thereby mitigating potential negative impacts on human well-being. Full article

Soil quality is a critical factor impacting agricultural productivity and ecosystem functions. Accurate assessment of soil quality is crucial for sustainable agricultural development. Xinjiang is the primary cotton-producing region in China. Its unique geographical condition, characterized by two basins surrounded by three mountains, results in distinct climatic conditions, soil-forming factors, and soil physical and chemical properties across different cotton-growing areas. The spatial differentiation patterns of soil quality and their primary factors in cotton-growing regions of different river basins are not yet fully understood. This study focused on four typical cotton-growing areas of Xinjiang, China. A total of 1588 plow-layer soil samples were collected, and 21 indicators covering soil physical, chemical, and environmental properties were measured. By constructing a minimum data set (MDS) and comparing the performance of linear (LS) and non-linear (NLS) scoring functions, the effects of geographical environmental factors on the spatial distribution patterns of soil quality in cotton fields of different basins were analyzed. The results showed the MDS, composed of data on soil bulk density and the contents of organic matter, available iron, available zinc, nickel, sand, and silt, could replace the total data set. The NLS-MDS was identified as the optimal assessment model. Its Nash–Sutcliffe efficiency coefficient (E_f = 0.84) and coefficient of determination (R² = 0.70) were both higher than those of the linear model (E_f = 0.79, R² = 0.66). The study also revealed significant spatial heterogeneity in soil quality across different cotton-growing areas. The average soil quality index (SQI) in the Aksu River Basin (SQI_NLS-MDS = 0.53) and Xiaohaizi Basin (SQI_NLS-MDS = 0.50) was significantly higher than that in the Kuitun River Basin (SQI_NLS-MDS = 0.44) and Manas River Basin (SQI_NLS-MDS = 0.41). Random forest analysis demonstrated that the relative importance of topographic (digital elevation model) and climatic factors (annual mean temperature, annual mean precipitation) on SQI was higher than that of the vegetation factor (normalized difference vegetation index). The strong interaction between topographic and climatic factors was the primary driver of the spatial distribution of soil quality. This study confirms significant spatial heterogeneity of soil quality in cotton fields across different river basins in Southern and Northern Xinjiang, and identifies the synergistic interaction between topographic and climatic factors as the dominant driver of this heterogeneity in arid regions. These findings provide a scientific basis for implementing site-specific agricultural management in arid regions. Full article

Terrestrial ecosystems serve as key carbon reservoirs and contribute substantially to global carbon cycling and climate regulation. Shaanxi Province (SP) is located along China’s north–south geographical boundary and climatic transition zone, making it crucial to understand how carbon stocks change within its ecosystems. This study analyzed the land-use patterns, influencing factors, and spatiotemporal dynamics of carbon storage across three regions (Shanbei, Guanzhong, and Shannan) in SP. The results indicated that: (1) From 2000 to 2020, cropland and barren land areas in SP decreased significantly, while the forest land area increased markedly. Total carbon storage in SP increased from 1688.55 Tg in 2000 to 1726.12 Tg in 2020, with the highest accumulation observed in Shannan, followed by Shanbei and Guanzhong. (2) Forest land acted as the most significant carbon sink; its contribution to SP’s total carbon storage increased from 54.98% in 2000 to 60.28% in 2020. (3) Carbon storage across the three regions was positively correlated with elevation, slope, soil silt content, and precipitation, but negatively correlated with soil sand content, gross domestic product, and population distribution. (4) Geographical detector analysis identified precipitation as the key influencing factor for carbon storage in Shanbei and Guanzhong, whereas the primary factors in Shannan were temperature, elevation, and slope. This study recommends future land use priorities: maintaining grassland dominance in Shanbei, scientifically optimizing the planning of cropland and impervious land in Guanzhong, and sustaining current forest protection and management in Shannan. These results provide vital quantitative support and important references for ecologically sustainable development and the realization of China’s dual-carbon goals in SP. Full article

Although the green belt around Greater Lomé (GBGL) is a vital ecological buffer, it is currently facing significant degradation. This decline appears to be associated with a combination of various socioeconomic uses by the local community and formal operations of established businesses. Grounded in the cultural materialism framework, this study aims to contribute to a better understanding of the dynamics of the socioeconomic uses of the green belt around Greater Lomé in a context of degradation and investigates the dynamics of these socioeconomic uses and their environmental impacts through a multidisciplinary methodology. This approach combines anthropological analysis based on field observation, 53 semi-structured interviews and 5 focus groups, a quantitative questionnaire survey (n = 384) and an analysis of land use and land cover (LULC) dynamics derived from Landsat imagery (2003–2023). The results reveal six main types of socioeconomic uses of the GBGL (notably land transactions, agriculture, breeding and grazing, exploitation of wood energy, timber and utility wood, sand mining, and waste disposal), which lead to complex social dynamics ranging from conflicts to alliances among stakeholders. The LULC dynamics analysis indicates a staggering 468.26% expansion in built-up areas over the last 20 years, at the expense of swamp vegetation/gallery forest (−76.79%), tree-and-shrub savanna (−53.47%) and plantations (−49.43). This study provides a scientific basis supporting the urgent necessity to establish the GBGL as a legally protected entity and argues in favour of an inclusive management model that is designed to reconcile the socioeconomic survival needs of local populations with sustainable preservation of essential ecosystem services. Full article

Topsoil sand content (TSC) is a critical indicator of soil degradation in black soil regions, yet its long-term dynamics remain poorly quantified. To address this, we developed an automated Landsat-based framework on Google Earth Engine (GEE) for mapping cropland TSC across the Northeast China Black Soil Region (NCBSR) from 1984 to 2023. The methodology integrates a hierarchical bare-soil extraction strategy using the Normalized Difference Bare Soil Index (NDBSI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Tillage Index (NDTI) with a Random Forest (RF) model optimized by three-band spectral indices and a “prediction-first” compositing workflow. Results demonstrate that the bare-soil extraction achieved an overall accuracy of 96%, while the TSC retrieval model maintained robust performance with a coefficient of determination (R²) of 0.80 and a root mean square error (RMSE) of 9.68%, together with satisfactory temporal transferability. Long-term mapping revealed a significant biphasic evolutionary trajectory: 23.4% of croplands experienced soil coarsening predominantly before 2000, followed by a partial reversal and stabilization in later decades. This framework provides a high-resolution, multi-decadal baseline for monitoring soil physical degradation and supports sustainable agricultural management in global black soil regions. Full article

Soil physicochemical and biochemical properties are fundamental to soil processes and ecosystem functioning in forest environments, yet their responses to dominant tree species in humid montane regions remain largely ununderstood. This study examined the effects of three widespread broadleaf species—Quercus pontica, Quercus petraea, and Fagus orientalis—on soil physical, chemical, and biochemical properties in natural forests in the Eastern Black Sea region, where these species play key ecological roles in structuring forest composition and biogeochemical processes. A total of 15 soil samples (5 per forest type) were collected under comparable climatic and geological conditions and analyzed for particle-size distribution, pH, electrical conductivity (EC), soil organic carbon, and key microbial activity indicators. Significant differences in soil properties were detected among forest types. Soils under Q. pontica were characterized by the lowest silt content and pH, but the highest sand content, soil organic carbon, microbial biomass carbon (C_mic), and microbial respiration. In contrast, soils under Q. petraea exhibited the highest clay content and pH, whereas F. orientalis soils showed lower sand content, EC, soil organic carbon, microbial biomass nitrogen (N_mic), and basal respiration. Multivariate analyses revealed that soil texture, pH, and C_mic are key factors driving soil differentiation across forest types. These patterns indicate that species-specific litter inputs and belowground processes regulate soil biochemical functioning by altering resource availability and habitat conditions. Crucially, this study sheds light on the soil-forming responses of these ecologically dominant species and their impacts on carbon cycle pathways and microbial dynamics at the regional scale. Overall, the study shows that tree species identity is a critical factor influencing soil function, with significant consequences for forest management, carbon sequestration strategies, and ecosystem resilience to changing environmental conditions. Full article

Timely and accurate soil property information is critical for sustainable agriculture and precision nutrient management. Conventional laboratory methods are accurate but costly and labor-intensive, restricting their feasibility for high-density soil mapping. Low-cost, portable near-infrared (NIR) spectroscopy presents a promising alternative for rapid, on-site, and non-destructive soil analysis. This study aimed to evaluate the potential of a low-cost, portable NIR sensor (NeoSpectra) for the quantitative prediction of key soil properties in paddy fields from Southeastern China. The target properties were soil organic matter (SOM), total nitrogen (TN), pH, and particle size fractions (clay, silt, and sand). A total of 995 soil samples were collected from representative paddy fields in the region and spectra measurements were conducted in the laboratory on air-dried samples. We developed and compared the performance of multiple machine learning algorithms, including partial least squares regression (PLSR), Cubist, random forest (RF) and memory-based learning (MBL), to build robust calibration models. The predictive models showed substantial performance for SOM and TN, indicating high accuracy (R² > 0.75, LCCC > 0.85, RPD > 2) for quantitative prediction. Predictions for pH, silt, sand, and clay were less accurate (R² of 0.48–0.53, LCCC of 0.67–0.71, RPD of 1.39–1.49), suggesting the sensor’s utility is limited to indicating general trends for these properties. Among the tested algorithms, MBL consistently provided the most accurate and robust predictions across the majority of soil properties. Our findings demonstrate that the low-cost portable NIR sensor, when coupled with appropriate machine learning algorithms, is a powerful and viable tool for the rapid and reliable estimation of critical paddy soil fertility properties (SOM and TN). This technology has significant potential to support field-level soil health monitoring, precision fertilization strategies, and sustainable land management in the agricultural systems of Southeastern China. Full article

High-precision sediment distribution maps are indispensable for nearshore sediment dynamics and ecology and nearshore resource management. Using grain-size data of surface sediments from the nearshore waters of Lianyungang and auxiliary datasets including bathymetric and hydrodynamic conditions, this study assessed Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR) for predicting sediment grain-size fractions and mapping sediment substrate types. All three models capture the spatial gradient of sediment grain size from fine to coarse from the nearshore to the offshore regions, but differ in preserving local heterogeneity and defining transition boundaries: XGBoost delivers the most balanced performance by preserving grain-size variability, reducing boundary mixing, and improving the identification of classes with limited samples; RF excels in robust delineation of gradual transitions, whereas SVR tends to produce fragmented boundaries and unstable performance for classes with limited samples. Feature importance reveals that hydrodynamic drivers dominate the spatial distribution of sand, whereas terrain indices are more influential for the clay distribution pattern, confirming the role of microtopography in modulating fine-sediment trapping. Overall, this study improves mapping accuracy and supports marine spatial planning and coastal infrastructure design. Full article

Agricultural land-use conversion in high-altitude cold-arid inland river basins profoundly affects soil ecosystems. This study investigates the middle and lower reaches of the Bayin River Basin (Qaidam Basin, China) at approximately 3000 m elevation. We examined a continuous, reversible gradient of land-use intensity ranging from intensively managed cultivated land and orchards to marginal farmland abandoned owing to salinisation and low fertility. Using a multi-model fusion framework combining geostatistics, random forest regression and partial least-squares path modelling, we quantified the spatial patterns of soil properties and the drivers of soil organic carbon (SOC). Compared with marginal farmland, both cultivated land and orchards showed markedly higher SOC content (10.7–41.1% increase), elevated total nitrogen (TN) and clay content, and reduced electrical conductivity and sand fraction. These changes demonstrate that abandonment of marginal farmland impairs SOC accumulation while accelerating soil degradation and salinisation. SOC and TN exhibited strong spatial autocorrelation over distances exceeding 27 km, largely controlled by broad-scale factors such as topography and climate. The Random Forest and Partial Least Squares Path Modeling consistently reveal a close synergistic variation between Total Nitrogen (TN) and Soil Organic Carbon (SOC). TN exerts a direct positive driving effect on SOC, while land use intensity positively affects SOC through an indirect pathway: “sand content drives land use → enhances vegetation cover → increases TN.” Reverse modeling has validated a similar driving effect of SOC on TN. This study offers practical pathways for the sustainable management of marginal farmland and the enhancement of carbon sinks, addressing a common issue in China and other developing countries. Full article

Illegal logging is a major driver of tropical deforestation, accounting for the majority of timber harvested in many tropical countries and degrading many protected areas, due to both weak law enforcement capacity and corruption. Commercial logging is illegal in Peru’s Allpahuayo-Mishana National Reserve, a state protected area, but clandestine logging operations persist and affect its biodiversity, including the endemic bird species associated with its rare Amazonian white-sand forests. We examined the effects of illegal logging operations on white-sand forest understory bird communities as sentinels of biodiversity. We sampled birds with mist nets at 12 study sites in unlogged forest and forest regenerating between 1 and 10 years after timber harvest, capturing and releasing 348 birds representing 54 species in 16 families. Forest structure differed significantly between forest treatments, with canopy cover in logged forest significantly lower than in unlogged forest. All avian foraging guilds tested (including ant followers, other insectivores, frugivores, granivores, and nectarivores) responded significantly to changes in one or more forest structure characteristics we measured. The abundance of ant followers and other insectivores was positively correlated with canopy cover, while granivore abundance was positively correlated with subcanopy cover, and both frugivore and nectarivore abundance was negatively correlated with the numbers of trees in white-forest stands. We also took a rare opportunity to compare avian foraging guilds and relative abundance using capture data collected at the same white-sand forest sites in both 2005 and 2023. Over this 18-year period, the total number of understory birds and ant followers in particular declined, whereas other insectivores increased with time since logging. Our results demonstrate that logging has significant influences on white-sand forest habitat structure and bird community dynamics for decades after logging events. Illegal logging threatens forests and wildlife in many tropical protected areas, and we recommend their managers prioritize both preventing illegal logging and mitigating its negative effects to effectively conserve biodiversity. Full article

Accurate estimation of topsoil moisture (TSM) is essential for optimizing agricultural practices, particularly in the context of precision farming. This study evaluates the use of high-resolution synthetic aperture radar (SAR) imagery from TerraSAR-X (X-band) and Radarsat-2 (C-band) for estimating TSM over bare agricultural soils, at both plot and intra-plot spatial scales. The experiment was conducted over a 420 km² area in southwest France, comprising 29 agricultural plots with varying topography, soil texture, and land management practices. Extensive in situ measurements of TSM, soil texture, and surface roughness were collected over multiple dates. A random forest regression model was developed to estimate soil moisture, using radar backscatter coefficients, incidence angles, soil texture components (clay, silt, sand), and roughness parameters (Hrms, correlation length) as input features. The modeling approach was applied at multiple spatial scales by extracting satellite signals within circular buffers of varying radius (5 to 30 m), as well as at the plot scale. Results indicate that estimation performance improves with increasing buffer size, with the best results achieved at the 30 m intra-plot scale (R² > 0.8, RMSE < 4 m³·m⁻³), outperforming plot-scale estimates. Both C-band and X-band data provided reliable results, with a slight advantage when combining data from multiple incidence angles. The inclusion of surface roughness and soil texture significantly improved model accuracy, underlining the importance of accounting for local soil properties in radar-based moisture retrieval. The intra-plot variability of TSM was found to be substantial, often exceeding inter-plot differences, highlighting the necessity for high spatial resolution in moisture monitoring. This study demonstrates the value of combining ground observations with multi-frequency SAR data and machine learning for high-resolution soil moisture mapping. The approach supports more precise water management strategies and contributes to sustainable agricultural development through informed decision-making. Full article