Search Results (1,808)

Under the context of global change, forest cover, as a critical component of terrestrial ecosystems, exerts a profound influence on regional ecological security and sustainable development through its spatiotemporal evolution. Current research on forest cover change primarily focuses on pattern description and single-factor driver analysis, with insufficient in-depth exploration of the interactions among multiple factors and their associated nonlinear mechanisms. To address this gap, this study focuses on the Wumeng Mountain area, a typical ecologically fragile karst region in Southwest China. By comprehensively employing methods such as Theil–Sen Median trend analysis, land use transfer matrix, standard deviation ellipse, and spatial autocorrelation analysis, this study systematically reveals the spatiotemporal evolution characteristics of forest cover from 1985 to 2024. On this basis, an integrated eXtreme Gradient Boosting–SHapley Additive exPlanations (XGBoost-SHAP) model is introduced to construct an indicator system comprising 16 driving variables, including elevation, slope, aspect, temperature, precipitation, soil type, soil pH, soil thickness, soil organic matter, soil moisture content, GDP, population, distance from water, distance from railway, distance from grade highway, and distance from government. This model quantifies the influence intensity of each driving factor on forest change. The main findings are as follows: (1) From 1985 to 2024, the forest cover rate in the Wumeng Mountain area significantly increased from 54.7% to 60.2%, exhibiting a “high-low-high” heterogeneous spatial distribution pattern along the northeast-southwest axis; (2) Forest increase primarily originated from the conversion of cropland and grassland, with contribution rates reaching 93.58% and 5.9%, respectively, indicating an overall trend of “increase in low-value areas and decrease in high-value areas”; (3) Forest cover change is driven by both natural and anthropogenic factors, with dominant driving factors exhibiting phased replacement over time. Overall, this is manifested as long-term stable constraints exerted by natural background factors, alongside strong disturbances from anthropogenic factors such as social-economic, and transportation-related activities. Natural factors remain the primary driving force behind changes in forest cover. The core findings of this study elucidate the complex driving factors of forest change in karst mountainous areas, thereby providing scientific support for the precise management of regional forest resources, the planning of ecological restoration projects, and the implementation of sustainable development strategies. Full article

Dietary transition is reshaping cropland demand and intensifying the challenge of matching food demand with land supply in rapidly urbanizing regions. This study examines how different dietary structure scenarios generate differentiated cropland demand, how these demands match with land supply under alternative development pathways, and how the land system responds when diet-driven demand is incorporated into land-use simulation. Using Jiangsu Province, China, as a case study, we developed a coupled diet–land simulation framework. On the demand side, five dietary structure scenarios—current, balanced, U.S., Japanese, and Greek—were constructed based on seven food categories, and their cropland demand in 2035 and 2050 was estimated using the cropland footprint approach and LSTM forecasting. On the supply side, the GeoSOS-FLUS model was used to simulate future land-use patterns under four development scenarios: natural development, cultivated land protection, ecological protection, and economic development. The cropland demand associated with each dietary scenario was then introduced into the land-use simulation process as an external demand constraint to identify land-system feedbacks and scenario differences. The results show that cropland demand differs markedly across dietary scenarios, forming a clear gradient from moderate-demand to high-demand diets. These differences are driven primarily by changes in the composition of key food categories, especially grains, livestock and poultry meat, plant oils, and fruits, rather than by proportional increases across all foods. In terms of supply–demand matching, the cultivated land protection scenario provides the strongest support for high-demand diets, whereas the natural development, ecological protection, and economic development scenarios are more compatible with moderate-demand dietary pathways. Once diet-driven demand is incorporated into land-use simulation, the land system shows clear sensitivity and strong scenario dependence. High-demand dietary scenarios intensify cropland compensation pressure and trigger structural reallocation among cultivated land and flexible land types. Under natural development, the response is mainly reflected in cropland expansion and grassland compression; under cultivated land protection and ecological protection, it is expressed more through substitutions among grassland, water bodies, and unused land; under economic development, the most prominent feedback is the competitive reallocation among cultivated land, construction land, and water bodies, with high dietary demand even constraining construction land expansion. Overall, the robustness of cropland supply–demand matching depends not only on the scale of dietary demand but also on how different dietary pathways interact with development-oriented land-use structures. Full article

Vicarious calibration is a technique that makes use of radiometrically stable targets such as dry lakebeds, desert sites, and open grasslands for the post-launch calibration of a satellite sensor. Top-of-the-atmosphere radiances or reflectances are provided from those sites for the calibration of a sensor. The reflectance of a remote sensing vicarious calibration site is measured by ratioing the signal from a ground target to the signal from a reference target, often a white panel made of PTFE whose reflectance is known. When physically mapping a vicarious calibration site prior to a satellite sensor overflight, there can be elapsed times between the two measurements as great as 10 min. The solar illumination can vary on time scales relevant to the time between measurements of a ground target and a reference panel, impacting the variance in the measured reflectance. In this work, we explore the impact of a temporal delay between two measurements taken outdoors on the Type A uncertainties in their ratios. A factor of 3 reduction in the Coefficient of Variation of the ratio taken simultaneously versus sequentially with delays on the order of 10 min was realized. Implications for protocols employed to measure the surface reflectance at sites used for the vicarious calibration of aircraft and satellite sensors are discussed. Full article

The administrative boundaries of ecosystems do not necessarily align with natural watershed boundaries, which is a significant reason for the current inefficiency and pronounced conflicts in ecological governance. Using the watershed as the fundamental unit, this study assessed the forest ecosystem services (FES) of the Beihai Wetland watershed in Tengchong (As of 2025). Forest vegetation was classified to the formation level, and the functional value method was employed. The results showed the following order of service values: regulating services > provisioning services > supporting services > cultural services. Biodiversity was identified as the most valuable ecosystem function. The study further revealed that factors such as stand type, stand age, and altitude influence the total FES value within the watershed. Analysis of FES per unit stand (1 ha) indicated that Lithocarpus variolosus Franch. Chun (natural forest) exhibited the highest value. Through in-depth analysis of linear correlations and spatial associations of FES per unit stand, a synergy-trade-off visualization was constructed. This revealed that natural forests in the upper watershed may exert systemic effects on nutrient cycling in the lower watershed. The results obtained at the formation level provide support for the development of watershed-based forest tending plans. Moreover, studying FES using the watershed as a unit represents a practical exploration of the “life community of mountains, rivers, forests, farmlands, lakes, grasslands, and deserts” and offers a potential reference for maintaining the ecological security and supporting the ecological protection and restoration of the Beihai watershed. Full article

In arid and semi-arid regions, the cultivation of artificial grasslands commonly suffers from low productivity due to insufficient water supply. The rational application of water-retaining agents is an important approach to alleviating production constraints in artificial grasslands facing resource-based water scarcity. This study investigated two types of water-retaining agents [starch-grafted acrylate water-retaining agent (B1) and polyacrylamide water-retaining agent (B2)] and four application rates [0 kg·hm⁻² (CK), 30 kg·hm⁻² (T1), 60 kg·hm⁻² (T2), 90 kg·hm⁻² (T3)], systematically analyzing their effects on the growth, osmotic adjustment substances, antioxidant enzyme activities, and yield of alfalfa. The results showed that alfalfa plant height, stem diameter, leaf area, branch number, soluble sugar (SS), soluble protein (SP), and proline (Pro) all exhibited a decreasing trend with increasing cutting times. The activities of superoxide dismutase (SOD), catalase (CAT), and peroxidase (POD) in alfalfa leaves initially increased and then decreased with increasing application rates of water-retaining agents, while malondialdehyde (MDA) content showed a decreasing trend. Under the B2T2 treatment, both alfalfa yield and water-use efficiency (WUE) reached their highest values, recorded as 4931.97 kg·hm⁻² (2022), 6021.44 kg·hm⁻² (2023) and 2.19 kg·m⁻³ (2022), 2.39 kg·m⁻³ (2023), respectively. Based on the principal component analysis for comprehensive evaluation, the B2T2 treatment (polyacrylamide water-retaining agent applied at 60 kg·hm⁻²) achieved the highest comprehensive score in both years and could synergistically improve alfalfa yield and water-use efficiency. However, its applicability in the Yellow River irrigation region of Gansu Province and similar ecological areas still requires further verification through field trials. Full article

Scientifically assessing the spatiotemporal evolution of regional carbon storage is of great significance for achieving the “dual carbon” goals and optimizing territorial spatial patterns. This study integrated the PLUS and InVEST models to systematically reconstruct the spatiotemporal pattern of carbon storage in Hebei Province from 2000 to 2020, simulate its evolution trajectory under different scenarios in 2030, and identify its driving mechanisms using the GeoDetector model. The main findings are as follows: (1) From 2000 to 2020, cropland was the dominant land use type in Hebei Province, and carbon storage exhibited a spatial pattern of “high in the northwest, low in the southeast.” Carbon storage increased from 16.23 × 10⁸ t to 16.31 × 10⁸ t, with a significantly slowed growth rate after 2010. (2) Multi-scenario simulations for 2030 indicate that under the natural development and economic priority scenarios, construction land expands significantly while cropland and grassland continue to decrease. In contrast, carbon storage shows an increasing trend under the ecological protection and cropland protection scenarios. (3) Driving factor analysis reveals that the spatial differentiation of carbon storage is primarily controlled by natural factors such as slope, elevation, and NDVI, while the explanatory power of anthropogenic factors, particularly population density, has significantly increased. The interaction between NDVI and slope exhibits a synergistic enhancement effect. This study elucidates the coupling mechanisms between land use change and carbon storage under different policy orientations, providing a scientific basis for territorial spatial optimization and the formulation of differentiated carbon neutrality pathways in Hebei Province. Full article

As a major ecological safeguard in northwestern China and an important corridor for the Belt and Road Initiative, the Hexi Corridor holds strategic significance for improving landscape structure and enhancing regional ecological security. Focusing on the Hexi Corridor, this study develops a landscape ecological risk (LER) index based on land use (LU) data from 2000, 2010, and 2020. The study employs ArcGIS spatial analysis and XGBoost-SHAP, an interpretable machine learning method, to analyze the spatiotemporal evolution of LU and LERs, as well as their driving factors. Furthermore, the Markov-FLUS model is utilized to simulate and predict LU and LER spatial patterns under multiple scenarios for 2030. The results show that: (1) The dominant land type in the Hexi Corridor is unused land, accounting for 67.33%. During the research period, the extents of unused land, grassland, and forestland showed a steady decline, while built-up land and cropland increased. (2) LERs are categorized into five types, with high risk being the most prevalent, accounting for 52.02%. Between 2000 and 2020, the total area of higher and high risks decreased by 4312 km², indicating an overall decrease in LER across the region. (3) LER is primarily influenced by annual rainfall, population density, distance to main roads, and distance to rivers. (4) Marked variations in LU patterns and LER are observed across different development scenarios projected for 2030. Full article

Amidst global urbanization and rising psychological stress, urban green spaces are increasingly recognized as critical infrastructure for sustainable urban development and public health. However, the mechanisms by which summer vegetation structure mediates both physiological and psychological restoration, and the interplay between these two dimensions, remain poorly understood. Understanding these mechanisms is essential for designing sustainable, health-promoting urban environments that can support growing urban populations in a warming climate. This study employed a controlled field experiment in Xi’an during summer to examine the effects of five vegetation structure types (Single-Layer Grassland, single-layer woodland, tree–shrub–grass composite woodland, tree–grass composite woodland, and a non-vegetated square) on university students’ physiological (heart rate variability) and psychological (perceived restorativeness and affective states) restoration. Following stress induction, 300 participants engaged with the green spaces through both quiet sitting and walking. The results revealed three key findings: (1) the tree–shrub–grass composite woodland consistently showed the most favorable trends other vegetation types across all psychological restoration dimensions, while also showing favorable trends in physiological recovery, underscoring the importance of structural complexity for restorative quality; (2) walking significantly enhanced physiological recovery compared to seated observation across all settings, confirming the role of physical activity as a critical activator of green space benefits; (3) correlation analysis identified a specific cross-system association: the R-R interval recovery value showed a weak but significant correlation with positive affect (PA) scores, suggesting that physiological calmness and positive emotional experience are linked, yet their weak coupling under short-term exposure indicates they may operate as parallel processes with distinct temporal dynamics. These findings indicate that the restorative potential of summer green spaces emerges from an integrated framework combining vegetation complexity and activity support. We propose that future sustainable landscape design should prioritize multi-layered vegetation structures as nature-based solutions that simultaneously enhance human well-being and urban resilience. These findings provide empirical evidence for integrating health-promoting green infrastructure into sustainable urban planning frameworks, supporting multiple Sustainable Development Goals (SDGs), including SDG 3 (Good Health and Well-being), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action). Full article

Live Fuel Moisture Content (LFMC) is a key determinant of vegetation flammability and fire behaviour, yet LFMC products have traditionally relied on coarse-resolution sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS, 500 m), limiting their utility for fine-scale fire management. This study introduces the first continental-scale operational LFMC product for Australia derived from Sentinel-2 imagery at 20 m resolution. We developed a Random Forest regression model trained on approximately 680,000 paired Sentinel-2 reflectance and MODIS-LFMC samples (2015–2022) to emulate outputs from the Australian Flammability Monitoring System (AFMS), a MODIS-based pre-operational LFMC product. Model evaluation against AFMS showed strong agreement for grasslands (R² = 0.83, RMSE = 32.45%) and moderate performance for forests (R² = 0.43, RMSE = 20.84%) and shrublands (R² = 0.21, RMSE = 10.28%). Validation using 2279 in situ LFMC measurements from Globe-LFMC 2.0 indicated improved accuracy at homogeneous sites (NDVI CV ≤ 20th percentile: R² = 0.42, RMSE = 31.39%). Additionally, when validating with a dedicated field campaign specifically designed for Sentinel-2 LFMC assessment, the model achieved its highest accuracy (R² = 0.53, RMSE = 32.14%), highlighting the importance of tailored ground protocols for satellite product validation. Predicted LFMC also reproduced observed seasonal dynamics at sites with frequent field monitoring. Despite variability across vegetation types, the Sentinel-2 LFMC product effectively captured spatial patterns and seasonal dynamics, providing a step change in monitoring vegetation moisture at landscape scales. This high-resolution dataset offers actionable intelligence for prescribed burning, fuel treatment planning, and fire behaviour modelling in fire-prone environments. Full article

Land use change has direct human impacts and profoundly alters the structure and function of terrestrial ecosystems. Numerous studies have explored land use change dynamics in the context of socio-economics, often overlooking the influence of climate change on the potential suitability of land use. To address this gap, we propose an integrated framework combining CLUE-S and MaxEnt models to analyze how land use in Tai’an City, Shandong Province, China, responds to future socio-economic and climate change scenarios. The CLUE-S model, based on land demand, and the MaxEnt model, based on suitability assessment, can effectively explore the trends of land change under the influence of human activities and global warming. This study maps the spatial distributions of land use under socio-economic development and four climate change pathways. Overall, the AUC values of the CLUE-S model were all greater than 0.7, and those of the MaxEnt model were all greater than 0.5, indicating that the results of both are relatively reliable. Our study reveals that, within the baseline development (BL) scenario, cultivated land, forest land, grassland, and unused land are projected to decrease between 2020 and 2040. Conversely, the expansion of water bodies and built land will keep growing. In addition, climate change is expected to enhance the suitability of cultivated land between 2020 and 2040, while reducing that of forest land, grassland, unused land, and built land, with only minimal effects on water bodies. Finally, our framework projected that the most widespread priority areas are cultivated land, followed by forest, grassland, water, built land, and unused land. These priority areas are largely determined by human activities, while the influence of climate change is relatively small. Our research framework has broad applicability to the other regions. Considering the MaxEnt model within the framework is beneficial for excluding unsuitable distribution areas of land use types in the CLUE-S model, which will provide new insights for the sustainable use of land resources. Full article

Land-use change contributes significantly to climate change mitigation through biophysical changes (albedo, α) and biogeochemical (greenhouse gases, GHG) emissions (here refers to methane, CH₄, and nitrous oxide, N₂O). While the impact of grassland–cropland conversion on global warming potential (GWP) is well-documented globally, research remains scarce in the saline-alkaline agropastoral transition zone (APTZ) of the western Songnen Plain, Northeast China, an ecotone uniquely characterized by soil-crusting and seasonal inundation. We conducted in situ bi-weekly measurements of N₂O and CH₄ fluxes (June–September) to acquire growing season ${\mathrm{GWP}}_{{\mathrm{N}}_2\mathrm{O}}$ and ${\mathrm{GWP}}_{{\mathrm{C}\mathrm{H}}_4}$, alongside α. The study compared an undisturbed fenced meadow (FMD) with three adjacent land-use types, clipped meadow (CMD), saline-alkaline meadow (SAL), and paddy rice field (PDY), converted from FMD from 2018 to 2022. Annual α-induced GWP (GWPΔα) was positive across all converted sites (CMD, SAL, and PDY), indicating a warming effect due to lower α compared to FMD. The PDY exhibited the highest CH₄ emission (5.04 kg CO₂ m⁻² yr⁻¹), exceeding other land uses by three orders of magnitude (p < 0.05). Conversely, N₂O emissions remained consistently minimal and stable across all sites. When integrating the net ecosystem exchange of CO₂ (NEE), the PDY functioned as a net warming source. In contrast, the warming effects of α and non-CO₂ GHGs were effectively offset by the NEE in other land uses. Machine learning identified soil water content (SWC) as the dominant predictor of α across all land uses in growing season. However, a mechanistic divergence was observed, i.e., α in low saline-alkali ecosystems (FMD, CMD and PDY) was shaped by coupled biotic and soil moisture controls, whereas in the degraded SAL ecosystem, α is almost exclusively abiotic-driven. These findings demonstrate that land-use conversion in the Songnen Plain governs complex land-surface feedbacks through distinct pathways. This study provides a quantitative framework for integrating biophysical and biogeochemical impacts to optimize land management for climate resilience in saline-alkaline agropastoral ecotones. Full article

Urban wetlands are acoustic hotspots where vegetation structure, hydrological dynamics, and anthropogenic noise interact, yet multi-season assessments of how vegetation influences avian soundscapes are limited. This study explored bird soundscape dynamics across forest, open forest grassland, and meadow habitats in Nanjing Xinjizhou National Wetland Park, eastern China, using passive acoustic monitoring during spring and autumn 2023. Twelve sampling points (four per vegetation type) were established, and six acoustic indices were calculated, including the Acoustic Complexity Index (ACI), Acoustic Diversity Index (ADI), Acoustic Evenness Index (AEI), Bioacoustic Index (BIO), Normalized Difference Soundscape Index (NDSI), and Acoustic Entropy Index (H). were calculated from 48-h recordings each season. Random forest models and redundancy analysis assessed the relationships between acoustic indices, fine-scale vegetation parameters (e.g., crown width, tree height, species richness), and anthropogenic factors (e.g., distance to roads/trails, surface hardness). Vegetation structure, particularly crown width, was the primary driver of avian acoustic diversity, with broad-crowned forests consistently exhibiting the highest acoustic complexity. In spring, anthropogenic factors such as trail and road proximity dominated soundscape variation, suppressing biological sounds. In autumn, with reduced human presence, vegetation structure emerged as the dominant factor, while bioacoustic activity remained elevated despite reduced peaks in acoustic complexity. Proximity to roads increased low-frequency (1–2 kHz) noise and suppressed mid-frequency (4–8 kHz) bird vocalizations, but trees with crown widths ≥4 m maintained higher acoustic diversity even near disturbance sources. This study demonstrates that vegetation structure mediates both resource availability and sound propagation, buffering the effects of anthropogenic disturbance in frequency-specific ways. Multi-season sampling is crucial for understanding the dynamic interplay between vegetation phenology and human activity that shapes urban wetland soundscapes. Full article

Accurate soil moisture assessment is essential for effective agricultural management in the southern US, where water availability has a significant impact on crop productivity. This study evaluates the Soil Moisture Active Passive (SMAP) Level-4 daily soil moisture product using in situ measurements from Natural Resources Conservation Service (NRCS) Soil Climate Analysis Network (SCAN) stations and the US. Climate Reference Network (USCRN) across diverse agroecosystems in Texas from 2016 to 2024. SMAP’s performance was examined across ten climate zones and six major land cover types, including urban regions, pastureland, grassland, rangeland, shrubland, and deciduous forests. Statistical metrics, including the coefficient of determination (R²), Root Mean Square Error (RMSE), Bias, and unbiased RMSE (ubRMSE) were used to evaluate the agreement between SMAP-derived and in situ soil moisture measurements. Results show that SMAP effectively captures seasonal soil moisture dynamics but exhibits spatially variable accuracy. The highest agreement was observed at Panther Junction (R² = 0.57, RMSE = 2.29%), followed by Austin (R² = 0.57, RMSE = 9.95%). While a weaker coefficient of determination was observed at PVAMU (R² = 0.28, RMSE = 11.28%) and Kingsville (R² = 0.11, RMSE = 7.33%), likely due to heterogeneity in land cover, and urbanized landscapes in these stations. Applying the quantile mapping bias correction methods significantly reduced RMSE and improved the accuracy of SMAP soil moisture data at some in situ measurement stations. The results highlight the importance of station-specific calibration and the integration of satellite and ground-based measurements to improve soil moisture monitoring for agriculture and drought management in Texas and similar regions. Full article

Vegetation restoration in water-limited regions typically increases evapotranspiration (ET) while reducing runoff. Over the past four decades, Daihai Lake in China’s northwest inland river basin has experienced significant shrinkage. Previous studies attribute this primarily to climate change and water resource exploitation, yet the impact of vegetation dynamics remains insufficiently examined. This study analyzed changes in the water budget across different vegetation types in the Daihai Lake Basin, based on remote sensing-derived precipitation and ET data, and employed correlation analysis to examine the relationships between environmental factors (such as climate change, afforestation projects, and water-saving irrigation) and lake shrinkage. Our findings revealed that afforestation has expanded forest cover by 69.42 km² since 2000, accounting for 73.95% of the total forest area. Notably, forest ET demonstrated the strongest negative correlation (r = −0.89, p < 0.001) with lake area among all vegetation types. Grasslands emerged as the primary water-surplus vegetation, contributing 81.34% to the basin’s total water surplus. The synergistic effects of precipitation reduction, temperature increase, and enhanced ET from forest expansion drove the shrinkage of the lake. These results highlight the need for science-based vegetation management in arid and semi-arid regions, where we recommend adopting shrub-grass combined restoration approaches to enhance the sustainability of ecological restoration. Full article

Changes in land use and land cover (LULC) are among the leading contributors to global environmental transformation. Analyzing these dynamics is essential for understanding historical land utilization patterns and identifying the key drivers behind such shifts. This research focuses on LULC changes in the Kunar region of eastern Afghanistan. To classify the LULC types, the study area was divided into nine major classes using the Support Vector Machine (SVM) algorithm, based on Landsat 07 Enhanced Thematic Mapper Plus (ETM+) data for 2004 and Landsat 8 Operational Land Imager (OLI) data for 2014 and 2024. Past and present changes were evaluated using ArcGIS 10.8, while future scenarios for 2034 and 2044 were simulated using the Land Change Modeler (LCM) embedded in the TerrSet platform, combined with the Cellular Automata–Markov Chain (CA-MC) model with 90% kappa agreement validation value. From 2004 to 2024, grassland expanded significantly from 68.93% (3406 km²) to 73.94% (3654 km²). Built-up areas grew from 0.59% (29.10 km²) in 2014 to 1.02% (50.39 km²) in 2024. Conversely, dense forest cover declined from 27.50% (1358.90 km²) to 22.96% (1134.75 km²), a decrease of 224.15 km². Barren land, after a temporary increase, also showed a net decline. Projections for 2034 and 2044 suggest a further reduction in forested areas to 1077 km², while grasslands and urbanized zones are expected to increase to 3690 km² and 60.63 km², respectively. These trends emphasize a swift transition in land use patterns, primarily driven by the conversion of forested and barren landscapes into settlements and grasslands. The findings underline the urgent need for implementing sustainable land management strategies to curb environmental degradation and ensure balanced land resource utilization in the future. Full article