Search Results (865)

Forest cover dynamics strongly influence ecological integrity and resource sustainability, particularly in ecotonal landscapes, where vegetation is highly sensitive to climate variability, long-term climate change, and anthropogenic disturbances. This study examined Forest Land (FL), representing all areas of dense, canopy-forming woody vegetation with forest-like structure, aggregated from SANLC classes, in relation to eight other land cover classes across three periods: 1990–2014, 2014–2022, and 1990–2022. The study used South African National Land Cover datasets and the TerrSet–LiberaGIS Land Change Modeller to quantify changes in magnitude, direction, and source–sink relationships. Analyses included post-classification comparison to determine spatial changes, transition matrices to identify land-cover conversions, and asymmetric gain–loss metrics to reveal sources and sinks of forest change. The result shows that between 1990 and 2014, forests remained marginal and fragmented in the eastern central part of the study area, while shrubland increased from 40.4% to 60.2% at the expense of grasslands, cultivated land, bare land, wetlands, and forest land. From 2014 to 2022, FL regeneration was pronouncedly increased from 2% to 6%, especially along riparian corridors and reservoir margins, coinciding with shrubland decline (99.3%) and grassland recovery (261.2%). Over the entire 1990–2022 period, FL increased from 2.4% to 6% expanding into bare land, cultivated land, grassland, shrubland, and wetlands. Asymmetric analysis indicated that forests acted as a sink during the first period but as a source of ecological resilience in the second and final. These findings demonstrate strong vegetation feedback to hydrological and anthropogenic drivers. Overall, the findings underscore the potential for forest recovery to enhance biodiversity, ecosystem services, carbon storage, and hydrological regulation, while identifying priority areas for riparian conservation and integrated catchment management. Full article

Vegetation phenology, particularly the start of the growing season (SOS) date, is a key indicator of the climate sensitivity of ecosystems, yet its accurate prediction remains challenging. This study investigates the SOS of Hungarian grasslands between 2000 and 2023 using MODIS NDVI data, testing ten process-based models of varying complexity. Model parameters were optimized with the differential evolution algorithm under three calibration strategies: generic (GEN, aiming for a single model setting for the country), grassland-type (GEN GRASS, where grasslands are first categorized and then a type-specific parameterization is sought), and pixel-level (PIX, where model parameterization is performed for each pixel separately). The models with the lowest RMSE values were AGSI and AHSGSI (driven by temperature, vapor pressure deficit, and photoperiod) under PIX (RMSE = 3.3 days), AGSIwSW (driven by temperature, soil water content, and photoperiod) under GEN and GEN GRASS (RMSE = 7.6 and 6.3 days, respectively), and MGDDwPP (driven by temperature and photoperiod) under GEN (RMSE = 7.6 days). Considering the Akaike Information Criteria, the simplest GDD model (driven by temperature only) was the proposed one under PIX, while MGDDwPP was identified as the best model both in GEN and GEN GRASS. Residual analysis revealed relatively strong co-variation between model errors and some basic climate anomalies (most of all spring temperature and soil water content), enabling statistical corrections that reduced bias close to zero across all models. Integrating local climate and soil information into phenology models enhances their accuracy for grassland SOS estimation in Central Europe. Full article

This study examines the relationships between land use, climate, and nature-based adaptation in coastal and island regions of South Korea, focusing on the counties of Muan and Shinan along the southwest coast. Using land use data (2014) and meteorological data (2001–2010), Spearman correlation analysis was applied to assess the associations between six land-use categories and eight climatic indicators, including temperature extremes, tropical nights, and precipitation patterns. Results show that built-up and agricultural areas are closely linked to higher maximum temperatures and more frequent heatwaves, indicating greater climatic vulnerability. Conversely, wetlands, and bare lands demonstrate significant cooling effects, acting as natural buffers against rising temperatures. Wetlands play dual roles in supporting initial hydrological heat mitigation but enhancing nocturnal heat retention during prolonged heatwaves. Forests and grasslands emerged as important land-use types that can help reduce the number of tropical night days. These findings underscore the importance of nature-based land management—such as forest expansion, wetland conservation, and vegetation restoration—for mitigating heat stress and enhancing climate resilience. This study calls for extending national climate adaptation policies beyond urban areas to support aging, and therefore vulnerable, coastal and island populations facing the intensifying effects of climate change. Full article

Reclamation measures are essential tools for enhancing ecosystem functions and promoting ecological sustainability. This study focused on the Jiangnan mining area within the Muli coalfield in Qinghai Province, China. Four organic fertilizer reclamation treatments were established, namely, unfertilized control (CK, 0), low fertilizer (LF, consisting of sheep manure at 165 m³/ha and commercial organic fertilizer at 7.5 t/ha), medium fertilizer (MF, using 330 m³/ha of sheep manure and 15.0 t/ha of commercial organic fertilizer), and high fertilizer (HF, using 495 m³/ha of sheep manure and 22.5 t/ha of commercial organic fertilizer), with a natural meadow near the experimental site selected as a reference for evaluation. Through a field vegetation survey and indoor analysis, the primary productivity, water conservation, carbon cycle, nitrogen cycle, and phosphorus cycle of five ecosystem functions and ecosystem multifunctionality (EMF) were quantified, and the trade-off relationships among ecosystem functions were analyzed. The findings indicate the following: (1) Compared to the unfertilized control, organic fertilizer reclamation significantly enhanced all individual ecosystem functions and EMF, with the EMF value under the high-fertilizer treatment (EMF = 0.69) even exceeding that of the natural grassland (EMF = 0.60). (2) This intervention altered the original trade-off patterns (E_RMSD = 0.03), intensifying trade-offs among multiple ecological functions (E_RMSD = 0.09), whereas natural grassland exhibited the strongest trade-off intensity (E_RMSD = 0.26). In summary, while organic fertilizer reclamation effectively enhances the multifunctionality of alpine mining ecosystems, it also amplifies trade-off effects among ecological functions to varying degrees. Therefore, future long-term positioning observations are required to evaluate the ecological stability and sustainability of this restoration technology under extreme climatic conditions and to further explore reasonable grazing and mowing management plans in order to coordinate multiple ecological functions, thereby promoting the development of the reclamation ecosystem in alpine mining areas toward coordination and health. Full article

The alpine grassland is one of the most representative ecosystems on the Qinghai–Tibet Plateau, Growth monitoring is fundamental for the alpine grassland maintenance and husbandry sustainability. In this study, by the integration of regression model, principal component analysis, and SHAP-enhanced machine learning, a comprehensive growth index (CGI) was proposed for the accurate and quick assessment of alpine grassland growth in Qinghai Province, located in the eastern Qinghai–Tibet Plateau. The temporal and spatial growth behaviors of the main grassland types over 2001–2023 were then determined and the differences in key driving factors and their responses explored. The results indicated that the CGI composed of KNDVI, EVI, MSAVI, GNDVI and CVI characterized the typical ecological and physical parameters related to grassland growth, proved to be optimal and efficient in long-term growth monitoring. Alpine grassland growth fluctuated but gradually increased from 2001 to 2023, but individual types exhibited different trends. In particular, the two main types of alpine meadow and alpine steppe displayed the weakest increasing trend in growth, with the good-growth and continuous-increasing area proportions of 26.01% and 18.03%, 70.45% and 74.72%, respectively. Soil total nitrogen was the most critical common factor and significantly increased the growth across all five grassland types, then followed by grazing intensity and precipitation, which exhibits diverse effects on the individual types. The result implies the significant heterogeneity in the key driviers which affect the alpine grassland growth over large scale. Full article

Broad-scale assessments often track forest productivity, yet they rarely quantify how soil conditions determine whether these gains persist as long-lived carbon and generate measurable economic value. This study focused on Northeast China, where forests include boreal coniferous stands dominated by Dahurian larch, temperate conifer–broadleaf mixed forests with Korean pine, and temperate deciduous broadleaf forests dominated by Mongolian oak. We combined GLASS net primary productivity and ESA CCI Land Cover to delineate forest pixels, used 2000 to 2005 as the baseline, and converted productivity anomalies into pixel level carbon economic value using a consistent pricing rule. Forest NPP increased significantly during 2000 to 2018 (slope = 1.57, p = 0.019), and carbon economic value also increased over time during 2006 to 2018 (slope = 2.24, p = 0.002), with the highest values in core mountain forests and lower values in the western forest–grassland transition zone. Correlation analysis, explainable random forests, and variance partitioning characterized spatial and temporal dynamics from 2000 to 2018 and identified environmental controls. Carbon value increased over time and showed marked spatial heterogeneity that mirrored productivity patterns in core mountain forests. Climate was the dominant predictor of value, while higher soil pH and clay content were negatively associated with value. The random forest model explained about 70% of the variance in carbon value (R² = 0.695), and variance partitioning indicated substantial unique and joint contributions from climate and soil alongside secondary topographic effects. The automatable framework enables periodic updates with new satellite composites, supports ecological compensation zoning, and informs soil-oriented interventions that enhance the monetized value of forest carbon sinks in data-limited regions. Full article

Surface soil moisture (SSM) is essential for crop growth, irrigation management, and drought monitoring. However, conventional field-based measurements offer limited spatial and temporal coverage, making it difficult to capture environmental variability at scale. This study introduces a multimodal soil moisture estimation framework that combines synthetic aperture radar (SAR), optical imagery, vegetation indices, digital elevation models (DEM), meteorological data, and spatio-temporal metadata. To strengthen model performance and adaptability, an intermediate fine-tuning strategy is applied to two datasets comprising 10,571 images and 3772 samples. This approach improves generalization and transferability across regions. The framework is evaluated across diverse agro-ecological zones, including farmlands, alpine grasslands, and environmentally fragile areas, and benchmarked against single-modality methods. Results with RMSE 4.5834% and $R^2$ 0.8956 show consistently high accuracy and stability, enabling the production of reliable field-scale soil moisture maps. By addressing the spatial and temporal challenges of soil monitoring, this framework provides essential information for precision irrigation. It supports site-specific water management, promotes efficient water use, and enhances drought resilience at both farm and regional scales. Full article

Net ecosystem exchange (NEE) is a central metric for assessing carbon cycling, and its accurate quantification is critical for understanding terrestrial-atmosphere carbon exchange dynamics. However, in complex alpine regions, high-resolution NEE estimation remains challenging due to limited observations and heterogeneous surface processes. To address this, we developed a multimodal feature fusion model (Multimodal-CNN-Attention-RF, MMCA-RF) that integrates convolutional neural networks (CNN) and random forest (RF) for NEE estimation in the Babao River Basin on the northeastern Tibetan Plateau. The model incorporates a cross-modal attention mechanism to dynamically optimize feature interactions, thereby better capturing the spatially heterogeneous responses of vegetation to environmental drivers. Results demonstrate that MMCA-RF exhibits strong stability and generalization, with R² values of 0.89 (training) and 0.85 (testing). Based on model outputs, the Babao River Basin acted as a carbon sink during 2017–2023, with a mean annual NEE of −100.86 gC m⁻² yr⁻¹. Spatially, NEE showed pronounced heterogeneity, while seasonal variation followed a unimodal pattern. Among vegetation types, grasslands contributed the largest total carbon sink, whereas open woodlands showed the highest sequestration efficiency per unit area. Driver analysis identified temperature as the dominant control on NEE spatial variation, with interactions between temperature, precipitation, and topography further enhancing heterogeneity. This study provides a high-accuracy modeling approach for monitoring carbon cycling in alpine ecosystems and offers insights into the stability of regional carbon pools under climate change. Full article

To investigate the influence of land use landscape patterns on lake water quality in the basin, the land use and water quality data of the Yilong Lake Basin from 1993 to 2023 were analyzed with a geographic information system, remote sensing, and landscape ecology methods in this research. The results show that (1) the land use landscape pattern and water quality of the Yilong Lake Basin had significant changes: the lake surface area, farmland, and shrubland declined, with grassland showing the sharpest decrease and serving as the main source of conversion to other land types, while forest land expanded and built-up land increased by five times. The landscape pattern analysis showed that the aggregation degree of the core habitat in the basin increased and the landscape had decreased patch density and increased heterogeneity. Regarding water quality, the concentrations of total nitrogen (TN), total phosphorus (TP), and ammonium nitrogen (NH₄⁺-N); permanganate index (I_Mn); and biochemical oxygen demand over 5 days (BOD₅) decreased. Furthermore, the concentration of dissolved oxygen (DO) increased and the concentration of chlorophyll-a (Chl-a) fluctuated for a long time but did not decrease dramatically at the end of the period compared with the beginning. In general, the eutrophication degree of Yilong Lake slightly decreased. (2) The landscape configuration strongly shaped the water quality: the redundancy analysis (RDA) revealed that the edge density (ED), landscape shape index (LSI), largest patch index (LPI), and patch density (PD) were negatively associated with the eutrophication of Yilong Lake (TN, TP, NH₄⁺-N, Chl-a), whereas the contagion index (CONTAG) was positively associated; the Shannon’s diversity index (SHDI) was closely linked with TN and I_Mn but negatively with DO; and the patch cohesion index (COHESION) had a low interpretation power for water quality changes. In particular, larger and more cohesive ecological patches supported a higher DO, while an increased patch density was linked to an elevated I_Mn and reduced DO. These results indicate that the restoration of key ecological patches and enhanced landscape cohesion helped to improve the water quality, whereas increased patch density and landscape heterogeneity negatively affected it. (3) In the past 30 years, the ecological management and protection work on Yilong Lake, such as returning farmland to forests and lakes, wetland restoration, and sewage pipe network construction, achieved remarkable results that were reflected in the change in the relationship between land use landscape pattern and water quality in the basin. However, human activities still affected the dynamic evolution of water quality: the expansion of built-up land increased the patch density, the reduction in shrubland and grassland weakened natural filtration, and the rapid urbanization process introduced more pollution sources. Although the increase in forest land helped to improve the water quality, the effect was not fully developed. These findings provide a scientific basis for the management and ecological restoration of plateau lakes. Strengthening land use planning, controlling urban expansion, and maintaining ecological patches are essential for sustaining water quality and promoting the coordinated development of the ecology and economy in the Yilong Lake Basin. Full article

Rural development significantly contributes to global carbon emissions. While China’s dual-carbon goals are critical for global climate mitigation, surging rural emissions and regional disparities challenge their realization. Implementing village-scale horizontal carbon compensation zoning offers a strategic solution, though empirical evidence at this granularity remains scarce. Addressing this gap, this study conducts an empirical analysis of Laiyuan County in North China, integrating field data with village-scale carbon budget accounting. A multi-dimensional evaluation system was developed to classify and refine compensation zones. The results showed that (1) Laiyuan County exhibits a distinct “core–periphery” carbon budget pattern, with overall emissions exceeding carbon sinks. 46.6% of villages and 61.1% of townships are net carbon sources. Human respiration and domestic waste dominate the emission structure, while forests, grasslands, and shrublands provide the overwhelming majority of carbon sinks. Farmland contributes only limited sequestration, indicating an urgent need to enhance its sink capacity. (2) The multidimensional framework that incorporates Economic Contribution Coefficient (ECC), Carbon Emission Intensity (CEI), Ecological Support Coefficient (ESC), and Territorial Development Intensity (TDI) effectively guides compensation zoning, revealing positive CEI-TDI/ESC-ECC and U-shaped CEI-ECC/CEI-ESC relationships. These patterns underscore the necessity of integrated ecological–economic planning. (3) Villages can be systematically categorized into Payment Zones, Recipient Zones, and Equilibrium Zones. Integration with territorial planning further delineates 11 functional subregions, highlighting critical conflicts in subregions of Payment Zone-Permanent Basic Farmland and Payment Zone-Ecological Conservation Redline. This study advances methodologies for village-scale carbon management and provides actionable insights for achieving dual-carbon goals in rural areas of North China and beyond. Full article

Carbon sequestration by vegetation and soil conservation are vital components in balancing greenhouse gas emissions and enhancing terrestrial ecosystem carbon sinks. They also represent an efficient pathway towards achieving carbon neutrality objectives and addressing numerous environmental challenges arising from global warming. Soil and water conservation, as crucial elements of ecological civilisation development, constitute a key link in realising carbon neutrality. This study systematically quantifies and forecasts the spatiotemporal characteristics of carbon sink capacity in soil and water conservation within the study area of Puding County, a typical karst region in Guizhou Province, China. Following a research approach of “mechanism elucidation–model construction–categorised estimation”, we established a carbon sink calculation system based on the dual mechanisms of vertical biomass carbon fixation via vegetative measures and horizontal soil organic carbon (SOC) retention using engineering measures. This system combines forestry, grassland, and engineering, with the aim of quantifying regional carbon sinks. Machine learning regression algorithms such as Random Forest, ExtraTrees, CatBoost, and XGBoost are used for backtracking estimation and optimisation modelling of soil and water conservation as carbon sinks from 2010 to 2022. The results show that the total carbon sink capacity of soil and water conservation in Puding County in 2017 was 34.53 × 10⁴ t, while the contribution of engineering measures was 22.37 × 10⁴ t. The spatial distribution shows a pattern of “higher in the north and lower in the south”. There are concentration hotspots in the central and western regions. Model comparison demonstrates that the Random Forest and extreme gradient boosting regression models are the best models for plantations/grasslands and engineering measures, respectively. The LSTM model was applied to predict carbon sink variables over the next ten years (2025–2034), showing that the overall situation is relatively stable, with only slight local fluctuations. This study solves the problem of the lack of quantitative data on soil and water conservation as carbon sinks in karst areas and provides a scientific basis for regional ecological governance and carbon sink management. Our findings demonstrate the practical significance of promoting the realisation of the “double carbon” goal. Full article

The effect of nitrogen (N) deposition on soil organic carbon (SOC) and the underlying mechanisms in grassland ecosystems remain a topic of debate. Moreover, previous research has primarily concentrated on interaction between carbon (C) and N cycles in response to N deposition, with less attention paid to how N-induced phosphorus (P) deficits impact SOC sequestration. To further investigate whether soil microbial stoichiometry influences SOC sequestration under N enrichment, we conducted a field experiment involving N and P additions. The soil properties, nutrients within plant leaves and microbial biomass, and the potential activity of eco-enzymes related to microbial nutrient acquisition were measured. Results showed that SOC did not significantly change with N addition, and SOC significantly increased with addition of N and P together, which suggested that the SOC was depleted with N addition. Soil available phosphorus and microbial biomass phosphorus (MBP) did not significantly decrease alongside N addition, which suggested that microbes alleviated P limitation. Microbial metabolic limitation analysis showed microbial P limitation was enhanced by N10 treatment. At the same time, microbial P limitation enhanced microbial C limitation. Consequently, microbes also required more C as an energy resource to invest in enzyme production. Microbial P and C limitations were both significantly negatively correlated with SOC. Results from SEM analysis also showed that the MBC:MBP ratio was significantly negatively correlated with SOC. These results support the idea that consumer-driven nutrient recycling shapes the dynamics of SOC. Therefore, nitrogen deposition-induced MBC:MBP imbalance may regulate SOC in alpine grassland ecosystems. Full article

Understanding evapotranspiration (ET) dynamics under community composition transitions in grasslands is crucial for interpreting alpine ecosystem responses to climate change. We investigated variations in ET and its components during the growing season across five alpine grassland transition types in the Source Region of the Yellow River (SRYR) from 1986 to 2018, integrating climatic, vegetation, and soil factors. Under warming and wetting conditions, ET increased significantly by 1.17 mm yr⁻¹, accounting for 79.39% of annual precipitation, while soil moisture declined slightly. A pronounced temperature–precipitation decoupling emerged between alpine meadow-origin (AM-origin) and alpine steppe-origin (AS-origin) transitions, indicating differential hydrological responses driven by community composition. Vegetation growth increased across all transitions, yet its regulation of ET components varied by transition type. Transpiration dominated ET increases, contributing over 80% in AM-origin and 100% in AS-origin transitions. Soil evaporation exhibited contrasting trends: decreasing in AS-origin transitions due to enhanced soil insulation from vegetation growth, but increasing in AM-origin transitions, thereby reducing soil moisture. Interannual ET growth rates and seasonal fluctuations were greater in AM-origin than in AS-origin transitions. A critical turning point in ET trends, caused by changes in precipitation, revealed the divergent hydrological trajectories among the transitions. In AM-origin transitions, temperature primarily drove ET increases, causing soil drying (strongest in AM to TS), whereas in AS-origin transitions, precipitation dominated, resulting in soil wetting (more pronounced in AS to AM). These findings demonstrate that the directionality of compositional transitions governs hydrological responses more strongly than absolute vegetation states. Full article

Soil microbial communities face the combined pressures of climate change and biodiversity loss, yet how these stressors interact to shape ecosystem function remains a critical uncertainty. To investigate this, we established a constructed grassland plant community and conducted a fully factorial experiment manipulating plant diversity (1, 3, and 6 species), temperature (ambient, +2 °C), and precipitation (ambient, +50%). High-throughput 16S rRNA gene sequencing revealed that plant diversity exerted a stronger influence on soil bacterial community structure than did warming or precipitation changes. Beta diversity analysis revealed a distinct clustering of bacterial communities corresponding to the plant diversity gradient. This shift was characterized by a consistent enrichment of the metabolically versatile genus Sphingomonas in medium-diversity plots that experienced elevated precipitation, suggesting a predicted potential for enhanced organic matter decomposition. Despite overall stability in alpha diversity, the interaction between plant diversity and warming significantly modulated bacterial diversity and dominance patterns. Our findings highlight that plant diversity plays a key role in mediating soil bacterial responses to simulated climate factors in the short term. Incorporating these plant–soil feedback mechanisms into ecological models appears crucial for advancing predictions of ecosystem dynamics under future climate conditions. Full article

Wildfires can drastically alter ecological landscapes in just a few days, while it takes years of post-fire recovery for vegetation to return to its former pre-fire state. Assessing changes in vegetation can help with understanding how the hydrological components in the wildfire-affected areas contribute to potential vegetation shifts. This case study of the Los Angeles Bobcat Fire in 2020 uses Google Earth Engine (GEE) and Python 3.10.18 to access and visualize variations in Difference Normalized Burn Ratio (dNBR) area, Normalized Difference Vegetation Index (NDVI), and OpenET’s evapotranspiration (ET) across three dominant National Land Cover Database (NLCD) vegetation classes and dNBR classes via monthly time series and seasonal analysis from 2016 to 2024. Burn severity was determined based on Landsat-derived dNBR thresholds defined by the United Nations Office for Outer Space Affairs UN-Spider Knowledge Portal. Our study showed a general reduction in dNBR class area percentages, with High Severity (HS) dropping from 15% to 0% and Moderate Severity (MS) dropping from 45% to 10%. Low-Severity (LS) areas returned to 25% after increasing to 49% in May of 2022, led by vegetation growth. The remaining area was classified as Unburned and Enhanced Regrowth. Within our time series analysis, HS areas showed rapid growth compared to MS and LS areas for both ET and NDVI. Seasonal analysis showed most burn severity levels and vegetation classes increasing in median ET and NDVI values while 2024’s wet season median NDVI decreased compared to 2023’s wet season. Despite ET and NDVI continuing to increase post-fire, recent 2024 NLCD data shows most Forests and Shrubs remain as Grasslands, with small patches recovering to pre-fire vegetation. Using GEE, Python, and available satellite imagery demonstrates how accessible analytical tools and data layers enable wide-ranging wildfire vegetation studies, advancing our understanding of the impact wildfires have on ecosystems. Full article