Search Results (961)

Bare land exposed by glacier retreat provides new opportunities for ecosystem development. Investigating primary vegetation succession in deglaciated regions can provide significant insights for ecological restoration, particularly for future climate change scenarios. Nonetheless, research on this topic in the Qinghai–Tibet Plateau has been exceedingly limited. This study aimed to investigate vegetation succession in the deglaciated area of the Zepu glacier during the Little Ice Age in southeastern Tibet. Quadrat surveys were performed on arboreal communities, and trends in vegetation change were assessed utilizing multi-year (1986–2024) remote sensing data. The findings indicate that vegetation succession in the Zepu glacier deglaciated area typically adheres to a sequence of bare land–shrub–tree, divided into four stages: (1) shrub (species include Larix griffithii Mast., Hippophae rhamnoides subsp. yunnanensis Rousi, Betula utilis D. Don, and Populus pseudoglauca C. Wang & P. Y. Fu); (2) broadleaf forest primarily dominated by Hippophae rhamnoides subsp. yunnanensis Rousi; (3) mixed coniferous–broadleaf forest with Hippophae rhamnoides subsp. yunnanensis Rousi and Populus pseudoglauca C. Wang & P. Y. Fu as the dominant species; and (4) mixed coniferous–broadleaf forest dominated by Picea likiangensis (Franch.) E. Pritz. Soil depth and NDVI both increase with succession. Species diversity is significantly higher in the third stage compared to other successional stages. In addition, soil moisture content is significantly greater in the broadleaf-dominated communities than in the conifer-dominated communities. An analysis of NDVI from 1986 to 2024 reveals an overall positive trend in vegetation recovery in the area, with 93% of the area showing significant vegetation increase. Temperature is the primary controlling factor for this recovery, showing a positive correlation with vegetation cover. The results indicate that Key ecological indicators—including species composition, diversity, NDVI, soil depth, and soil moisture content—exhibit stage-specific patterns, reflecting distinct phases of primary succession. These findings enhance our comprehension of vegetation succession in deglaciated areas and their influencing factors in deglaciated areas, providing theoretical support for vegetation restoration in climate change. Full article

Vegetation dynamics are complexly influenced by multiple factors such as climate, human activities, and topography. In recent years, the frequency, intensity, and diversity of human activities have increased, placing substantial pressure on the growth of vegetation. Arid and semi-arid regions are particularly sensitive to climate change, and climate change and large-scale ecological restoration have led to significant changes in the dynamic of dryland vegetation. However, few studies have explored the nonlinear relationships between these factors and vegetation dynamic. In this study, we integrated trend analysis (using the Mann–Kendall test and Theil–Sen estimation) and machine learning algorithms (XGBoost-SHAP model) based on long time-series remote sensing data from 2001 to 2020 to quantify the nonlinear response patterns and threshold effects of bioclimatic variables, topographic features, soil attributes, and anthropogenic factors on vegetation dynamic. The results revealed the following key findings: (1) The kNDVI in the study area showed an overall significant increasing trend (p < 0.01) during the observation period, of which 26.7% of the area showed a significant increase. (2) The water content index (Bio 23, 19.6%), the change in land use (15.2%), multi-year average precipitation (pre, 15.0%), population density (13.2%), and rainfall seasonality (Bio 15, 10.9%) were the key factors driving the dynamic change of vegetation, with the combined contribution of natural factors amounting to 64.3%. (3) Among the topographic factors, altitude had a more significant effect on vegetation dynamics, with higher altitude regions less likely to experience vegetation greening. Both natural and anthropogenic factors exhibited nonlinear responses and interactive effects, contributing to the observed dynamic trends. This study provides valuable insights into the driving mechanisms behind the condition of vegetation in arid and semi-arid regions of China and, by extension, in other arid regions globally. Full article

Potato (Solanum tuberosum L.) crop yields may be reduced by nitrogen deficiency stress tolerance. An evaluation of 144 tetraploid potato genotypes was carried out during two consecutive seasons (2019 and 2020), with the objective of characterizing their variability in key physiological and agronomic parameters. Physiological parameters included chlorophyll content and fluorescence, stomatal conductance, NDVI, leaf area, and perimeter, while agronomic characteristics such as yield, tuber fresh weight, tuber number, starch content, dry matter, and reducing sugars were evaluated. To genotype the population, the GGP V3 Potato array was used, generating 18,259 high-quality SNP markers. Marker–trait association analysis was conducted using the GWASpoly package in R, applying Q + K linear mixed models to enhance precision. This methodology enabled the identification of 18 SNP markers that exhibited statistically significant associations with the traits analyzed in both trials and periods, relating them to genes whose functional implication has already been described. Genetic loci associated with chlorophyll content and tuber number were detected across non-stress and stress treatments, while markers linked to leaf area and leaf perimeter were identified specifically under nitrogen deficiency stress. The genomic distribution of these markers revealed that genetic markers or single-nucleotide polymorphisms (SNPs) correlated with phenotypic traits under non-stress conditions were predominantly located on chromosome 11, whereas SNPs linked to stress responses were mainly identified on chromosomes 2 and 3. These findings contribute to understanding the genetic mechanisms underlying potato tolerance to nitrogen deficiency stress, offering valuable insights for the development of future marker-assisted selection programs aimed at improving nitrogen use efficiency and stress resilience in potato breeding. Full article

The Inner Mongolia Plateau (IMP), situated in the arid and semi-arid ecological transition zone of northern China, is particularly vulnerable to both climate change and human activities. Understanding the spatiotemporal vegetation dynamics and their driving forces is essential for regional ecological management. This study employs Sen’s slope estimation, BFAST analysis, residual trend method and Geodetector to analyze the spatial patterns of Normalized Difference Vegetation Index (NDVI) variability and distinguish between climatic and anthropogenic influences. Key findings include the following: (1) From 1982 to 2022, vegetation cover across the IMP exhibited a significant greening trend. Zonal analysis showed that this spatial heterogeneity was strongly regulated by regional hydrothermal conditions, with varied responses across land cover types and pronounced recovery observed in high-altitude areas. (2) In the western arid regions, vegetation trends were unstable, often marked by interruptions and reversals, contrasting with the sustained greening observed in the eastern zones. (3) Vegetation growth was primarily temperature-driven in the eastern forested areas, precipitation-driven in the central grasslands, and severely limited in the western deserts due to warming-induced drought. (4) Human activities exerted dual effects: significant positive residual trends were observed in the Hetao Plain and southern Horqin Sandy Land, while widespread negative residuals emerged across the southern deserts and central grasslands. (5) Vegetation change was driven by climate and human factors, with recovery mainly due to climate improvement and degradation linked to their combined impact. These findings highlight the interactive mechanisms of climate change and human disturbance in regulating terrestrial vegetation dynamics, offering insights for sustainable development and ecosystem education in climate-sensitive systems. Full article

Landslide susceptibility assessment is critical for hazard mitigation and land-use planning. This study evaluates the impact of two different non-landslide sampling methods—random sampling and sampling constrained by the Global Landslide Hazard Map (GLHM)—on the performance of various machine learning and deep learning models, including Naïve Bayes (NB), Support Vector Machine (SVM), SVM-Random Forest hybrid (SVM-RF), and XGBoost. The study area is a 2 km buffer zone along the Duku Highway in Xinjiang, China, with 102 landslide and 102 non-landslide points extracted by aforementioned sampling methods. Models were tested using ROC curves and non-parametric significance tests based on 20 repetitions of 5-fold spatial cross-validation data. GLHM sampling consistently improved AUROC and accuracy across all models (e.g., AUROC gains: NB +8.44, SVM +7.11, SVM–RF +3.45, XGBoost +3.04; accuracy gains: NB +11.30%, SVM +8.33%, SVM–RF +7.40%, XGBoost +8.31%). XGBoost delivered the best performance under both sampling strategies, reaching 94.61% AUROC and 84.30% accuracy with GLHM sampling. SHAP analysis showed that GLHM sampling stabilized feature importance rankings, highlighting STI, TWI, and NDVI as the main controlling factors for landslides in the study area. These results highlight the importance of hazard-informed sampling to enhance landslide susceptibility modeling accuracy and interpretability. Full article

The ecological environment is crucial for human survival and development. As ecological issues become more pressing, studying the spatiotemporal evolution of ecological quality (EQ) and its driving mechanisms is vital for sustainable development. This study, based on MODIS data from 2000 to 2022 and the Google Earth Engine platform, constructs a remote sensing ecological index for the Beibu Gulf Urban Agglomeration and analyzes its spatiotemporal evolution using Theil–Sen trend analysis, Hurst index (HI), and geographic detector. The results show the following: (1) From 2000 to 2010, EQ improved, particularly from 2005 to 2010, with a significant increase in areas of excellent and good quality due to national policies and climate improvements. From 2010 to 2015, EQ degraded, with a sharp reduction in areas of excellent quality, likely due to urban expansion and industrial pressures. After 2015, EQ rebounded with successful governance measures. (2) The HI analysis indicates that future changes will continue the past trend, especially in areas like southeastern Chongzuo and northwestern Fangchenggang, where governance efforts were effective. (3) EQ shows a positive spatial correlation, with high-quality areas in central Nanning and Fangchenggang, and low-quality areas in Nanning and Beihai. After 2015, both high–high and low–low clusters showed changes, likely due to ecological governance measures. (4) NDBSI (dryness) is the main driver of EQ changes (q = 0.806), with significant impacts from NDVI (vegetation coverage), LST (heat), and WET (humidity). Urban expansion’s increase in impervious surfaces (NDBSI rise) and vegetation loss (NDVI decline) have a synergistic effect (q = 0.856), significantly affecting EQ. Based on these findings, it is recommended to control construction land expansion, optimize land use structure, protect ecologically sensitive areas, and enhance climate adaptation strategies to ensure continuous improvement in EQ. Full article

Investigating land cover patterns, changes in carbon stocks, and forecasting future conditions are essential for formulating regional sustainable development strategies and enhancing ecological and environmental quality. This study centers on Guiyang, a mountainous urban area in southwestern China, to analyze the dynamic changes in land cover and their effects on carbon stocks from 2000 to 2035. A carbon stocks assessment framework was developed using a cellular automaton-based artificial neural network model (CA-ANN), the InVEST model, and the geographical detector model to predict future land cover changes and identify the primary drivers of variations in carbon stocks. The results indicate that (1) from 2000 to 2020, impervious surfaces expanded significantly, increasing by 199.73 km². Compared to 2020, impervious surfaces are projected to increase by 1.06 km², 13.54 km², and 34.97 km² in 2025, 2030, and 2035, respectively, leading to further reductions in grassland and forest areas. (2) Over time, carbon stocks in Guiyang exhibited a general decreasing trend; spatially, carbon stocks were higher in the western and northern regions and lower in the central and southern regions. (3) The level of greenness, measured by the normalized vegetation index (NDVI), significantly influenced the spatial variation of carbon stocks in Guiyang. Changes in carbon stocks resulted from the combined effects of multiple factors, with the annual average temperature and NDVI being the most influential. These findings provide a scientific basis for advancing low-carbon development and constructing an ecological civilization in Guiyang. Full article

Objectives: The major sugarcane-producing regions of Guangxi represent a critical agricultural zone in China. Investigating the mechanisms of land use change and carbon storage dynamics in this area is essential for optimizing regional ecological security and promoting sustainable development. Methods: Employing the land use transfer matrix, the InVEST model and the Geodetector model to analyze carbon storage changes and identify key driving factors and their interactive effects. Results: (1) From 2011 to 2022, Guangxi’s major sugarcane-producing regions experienced significant land use changes: reductions in cultivated land, grassland and water bodies alongside expansions of forest, bare land and construction land. (2) The total carbon storage in Guangxi’s major sugarcane-producing regions has increased from 2011 to 2018 by 0.99%, representing 1627.03 and 1643.10 million tons, while it has decreased by 0.1% in 2022 (1641.47 million tons) compared to 2018. (3) Cultivated land proportion and forest coverage rate were the primary drivers of spatial heterogeneity, followed by average slope and land urbanization rate. (4) Interaction analysis revealed strong synergistic effects among cultivated land proportion, forest coverage rate, NDVI and average slope, confirming multi-factor control over carbon storage changes. Conclusions: Carbon storage in the Guangxi sugarcane-producing regions is shaped by land use patterns and multi-factor interactions. Future strategies should optimize land use structures and balance urbanization with ecological protection to enhance regional carbon sequestration. Full article

We propose a dynamic monitoring and precision fertilization decision system for agricultural soil nutrients, integrating UAV remote sensing and GIS technologies to address the limitations of traditional soil nutrient assessment methods. The proposed method combines multi-source data fusion, including hyperspectral and multispectral UAV imagery with ground sensor data, to achieve high-resolution spatial and spectral analysis of soil nutrients. Real-time data processing algorithms enable rapid updates of soil nutrient status, while a time-series dynamic model captures seasonal variations and crop growth stage influences, improving prediction accuracy (RMSE reductions of 43–70% for nitrogen, phosphorus, and potassium compared to conventional laboratory-based methods and satellite NDVI approaches). The experimental validation compared the proposed system against two conventional approaches: (1) laboratory soil testing with standardized fertilization recommendations and (2) satellite NDVI-based fertilization. Field trials across three distinct agroecological zones demonstrated that the proposed system reduced fertilizer inputs by 18–27% while increasing crop yields by 4–11%, outperforming both conventional methods. Furthermore, an intelligent fertilization decision model generates tailored fertilization plans by analyzing real-time soil conditions, crop demands, and climate factors, with continuous learning enhancing its precision over time. The system also incorporates GIS-based visualization tools, providing intuitive spatial representations of nutrient distributions and interactive functionalities for detailed insights. Our approach significantly advances precision agriculture by automating the entire workflow from data collection to decision-making, reducing resource waste and optimizing crop yields. The integration of UAV remote sensing, dynamic modeling, and machine learning distinguishes this work from conventional static systems, offering a scalable and adaptive framework for sustainable farming practices. Full article

Accurate estimation aboveground biomass (AGB) in winter wheat is crucial for yield assessment but remains challenging to achieve non-destructively. Unmanned aerial vehicle (UAV)-based remote sensing offers a promising solution at the plot level. Traditional field sampling methods, such as random plant selection or full-quadrat harvesting, are labor intensive and may introduce substantial errors compared to the canopy-level estimates obtained from UAV imagery. This study proposes a novel method using Fractional Vegetation Coverage (FVC) to adjust field-sampled AGB to per-plant biomass, enhancing the accuracy of AGB estimation using UAV imagery. Correlation analysis and Variance Inflation Factor (VIF) were employed for feature selection, and estimation models for leaf, spike, stem, and total AGB were constructed using Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN) models. The aim was to evaluate the performance of multimodal data in estimating winter wheat leaves, spikes, stems, and total AGB. Results demonstrated that (1) FVC-adjusted per-plant biomass significantly improved correlations with most indicators, particularly during the filling stage, when the correlation between leaf biomass and NDVI increased by 56.1%; (2) RF and NN models outperformed SVM, with the optimal accuracies being R² = 0.709, RMSE = 0.114 g for RF, R² = 0.66, RMSE = 0.08 g for NN, and R² = 0.557, RMSE = 0.117 g for SVM. Notably, the RF model achieved the highest prediction accuracy for leaf biomass during the flowering stage (R² = 0.709, RMSE = 0.114); (3) among different water treatments, the R² values of water and drought treatments were higher 0.723 and 0.742, respectively, indicating strong adaptability. This study provides an economically effective method for monitoring winter wheat growth in the field, contributing to improved agricultural productivity and fertilization management. Full article

Understanding the detailed spatiotemporal variations in soil organic carbon (SOC) stocks is essential for assessing soil carbon sequestration potential. However, most existing studies predominantly focus on topsoil SOC stocks, leaving significant knowledge gaps regarding critical zones, depth-dependent variations, and key influencing factors associated with deeper SOC stock dynamics. This study adopted a comprehensive methodology that integrates random forest modeling, equal-area soil profile analysis, and space-for-time substitution to predict depth-specific SOC stock dynamics under climate warming in Northeast China’s forest ecosystems. By combining these techniques, the approach effectively addresses existing research limitations and provides robust projections of soil carbon changes across various depth intervals. The analysis utilized 63 comprehensive soil profiles and 12 environmental predictors encompassing climatic, topographic, biological, and soil property variables. The model’s predictive accuracy was assessed using 10-fold cross-validation with four evaluation metrics: MAE, RMSE, R², and LCCC, ensuring comprehensive performance evaluation. Validation results demonstrated the model’s robust predictive capability across all soil layers, achieving high accuracy with minimized MAE and RMSE values while maintaining elevated R² and LCCC scores. Three-dimensional spatial projections revealed distinct SOC distribution patterns, with higher stocks concentrated in central regions and lower stocks prevalent in northern areas. Under simulated warming conditions (1.5 °C, 2 °C, and 4 °C increases), both topsoil (0–30 cm) and deep-layer (100 cm) SOC stocks exhibited consistent declining trends, with the most pronounced reductions observed under the 4 °C warming scenario. Additionally, the study identified mean annual temperature (MAT) and normalized difference vegetation index (NDVI) as dominant environmental drivers controlling three-dimensional SOC spatial variability. These findings underscore the importance of depth-resolved SOC stock assessments and suggest that precise three-dimensional mapping of SOC distribution under various climate change projections can inform more effective land management strategies, ultimately enhancing regional soil carbon storage capacity in forest ecosystems. Full article

Soil available nitrogen (AN) is a critical nutrient for plant absorption and utilization. Accurately mapping its spatial distribution is essential for improving crop yields and advancing precision agriculture. In this study, 188 AN soil samples (0–20 cm) were collected at Heshan Farm, Nenjiang County, Heihe City, Heilongjiang Province, in 2023. The soil available nitrogen content ranged from 65.81 to 387.10 mg kg⁻¹, with a mean value of 213.85 ± 61.16 mg kg⁻¹. Sentinel-2 images and normalized vegetation index (NDVI) and enhanced vegetation index (EVI) time series data were acquired on the Google Earth Engine (GEE) platform in the study area during the bare soil period (April, May, and October) and the growth period (June–September). These remote sensing variables were combined with soil sample data, crop type information, and crop growth period data as predictive factors and input into a Random Forest (RF) model optimized using the Optuna hyperparameter tuning algorithm. The accuracy of different strategies was evaluated using 5-fold cross-validation. The research results indicate that (1) the introduction of growth information at different growth periods of soybean and maize has different effects on the accuracy of soil AN mapping. In soybean plantations, the introduction of EVI data during the pod setting period increased the mapping accuracy R² by 0.024–0.088 compared to other growth periods. In maize plantations, the introduction of EVI data during the grouting period increased R² by 0.004–0.033 compared to other growth periods, which is closely related to the nitrogen absorption intensity and spectral response characteristics during the reproductive growth period of crops. (2) Combining the crop types and their optimal period growth information could improve the mapping accuracy, compared with only using the bare soil period image (R² = 0.597)—the R² increased by 0.035, the root mean square error (RMSE) decreased by 0.504%, and the mapping accuracy of R² could be up to 0.632. (3) The mapping accuracy of the bare soil period image differed significantly among different months, with a higher mapping accuracy for the spring data than the fall, the R² value improved by 0.106 and 0.100 compared with that of the fall, and the month of April was the optimal window period of the bare soil period in the present study area. The study shows that when mapping the soil AN content in arable land, different crop types, data collection time, and crop growth differences should be considered comprehensively, and the combination of specific crop types and their optimal period growth information has a greater potential to improve the accuracy of mapping soil AN content. This method not only opens up a new technological path to improve the accuracy of remote sensing mapping of soil attributes but also lays a solid foundation for the research and development of precision agriculture and sustainability. Full article

Desertification presents a significant ecological challenge in arid and semi-arid regions, posing a severe threat to regional ecological security and sustainable development. This study introduces an integrated framework for desertification vulnerability assessment, combining the MEDALUS model with the XGBoost algorithm, to evaluate desertification dynamics across the Mongolian Plateau from 2000 to 2020 and project future trends under four Shared Socioeconomic Pathways (SSPs) for 2030. The findings are as follows: (1) Between 2000 and 2020, desertification vulnerability was most pronounced in the southern and western regions of the plateau, with lower vulnerability observed in the northern and eastern areas. High-vulnerability zones expanded over time, highlighting the need for targeted and prioritized management efforts. (2) Climate factors—particularly temperature, wind speed, and precipitation—emerged as the dominant drivers of desertification, followed by soil characteristics and vegetation (NDVI). The influence of human activities on desertification became increasingly significant, stressing the need for improved land management and sustainable practices. (3) Future risks show that desertification vulnerability in the Mongolian Plateau will intensify under high-emission scenarios (SSP3-7.0, SSP5-8.5), with significant expansion of high vulnerability areas. Lower-emission scenarios (SSP1-2.6, SSP2-4.5) may reduce some impacts, but high vulnerability will persist, highlighting the need for urgent climate mitigation and adaptation efforts. Full article

Mangrove forests serve as critical ecological barriers in coastal zones and play a vital role in global blue carbon sequestration strategies. In recent decades, China’s mangrove ecosystems have experienced complex interactions between degradation and restoration under intense coastal urbanization and systematic conservation efforts. However, the long-term spatiotemporal patterns and driving mechanisms of mangrove ecosystem health changes remain insufficiently quantified. This study developed a multi-temporal analytical framework using Landsat imagery (1986–2021) to derive kernel normalized difference vegetation index (kNDVI) time series—an advanced phenological indicator with enhanced sensitivity to vegetation dynamics. We systematically characterized mangrove growth patterns along China’s southeastern coast through integrated Theil–Sen slope estimation, Mann–Kendall trend analysis, and Hurst exponent forecasting. A Deep Forest regression model was subsequently applied to quantify the relative contributions of environmental drivers (mean annual sea surface temperature, precipitation, air temperature, tropical cyclone frequency, and relative sea-level rise rate) and anthropogenic pressures (nighttime light index). The results showed the following: (1) a nationally significant improvement in mangrove vitality (p < 0.05), with mean annual kNDVI increasing by 0.0072/yr during 1986–2021; (2) spatially divergent trajectories, with 58.68% of mangroves exhibiting significant improvement (p < 0.05), which was 2.89 times higher than the proportion of degraded areas (15.10%); (3) Hurst persistence analysis (H = 0.896) indicating that 74.97% of the mangrove regions were likely to maintain their growth trends, while 15.07% of the coastal zones faced potential degradation risks; and (4) Deep Forest regression id the relative rate of sea-level rise (importance = 0.91) and anthropogenic (nighttime light index, importance = 0.81) as dominant drivers, surpassing climatic factors. This study provides the first national-scale, 30 m resolution assessment of mangrove growth dynamics using kNDVI, offering a scientific basis for adaptive management and blue carbon strategies in subtropical coastal ecosystems. Full article

Anthropogenic activities and climate change have significantly altered runoff generation in the upper Han River basin, posing a challenge to the water supply sustainability for the Middle Route of the South-to-North Water Diversion Project. Land use/cover changes (LUCCs) affect hydrological processes by modifying evapotranspiration, infiltration and soil moisture content. Based on hydro-meteorological data from 1961 to 2023 and LUCC data series from 1985 to 2023, this study aimed to identify the temporal trend in hydro-meteorological variables, to quantify the impacts of underlying land surface and climate factors at different time scales and to clarify the effects of LUCCs and basin greening on the runoff generation process. The results showed that (1) inflow runoff declined at a rate of −1.71 mm/year from 1961 to 2023, with a marked shift around 1985, while potential evapotranspiration increased at a rate of 2.06 mm/year within the same time frame. (2) Annual climate factors accounted for 61.01% of the runoff reduction, while underlying land surface contributed 38.99%. Effective precipitation was the dominant climatic factor during the flood season, whereas potential evapotranspiration had a greater influence during the dry season. (3) From 1985 to 2023, the LUCC changed significantly, mainly manifested by the increasing forest area and decreasing crop land area. The NDVI also showed an upward trend over the years; the actual evapotranspiration increased by 1.163 billion m³ due to the LUCC. This study addresses the climate-driven and human-induced hydrological changes in the Danjiangkou Reservoir and provides an important reference for water resource management. Full article