Hanchen Duan

The Horqin Sandy Land (HSL), a critical ecological barrier in northern China and core area of the Three-North Shelterbelt Project, requires precise monitoring of aeolian desertification (AD) dynamics to maintain ecological stability in densely populated northern and northeastern China. This study innovatively integrated multi-source remote sensing parameters via Google Earth Engine to systematically compare three feature space models (Albedo-NDVI, Albedo-MSAVI, Albedo-KNDVI) and five machine learning algorithms for AD monitoring, while applying the Optimal Parameter Geographic Detector (OPGD) to quantify natural-anthropogenic driving mechanisms. High-resolution AD classification (5-year intervals, 2000–2020) revealed spatiotemporal dynamics, with machine learning models consistently outperforming feature space models. Random Forest (RF) achieved the highest accuracy (97.7%) and Kappa coefficient (0.967), effectively distinguishing non-desertified areas from extreme desertification. Temporal analysis showed a significant AD reversal: total desertified area declined to 30,166.35 km² by 2020, driven by ecological policies and climate amelioration. OPGD analysis indicated soil texture as the strongest single driver (q > 0.48), but factor interactions (e.g., SD ∩︀ LU) exerted greater explanatory power, highlighting compound effects of natural and anthropogenic factors. This study provides a robust framework for high-precision AD monitoring in semi-arid agro-pastoral zones, supporting global efforts toward zero land degradation.

The Qinghai–Tibet Plateau (QTP) is crucial for global climate regulation and ecological equilibrium. However, the phenomenon of global climate warming has increased the frequency of extreme weather events on the QTP, exerting substantial effects on both regional and global ecological systems. This study utilized long-term series NDVI and extreme climate indices to comprehensively evaluate the impact of extreme climatic changes on diverse vegetation types within the QTP. A variety of analytical methodologies, including trend analysis, a Mann–Kendall test, correlation analysis, and random forest importance ranking, were employed in this study. These methodologies were applied to investigate the distribution patterns and variation trends of diverse vegetation types and extreme climate indices. This comprehensive approach facilitated a detailed analysis of the responses of different vegetation types to interannual variability under extreme climatic conditions and enabled the assessment of the impact of extreme climate indices on these vegetation types. The findings have the following implications: (1) Except for forests, the annual NDVI for overall vegetation, meadows, steppes, deserts, and alpine vegetation in the QTP exhibits a significant upward trend (p < 0.01). Notably, meadows and deserts demonstrate the highest growth rates at 0.007/10y, whereas the annual NDVI of forests is not statistically significant (p > 0.05). Substantial increases in vegetation were predominantly detected in the central and northeastern regions of the QTP, while significant decreases were mostly observed in the southeastern and western regions. The area exhibiting significant vegetation increase (38.71%) considerably surpasses that of the area with a significant decrease (14.24%). (2) There was a statistically significant reduction (p < 0.05) in the number of days associated with extreme cold temperature indices, including CSDI, DTR, FD, ID, TN10p, and TX10p. In contrast, indices related to extremely warm temperatures, such as GSL, WSDI, SU25, TN90p, TNn, TNx, TX90p, and TXx, exhibited a statistically significant increase (p < 0.01). The pronounced rise in minimum temperatures, reflected by fewer cold days, has notably contributed to climate warming. Although extreme precipitation events have become less frequent, their intensity has increased. Notable spatial variations in extreme precipitation were observed, although no consistent changing pattern emerged. (3) The annual NDVI for non-forest vegetation types showed a significant negative correlation with most extreme cold temperature indices and a significant positive correlation with extreme warm temperature indices. A significant positive correlation (p < 0.05) between annual NDVI and extreme precipitation indices is found only in steppe and desert ecosystems, with no such correlation observed in other vegetation types. Both correlation analysis and random forest methodologies underscore the impact of extreme climate indices on vegetation variations, with the random forest model exhibiting superior capability in capturing nonlinear relationships. In conclusion, global climate change is projected to result in a heightened frequency of extreme warm events. Although these conditions might temporarily enhance vegetation growth, they are also associated with numerous detrimental impacts. Therefore, it is imperative to enhance awareness and take proactive measures for early warning and prevention.

For regional desertification control and sustainable development, it is critical to quickly and accurately understand the distribution pattern and spatial and temporal changes of deserts. In this work, five different machine learning algorithms are used to classify different desert types on the Qinghai–Tibetan Plateau (QTP), and their classification performance is evaluated on the basis of their classification results and classification accuracy. Then, on the basis of the best classification model, the Shapely Additive Explanations (SHAP) method is used to clarify the contribution of each classification feature to the identification of desert types during the machine learning classification process, both globally and locally. Finally, the independent and interactive effects of each factor on desert change on the Qinghai-Tibetan Plateau during the study period are quantitatively analyzed via geodetector. The main results are as follows: (1) Compared with other classification algorithms (GTB, CART, KNN, and SVM), the RF classifier achieves the best performance in classifying QTP desert types, with an overall accuracy (OA) of 87.11% and a kappa coefficient of 0.83. (2) From the perspective of the overall classification of deserts, the five features, namely, elevation, slope, VV, VH, and GLCM, contribute most significantly to the features. In terms of the influence of each classification feature on the extraction of different types of deserts, the radar backscattering coefficient VV serves the most important role in distinguishing sandy deserts; the VH is helpful in distinguishing the four types of deserts: rocky desert, alpine cold desert, sandy deserts, and loamy desert; slope is more effective in distinguishing between the two desert types (rocky desert and alpine cold desert) and other types of deserts; and elevation has a significant role in the identification of alpine cold deserts; and the short-wave infrared band SR_B7 has an important role in the identification of salt crusts and saline deserts. (3) During the study period, the QTP deserts exhibited a reversing trend, and the proportion of desert area decreased from 28.62% to 26.20%. (4) Compared with other factors, slope, precipitation, elevation, vegetation type, and the human footprint have greater effects on changes in the QTP desert area, and the interactions among the factors affecting changes in the desert area all show bidirectional enhancement or nonlinear enhancement effects.

The purpose of this study is to reveal the seasonal difference in vegetation variation and its seasonal response to climate factors in the Qilian Mountains (QM) under the background of global warming. Based on the MOD13 A2 normalized difference vegetation index (NDVI) data and meteorological data, this study analyzed the spatiotemporal dynamics and stability of vegetation in different seasons by using the mean value method, trend analysis and stability analysis method, and discussed their seasonal responses to climatic factors based on the correlation analysis method. The results show that the vegetation cover in the QM experienced a significant upward trend in the past 21 years, but there were obvious spatial differences in vegetation change in different seasons. The growth rate of vegetation in summer was the fastest, and summer vegetation provided the most significant contribution to the growing season vegetation. The order of vegetation stability in the QM among the seasons was growing season > summer > spring > autumn. The vegetation change was obviously affected by temperature in spring, while it was mainly controlled by precipitation in the growing season and summer. The response of vegetation to climatic factors was not significant in autumn. Our results can provide important data support for ecological protection in the QM and socioeconomic development in the Hexi Corridor.

The interaction between vegetation and soil is important for vegetation restoration and reconstruction during the succession of abandoned farmland. We chose four kinds of abandoned farmlands with the time of 1, 6, 12, and 22 years to experiment in the Loess Plateau. The community composition, community biomass, and soil nutrients of the four kinds of abandoned farmlands were studied by the method of temporal–spatial alternation, and the interaction effects among vegetation, biomass, soil nutrients, and abandonment time were analyzed. The results showed there were 33 species belonging to 13 families during the succession and 15 species of Gramineae and Compositae, accounting for 47% of the total community species. The succession trend of abandoned farmland was as follows: The vegetation was the weed community of Heteropappus altaicus + Artemisia capillaris in the beginning of the abandonment stage and became the weed community of Tragus racemosus + Enneapogon borealis after 6 years of abandonment. Then, Leymus secalinus became the dominant population in the community after 12 years of abandonment. When the succession lasted for 22 years, the vegetation became a common advantageous community of Stipa breviflora and Cleistogenes songorica. Soil moisture (SM) was positively correlated with soil nutrients and negatively correlated with abandonment time and community biomass. Soil organic matter (SOM), soil total nitrogen (STN), and alkali hydrolyzable nitrogen (AHN) were the highest in 0–10 cm soil layer, showing obvious surface accumulation. The three decreased with the soil layer, and there was a positive correlation among them. The abandonment time had a positive effect on the above-ground biomass (AGB) and below-ground biomass (BGB) and a negative effect on the SOM, STN, and AHN. The root/shoot ratio (R/S) was positively correlated with SOM and negatively correlated with abandonment time. With the progress of succession, the hierarchical differentiation of the community was gradually obvious, and the community structure begun to complicate. The community better adapted to the arid environment and toward the local top community succession.

Alpine meadow and alpine steppe are the two most widely distributed nonzonal vegetation types in the Qinghai-Tibet Plateau. In the context of global climate change, the differences in spatial-temporal variation trends and their responses to climate change are discussed. It is of great significance to reveal the response of the Qinghai-Tibet Plateau to global climate change and the construction of ecological security barriers. This study takes alpine meadow, alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau as the research objects. The normalized difference vegetation index (NDVI) data and meteorological data were used as the data sources between 2000 and 2018. By using the mean value method, threshold method, trend analysis method and correlation analysis method, the spatial and temporal variation trends in the alpine meadow, alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau were compared and analyzed, and their differences in the responses to climate change were discussed. The results showed the following: (1) The growing season length of alpine meadow was 145~289 d, while that of alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau was 161~273 d, and their growing season lengths were significantly shorter than that of alpine meadow. (2) The annual variation trends of the growing season NDVI for the alpine meadow, alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau increased obviously, but their fluctuation range and change rate were significantly different. (3) The overall vegetation improvement in the Qinghai-Tibet Plateau was primarily dominated by alpine steppe and alpine meadow, while the degradation was primarily dominated by alpine meadow. (4) The responses between the growing season NDVI and climatic factors in the alpine meadow, alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau had great spatial heterogeneity in the Qinghai-Tibet Plateau. These findings provide evidence towards understanding the characteristics of the different vegetation types in the Qinghai-Tibet Plateau and their spatial differences in response to climate change.

Currently, grazing exclosure is one of the most important grassland management measures for restoring all types of degraded alpine grassland in the Qinghai–Tibetan Plateau (QTP). The most widely distributed grassland ecosystems across the northeastern QTP are the alpine meadow (AM), alpine meadow steppe (AMS), and alpine steppe (AS). However, whether the impacts of fencing on vegetation characteristics and soil properties vary among different grassland types remains poorly understood despite that numerous individual studies have been conducted. This study investigated the vegetation characteristics and soil properties in fenced and grazed AM, AMS, and AS in the northeastern QTP. Grazing exclosure significantly increased the vegetation coverage and Shannon–Wiener diversity index in all the three grasslands. Plant species richness was significantly increased in AM, but there were no significant changes in AMS and AS. Aboveground biomass was significantly increased in AMS and AS but not significant in AM. Increase in the percentage of high-quality forage grasses was only observed in AMS. Fencing significantly decreased the soil bulk density (BD) and significantly increased soil organic carbon (SOC) and total nitrogen at a depth of 0–50 cm in AMS and AS but had no effect in AM. Our results indicate that the use of fencing for restoring degraded AM might not achieve the same expected results as in AS and AMS on the QTP.