The ecological environment quality of Inner Mongolia is a fundamental pillar for sustaining regional environmental health and safeguarding human well-being. Despite its critical importance, the region has faced escalating ecological challenges in recent years, yet the dynamics of its ecosystems and the underlying drivers of change remain largely underexplored. This study aims to assess the ecological environment quality in Inner Mongolia and investigate the impacts of a total of seventeen natural and human factors using remote sensing data and machine learning. The remote sensing ecological index (RSEI) was used to reveal the ecological environment quality and its spatiotemporal variation from 2000 to 2020 was analyzed. An interpretable machine learning framework, integrating Shapley Additive Explanations (SHAP) with eXtreme Gradient Boosting Trees (XGBoost), was then employed to uncover the complex driving mechanisms influencing the ecological environment quality. The results reveal a slight but clear improving trend in the ecological environment quality over the two decades, accompanied by pronounced spatial heterogeneity. Notably, net improvement is characterized by significant expansion of moderate and good areas, alongside contraction of poor and worst areas. The XGBoost-SHAP model identifies climatic factors as primary drivers, with precipitation as the most influential positive factor (exceeding 273.79 mm), while temperature variables act as dominant negative factors (suppressing the ecological environment quality beyond 3.02 °C and 11.71 °C). Relationships are predominantly nonlinear with critical thresholds. Interaction analysis further indicates that any single factor's effect highly depends on other factors. Crucially, these findings demonstrate that the overarching regional vegetation recovery is fundamentally a climate-driven process, wherein anthropogenic restoration efforts play a secondary role. By elucidating these nonlinear and threshold-dependent mechanisms, this study provides a robust, transferable basis for targeted ecological restoration and management strategies, offering critical insights for arid ecosystems in Inner Mongolia and globally.

Species richness and evenness have been widely used to investigate the spatiotemporal variation of α-diversity. However, some studies have indicated that a negative relationship exists between species richness and evenness. The question is how the differing sensitivity of α-diversity metrics and interactive behavior between richness and evenness affect the modeling of α-diversity variation. Here, we explored the response of species diversity, represented by three Hill numbers (i.e., species richness, exponential of Shannon index – expShannon, and inverse of Simpson index – invSimpson) focusing on the abundance of rare and common species, and Pielou index underlining the evenness of a community, to α-diversity variation through structural equation modeling (SEM). The model scheme integrated three categories of variables, spectral variation hypothesis (SVH), community pattern, and vertical structure, along the precipitation gradient spanning three steppes, including meadow steppe, typical steppe, and desert steppe. Our results showed that there were large differences in species richness across the three steppes, with v-shaped patterns emerging along the gradient (low-point in the typical steppe). Differences between steppes were diminished in the expShannon or invSimpson indices, though the v-shaped patterns persisted. The Pielou index showed the opposite pattern, with the peak in the typical steppe. Accordingly, a negative relationship between species richness and Pielou index was found across the three steppes. The concurrent increases in annual species number and dominant species abundance in response to precipitation variations led to the negative relationship. As a result, the SEM fitness on expShannon and invSimpson indices over the region was substantially diminished by the negative relationship. Overall, community pattern better explained the variation in species richness, invSimpson and Pielou indices. The performance of SVH differed among α-diversity metrics due to the collinearity with the variables of community pattern and vertical structure. This study emphasizes the variability of α-diversity metrics in response to environmental change. Particularly, distinguishing the asynchronous behaviors between species richness and evenness is paramount to account for α-diversity variation over heterogeneous ecosystems.

Monitoring aboveground biomass (AGB) is crucial for assessing, managing, and utilizing grassland ecosystems. While the technical form of combining remote sensing and machine learning algorithms is widely used to estimate AGB at a regional scale, few studies have assessed and compared the performance of popular algorithms on the typical steppe in northern China. In this study, the northern Xilinhot, a representative area of typical steppe in China, was selected as the study area to compare the performance of six widely used machine learning algorithms for AGB estimation, namely stepwise linear regression (SLR), partial least square regression (PLS), principal component regression (PCR), random forest (RF), support vector machines (SVM), and k-nearest neighbors (KNN). Additionally, the study explored the modeling capability of multisource variables from Sentinel imagery and auxiliary data. The results showed that (1) considering the aspects of prediction accuracy, noise resistance, ease of operation, and transferability, the SLR algorithm is more suitable for estimating typical steppe AGB in northern China at the Sentinel scale. (2) Vegetation Indices (VI) play a significant role in the development of selected models, with significant contributions from both traditional and soil-adjusted indices. (3) Sentinel C-band synthetic aperture radar (SAR) is unsuitable for modeling typical steppe AGB. (4) Among the selected environmental factors, only clay content and soil pH are significantly linearly correlated with AGB, while elevation, precipitation, temperature, soil pH, and sand content are advantageous for RF prediction. This study can provide important technical references for the research on AGB in typical steppe in northern China.

内蒙古乌拉盖河流域是典型的干旱半干旱牧区草原内陆河流域，生态系统极为脆弱，气候变暖和人类活 动能直接影响水文变化。本研究采用改进的SWAT水文模型、M-K趋势检验、降水-径流量双累积曲线以及情景分 析等方法，系统分析了1981—2020年乌拉盖河流域径流时空变化特征，并量化流域不同时期、不同河段气候变化和 人类活动对径流影响的差异。结果表明：SWAT模型在乌拉盖河流域的适用性良好，率定期及验证期的NSE及R2均 在0.62以上，PBLAS小于18.8%。近40 a在流域呈暖干化趋势下，径流量在上、中、下游均呈显著减少趋势，且在 2000年发生突变。气候变化和过度放牧、盲目开垦以及水利水库建设等人类活动对流域径流变化的贡献率分别为 95.84%和4.16%。人类活动对流域不同河段的贡献率也有差异，由上游到下游的贡献率分别为1.69%、4.36%和 5.03%。且在不同时间段内的贡献率也有较大的差异，由1980年的88.26%减少到2020年的25.47%，不同时间段内 不同人类活动方式导致径流变化趋势及幅度也有差异。研究结果能为牧区草原内陆河流域水资源的可持续利用 及合理调度提供参考依据。

Knowledge of grassland classification in a timely and accurate manner is essential for grassland resource management and utilization. Although remote sensing imagery analysis technology is widely applied for land cover classification, few studies have systematically compared the performance of commonly used methods on semi-arid native grasslands in northern China. This renders the grassland classification work in this region devoid of applicable technical references. In this study, the central Xilingol (China) was selected as the study area, and the performances of four widely used machine learning algorithms for mapping semi-arid grassland under pixel-based and object-based classification methods were compared: random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and naive Bayes (NB). The features were composed of the Landsat OLI multispectral data, spectral indices, Sentinel SAR C bands, topographic, position (coordinates), geometric, and grey-level co-occurrence matrix (GLCM) texture variables. The findings demonstrated that (1) the object-based methods depicted a more realistic land cover distribution and had greater accuracy than the pixel-based methods; (2) in the pixel-based classification, RF performed the best, with OA and Kappa values of 96.32% and 0.95, respectively. In object-based classification, RF and SVM presented no statistically different predictions, with OA and Kappa exceeding 97.5% and 0.97, respectively, and both performed significantly better than other algorithms. (3) In pixel-based classification, multispectral bands, spectral indices, and geographic features significantly distinguished grassland, whereas, in object-based classification, multispectral bands, spectral indices, elevation, and position features were more prominent. Despite the fact that Sentinel 1 SAR variables were chosen as an effective variable in object-based classification, they made no significant contribution to the grassland distinction.

Due to climate change and global warming, the frequency of sandstorms in northern China is increasing. Stipa breviflora, a dominant species in Eurasian grasslands, can help prevent desertification from becoming more serious. Studies on S. breviflora cover a wide range of fields. To the best of our knowledge, the present study is the first to sequence, assemble, and annotate the S. breviflora genome. In total, 2,781,544 contigs were assembled, and 2,600,873 scaffolds were obtained, resulting in a total length of 649,849,683 bp. The number of scaffolds greater than 1 kb was 70,770. We annotated the assembled genome (>121 kb), conducted a selective sweep analysis, and ultimately succeeded in assembling the Matk gene of S. breviflora. More importantly, our research identified 26 scaffolds that may be responsible for the drought tolerance of S. breviflora Griseb. In summary, the data obtained regarding S. breviflora will be of great significance for future research.

Global warming and human activities are complicating the spatial and temporal relationships between basin hydrologic processes and ecosystem quality (EQ), especially in arid and semi-arid regions. Knowledge of the synergy between hydrological processes and ecosystems in arid and semi-arid zones is an effective measure to achieve ecologically sustainable development. In this study, the inland river basin Ulagai River Basin (URB), a typical arid and semi-arid region in Northern China, was used as the study area; based on an improved hydrological model and remote-sensing and in situ measured data, this URB-focused study analyzed the spatial and temporal characteristics of hydrological process factors, such as precipitation, evapotranspiration (ET), surface runoff, lateral flow, groundwater recharge, and EQ and the synergistic relationships between them. It was found that, barring snowmelt, the hydrological process factors such as precipitation, ET, surface runoff, lateral flow, and groundwater recharge had a rising trend in the URB, since the 20th century. The rate of change was higher in the downstream areas when compared with what it was in the upstream and midstream areas. The multi-year average of EQ in the basin is 53.66, which is at a medium level and has an overall improving trend, accounting for 95.14% of the total area, mainly in the upstream, downstream southern, and downstream northern areas of the basin. The change in relationship between the hydrological process factors and EQ was found to have a highly synergistic effect. Temporally, EQ was consistent with the interannual trends of precipitation, surface runoff, lateral flow, and groundwater recharge. The correlation between the hydrological process factors and EQ was found to be higher than 0.7 during the study period. Spatially, the hydrological process factors had a synergistic relationship with EQ from strong to weak upstream, midstream, and downstream, respectively. In addition, ecosystem improvements were accelerated by government initiatives such as the policy of Returning Grazing Land to Grassland Project, which has played an important role in promoting soil and water conservation and EQ. This study provides theoretical support for understanding the relationship between hydrological processes and ecological evolution in arid and semi-arid regions, and it also provides new ideas for related research.

The desert steppe serves as a transitional zone between grasslands and deserts, and long-term monitoring of aboveground biomass (AGB) in the desert steppe is essential for understanding grassland changes. While AGB observation techniques based on multisource remote-sensing data and machine-learning algorithms have been widely applied, research on monitoring methods specifically for the desert steppe remains limited. In this study, we focused on the desert steppe of Inner Mongolia, China, as the study area and used field sampling data, MODIS data, MODIS-based vegetation indices (VI), and environmental factors (topography, climate, and soil) to compare the performance of four commonly used machine-learning algorithms: multiple linear regression (MLR), partial least-squares regression (PLS), random forest (RF), and support vector machine (SVM) in AGB estimation. Based on the optimal model, the spatial–temporal characteristics of AGB from 2000 to 2020 were calculated, and the driving forces of climate change and human activities on AGB changes were quantitatively analyzed using the random forest algorithm. The results are as follows: (1) RF demonstrated outstanding performance in terms of prediction accuracy and model robustness, making it suitable for AGB estimation in the desert steppe of Inner Mongolia; (2) VI contributed the most to the model, and no significant difference was found between soil-adjusted VIs and traditional VIs. Elevation, slope, precipitation, and temperature all had positive effects on the model; (3) from 2000 to 2020, the multiyear average AGB in the study area was 58.34 g/m2, exhibiting a gradually increasing distribution pattern from the inner region to the outer region (from north to south); (4) from 2000 to 2020, the proportions of grassland with AGB slightly and significantly increasing trend in the study area were 87.08% and 5.13%, respectively, while the proportions of grassland with AGB slightly and significantly decreasing trend were 7.76% and 0.05%, respectively; and (5) over the past 20 years, climate change, particularly precipitation, has been the primary driving force behind AGB changes of the study area. This research holds reference value for improving desert steppe monitoring capabilities and the rational planning of grassland resources.

为明确内蒙古地区不同时间尺度旱灾危险性分布特征，选取内蒙古地区1949—2018年SPEIbase v.2.6数据集，利用Theil-Sen趋势分析和Mann-Kendall检验法分析气象干旱时空演变特征，并基于干旱多年平均强度与加权综合评价模型，对研究区年、季节尺度的旱灾危险性进行了评价。结果表明：无论是年际变化还是在空间上，内蒙古年尺度和春、夏、秋3个季节尺度的气候整体呈显著干旱趋势；研究区以中等及以上等级的年尺度旱灾危险性为主，占总面积的71%。其中，高、极高危险性区域主要位于呼伦贝尔市和锡林郭勒盟东部；在春、夏两季，内蒙古干旱灾害危险性空间分布呈“北高南低”，高等级的危险性主要位于锡林郭勒草原和地处干旱区的阿拉善盟；秋、冬两季的高等级危险性区域显著减少，尤其是冬季，以东北地区的低、极低危险性为主要特征，与其干旱趋势变化特征较为一致。研究结果可为内蒙古地区干旱灾害风险管理提供参考依据。

The desert steppe in Inner Mongolia is an important part of the temperate grasslands in Central Asia and plays a key role in sustaining the regional pastoral livelihood and securing the ecological environment in northern China. Grassland net primary productivity (NPP) is an important indicator to ecosystem health, and is largely affected by the variation of precipitation, especially the frequent droughts in the desert steppe region. Monitoring the spatiotemporal changes of the NPP in relation with climate variation, especially with the drought events, is of great significance for assessing the total amount of grassland resources and predicting its future change trend, which is critically important for the development of grassland management regimes with the consideration of current and future climate changes. In the present study, we estimated the spatiotemporal variation of the NPP of the desert steppe in Inner Mongolia for the period from 2000 to 2019. We calculated the standardized precipitation evapotranspiration index (SPEI) of the study area, and analyzed its effects on vegetation NPP, especially for the drought effects. We found that the temporal trend line and Mann-Kendall statistical curve of vegetation NPP were basically consistent. Over the 20-year period, the NPP showed an overall downward trend during the plant growing season, or in summer and autumn period, but a slight upward trend in spring. Spatially, the plant growing season NPP slightly decreased on the 74.89% of the studied desert steppe area, the NPP in summer slightly decreased on the 72.25% of the area, while the NPP in spring slightly increased on the 61.8% of the area, and NPP in autumn significantly decreased on the 25.9% of the area. From the perspective of the persistence on different time scales, the proportion of moderate persistence-slight increase area covered 11.72% of the study area, and weak persistence-slight increase in spring covered 50.10% of the study area. The proportion of weak persistence-slight decrease in summer was 59.68%. The proportion of moderate persistence-slight decrease covered 18.83% in autumn. In the growing season, a positive correlation between NPP and SPEI accounted for 91.18% of the desert steppe area. Over 8% of the area showed a highly significant positive correlation, and 25.21% showed a significant positive correlation. Overall, 92.56% of the area showed a positive correlation in spring, and 87.26% showed a positive correlation in summer. A positive correlation was observed over 60.71% of the area in autumn. The research results show that the NPP results of CASA model simulating desert vegetation in Inner Mongolia are reliable. The SPEI drought index has good applicability in the study area. On the whole, the dynamic changes of NPP are closely related to drought. This research has important reference significance for regional ecosystem management and construction.

Evapotranspiration (ET), as the main ecological water consumer, is crucial to assess the ecological water budget and dry conditions in arid and semi-arid areas.The objective of this study was to characterize the spatiotemporal variations of ET and determine the major parameters affecting ET by using remote sensing data and climate data at annual and seasonal scales in Xilingol steppe, China.The results of this study showed that the annual ET gradually reduced from northeast to southwest in the Xilingol steppe, with the values fluctuating around 200 mm per year during 2000-2014.The seasonal value of the spatially averaged ET was in reducing order from summer, fall, and winter to spring, accounting for approximately 35%, 23%, 22%, and 20% of the annual ET, respectively.The largest ET appeared in summer in meadow steppe, typical steppe, and sandy vegetation steppe, while in the desert steppe, it occurred in winter, accounting for 39% of the annual ET.Precipitation and NDVI are the major parameters positively affecting ET in spring, summer, and fall.However, in winter, ET was positively correlated with temperature and negatively correlated with precipitation.The results indicated that the spatiotemporal characteristics and the affecting parameters of the actual ET vary seasonally and that the characteristics of the annual ET are mainly determined by the growing season (spring-fall).Moreover, vegetation growth is directly correlated with ET and sunshine hours rather than other parameters.Combining with the natural conditions, the conclusions can be deduced that the dry conditions in the meadow and typical steppes are probably caused by uneven precipitation distribution and high ET demands during the growing season, while the low annual precipitation combined with high winter evaporation is the main reason for water scarcity in the desert steppe.

Ecosystem succession and biodiversity change associated with grassland fires are crucial for the patterns and dynamics of ecosystem functioning and services. The reactions to fire by different grassland types vary diversely, and are determined by certain species assemblages and environments. However, there are still uncertainties concerning the role of fire in affecting grassland ecosystems and how the effects are sustained. By conducting a bibliometric analysis of related articles indexed in the Web of Science between 1984 and 2020, we firstly described the general trend of these articles over the recent decades (1984–2020). The major research progress in the effects of fire on grassland ecosystems was then systematically summarized based on three levels (individual level, community level, and ecosystem level) with eight topics. We concluded that strong persistence or resistance of adapted individuals facilitated community conversion to a novel environment, which temporally and spatially interacted with ecological factors. The novel habitats could maintain more frequent fires and change an ecosystem structure and functioning. Nonetheless, the transformation of ecosystem states will present more uncertainties on prospective succession trajectories, global carbon storage, and subsequent biodiversity conservation. This review is important to flourish biodiversity, as well as aid conservation policies and strategy making.

Previous studies have shown that climate changes and human activities are the main external factors affecting grassland. Therefore, quantitative assessment of their dominant role is of great significance for grassland management. This study selected Net primary productivity (NPP) as an indicator and quantitatively analyzed the relative impacts of climate change and human activities on Xilingol grassland during 1982–2018 by establishing different analysis scenarios. The results showed that the annual average grassland NPP was 251.13 g·C·m-2·a-1 from 1982 to 2018 and showed an increasing distribution pattern from west to east. During 1982–2018, the grassland NPP showed a slightly decreasing trend, with annual average decreases of 0.42 g·C·m-2·a-1 and changed significantly in 1998. The changing trend of grassland NPP was a significant difference during the two periods around 1998. Approximately 94.95% of the grassland showed a restoration trend in 1982–1998, and 76.47% of the grassland showed a degradation trend in 1998–2018. The climate-dominated grassland accounted for 87.76%, 75.4%, and 73.68% of the total grassland area in 1982–1998, 1998–2018, and 1982–2018, respectively. In comparison, the human-dominated grassland was 12.24%, 24.6%, and 26.32%, indicating that climate change is the main factor leading to the grassland NPP change, and human activities affect the change of NPP in local areas. Further analysis identified precipitation as the dominant climate factor affecting grassland, and mining and reclamation became the main form of human activities accelerating grassland degradation.

积雪是地表物质的重要组成部分,同时也是蒙古高原干旱半干旱区重要的水分来源.利用1982-2015年SM M R、SSM/I和SSM IS雪深产品以及同时期气象数据,分析了蒙古高原雪深时空变化特征及其对气候变化的响应.结果表明:(1)近年来,蒙古高原雪深出现缓慢减少趋势,以0.49 m m/10a的速率减少;一个积雪季内,10月-翌年2月为积雪积累阶段,2月雪深最大值可以达到28.34 m m,2月-3月为积雪消融阶段;(2)蒙古高原雪深与温度和降水的相关系数分别为-0.32和0.35(P<0.05),降水量对雪深的影响大于温度.

定量获取地表植被高精度时序及空间覆盖的叶面积指数(Leaf Area Index,LAI)是生态监测及农业生产应用的重要研究内容.通过使用Moderate Resolution Imaging Spectroradiometer(MODIS)植被冠层多角度观测MOD09GA数据及叶面积指数MOD15A2数据,发展了一种参数化的叶面积指数遥感反演方法并完成了必要的检验分析.研究使用基于辐射传输理论的RossThick LiSparse Reciprocal(RTLSR)核驱动模型及Scattering by Arbitrarily Inclined Leaves with Hotspot(SAILH)模型进行植被冠层辐射特征的提取,使用Anisotropic Index(ANIX)异质性指数作为指示植被冠层二向反射分布Bidirectional Reflectance Distribution Function(BRDF)的辅助特征信息,发展了基于数据机理(Data-Based Mechanistic,DBM)的植被叶面积指数建模和估算方法.通过必要的林地、农作物、草地植被实验区反演及数值分析可得知:①时间序列多角度遥感观测数据结合数据机理的叶面积指数估算方法,可实现模型参数的时序动态更新,改进叶面积指数估算结果的时序完整性及精度.②异质性指数可以用做指示植被冠层二向反射分布特征信息,可降低因观测数据几何条件差异所导致的反演结果不确定情况,同时能够补充植被时序生长过程表现的植被结构变化等动态特征.经研究实践,可将算法应用于时空尺度的叶面积指数估算,并能够为生态、农业应用提供植被的高精度遥感监测指标.

As a grassland type distributed in the desert steppe with warmer climate in the steppe region of central Asia, Stipa breviflora steppe has the following characteristics, i.e. transition and vulnerability. Therefore, it is susceptible to global climate changes and anthropogenic disturbances. The resistance and resilience of a plant community to the disturbances or climate changes mainly depend on plant functional traits of the dominant species. Yet there are few studies focus on the interaction between annual precipitation and grazing pressure on the variation of plant traits of desert steppe. We investigated the functional traits of S. breviflora under contrasting annual precipitations (wet and dry years) and different long-term grazing intensities (no grazing, light grazing, moderate grazing, MG, and heavy grazing, HG), and revealed the influence of grazing and the precipitation on the variability of functional traits of S. breviflora. Moreover, we discussed the vulnerable grassland type’s adaptation mechanism to grazing disturbances and precipitation changes, and its grazing adaptation strategy under different annual rainfall precipitation conditions. Plant height, coverage, above-ground biomass, leaf length, single leaf area of S. breviflora, were sensitive to grazing interference and precipitation, while leaf dry matter content, specific leaf area, and leaf nitrogen content were inert traits. S. breviflora’s resistance and resilience in response to grazing disturbance strongly depended on rainfall conditions. In the dry year, the functional traits of S. breviflora showed higher plasticity in responses to grazing treatment, in which moderate interference was favorable for the compensatory growth of S. breviflora. The functional traits of S. breviflora were not sensitive to the drastic changes of precipitation in grazing exclusion plot for many years. It was concluded that grazing in dry year had an adverse effect on S. breviflora desert steppe, and that light grazing or banning grazing in dry years is a better management practice for the desert steppe.

利用MOD10A1积雪产品逐像元提取积雪覆盖率、积雪日数、初雪日期、终雪日期,分析2000～2017年间蒙古高原各积雪参数的分布特征、变化及其与气温、降水的关系,为蒙古高原长期生态环境保护和应对全球变化制定适应对策提供参考依据.研究结果表明:(1)蒙古高原积雪覆盖率介于24～47％之间,且呈现“北部多南部少”的分布特点.积雪覆盖率最大出现在2002年,为46.02％;最小出现在2014年,为24.72％;1月份积雪覆盖率最大,为46.04％.积雪日数在46～82d之间,且呈现“北部长南部短”的分布特点,西北局部地区可达140d以上.积雪覆盖率和积雪日数以不明显的减少趋势为主,但西北部蒙古阿尔泰山、杭爱山,东部呼伦贝尔、锡林郭勒北侧和大兴安岭西侧小部分地区呈显著增长趋势.初雪日期北部出现早,南部晚,分布在第317～343d之间.终雪日期北部出现晚,南部早,分布在第45～63d之间.初雪日期和终雪日期均有不同程度的提前.(2)初步研究表明气温变化对积雪影响程度大于降水变化的影响.(3)整体上,蒙古高原积雪覆盖率随海拔升高而增大,但小尺度范围内,不同海拔地区积雪覆盖率变化多样.

Grassland is an important type of terrestrial ecosystem. Using remote sensing technology to study the change and driving force of native grassland productivity at large scale is an important way to understand the ecological status of grassland. In this study, potential and actual net primary productivity (NPP) of Xilingol steppe from 2000 to 2018 were examined based on climatic model and light-use efficiency model, respectively. NPP damage value driven by human activities was calculated from the difference between potential and actual NPP. The least square method was used to analyze the temporal and spatial variation of NPP in Xilingol and the driving role of climate and human activities on NPP. The results showed that NPP in Xilingol increased from west to east, with mean annual NPP being 271.54 g C·m-2·a-1, the area with increased NPP (grassland restoration) being 36500 km2, and the area with decreased NPP (grassland degradation) being 59900 km2. The potential NPP tended to rise under the driving force of temperature and precipitation, with an average annual increase of 6.5 g C·m-2·a-1, which indicated that regional climate played a positive role in the improvement of NPP in Xilingol steppe, and that human activities were the main driving force for grassland degradation. The value of NPP damage driven by human activities decreased from east to west and from south to north, with the highest value in Wuzhumuqin meadow and southern steppe. Human activities, such as mining and reclamation, had the most obvious negative impact on grassland NPP.

利用光能利用率模型和MODIS数据对内蒙古天然草原2000-2018年植被生长期草原净初级生产力进行连续动态监测,并在像元尺度利用最小二乘法分析了近20年植被净初级生产力(NPP)时空变化规律.结果表明:近20年中,内蒙古天然草原NPP在空间上呈由西向东递增分布规律,年均NPP为198.04 g C·m-2·a-1,潜在草地退化面积16.22万km2,其中重度、较重度面积分别为0.20万和1.11万km2,主要分布于人类活动密集区域,如矿区、建设用地及周边;在草地类型上,温性草原、温性草甸草原、温性荒漠以及温性荒漠草原潜在退化面积分别为5.22万、1.40万、4.04万和2.21万km2.通过分析NPP与气候因子的相关性表明:近20年,内蒙古草原NPP与降水具有显著相关性,与温度无相关性;温性草甸草原NPP对降水的响应最敏感,温性荒漠草原其次,温性草原对降水响应最低.研究还根据上述结果,围绕草地生态保护提出了建议.

Grassland health assessment is the basis for formulating grassland protection policy. However, there are few assessment methods that consider the angle of natural succession for northern China’s regional native grassland with excessive human activities. The main purpose of this study is to build an assessment system for these areas from the perspective of natural succession. Besides, the minimal cumulative resistance (MCR) model was used to extract potential ecological information from the study area as a supplementary reference for the assessment results. The result for Bayinxile pasture, a typical semiarid steppe with excessive human activities located in northern China, showed that: (1) The ecological function of eastern hilly area was better than that of other regions and the western area was lowest as a whole. (2) The river was the most important ecological network in the whole grassland in that it was of vital significance in the prevention of retrogressive succession and in the linking of ecological communities. (3) The density of ecological network was closely related to the intensity of human activities, and farmland and roads had great negative influence on the connection of the grassland ecological network. We further proposed an ecological control zone and made suggestions for Bayinxile ecological management to prevent grassland degradation based on the above results. This study should provide a new perspective for grassland health assessment and sustainable development of regional grassland.