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The general requirements of China's rural revitalization strategy are industrial prosperity, ecological livability and rich life. However, the traditional livestock breeding model has struggled to balance the dual requirements of production development and ecological protection, and it is urgent to inject new impetus and explore new development paths. At present, e-commerce has become a bridge between pastoral areas and cities, herdsmen and consumers. E-commerce participation is not only an important starting point for herdsmen to increase the added value and profit space of livestock products, but also an effective way to change the original breeding behavior based on the premise of destroying grassland. Therefore, this paper presents an in-depth study on the issue of e-commerce enabling grassland ecological restoration, aiming to provide more scientific and effective guidance for e-commerce to be used to achieve a win–win situation in economy and ecology. Therefore, based on the data of 271 herdsmen in pastoral areas of Inner Mongolia, we used the OLS model and the two-stage least squares (2SLS) method to identify the direct impact of herdsmen's e-commerce participation on grassland utilization pressure. The empirical results show that e-commerce participation can significantly decrease the grassland utilization pressure. The conclusion was still valid after alleviating endogeneity and conducting a robustness test. The results of a mechanism analysis suggest that the reduction effect of e-commerce participation on grassland utilization pressure is mainly due to price incentive, reputation incentive and place identity. Subjective norms can strengthen the inhibitory effect of e-commerce participation on grassland utilization pressure. Furthermore, a heterogeneity analysis demonstrates that e-commerce participation has a better, decreased impact on the grassland utilization pressure on the banners of China's rural e-commerce demonstration county program. Under a counterfactual assumption, if herdsmen who can participate in e-commerce choose not to do this, their grassland utilization pressure will increase. [ABSTRACT FROM AUTHOR]

Among the methods used to describe soil-available water in terms of yield, field water capacity and permanent wilting coefficient were considered as the upper and lower boundaries, respectively, for normal plant growth. This study investigated the soil properties and groundwater resources of the Hailiutu pasture in Inner Mongolia, China. Soil water characteristics and hydraulic conductivity function curves of the pasture soil were measured based on filter paper method. Experimental results showed that air-entry and residual value were determined to be 34.6% and 13.1%, corresponding to the field water capacity and permanent wilting coefficient, respectively, and the available water capacity was 218.0 mm. The downward velocity of the soil wetting front in the silt is 3.0 mm/h. Site investigation revealed that the pasture was a natural capillary barrier, and its critical suction could optimize the upper boundary of irrigation. Considering the limited underground water resources, shallow water level, and the transpiration of Caragana Korshinskii in the pasture, an available drip irrigation method was designed. The spacing between drip irrigation pipes is 60 cm, the spacing between drip holes is 60 cm, the drip irrigation volume per hole is 475.2~838.8 mL/d, the drip irrigation time is 110~200 min at each night. Each hectare pasture exhausts 13.5~22.5 m3 water each day. Field tests showed that the proposed method is feasible and can be used for irrigation in dryland pastures. [ABSTRACT FROM AUTHOR]

Unregulated livestock breeding and grazing can degrade grasslands and damage the ecological environment. The combination of remote sensing and artificial intelligence techniques is a more convenient and powerful means to acquire livestock information in a large area than traditional manual ground investigation. As a mainstream remote sensing platform, unmanned aerial vehicles (UAVs) can obtain high-resolution optical images to detect grazing livestock in grassland. However, grazing livestock objects in UAV images usually occupy very few pixels and tend to gather together, which makes them difficult to detect and count automatically. This paper proposes the GLDM (grazing livestock detection model), a lightweight and high-accuracy deep-learning model, for detecting grazing livestock in UAV images. The enhanced CSPDarknet (ECSP) and weighted aggregate feature re-extraction pyramid modules (WAFR) are constructed to improve the performance based on the YOLOX-nano network scheme. The dataset of different grazing livestock (12,901 instances) for deep learning was made from UAV images in the Hadatu Pasture of Hulunbuir, Inner Mongolia, China. The results show that the proposed method achieves a higher comprehensive detection precision than mainstream object detection models and has an advantage in model size. The m A P of the proposed method is 86.47%, with the model parameter 5.7 M. The average recall and average precision can be above 85% at the same time. The counting accuracy of grazing livestock in the testing dataset, when converted to a unified sheep unit, reached 99%. The scale applicability of the model is also discussed, and the GLDM could perform well with the image resolution varying from 2.5 to 10 cm. The proposed method, the GLDM, was better for detecting grassland grazing livestock in UAV images, combining remote sensing, AI, and grassland ecological applications with broad application prospects. [ABSTRACT FROM AUTHOR]

In this paper, we selected Xilingol League in Inner Mongolia, China, as the research area, based on the land-use data of five Landsat remote sensing images taken between 1980 and 2015. Then, we calculated the complex network eigenvalues, such as the average shortest path, betweenness centrality, and degree, to identify the key land-use types, stability, and ecological environment change regularity from the perspective of the land-use system. Finally, we used the Canonical Correspondence Analysis (CCA) method to explore the main driving forces behind changes in the land-use system, to provide scientific support for the study of changing trends in land-use, and regional grassland ecological management. The findings are shown below. First, in the last 35 years, grasslands have always been the major type of change in land-use transfer matrices. Grasslands play a controlling role in the whole land-use system. Second, grassland and cultivated land are the major "transfer out" type of land in the Xilingol area, while construction land and water area belong to the major "transfer in" type of land. Third, the average shortest path values of four transition matrix networks were all less than 1.5, indicating that the land-use system becomes less stable, but the average shortest path values of these four networks present an increasing trend, leading to a more stable development of the land-use system. However, on the whole, it shows an upward trend, and the land-use system is moving in a stable direction. Fourth, the average annual rainfall, population, topographic factors, GDP, and distance from settlements play a prominent role in determining the spatial distribution of change in land-use in Xilingol. [ABSTRACT FROM AUTHOR]

Grasshopper disasters threaten grassland animal husbandry, and overgrazing is widely recognized as one of the main causes of locust infestation in grassland regions. However, the impact of overgrazing on grasshopper disasters remains unclear. To address this knowledge gap, this study interviewed 541 households living in locust-prone areas in Inner Mongolia, China. The generalized Poisson model and OLS regression examined the relationship between herders' production behavior and locust disasters. The results showed that 42% of the herders had suffered from locusts more than three times over the past 15 years, with an average of 49 ha of grassland damaged per household. In addition, with the increase in grazing rates, the scale of locust disasters decreased before it increased. The results also showed that operating grassland areas and feeding forage reduced locust disasters significantly, while renting grassland areas and grazing rates worked oppositely. These results suggest that grazing intensity can make a significant difference in the occurrence of locust disasters. [ABSTRACT FROM AUTHOR]

The Payments for Ecosystem Services (PES) program is an innovative approach that provides economic incentives directly to natural resource exploiters in order to shape their behavior. Although the implementation of PES programs and the context in which these programs are implemented are often different across space, the spatial heterogeneities in the impacts of PES programs are often neglected in studies. In this study, we demonstrate the spatial and temporal dynamics of the Normalized Differential Vegetation Index (NDVI) in the grassland where China's Grassland Ecological Compensation Program (GECP) has been implemented, and we evaluate the impacts of the GECP on the NDVI. We found that most of the grassland in the GECP area showed small changes in NDVI between 2000 and 2010. On average, the GECP only had a marginal positive effect on the NDVI of grassland. Although the magnitude of impacts of the GECP was relatively small in most places where the program is implemented, we detected substantial heterogeneities in the impacts of the GECP on the NDVI. The impact of the GECP on the NDVI differed substantially, particularly between Inner Mongolia, Sichuan, and other provinces. Our findings suggest that there can be substantial heterogeneities in the impacts of PES programs across space, which can be leveraged to promote the efficacy of the GECP and many other PES programs around the world. [ABSTRACT FROM AUTHOR]

Biodiversity is sensitive to climate change and human activity. Grazing management practices have a profound impact on plant species–genetic diversity in grassland and woodland communities. In this study, we explored the responses of species and genetic diversity to grazing in Ulmus pumila L. communities in the Hunshandak Sandy Land, analyzed the relationship between species and genetic diversity, and revealed the effects of climate factors on them. We found that the dominant species were Spiraea trilobata, Caragana microphylla and Artemisia intramongolica in U. pumila communities. Plant species richness in the banned grazing (BG) and seasonal grazing (SG) communities was significantly higher than that in the delayed grazing (DG) community. Plant Simpson's diversity index showed a downward trend with increasing grazing duration. There was no difference in allelic richness in nuclear DNA (nrDNA) of U (U. pumila) and chloroplast DNA (cpDNA) of NU (other dominant species besides U. pumila) among grazing management types. The expected heterozygosity of U in nrDNA and cpDNA was significantly affected by grazing management, and the trend was BG > SG > DG. The genetic diversity of U was lower than that of NU. The genetic diversity characteristics of U in cpDNA were lower than those in nrDNA. The analysis of molecular variance (AMOVA) showed that 98.08% of the variation in U and 95.25% of the variation in NU was attributed within populations and the differences within grazing management types were 13.35% in U and 24.08% in NU (p < 0.001). The species richness of communities was positively correlated with the genetic diversity of U, NU and all dominant species (U + NU) in communities. The nineteen climatic variables together explained 94.24% and 79.08% of the total variation in U and NU genetic and species diversity. The mean temperature of the warmest quarter and temperature seasonality were the main factors affecting genetic diversity (p = 0.046; 0.01), while the maximum temperature of the warmest month was the main factor affecting species diversity (p = 0.05). [ABSTRACT FROM AUTHOR]

Wildfires spread rapidly and cause considerable ecological and socioeconomic losses. Inner Mongolia is among the regions in China that suffer the most from wildfires. A simple, effective model that uses fewer parameters to simulate wildfire spread is crucial for rapid decision-making. This study presents a region-specific technological process that requires a few meteorological parameters and limited grassland vegetation data to predict fire spreading dynamics in Inner Mongolia, based on cellular automata that emphasize the numeric evaluation of both heat sinks and sources. The proposed method considers a case that occurred in 2021 near the East Ujimqin Banner border between China and Mongolia. Three hypothetical grassland wildfires were developed using GIS technology to test and demonstrate the proposed model. The simulation results suggest that the model agrees well with real-world experience and can facilitate real-time decision-making to enhance the effectiveness of firefighting, fire control, and simulation-based training for firefighters. [ABSTRACT FROM AUTHOR]

The agro-pastoral ecotone serves as an important ecological transition zone between grassland and cropland. It plays a crucial role in providing forage and maintaining the ecological security of Northern China. China has initiated many ecological restoration projects in the agro-pastoral ecotone. Separating the affecting factors of anthropogenic activities and natural factors change on grassland may help guide future ecological restoration projects. This study investigated the grassland change over the agro-pastoral ecotone in Inner Mongolia (APEI) using land use data, vegetation index, and monthly climate data, social and economic data from 2010 to 2020. Two indicators were introduced to evaluate the grassland change in quality and quantity. The average annual maximum NDVI of grassland within each sample was used as a proxy for grassland quality, and the proportion of grassland within each sample was used as a proxy for grassland quantity. The driving mechanisms of grassland quality were examined on different types of grassland. The results showed that (1) from 2010 to 2020, the quantity of grassland in APEI slightly decreased while the quality of the grassland significantly improved; (2) the change in grassland quality was jointly caused by natural factors and human activities, with 66.77% being attributed to natural factors and 33.23% to human activities. In some areas, the alteration in grassland quality was solely determined by either the impacts of natural factors or human-induced disturbances; and (3) temperature and precipitation played different roles in affecting grassland quality among different types of grassland. In some areas, anthropogenic activities, such as farmland and population, would affect grassland quality. The findings of this study would contribute to the understanding of the mechanism of ecological environmental changes in the agro-pastoral ecotone and guide the planning and implementation of future ecological restoration projects in agro-pastoral areas. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) is a key indicator of vegetation structure and function, and its products have a wide range of applications in vegetation condition assessment and usually act as important input parameters for ecosystem modeling. Grassland plays an important role in regional climate change and the global carbon cycle and numerous studies have focused on the product-based analysis of grassland vegetation changes. However, the performance of various LAI products and their discrepancies across different grassland types in drylands remain unclear. Therefore, it is critical to assess these products prior to application. We evaluated the accuracy of four commonly used LAI products (GEOV2, GLASS, GLOBMAP, and MODIS) using LAI reference maps based on both bridging and cross-validation approaches. Under different grassland types, the GLASS LAI performed better in meadow steppe (R2 = 0.26, RMSE = 0.41 m2/m2) and typical steppe (R2 = 0.32, RMSE = 0.38 m2/m2); the GEOV2 LAI performed better in desert steppe (R2 = 0.39, RMSE = 0.30 m2/m2). When we assessed their spatial and temporal discrepancies during the period from 2010 to 2019, the four LAI products overall showed a high spatial and temporal consistency across the region. Compared with GLASS LAI, the most consistent to least consistent correlations can be ordered by GEOV2 LAI (R2 = 0.94), MODIS LAI (R2 = 0.92), and GLOBMAP LAI (R2 = 0.87). The largest differences in LAI throughout the year occurred in July for all grassland types. Limited by the location and number of sample plots, we mainly focused on spatial and temporal variations. The spatial heterogeneity of land surface is pervasive, especially in vast grassland areas with rich grassland types, and the results of this study can provide a basis for the application of the product in different grassland types. Furthermore, it is essential to develop highly accurate and reliable satellite-based LAI products focused on grassland from the regional to the global scale according to these popular approaches, which is the next step in our work plan. [ABSTRACT FROM AUTHOR]

Ecological protection and high-quality development of the Yellow River Basin (YRB), China, aroused remarkable concerns from China's Central Government, and has been a major national strategy. The Inner Mongolia reach of the Yellow River Basin (IM-YRB) is a typical dryland with pervasive vegetation restoration through the actions of the ecological projects that have been conducted in recent years. However, how climate changes and human activities, such as land use and land cover (LULC) changes, jointly impact vegetation variations in this region remains poorly understood. Here, using an explainable machine learning technique, we evaluated linkages between the kernel normalized difference vegetation index (kNDVI) and air temperature, precipitation, soil moisture, and LULC changes, and relevant marginal contributions of these four drivers to the observed vegetation changes. The grassland fraction on a pixel level was selected as the quantitative LULC variable due to its key role in regional LULC. We found that interannual kNDVI changes in most areas of this study region were negatively sensitive to temperature, but positively sensitive to precipitation and soil moisture, with area fractions of 71.74%, 96.93%, and 89.33%, respectively. The area fraction of negative kNDVI sensitivity to LULC was roughly equivalent to that of positive kNDVI sensitivity. The contributions of air temperature, precipitation, soil moisture, and LULC to overall kNDVI changes were 21.54%, 33.32%, 32.19%, and 12.95%, respectively. Moisture conditions also play a critical role in vegetation changes, which was reflected by the fluctuating growth of kNDVI as interannual changes in precipitation. Nonetheless, kNDVI changes are also affected by LULC, and LULC became the dominant factor behind the kNDVI anomalies over the grassland restoration regions from barren over the IM-YRB. This research provides theoretical support for dryland vegetation restoration under the influence of climate change. [ABSTRACT FROM AUTHOR]

Herders' livelihood strategies are functions of the capitals at their disposal. Although this thesis has been proved, it has not been applied to livelihood research in the context of conservation initiatives. The Chinese government implemented the Grassland Ecological Compensation Policy (GECP) in 2011. However, the impact of the policy on herders' livelihoods is still unclear. This study measured the variation in herders' livelihood strategies in the desert grassland of Inner Mongolia, China, after the implementation of the GECP. This study also analyzed the impacts of livelihood capitals on the livelihood strategies, revealing the shortages of different livelihood strategies. The results showed the following: After the GECP, (1) herders' livelihood strategies could be grouped into five types based on livelihood capitals. The livelihood capitals of herders varied greatly, resulting in large economical gaps among households with different livelihood strategies. (2) The herders' livelihood strategies were affected by factors including the education, age, social communication, amount of livestock, income, and subsidy of the herders. (3) The main income sources of different livelihood strategies were both subsidy and livestock husbandry. In general, the GECP drives the differentiation of herders' livelihoods remarkably. Future conservation initiatives should take the shortages of different livelihood strategies into account. [ABSTRACT FROM AUTHOR]

Coal is an important energy resource in the world, especially in China. Extensive coal exploitation seriously damaged the grassland and its fragile ecosystem. However, temporal and spatial impact laws of open-pit coal exploitation on Landscape Ecological Health (LEH) of semi-arid grasslands are still not clear. Therefore, the main objective of this paper is to study impact of Large-scale Open-pit Coal Base (LOCB) on the LEH of semi-arid grasslands from the perspectives of temporal and spatial. Taking Shengli LOCB of Xilinguole grassland in Inner Mongolia as an example, we demonstrate a conceptual model of LOCB impact on LEH of semi-arid grasslands, and establish a research system called landscape Index-pattern Evolution-Driving force-Spatial statistics (IEDS). A complete process integrated from investigation, monitoring, and evaluation to the analysis of impact laws was developed. Result indicated that coal mining causes gradual increase of landscape patches, landscape fragmentation, gradual decline of landscape connectivity, complexity and irregularity of landscape shape, enhancement of landscape heterogeneity and complexity, gradual decline of landscape stability, gradual decrease of grassland landscape and annual increase of unhealthy grassland landscape. The LEH of grassland basically belongs to the state of slight deterioration. In the past 15 years, the spatial and temporal distribution characteristics of LEH in the study area are similar. This study provides scientific reference for ecological disturbance research, environmental protection, landscape planning, restoration and renovation of ecological environment in mining areas. At the same time, future research should integrate geological, hydrological, soil, vegetation, microorganisms, animals, climate, and other perspectives to study the impact of mining on landscape ecology deeply. [ABSTRACT FROM AUTHOR]

Grassland is the primary land use in China, which has experienced extensive degradation in recent decades due to overexploitation. Here, we conducted field experiments to quantify the degraded grassland's recovery rate in Northeast Inner Mongolia in response to restoration measures, including fallow + enclosure (FE) and mowing + enclosure (ME) in comparison to livestock grazing (LG), since 2005. Plant community properties were surveyed and aboveground biomass (AGB) sampled in summer 2013. Our results showed that the regional dominant species Leymus chinensis retained its dominance under FE, whereas a range of forb species gained dominance under LG. Vegetative cover was maximal under FE and minimal under LG. The least amount of vegetation development and AGB were observed under LG. However, plant diversity showed an opposite pattern, with maximal diversity under LG and minimal under FE. Statistical analysis revealed that AGB was negatively associated with plant diversity for all treatments except ME. For ME, a positive AGB-diversity relationship was characterized, suggesting that mowing intensity was a controlling factor for the AGB-diversity relationship. Overall, these results demonstrated that enclosure plus mowing represented an effective conservation measure that provided fair support to forage production and a progressive pathway to a more resilient grassland system. [ABSTRACT FROM AUTHOR]

The main feature of grassland degradation is the change in the vegetation community structure. Hyperspectral-based grassland community identification is the basis and a prerequisite for large-area high-precision grassland degradation monitoring and management. To obtain the distribution pattern of grassland communities in Xilinhot, Inner Mongolia Autonomous Region, China, we propose a systematic classification method (SCM) for hyperspectral grassland community identification using China's ZiYuan 1-02D (ZY1-02D) satellite. First, the sample label data were selected from the field-collected samples, vegetation map data, and function zoning data for the Nature Reserve. Second, the spatial features of the images were extracted using extended morphological profiles (EMPs) based on the reduced dimensionality of principal component analysis (PCA). Then, they were input into the random forest (RF) classifier to obtain the preclassification results for grassland communities. Finally, to reduce the influence of salt-and-pepper noise, the label similarity probability filter (LSPF) method was used for postclassification processing, and the RF was again used to obtain the final classification results. The results showed that, compared with the other seven (e.g., SVM, RF, 3D-CNN) methods, the SCM obtained the optimal classification results with an overall classification accuracy (OCA) of 94.56%. In addition, the mapping results of the SCM showed its ability to accurately identify various ground objects in large-scale grassland community scenes. [ABSTRACT FROM AUTHOR]

The evolution of meandering rivers continues to attract considerable attention in research and for practical applications, given that it is closely associated with the safety of river systems and riparian zones. There has been much discussion regarding the various channel planform features exhibited by meandering rivers under different river systems and riparian conditions. The Yimin River is a good example and is located southeast of the Hulun Buir Grassland, which is characterised by a fragile ecosystem and little anthropological activity along with active flow during the non-frozen season from May to November each year and relatively low sediment discharge compared with the Yellow River and Mississippi River. Improved analysis of the evolution of the Yimin River from 1975 to 2019 can support increased local species diversity and more effective flood risk and river management. With the combined Google Earth Engine (GEE) platform and the Geographic Information Systems (GIS) technique, remote sensing images, including Landsat images and global surface water data, are used to analyse the channel planform features of the freely meandering river channel in the middle and lower Yimin River. The results show that the percentage of low sinuosity channel bends was higher than that of high-sinuosity bends. Although the bends with an amplitude greater than 0.48 km and sinuosity greater than 2.3 have an evident upstream-skewed trend, the main channel planform features were downstream skewed with 1499 such bends. The river system conditions in the Yimin River, including lower sediment discharge and vegetation cover, are conducive to the development of downstream-skewed bends. The high-sinuosity bends were found to have a relatively larger ratio during 1981–2000, a period with higher mean annual streamflow compared with other time periods. [ABSTRACT FROM AUTHOR]

Estimating the Start of Growing Season (SOS) of grassland on the global scale is an important scientific issue since it can reflect the response of the terrestrial ecosystem to environmental changes and determine the start time of grazing. However, most remote sensing data has coarse- temporal and spatial resolution, resulting in low accuracy of SOS retrieval based on remote sensing methods. In recent years, much research has focused on multi-source data fusion technology to improve the spatio-temporal resolution of remote sensing information, and to provide a feasible path for high-accuracy remote sensing inversion of SOS. Nevertheless, there is still a lack of quantitative evaluation for the accuracy of these data fusion methods in SOS estimation. Therefore, in this study, the SOS estimation accuracy is quantitatively evaluated based on the spatio-temporal fusion daily datasets through the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and other models in Xilinhot City, Inner Mongolia, China. The results show that: (1) the accuracy of SOS estimation based on spatio-temporal fusion daily datasets has been slightly improved, the average Root Mean Square Error (RMSE) of SOS based on 8d composite datasets is 11.1d, and the best is 9.7d ( f s t a r f m 8 ); (2) the estimation accuracy based on 8d composite datasets ( R M S E ¯ = 11.1d) is better than daily fusion datasets ( R M S E ¯ = 18.2d); (3) the lack of the Landsat data during the SOS would decrease the quality of the fusion datasets, which ultimately reduces the accuracy of the SOS estimation. The R M S E ¯ of SOS based on all three models increases by 11.1d, and the STARFM is least affected, just increases 2.7d. The results highlight the potential of the spatio-temporal data fusion method in high-accuracy grassland SOS estimation. It also shows that the dataset fused by the STARFM algorithm and composed for 8 days is better for SOS estimation. [ABSTRACT FROM AUTHOR]

Drought has been approved to affect the process of terrestrial ecosystems from different organizational levels, including individual, community, and ecosystem levels; however, which traits play the dominant role in the resistance of plant to drought is still unclear. The experiment was conducted in semi-arid temperate grassland and included six paired control and drought experimental plots. The drought treatment was completely removed from precipitation treatments from 20 June to 30 August 2013. At the end of the growing season in 2013, we removed the rain cover for ecosystem recovery in 2014. The results demonstrated that drought treatment increased the coverage of and abundance Heteropappus altaicus, Potentilla bifurca, and Artemisia scoparia by 126.2–170.0% and 63.4–98.9%, but decreased that of Artemisia frigida, Dontostemon dentatus, and Melissilus ruthenicu by 46.2–60.2% and 49.6–60.1%. No differences in coverage and abundance of Agropyron cristatum, Stipa kiylovii, and Cleistogenes squarrosa were found between control and drought treatment. The coverage and abundance of Stipa kiylovii have exceeded the original level before the drought stress, but Heteropappus altaicus still had not recovered in the first year after the disturbance. Our findings indicate that plant functional traits are important for the understanding of the resistance and resilience of plants to drought stress, which can provide data support for grassland management. [ABSTRACT FROM AUTHOR]

In grassland open-pit mining areas, net primary productivity (NPP) is mainly affected by climate conditions and human activities. The identification and assessment of the influence of human activities on NPP is important for mining production and the implementation of ecological restoration. In this study, we explored the influence of human activities on the NPP in the Shengli mining area in Inner Mongolia, China by using the Carnegie–Ames–Stanford Approach (CASA) model and the Chikugo model, in which a calibration method was applied. An analysis of four representative years showed that the proportion of NPP induced by human activities reached 56.2%, that the percentage of pixels with an inhibitory effect on NPP was 99% in 2011 with the highest intensity of mining activity, and that these two values decreased to 11.9% and 69% in 2020, respectively, with the steady implementation of ecological restoration. Moreover, from the analysis of global and local spatial correlation, mining activities and ecological restoration aggravated and weakened the aggregation of NPP induced by human activities, respectively. [ABSTRACT FROM AUTHOR]

Grassland classification is crucial for grassland management. One commonly used method utilizes remote sensing vegetation indices (VIs) to map grassland classes at various scales. However, most grassland classifications were conducted as case studies in a small area due to lack of field data sources. At a small scale, classification is reliable; however, great uncertainty emerges when extended to other areas. In this study, large amounts of field observations (more than 30,000 aerial photos) were obtained using unmanned aerial vehicle photography in Inner Mongolia, China, during the peak period of grassland growth in 2018 and 2019. Then, four machine learning classification algorithms were constructed based on characteristic indices of MODIS NDVI in the growing season to map grassland classes of Inner Mongolia. Finally, the spatial distribution and temporal variation of temperate grassland classes were analyzed. Results showed that: (1) Among all characteristic indices, the maximum, average, and sum of MODIS NDVI from July to September during 2015 to 2019 greatly affected grassland classification. (2) The random forest method exhibited the best performance with overall accuracy and kappa coefficient being 72.17% and 0.62, respectively. (3) Compared with the grassland class mapped in the 1980s, 30.98% of grassland classes have been transformed. Our study provides a technological basis for effective and accurate classification of the temperate steppe class and a theoretical foundation for sustainable development and restoration of the temperate steppe ecosystem. [ABSTRACT FROM AUTHOR]

Plant phenology, especially the timing of the start and the end of the vegetation growing season (SOS and EOS), plays a major role in grassland ecosystem carbon cycles. As the second-largest grassland country in the world, China's grasslands are mainly distributed in the northern cold temperate climate zone. The accuracies and relations of plant phenology estimations from multialgorithms and data resources are poorly understood. Here, we investigated vegetation phenology in two typical cold temperate grasslands, Haibei (HB) and Inner Mongolia (NM) grasslands, in China from 2001 to 2017. Compared to ground vegetation phenology observations, we analyzed the performance of the moderate resolution imaging spectroradiometer MODIS phenology products (MCD12Q2) and two remote sensing-based vegetation phenology algorithms from the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) time series (five satellite-based phenology algorithms). The optimal algorithm was used to compare with eddy covariance (EC)-based carbon phenology, and to calculate the thresholds of carbon phenology periods (SOSt and EOSt) in each site. Results showed that satellite-based phenology estimations (all five algorithms in this study) were strongly coupled with the temporal variation of the observed phenological period but significantly overestimated the SOS, predicting it to be over 21 days later than the field data. The carbon phenology thresholds of HB grassland (HB_SOSt and HB_EOSt) had a significant upward trend, with the multiyear average values being 0.14 and 0.29, respectively. In contrast, the thresholds of NM grasslands (NM_SOSt and NM_EOSt) also showed a certain upward trend, but it was not significant (p > 0.05), with the multiyear average values being 0.17 and 0.2, respectively. Our study suggested the thresholds of carbon phenology periods (SOSt and EOSt, %) could be simply and effectively estimated based on their significant relationship with the EC-based maximum of gross primary productivity observations (GPPmax) at a specific site and time. Therefore, this study suggested the thresholds of carbon phenology were not fixed even in a specific ecosystem, which also provided simple bridges between satellite-based vegetation phenology and EC-based carbon phenology in similar grasslands. [ABSTRACT FROM AUTHOR]