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The spatial pattern of soil bulk density in the grasslands of northern China largely remains undefined, which raised uncertainty in understanding and modeling various soil processes in large spatial scale. Based on the measured data of soil bulk density available from soil survey reports from the grasslands of northern China, we constructed a soil Stratified Pedotransfer function (SPTF) from the surface soil bulk density. Accordingly, the stratified bulk density data of soil vertical profile was reconstructed, and the estimation of soil bulk density data in horizontal space was performed. The results demonstrated that the soil bulk density of the grasslands of northern China was typically high in the central and northwestern regions and low in the eastern and mountainous regions. Mean soil bulk density of the grasslands was 1.52 g·cm−3. According to geographical divisions, the highest soil bulk density was observed in the Tarim basin, with mean soil bulk density of 1.91 g·cm−3. Conversely, the lowest soil bulk density was observed in the Tianshan Mountain area, with mean soil bulk density of 1.01 g·cm−3. Based on data obtained on various types of grasslands, the soil bulk density of alpine meadow was the lowest, with a mean soil bulk density of 0.75 g·cm−3, whereas that of temperate desert was the highest, with mean soil bulk density of 1.80 g·cm−3. Mean prediction error, root mean square deviation, relative error, and multiple correlation coefficient of soil bulk density data pertaining to surface layer (0–10 cm) in the grasslands of northern China were 0.018, 0.223, 16.2%, and 0.5386, respectively. The approach of employing multiple data sources via soil transfer function improved the estimation accuracy of soil bulk density from stratified soils data at the large scale. Our study would promote the accurate assessment of grassland carbon storage and fine land characteristics mapping.
Yuxin Qiao; Huazhong Zhu; Huaping Zhong; Yuzhe Li. Stratified Data Reconstruction and Spatial Pattern Analyses of Soil Bulk Density in the Northern Grasslands of China. ISPRS International Journal of Geo-Information 2020, 9, 682 .
AMA StyleYuxin Qiao, Huazhong Zhu, Huaping Zhong, Yuzhe Li. Stratified Data Reconstruction and Spatial Pattern Analyses of Soil Bulk Density in the Northern Grasslands of China. ISPRS International Journal of Geo-Information. 2020; 9 (11):682.
Chicago/Turabian StyleYuxin Qiao; Huazhong Zhu; Huaping Zhong; Yuzhe Li. 2020. "Stratified Data Reconstruction and Spatial Pattern Analyses of Soil Bulk Density in the Northern Grasslands of China." ISPRS International Journal of Geo-Information 9, no. 11: 682.
Models constitute the primary approaches for predicting terrestrial ecosystem gross primary production (GPP) at regional and global scales. Many satellite-based GPP models have been developed due to the simple algorithms and the low requirements of model inputs. The performances of these models are well documented at the biome level. However, their performances among vegetation subtypes limited by different environmental stresses within a biome remains largely unexplored. Taking grasslands in northern China as an example, we compared the performance of eight satellite-based GPP models, including three light-use efficiency (LUE) models (vegetation photosynthesis model (VPM), modified VPM (MVPM), and moderate resolution imaging spectroradiometer GPP algorithm (MODIS-GPP)) and five statistical models (temperature and greenness model (TG), greenness and radiation model (GR), vegetation index model (VI), alpine vegetation model (AVM), and photosynthetic capacity model (PCM)), between the water-limited temperate steppe and the temperature-limited alpine meadow based on 16 site-year GPP estimates at four eddy covariance (EC) flux towers. The results showed that all the GPP models performed better in the alpine meadow, particularly in the alpine shrub meadow (R2 ≥ 0.84), than in the temperate steppe (R2 ≤ 0.68). The performance varied greatly among the models in the temperate steppe, while slight intermodel differences existed in the alpine meadow. Overall, MVPM (of the LUE models) and VI (of the statistical models) were the two best-performing models in the temperate steppe due to their better representation of the effect of water stress on vegetation productivity. Additionally, we found that the relatively worse model performances in the temperate steppe were seriously exaggerated by drought events, which may occur more frequently in the future. This study highlights the varying performances of satellite-based GPP models among vegetation subtypes of a biome in different precipitation years and suggests priorities for improving the water stress variables of these models in future efforts.
Liangxia Zhang; Decheng Zhou; Jiangwen Fan; Qun Guo; Shiping Chen; Ranghui Wang; Yuzhe Li. Contrasting the Performance of Eight Satellite-Based GPP Models in Water-Limited and Temperature-Limited Grassland Ecosystems. Remote Sensing 2019, 11, 1333 .
AMA StyleLiangxia Zhang, Decheng Zhou, Jiangwen Fan, Qun Guo, Shiping Chen, Ranghui Wang, Yuzhe Li. Contrasting the Performance of Eight Satellite-Based GPP Models in Water-Limited and Temperature-Limited Grassland Ecosystems. Remote Sensing. 2019; 11 (11):1333.
Chicago/Turabian StyleLiangxia Zhang; Decheng Zhou; Jiangwen Fan; Qun Guo; Shiping Chen; Ranghui Wang; Yuzhe Li. 2019. "Contrasting the Performance of Eight Satellite-Based GPP Models in Water-Limited and Temperature-Limited Grassland Ecosystems." Remote Sensing 11, no. 11: 1333.
Grasslands in northern China form an important ecological barrier that prevents and controls desertification. The Beijing–Tianjin Sand Source Control (BTSSC) Project has been implemented to restore grassland in order to control sand sourced pollution. This study aimed to understand the impacts of four applied restoration practices on the productivity, composition, and species diversity of vegetation communities in the BTSSC Project. The results indicated the following: (1) All the restoration practices tended to increase the height and cover of communities, and the effect was most obvious where grazing was excluded; (2) total biomass (87%), above-ground biomass (164%) and below-ground biomass (58%) only increased consistently when grazing was excluded from the steppe; (3) fenced and grazing exclusion practice significantly increased the abundance of species in communities, but all the practices tended to decrease the evenness of species; and, (4) the correlation analysis revealed that the Shannon–Wiener diversity index, and Pielou evenness index, showed significant negative correlations with the above-ground biomass of grassland communities after restoration, while no significant relationships were shown in reference plots. Our comparison of applied practices in the BTSSC project revealed that grazing exclusion might be a high priority for more successful restoration in this region.
Yuzhe Li; Jiangwen Fan; Hailing Yu. Grazing Exclusion, a Choice between Biomass Growth and Species Diversity Maintenance in Beijing—Tianjin Sand Source Control Project. Sustainability 2019, 11, 1941 .
AMA StyleYuzhe Li, Jiangwen Fan, Hailing Yu. Grazing Exclusion, a Choice between Biomass Growth and Species Diversity Maintenance in Beijing—Tianjin Sand Source Control Project. Sustainability. 2019; 11 (7):1941.
Chicago/Turabian StyleYuzhe Li; Jiangwen Fan; Hailing Yu. 2019. "Grazing Exclusion, a Choice between Biomass Growth and Species Diversity Maintenance in Beijing—Tianjin Sand Source Control Project." Sustainability 11, no. 7: 1941.
Livestock grazing is an important determinant of species diversity and plant growth. Overgrazing is identified as one of the most important disturbances resulting in grassland degradation. Although many restoration practices have been implemented, grazing exclusion is one of the most effective methods to restore degraded grasslands. We explored the impact of five years of grazing exclusion on plant growth and species diversity in four types of grasslands: temperate steppe (TS), swamp meadow (SM), alpine steppe (AS), and alpine meadow (AM). Our results showed that grazing exclusion increased plant height, coverage, biomass, and species diversity in all four grasslands. The aboveground biomass in AM (180.8%), TS (117.3%), and SW (105.9%) increased significantly more than AS (10.1%). Grazing exclusion in AM had the greatest effect on proportion of palatable species, and the increase in palatable species in AM was higher than that of the other grassland types significantly. Species diversity increased significantly within the enclosure in SM (23.9%) and AM (20.8%). Our results indicate that grazing exclusion is an effective management strategy to restore degraded grasslands and it works best in alpine meadow. This study contributes to the growing theoretical basis for grassland management strategies and has a significant effect on sustainable development for grassland resources and pastoral areas.
Suizi Wang; Jiangwen Fan; Yuzhe Li; Lin Huang. Effects of Grazing Exclusion on Biomass Growth and Species Diversity among Various Grassland Types of the Tibetan Plateau. Sustainability 2019, 11, 1705 .
AMA StyleSuizi Wang, Jiangwen Fan, Yuzhe Li, Lin Huang. Effects of Grazing Exclusion on Biomass Growth and Species Diversity among Various Grassland Types of the Tibetan Plateau. Sustainability. 2019; 11 (6):1705.
Chicago/Turabian StyleSuizi Wang; Jiangwen Fan; Yuzhe Li; Lin Huang. 2019. "Effects of Grazing Exclusion on Biomass Growth and Species Diversity among Various Grassland Types of the Tibetan Plateau." Sustainability 11, no. 6: 1705.
Soil organic carbon (SOC) plays an important role in global carbon cycling and is increasingly important to the ecosystem. An accurate SOC content map would significantly contribute to the proper application of ecological modeling. Therefore, there is a need to accurately estimate and map SOC content in grasslands. We evaluated various methods for estimating the SOC content of grasslands using field soil sampling data and auxiliary data in the pastoral area. The results showed that (1) SOC is affected by various factors, including geographic location, soil, topography, and climate. Single-variable SOC models account for 2%–72% of the variations in the grassland SOC. (2) Based on the correlation of environmental variables of SOC, normalized difference vegetation index, annual precipitation, annual average temperature, elevation, and moisture index were explored as critical auxiliary data to predict SOC content. We established multi-factor weighted regression model (MWRM). (3) We compared three spatial estimation methods, including inverse distance weighting, regression kriging, and MWRM, to determine a suitable method for SOC mapping. Our results indicate that among the three spatial estimation methods, MWRM provides the lowest prediction error (RMSE = 4.85 g/kg; MAE = 3.47 g/kg; MRE = 24.04%) and highest R2 (0.89) and Lin's concordance (0.94) values in the spatial estimation at a 0–10 cm soil layer. (4) Therefore, we applied MWRM to predict SOC content at various layers, and its SOC content prediction in the topsoil (0–20 cm) is better than that in the subsurface (20–30 cm) and subsoil (30–40 cm). The SOC content spatial distribution demonstrated a similar pattern for each soil layer and the SOC content gradually decreased with increasing soil depth.
Suizi Wang; Jiangwen Fan; Huaping Zhong; Yuzhe Li; Huazhong Zhu; Yuxin Qiao; Haiyan Zhang. A multi-factor weighted regression approach for estimating the spatial distribution of soil organic carbon in grasslands. CATENA 2018, 174, 248 -258.
AMA StyleSuizi Wang, Jiangwen Fan, Huaping Zhong, Yuzhe Li, Huazhong Zhu, Yuxin Qiao, Haiyan Zhang. A multi-factor weighted regression approach for estimating the spatial distribution of soil organic carbon in grasslands. CATENA. 2018; 174 ():248-258.
Chicago/Turabian StyleSuizi Wang; Jiangwen Fan; Huaping Zhong; Yuzhe Li; Huazhong Zhu; Yuxin Qiao; Haiyan Zhang. 2018. "A multi-factor weighted regression approach for estimating the spatial distribution of soil organic carbon in grasslands." CATENA 174, no. : 248-258.
We used unmanned aerial vehicles (UAVs) to carry out a relatively complete population census of large wild herbivores in Maduo County on the Tibetan Plateau in the spring of 2017. The effective area covered by aerial surveys was 326.6 km2, and 23,784 images were acquired. Interpretation tag libraries for UAV images were created for wild animals, including Kiang (Equus kiang), Tibetan gazelle (Procapra picticaudata), and blue sheep (Pseudois nayaur), as well as livestock, including yaks and Tibetan sheep. Large wild herbivores in the survey transect were identified through manual imagery interpretation. Densities ranged from 1.15/km2 for Kiang, 0.61/km2 for Tibetan gazelle, 0.62/km2 for blue sheep, 4.12/km2 for domestic yak, and 7.34/km2 for domestic sheep. A method based on meadows in the cold and warm seasons was used for estimating the densities and numbers of large wild herbivores and livestock, and was verified against records of livestock numbers. Population estimates for Kiang, Tibetan gazelle, blue sheep, domestic yak, and domestic sheep were 17,109, 15,961, 9324, 70,846, and 102,194, respectively. Based on published consumption estimates, the results suggest that domestic stock consume 4.5 times the amount of vegetation of large wild herbivores. Compared with traditional ground survey methods, performance of UAV remote sensing surveys of large wild herbivore populations was fast, economical and reliable, providing an effective future method for surveying wild animals.
Xingjian Guo; Quanqin Shao; Yuzhe Li; Yangchun Wang; Dongliang Wang; Jiyuan Liu; Jiangwen Fan; Fan Yang. Application of UAV Remote Sensing for a Population Census of Large Wild Herbivores—Taking the Headwater Region of the Yellow River as an Example. Remote Sensing 2018, 10, 1041 .
AMA StyleXingjian Guo, Quanqin Shao, Yuzhe Li, Yangchun Wang, Dongliang Wang, Jiyuan Liu, Jiangwen Fan, Fan Yang. Application of UAV Remote Sensing for a Population Census of Large Wild Herbivores—Taking the Headwater Region of the Yellow River as an Example. Remote Sensing. 2018; 10 (7):1041.
Chicago/Turabian StyleXingjian Guo; Quanqin Shao; Yuzhe Li; Yangchun Wang; Dongliang Wang; Jiyuan Liu; Jiangwen Fan; Fan Yang. 2018. "Application of UAV Remote Sensing for a Population Census of Large Wild Herbivores—Taking the Headwater Region of the Yellow River as an Example." Remote Sensing 10, no. 7: 1041.
Carbon-use efficiency (CUE) is the proportion of gross primary production converted to net primary production. Changes to CUE strongly influence ecosystem carbon budgets and turnover. Little is known about the response of ecosystem CUE to human-induced land-use change, which limits the accurate evaluation of the environmental influence of large-scale steppe-use changes in northern China. We investigated the components of ecosystem carbon exchange and CUE under three typical steppe-use patterns in Xilinhot, Inner Mongolia. The results showed that CUE in grazing and grazing-excluded steppe were not significantly different (both over 0.7) but were significantly higher than in cultivated steppe (0.57). Ecosystem respiration and its components, including autotrophic respiration (Ra), aboveground respiration, heterotrophic respiration and belowground respiration showed significant negative correlation with CUE. Ra is the most important factor explaining the variation of CUE between different steppe-use patterns (p < 0.001, 97%); Ra change may be the primary factor driving CUE variation between steppe-use patterns. Leaf area index of different grassland-use patterns also showed a significant negative correlation with CUE (p < 0.001, 91%). These findings may help to improve accurate prediction of the environmental and climatic consequences of large-scale land-use change.
Yuzhe Li; Jiangwen Fan; Zhongmin Hu. Comparison of Carbon-Use Efficiency Among Different Land-Use Patterns of the Temperate Steppe in the Northern China Pastoral Farming Ecotone. Sustainability 2018, 10, 487 .
AMA StyleYuzhe Li, Jiangwen Fan, Zhongmin Hu. Comparison of Carbon-Use Efficiency Among Different Land-Use Patterns of the Temperate Steppe in the Northern China Pastoral Farming Ecotone. Sustainability. 2018; 10 (2):487.
Chicago/Turabian StyleYuzhe Li; Jiangwen Fan; Zhongmin Hu. 2018. "Comparison of Carbon-Use Efficiency Among Different Land-Use Patterns of the Temperate Steppe in the Northern China Pastoral Farming Ecotone." Sustainability 10, no. 2: 487.
Unmanned aerial vehicle surveys were conducted in the summer season of 2016 and the winter season of 2017 to investigate the large wild herbivore population, including kiangs, Tibetan gazelles and bharals, in Madoi County; the source region of the Yellow River. The study generated forage grass production data in 30 m spatial resolution in Madoi County in 2016 using a downscaling algorithm; estimated a forage-livestock balance including wild animals and domestic animals; and analyzed the effect of the large wild herbivore population on the balance between forage grass and herbivory in Madoi County. The large wild herbivore population was estimated based on the density of the animals in the survey sample strip and compared and verified with available statistical data and the two survey results from the summer season of 2016 and winter season of 2017. The results showed that: (1) in the winter season of 2017, the populations of kiang, Tibetan gazelle and bharal were 17,100, 16,000 and 9300, respectively, while the populations of domestic yak, Tibetan sheep and horse were 70,800, 102,200 and 1200, respectively. The total population of large wild herbivores and domestic animals was 475,000 (sheep units). The ratio (in sheep units) between large wild herbivores and domestic animals was 1:4.5; (2) When only considering domestic animals, the grazing pressure index was 1.13, indicating slight overloading of the grassland. When considering domestic animals and large wild herbivores (kiang, Tibetan gazelle and bharal), the grazing pressure index was 1.38, suggesting moderate overloading of the grassland; (3) If large wild herbivores are not taken into consideration when the forage-livestock balance is calculated, the grazing pressure will be under-estimated by 22%. Overgrazing is the major cause of grassland degradation in Madoi County. An additional 79,000 tons of hay or a 30% reduction in domestic animals is required to maintain a forage-livestock balance in Madoi County.
Fan Yang; Quanqin Shao; Xingjian Guo; Yuzhi Tang; Yuzhe Li; Dongliang Wang; Yangchun Wang; Jiangwen Fan. Effect of Large Wild Herbivore Populations on the Forage-Livestock Balance in the Source Region of the Yellow River. Sustainability 2018, 10, 340 .
AMA StyleFan Yang, Quanqin Shao, Xingjian Guo, Yuzhi Tang, Yuzhe Li, Dongliang Wang, Yangchun Wang, Jiangwen Fan. Effect of Large Wild Herbivore Populations on the Forage-Livestock Balance in the Source Region of the Yellow River. Sustainability. 2018; 10 (2):340.
Chicago/Turabian StyleFan Yang; Quanqin Shao; Xingjian Guo; Yuzhi Tang; Yuzhe Li; Dongliang Wang; Yangchun Wang; Jiangwen Fan. 2018. "Effect of Large Wild Herbivore Populations on the Forage-Livestock Balance in the Source Region of the Yellow River." Sustainability 10, no. 2: 340.