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Yuhai Bao
Inner Mongolia Key Laboratory of Remote Sensing and Geographic Information Systems, Inner Mongolia Normal University, Hohhot 010022, China

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Journal article
Published: 24 August 2021 in Remote Sensing
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Inner Mongolia in China is a typically arid and semi-arid region with vegetation prominently affected by global warming and human activities. Therefore, investigating the past and future vegetation change and its impact mechanism is important for assessing the stability of the ecosystem and the ecological policy formulation. Vegetation changes, sustainability characteristics, and the mechanism of natural and anthropogenic effects in Inner Mongolia during 2000–2019 were examined using moderate resolution imaging spectroradiometer normalized difference vegetation index (NDVI) data. Theil–Sen trend analysis, Mann–Kendall method, and the coefficient of variation method were used to analyze the spatiotemporal variability characteristics and sustained stability of the NDVI. Furthermore, a trend estimation method based on a Seasonal Trend Model (STM), and the Hurst index was used to analyze breakpoints and change trends, and predict the likely future direction of vegetation, respectively. Additionally, the mechanisms of the compound influence of natural and anthropogenic activities on the vegetation dynamics in Inner Mongolia were explored using a Geodetector Model. The results show that the NDVI of Inner Mongolia shows an upward trend with a rate of 0.0028/year (p< 0.05) from 2000 to 2019. Spatially, the NDVI values showed a decreasing trend from the northeast to the southwest, and the interannual variation fluctuated widely, with coefficients of variation greater than 0.15, for which the high-value areas were in the territory of the Alxa League. The areas with increased, decreased, and stable vegetation patterns were approximately equal in size, in which the improved areas were mainly distributed in the northeastern part of Inner Mongolia, the stable and unchanged areas were mostly in the desert, and the degraded areas were mainly in the central-eastern part of Inner Mongolia, it shows a trend of progressive degradation from east to west. Breakpoints in the vegetation dynamics occurred mainly in the northwestern part of Inner Mongolia and the northeastern part of Hulunbuir, most of which occurred during 2011–2014. The future NDVI trend in Inner Mongolia shows an increasing trend in most areas, with only approximately 10% of the areas showing a decreasing trend. Considering the drivers of the NDVI, we observed annual precipitation, soil type, mean annual temperature, and land use type to be the main driving factors in Inner Mongolia. Annual precipitation was the first dominant factor, and when these four dominant factors interacted to influence vegetation change, they all showed interactive enhancement relationships. The results of this study will assist in understanding the influence of natural elements and human activities on vegetation changes and their driving mechanisms, while providing a scientific basis for the rational and effective protection of the ecological environment in Inner Mongolia.

ACS Style

Yao Kang; Enliang Guo; Yongfang Wang; Yulong Bao; Yuhai Bao; Naren Mandula. Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019. Remote Sensing 2021, 13, 3357 .

AMA Style

Yao Kang, Enliang Guo, Yongfang Wang, Yulong Bao, Yuhai Bao, Naren Mandula. Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019. Remote Sensing. 2021; 13 (17):3357.

Chicago/Turabian Style

Yao Kang; Enliang Guo; Yongfang Wang; Yulong Bao; Yuhai Bao; Naren Mandula. 2021. "Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019." Remote Sensing 13, no. 17: 3357.

Journal article
Published: 06 May 2021 in Sensors
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Proximal sensing offers a novel means for determination of the heavy metal concentration in soil, facilitating low cost and rapid analysis over large areas. In this respect, spectral data and model variables play an important role. Thus far, no attempts have been made to estimate soil heavy metal content using continuum-removal (CR), different preprocessing and statistical methods, and different modeling variables. Considering the adsorption and retention of heavy metals in spectrally active constituents in soil, this study proposes a method for determining low heavy metal concentrations in soil using spectral bands associated with soil organic matter (SOM) and visible–near-infrared (Vis–NIR). To rapidly determine the concentration of heavy metals using hyperspectral data, partial least squares regression (PLSR), principal component regression (PCR), and support vector machine regression (SVMR) statistical methods and 16 preprocessing combinations were developed and explored to determine an optimal combination. The results showed that the multiplicative scatter correction and standard normal variate preprocessing methods evaluated with the second derivative spectral transformation method could accurately determine soil Cr and Ni concentrations. The root-mean-square error (RMSE) values of Vis–NIR model combinations with PLSR, PCR, and SVMR were 0.34, 3.42, and 2.15 for Cr, and 0.07, 1.78, and 1.14 for Ni, respectively. Soil Cr and Ni showed strong spectral responses to the Vis–NIR spectral band. The R2 value of the Vis–NIR-based PLSR model was higher than 0.99, and the RMSE value was 0.07–0.34, suggesting higher stability and accuracy. The results were more accurate for Ni than Cr, and PLSR showed the best performance, followed by SVMR and PCR. This perspective has critical implications for guiding quantitative biogeochemical analysis using proximal sensing data.

ACS Style

Aru Han; Xiaoling Lu; Song Qing; Yongbin Bao; Yuhai Bao; Qing Ma; Xingpeng Liu; Jiquan Zhang. Rapid Determination of Low Heavy Metal Concentrations in Grassland Soils around Mining Using Vis–NIR Spectroscopy: A Case Study of Inner Mongolia, China. Sensors 2021, 21, 3220 .

AMA Style

Aru Han, Xiaoling Lu, Song Qing, Yongbin Bao, Yuhai Bao, Qing Ma, Xingpeng Liu, Jiquan Zhang. Rapid Determination of Low Heavy Metal Concentrations in Grassland Soils around Mining Using Vis–NIR Spectroscopy: A Case Study of Inner Mongolia, China. Sensors. 2021; 21 (9):3220.

Chicago/Turabian Style

Aru Han; Xiaoling Lu; Song Qing; Yongbin Bao; Yuhai Bao; Qing Ma; Xingpeng Liu; Jiquan Zhang. 2021. "Rapid Determination of Low Heavy Metal Concentrations in Grassland Soils around Mining Using Vis–NIR Spectroscopy: A Case Study of Inner Mongolia, China." Sensors 21, no. 9: 3220.

Journal article
Published: 13 February 2021 in Remote Sensing
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In recent years, global warming and intense human activity have been responsible for significantly altering vegetation dynamics on the Mongolian Plateau. Understanding the long-term vegetation dynamics in this region is important to assess the impact of these changes on the local ecosystem. Long-term (1982–2015), satellite-derived normalized difference vegetation index (NDVI) datasets were used to analyse the spatio-temporal patterns of vegetation activities using linear regression and the breaks for additive season and trend methods. The links between these patterns and changes in temperature, precipitation (PRE), soil moisture (SM), and anthropogenic activity were determined using partial correlation analysis, the residual trends method, and a stepwise multiple regression model. The most significant results indicated that air temperature and potential evapotranspiration increased significantly, while the SM and PRE had markedly decreased over the past 34 years. The NDVI dataset included 71.16% of pixels showing an increase in temperature and evaporation during the growing season, particularly in eastern Mongolia and the southern border of the Inner Mongolia Autonomous region, China. The proportion indicating the breakpoint of vegetation dynamics was 71.34% of pixels, and the trend breakpoints mainly occurred in 1993, 2003, and 2010. The cumulative effects of PRE and SM in the middle period, coupled with the short-term effects of temperature and potential evapotranspiration, have had positive effects on vegetation greening. Anthropogenic factors appear to have positively impacted vegetation dynamics, as shown in 81.21% of pixels. We consider rapid economic growth, PRE, and SM to be the main driving factors in Inner Mongolia. PRE was the main climatic factor, and combined human and livestock populations were the primary anthropogenic factors influencing vegetation dynamics in Mongolia. This study is important in promoting the continued use of green projects to address environmental change in the Mongolian Plateau.

ACS Style

Enliang Guo; Yongfang Wang; Cailin Wang; Zhongyi Sun; Yulong Bao; Naren Mandula; Buren Jirigala; Yuhai Bao; He Li. NDVI Indicates Long-Term Dynamics of Vegetation and Its Driving Forces from Climatic and Anthropogenic Factors in Mongolian Plateau. Remote Sensing 2021, 13, 688 .

AMA Style

Enliang Guo, Yongfang Wang, Cailin Wang, Zhongyi Sun, Yulong Bao, Naren Mandula, Buren Jirigala, Yuhai Bao, He Li. NDVI Indicates Long-Term Dynamics of Vegetation and Its Driving Forces from Climatic and Anthropogenic Factors in Mongolian Plateau. Remote Sensing. 2021; 13 (4):688.

Chicago/Turabian Style

Enliang Guo; Yongfang Wang; Cailin Wang; Zhongyi Sun; Yulong Bao; Naren Mandula; Buren Jirigala; Yuhai Bao; He Li. 2021. "NDVI Indicates Long-Term Dynamics of Vegetation and Its Driving Forces from Climatic and Anthropogenic Factors in Mongolian Plateau." Remote Sensing 13, no. 4: 688.

Journal article
Published: 11 February 2021 in Remote Sensing
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The accurate estimation of grassland vegetation parameters at a high spatial resolution is important for the sustainable management of grassland areas. Unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) sensors with a single laser beam emission capability can rapidly detect grassland vegetation parameters, such as canopy height, fractional vegetation coverage (FVC) and aboveground biomass (AGB). However, there have been few reports on the ability to detect grassland vegetation parameters based on RIEGL VUX-1 UAV LiDAR (Riegl VUX-1) systems. In this paper, we investigated the ability of Riegl VUX-1 to model the AGB at a 0.1 m pixel resolution in the Hulun Buir grazing platform under different grazing intensities. The LiDAR-derived minimum, mean, and maximum canopy heights and FVC were used to estimate the AGB across the entire grazing platform. The flight height of the LiDAR-derived vegetation parameters was also analyzed. The following results were determined: (1) The Riegl VUX-1-derived AGB was predicted to range from 29 g/m2 to 563 g/m2 under different grazing conditions. (2) The LiDAR-derived maximum canopy height and FVC were the best predictors of grassland AGB (R2 = 0.54, root-mean-square error (RMSE) = 64.76 g/m2). (3) For different UAV flight altitudes from 40 m to 110 m, different flight heights showed no major effect on the derived canopy height. The LiDAR-derived canopy height decreased from 9.19 cm to 8.17 cm, and the standard deviation of the LiDAR-derived canopy height decreased from 3.31 cm to 2.35 cm with increasing UAV flight altitudes. These conclusions could be useful for estimating grasslands in smaller areas and serving as references for other remote sensing datasets for estimating grasslands in larger areas.

ACS Style

Xiang Zhang; Yuhai Bao; Dongliang Wang; Xiaoping Xin; Lei Ding; Dawei Xu; Lulu Hou; Jie Shen. Using UAV LiDAR to Extract Vegetation Parameters of Inner Mongolian Grassland. Remote Sensing 2021, 13, 656 .

AMA Style

Xiang Zhang, Yuhai Bao, Dongliang Wang, Xiaoping Xin, Lei Ding, Dawei Xu, Lulu Hou, Jie Shen. Using UAV LiDAR to Extract Vegetation Parameters of Inner Mongolian Grassland. Remote Sensing. 2021; 13 (4):656.

Chicago/Turabian Style

Xiang Zhang; Yuhai Bao; Dongliang Wang; Xiaoping Xin; Lei Ding; Dawei Xu; Lulu Hou; Jie Shen. 2021. "Using UAV LiDAR to Extract Vegetation Parameters of Inner Mongolian Grassland." Remote Sensing 13, no. 4: 656.

Journal article
Published: 05 January 2021 in Sustainability
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An important component in improving the quality of forests is to study the interference intensity of forest fires, in order to describe the intensity of the forest fire and the vegetation recovery, and to improve the monitoring ability of the dynamic change of the forest. Using a forest fire event in Bilahe, Inner Monglia in 2017 as a case study, this study extracted the burned area based on the BAIS2 index of Sentinel-2 data for 2016–2018. The leaf area index (LAI) and fractional vegetation cover (FVC), which are more suitable for monitoring vegetation dynamic changes of a burned area, were calculated by comparing the biophysical and spectral indices. The results showed that patterns of change of LAI and FVC of various land cover types were similar post-fire. The LAI and FVC of forest and grassland were high during the pre-fire and post-fire years. During the fire year, from the fire month (May) through the next 4 months (September), the order of areas of different fire severity in terms of values of LAI and FVC was: low > moderate > high severity. During the post fire year, LAI and FVC increased rapidly in areas of different fire severity, and the ranking of areas of different fire severity in terms of values LAI and FVC was consistent with the trend observed during the pre-fire year. The results of this study can improve the understanding of the mechanisms involved in post-fire vegetation change. By using quantitative inversion, the health trajectory of the ecosystem can be rapidly determined, and therefore this method can play an irreplaceable role in the realization of sustainable development in the study area. Therefore, it is of great scientific significance to quantitatively retrieve vegetation variables by remote sensing.

ACS Style

Aru Han; Song Qing; Yongbin Bao; Li Na; Yuhai Bao; Xingpeng Liu; Jiquan Zhang; Chunyi Wang. Short-Term Effects of Fire Severity on Vegetation Based on Sentinel-2 Satellite Data. Sustainability 2021, 13, 432 .

AMA Style

Aru Han, Song Qing, Yongbin Bao, Li Na, Yuhai Bao, Xingpeng Liu, Jiquan Zhang, Chunyi Wang. Short-Term Effects of Fire Severity on Vegetation Based on Sentinel-2 Satellite Data. Sustainability. 2021; 13 (1):432.

Chicago/Turabian Style

Aru Han; Song Qing; Yongbin Bao; Li Na; Yuhai Bao; Xingpeng Liu; Jiquan Zhang; Chunyi Wang. 2021. "Short-Term Effects of Fire Severity on Vegetation Based on Sentinel-2 Satellite Data." Sustainability 13, no. 1: 432.

Journal article
Published: 05 October 2020 in Water
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Understanding the variations of future drought under climate warming can provide the basis for mitigation efforts. This study utilized the standardized precipitation evapotranspiration index (SPEI), empirical mode decomposition (EMD) and empirical orthogonal function analysis (EOF) to predict the spatiotemporal variation of future drought under the representative concentration pathway RCP4.5 and RCP8.5 scenarios within the Mongolian Plateau over the period 2020–2100. The SPEI was computed using temperature and precipitation data generated by the fifth stage of the Coupled Model Intercomparison Project (CMIP5). The results under both the RCP4.5 and RCP8.5 scenarios showed increasing changes in temperature and precipitation. Both scenarios indicated increases in drought, with those under RCP8.5 much more extreme than that under RCP4.5. Under both scenarios, the climate showed an abrupt change to become drier, with the change occurring in 2041 and 2054 for the RCP4.5 and RCP8.5 scenarios, respectively. The results also indicated future drought to be more extreme in Mongolia than in Inner Mongolia. The simulated drought pattern showed an east–west antiphase and a north–south antiphase distribution based on EOF. The frequency of drought was higher under RCP8.5 compared to that under RCP4.5, with the highest frequencies under both scenarios occurring by the end of the 21st century, followed by the mid-21st century and early 21st century. The findings of this research can provide a solid foundation for the prevention, early warning and mitigation of drought disasters within the context of climate change in the Mongolian Plateau.

ACS Style

Yongzhen Li; Siqin Tong; Yongbin Bao; Enliang Guo; Yuhai Bao. Prediction of Droughts in the Mongolian Plateau Based on the CMIP5 Model. Water 2020, 12, 2774 .

AMA Style

Yongzhen Li, Siqin Tong, Yongbin Bao, Enliang Guo, Yuhai Bao. Prediction of Droughts in the Mongolian Plateau Based on the CMIP5 Model. Water. 2020; 12 (10):2774.

Chicago/Turabian Style

Yongzhen Li; Siqin Tong; Yongbin Bao; Enliang Guo; Yuhai Bao. 2020. "Prediction of Droughts in the Mongolian Plateau Based on the CMIP5 Model." Water 12, no. 10: 2774.

Journal article
Published: 11 August 2020 in Remote Sensing
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Global land-cover products play an important role in assisting the understanding of climate-related changes and the assessment of progress in the implementation of international initiatives for the mitigation of, and adaption to, climate change. However, concerns over the accuracies of land-cover products remain, due to the issue of validation data uncertainty. The volunteer-based Degree Confluence Project (DCP) was created in 1996, and it has been used to provide useful ground-reference information. This study aims to investigate the impact of DCP-based validation data uncertainty and the thematic issues on map accuracies. We built a reference dataset based on the DCP-interpreted dataset and applied a comparison for three existing global land-cover maps and DCP dataset-based probability maps under different classification schemes. The results of the obtained confusion matrices indicate that the uncertainty, including the number of classes and the confusion in mosaic classes, leads to a decrease in map accuracy. This paper proposes an informative classification scheme that uses a matrix structure of unaggregated land-cover and land-use classes, and has the potential to assist in the land-cover interpretation and validation processes. The findings of this study can potentially serve as a guide to select reference data and choose/define appropriate classification schemes.

ACS Style

Tana Qian; Tsuguki Kinoshita; Minoru Fujii; Yuhai Bao. Analyzing the Uncertainty of Degree Confluence Project for Validating Global Land-Cover Maps Using Reference Data-Based Classification Schemes. Remote Sensing 2020, 12, 2589 .

AMA Style

Tana Qian, Tsuguki Kinoshita, Minoru Fujii, Yuhai Bao. Analyzing the Uncertainty of Degree Confluence Project for Validating Global Land-Cover Maps Using Reference Data-Based Classification Schemes. Remote Sensing. 2020; 12 (16):2589.

Chicago/Turabian Style

Tana Qian; Tsuguki Kinoshita; Minoru Fujii; Yuhai Bao. 2020. "Analyzing the Uncertainty of Degree Confluence Project for Validating Global Land-Cover Maps Using Reference Data-Based Classification Schemes." Remote Sensing 12, no. 16: 2589.

Original paper
Published: 01 August 2020 in Theoretical and Applied Climatology
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Global warming has exerted increasingly serious impacts on the society, economy, and environment across Inner Mongolia, China. In this context, Sen’s slope method and Morlet wavelet transformation were employed to analyze the spatiotemporal variation characteristics of heat waves (HWs) and cold waves (CWs) based on excess factors (EFs) during 1961–2016. The Pearson correlation coefficient was applied to explore four climate indices’ potential relation with the EFs. The results show that the intensity and frequency of excess heat factors (EHFs) were increasing, and the increasing rate in high-latitude regions was higher than that in low-latitude regions. Excess cold factors (ECFs) showed a decreasing trend in almost all meteorological stations; severity of ECFs was increasing in the northeastern region. The EFs had a periodic variation of 1–4 years and crossover on the interdecadal scale. Climatic indices had a greater relation with ECFs than EHFs, but the previous year remained stable with a greater link with EHFs in the same year for climatic indices. The Arctic Oscillation index (AOI) and the North Atlantic Oscillation (NAO) were negatively correlated with the duration and frequency of ECFs for most meteorological stations, but there was a significant positive correlation with their severity. Multivariate El Niño/Southern Oscillation (ENSO) index (MEI) and Pacific Decadal Oscillation (PDO) had higher link with ECFs in the midwest than that in other regions. MEI and AOI had a notable relation with the severity and frequency of EHFs compared with the other two climatic indices. In the warm and cold periods, the atmospheric circulation showed different airflow convergence and divergence around Inner Mongolia, which may affect the spatiotemporal characteristics of CWs and HWs.

ACS Style

Enliang Guo; Yongfang Wang; Yuhai Bao; Zhongyi Sun; Yulong Bao; Lai Quan. Spatiotemporal variation of heat and cold waves and their potential relation with the large-scale atmospheric circulation across Inner Mongolia, China. Theoretical and Applied Climatology 2020, 142, 643 -659.

AMA Style

Enliang Guo, Yongfang Wang, Yuhai Bao, Zhongyi Sun, Yulong Bao, Lai Quan. Spatiotemporal variation of heat and cold waves and their potential relation with the large-scale atmospheric circulation across Inner Mongolia, China. Theoretical and Applied Climatology. 2020; 142 (1-2):643-659.

Chicago/Turabian Style

Enliang Guo; Yongfang Wang; Yuhai Bao; Zhongyi Sun; Yulong Bao; Lai Quan. 2020. "Spatiotemporal variation of heat and cold waves and their potential relation with the large-scale atmospheric circulation across Inner Mongolia, China." Theoretical and Applied Climatology 142, no. 1-2: 643-659.

Journal article
Published: 19 May 2020 in Remote Sensing
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High-resolution gridded population data are important for understanding and responding to many socioeconomic and environmental problems. Local estimates of the population allow officials and researchers to make a better local planning (e.g., optimizing public services and facilities). This study used a random forest algorithm, on the basis of remote sensing (i.e., satellite imagery) and social sensing data (i.e., point-of-interest and building footprint), to disaggregate census population data for the five municipal districts of Zhengzhou city, China, onto 100 × 100 m grid cells. We used a statistical tool to detect areas with an abnormal population density; e.g., areas containing many empty houses or houses rented by more people than allowed, and conducted field work to validate our findings. Results showed that some categories of points-of-interest, such as residential communities, parking lots, banks, and government buildings were the most important contributing elements in modeling the spatial distribution of the residential population in Zhengzhou City. The exclusion of areas with an abnormal population density from model training and dasymetric mapping increased the accuracy of population estimates in other areas with a more common population density. We compared our product with three widely used gridded population products: Worldpop, the Gridded Population of the World, and the 1-km Grid Population Dataset of China. The relative accuracy of our modeling approach was higher than that of those three products in the five municipal districts of Zhengzhou. This study demonstrated potential for the combination of remote and social sensing data to more accurately estimate the population density in urban areas, with minimum disturbance from the abnormal population density.

ACS Style

Ge Qiu; Yuhai Bao; Xuchao Yang; Chen Wang; Tingting Ye; Alfred Stein; Peng Jia. Local Population Mapping Using a Random Forest Model Based on Remote and Social Sensing Data: A Case Study in Zhengzhou, China. Remote Sensing 2020, 12, 1618 .

AMA Style

Ge Qiu, Yuhai Bao, Xuchao Yang, Chen Wang, Tingting Ye, Alfred Stein, Peng Jia. Local Population Mapping Using a Random Forest Model Based on Remote and Social Sensing Data: A Case Study in Zhengzhou, China. Remote Sensing. 2020; 12 (10):1618.

Chicago/Turabian Style

Ge Qiu; Yuhai Bao; Xuchao Yang; Chen Wang; Tingting Ye; Alfred Stein; Peng Jia. 2020. "Local Population Mapping Using a Random Forest Model Based on Remote and Social Sensing Data: A Case Study in Zhengzhou, China." Remote Sensing 12, no. 10: 1618.

Journal article
Published: 02 May 2020 in Sustainability
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Land use/cover change (LUCC) is becoming one of the most important and interesting problems in the study of global environmental change. Identifying the spatiotemporal behavior and associated driving forces behind changes in land use is crucial for the regional sustainable utilization of land resources. In this study, we consider the four municipalities of China (Beijing, Tianjin, Shanghai, and Chongqing) and compare their spatiotemporal changes in land use from 1990 to 2015 by employing intensity analysis and barycenter migration models. We then discuss their driving forces. The results show that the largest reduction and increase variations were mainly concentrated in arable and construction land, respectively. The decrement and increment were the largest in Shanghai, followed by Beijing and Tianjin, and the least in Chongqing. Furthermore, the results of the barycenter migration model indicate that in addition to Beijing, the migration distances of construction land were longer than those of arable land in three other cities. Moreover, the application of intensity analysis revealed that the rate of land use change was also the greatest in Shanghai and the slowest in Chongqing during the whole study period, with all of their arable land being mainly transformed into construction land. The driving force analysis results suggest that the spatial and temporal patterns of land use change were the results of the socio-economic development, national policies, and major events. In other words, where there was a high rate of economic and population growth, the intensity of land use change was relatively large.

ACS Style

Siqin Tong; Gang Bao; Ah Rong; Xiaojun Huang; Yongbin Bao; Yuhai Bao. Comparison of the Spatiotemporal Dynamics of Land Use Changes in Four Municipalities of China Based on Intensity Analysis. Sustainability 2020, 12, 3687 .

AMA Style

Siqin Tong, Gang Bao, Ah Rong, Xiaojun Huang, Yongbin Bao, Yuhai Bao. Comparison of the Spatiotemporal Dynamics of Land Use Changes in Four Municipalities of China Based on Intensity Analysis. Sustainability. 2020; 12 (9):3687.

Chicago/Turabian Style

Siqin Tong; Gang Bao; Ah Rong; Xiaojun Huang; Yongbin Bao; Yuhai Bao. 2020. "Comparison of the Spatiotemporal Dynamics of Land Use Changes in Four Municipalities of China Based on Intensity Analysis." Sustainability 12, no. 9: 3687.

Journal article
Published: 20 November 2019 in Sustainability
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Grassland biomass is an essential part of the regional carbon cycle. Rapid and accurate estimation of grassland biomass is a hot topic in research on grassland ecosystems. This study was based on field-measured biomass data and satellite remote sensing data from the Moderate resolution imaging spectroradiometer (MODIS). A generalized linear model (GLM) was used to analyze the aboveground biomass (AGB), dynamic changes, and relevance of climatic factors of the typical/desert steppe in Inner Mongolia during the growing seasons from May 2009 to October 2015. The results showed that: (1) The logarithmic function model with the ratio vegetation index (RVI) as the independent variable worked best for the typical steppe area in Inner Mongolia, while the power function model with the normalized differential vegetation index (NDVI) as the independent variable worked best for the desert steppe area. The R2 values at a spatial resolution of 250 m were higher than those at a spatial resolution 500 m. (2) From 2009 to 2015, the highest values of AGB in the typical steppe and desert steppe of Inner Mongolia both appeared in 2012, and were 41.9 Tg and 7.0 Tg, respectively. The lowest values were 30.7 Tg and 5.8 Tg, respectively, in 2009. (3) The overall spatial distribution of AGB decreased from northeast to southwest. It also changed considerably over time. From May to August, AGB at the same longitude increased from south to north with seasonal variations; from August to October, it increased from north to south. (4) A variation partitioning analysis showed that in both the typical steppe and desert steppe, the combined effect of precipitation and temperature contributed the most to the aboveground biomass. The individual effect of temperature contributed more than precipitation in the typical steppe, while the individual effect of precipitation contributed more in the desert steppe. Thus, the hydrothermal dynamic hypothesis was used to explain this pattern. This study provides support for grassland husbandry management and carbon storage assessment in Inner Mongolia.

ACS Style

Xiumei Wang; Jianjun Dong; Taogetao Baoyin; Yuhai Bao. Estimation and Climate Factor Contribution of Aboveground Biomass in Inner Mongolia’s Typical/Desert Steppes. Sustainability 2019, 11, 6559 .

AMA Style

Xiumei Wang, Jianjun Dong, Taogetao Baoyin, Yuhai Bao. Estimation and Climate Factor Contribution of Aboveground Biomass in Inner Mongolia’s Typical/Desert Steppes. Sustainability. 2019; 11 (23):6559.

Chicago/Turabian Style

Xiumei Wang; Jianjun Dong; Taogetao Baoyin; Yuhai Bao. 2019. "Estimation and Climate Factor Contribution of Aboveground Biomass in Inner Mongolia’s Typical/Desert Steppes." Sustainability 11, no. 23: 6559.

Journal article
Published: 30 October 2019 in Sustainability
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Droughts are among the more costly natural hazards, and drought risk analysis has become urgent for the proper planning and management of water resources in grassland ecosystems. We chose Songnen grassland as a case study, used a standardized precipitation evapotranspiration index (SPEI) to model drought characteristics, employed run theory to define the drought event, and chose copula functions to construct the joint distribution for drought variables. We applied two kinds of return periods to conduct a drought risk assessment. After evaluating and comparing several distribution functions, drought severity (DS) was best described by the generalized extreme value (GEV) distribution, whereas drought duration (DD) was best fitted by gamma distribution. The root mean square error (RMSE) and Akaike Information Criterion (AIC) goodness-of-fit measures to evaluate their performance, the best-performing copula is Frank copula to model the joint dependence structure for each drought variables. The results of the secondary return periods indicate that a higher risk of droughts occurs in Keshan county, Longjiang county, Qiqiha’er city, Taonan city, and Baicheng city. Furthermore, a relatively lower risk of drought was found in Bei’an city, Mingquan county, Qinggang county, and qian’an county, and also in the Changling county and Shuangliao city. According to the calculation of the secondary return periods, which considered all possible scenarios in our study, we found that the secondary return period may be the best indicator for evaluating grassland ecosystem drought risk management.

ACS Style

Rina Wu; Jiquan Zhang; Yuhai Bao; Enliang Guo. Run Theory and Copula-Based Drought Risk Analysis for Songnen Grassland in Northeastern China. Sustainability 2019, 11, 6032 .

AMA Style

Rina Wu, Jiquan Zhang, Yuhai Bao, Enliang Guo. Run Theory and Copula-Based Drought Risk Analysis for Songnen Grassland in Northeastern China. Sustainability. 2019; 11 (21):6032.

Chicago/Turabian Style

Rina Wu; Jiquan Zhang; Yuhai Bao; Enliang Guo. 2019. "Run Theory and Copula-Based Drought Risk Analysis for Songnen Grassland in Northeastern China." Sustainability 11, no. 21: 6032.

Journal article
Published: 17 October 2019 in Sustainability
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Continuous climate warming in the last few decades has led to global climate anomalies, resulting in frequent drought events in arid/semiarid regions with fragile and sensitive ecological environment. The Mongolian Plateau (MP) is located at the mid-latitude arid/semiarid climate region, which is deemed as the most sensitive region in response to global climate change. In order to understand the spatiotemporal characteristics of droughts in Mongolian Plateau under changing climate, we divided the study area into three climatic regions via Köppen climate classification. Then, the seasonal and annual drought trends were analyzed by standardized precipitation evaporation index (SPEI), which is a function of monthly mean temperatures, highest temperatures, lowest temperatures and precipitations, collected from the 184 meteorological stations from 1980 to 2015. Mann–Kendall (MK) test was employed to detect if there is an abrupt change of annual drought, while the empirical orthogonal function method (EOF) was adopted to investigate the spatiotemporal characteristics of droughts across the Mongolian Plateau. Results from MK test illustrated that the SPEI-12 exhibited statistically significant downward trends (a < 0.05) for all three climatic regions of the Mongolian Plateau.EOF spatial analysis indicated that Region III experienced the most severe drought from 1980 to 2015. During the 35 years period, an abrupt change of drought was detected in 1999. Before year 1999, the climate was relatively humid. However, the entire region became more arid after year 1999, reflected by remarkably increased frequency and intensity of drought. SPEI-3 revealed the trend of drought at seasonal scale. We found that drought became more severe in spring, summer, and fall seasons for the entire MP. However, winter became more humid. Different climate regions exhibited quite different drought seasonality: Region I experienced a severe arid trend in summer and fall. For Region II and III, summer became more arid. All three regions became more humid in winter season, especially for Region I, with the Sen’s slope of 0.0241/a.

ACS Style

Laiquan Jin; Jiquan Zhang; Ruoyu Wang; Yuhai Bao; Enliang Guo. Analysis for Spatio-Temporal Variation Characteristics of Droughts in Different Climatic Regions of the Mongolian Plateau Based on SPEI. Sustainability 2019, 11, 5767 .

AMA Style

Laiquan Jin, Jiquan Zhang, Ruoyu Wang, Yuhai Bao, Enliang Guo. Analysis for Spatio-Temporal Variation Characteristics of Droughts in Different Climatic Regions of the Mongolian Plateau Based on SPEI. Sustainability. 2019; 11 (20):5767.

Chicago/Turabian Style

Laiquan Jin; Jiquan Zhang; Ruoyu Wang; Yuhai Bao; Enliang Guo. 2019. "Analysis for Spatio-Temporal Variation Characteristics of Droughts in Different Climatic Regions of the Mongolian Plateau Based on SPEI." Sustainability 11, no. 20: 5767.

Journal article
Published: 15 March 2019 in Forests
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As the main defoliators of coniferous forests in Shandong Province, China, pine caterpillars (including Dendrolimus suffuscus suffuscus Lajonquiere, D. spectabilis Butler, and D. tabulaeformis Tsai et Liu) have caused substantial forest damage, adverse economic impacts, and losses of ecosystem resources. Therefore, elucidating the effects of drought on the outbreak of these pests is important for promoting forestry production and ecological reconstruction. Accordingly, the aim of the present study was to analyse the spatiotemporal variation of drought in Shandong Province, using the Standard Precipitation Index, and to investigate the impact of drought on the outbreak of pine caterpillar infestations. Future trends in drought and pine caterpillar populations were then estimated using the Hurst exponent. The results showed that: (1) Drought decreased gradually and showed a wetting trend from 1981 to 2012, with frequency decreasing on a decadal scale as follows: 1980s > 1990s > 2000s > 2010s; (2) The total area of pine caterpillar occurrence decreased strongly from 1992 to 2012; (3) Long-term or prolonged drought had a greater positive impact on pine caterpillar outbreak than short-term drought; (4) In the future, a greater portion of the province’s area will experience increased wetting conditions (57%) than increased drought (43%), and the area of pine caterpillar outbreak is estimated to decrease overall. These findings help elucidate the relationship between drought and pine caterpillar outbreak in Shandong Province and, hence, provide a basis for developing preventive measures and plans.

ACS Style

Yongbin Bao; Fei Wang; Siqin Tong; Li Na; Aru Han; Jiquan Zhang; Yuhai Bao; Yunchi Han; Qiumei Zhang. Effect of Drought on Outbreaks of Major Forest Pests, Pine Caterpillars (Dendrolimus spp.), in Shandong Province, China. Forests 2019, 10, 264 .

AMA Style

Yongbin Bao, Fei Wang, Siqin Tong, Li Na, Aru Han, Jiquan Zhang, Yuhai Bao, Yunchi Han, Qiumei Zhang. Effect of Drought on Outbreaks of Major Forest Pests, Pine Caterpillars (Dendrolimus spp.), in Shandong Province, China. Forests. 2019; 10 (3):264.

Chicago/Turabian Style

Yongbin Bao; Fei Wang; Siqin Tong; Li Na; Aru Han; Jiquan Zhang; Yuhai Bao; Yunchi Han; Qiumei Zhang. 2019. "Effect of Drought on Outbreaks of Major Forest Pests, Pine Caterpillars (Dendrolimus spp.), in Shandong Province, China." Forests 10, no. 3: 264.

Journal article
Published: 12 December 2018 in Sustainability
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Land use/cover change (LUCC) is one of the major environmental changes and has become a hot topic in the study of global change. Based on four land use classification maps, this study used the intensity analysis method to quantitatively monitor the land use changes which occurred in Inner Mongolia during 1980–2015. The results showed that changes occurred although the trends of corresponding land use types were different (increase or decrease), and the land use changes had an obvious increasing or decreasing trend before and after 2000, respectively. Generally, woodland, high-coverage grassland, and moderate-coverage grassland decreased and the other land use types increased during 1980–2015. In addition, the changes had great differences in spatial distribution. The area of grassland had the largest decrease, indicating that the quality of grassland has declined in Inner Mongolia. The variation rate of land use in 1980–1990 was faster than the rates in 1990–2000 and 2000–2015.

ACS Style

Siqin Tong; Zhenhua Dong; Jiquan Zhang; Yongbin Bao; Ari Guna; Yuhai Bao. Spatiotemporal Variations of Land Use/Cover Changes in Inner Mongolia (China) during 1980–2015. Sustainability 2018, 10, 4730 .

AMA Style

Siqin Tong, Zhenhua Dong, Jiquan Zhang, Yongbin Bao, Ari Guna, Yuhai Bao. Spatiotemporal Variations of Land Use/Cover Changes in Inner Mongolia (China) during 1980–2015. Sustainability. 2018; 10 (12):4730.

Chicago/Turabian Style

Siqin Tong; Zhenhua Dong; Jiquan Zhang; Yongbin Bao; Ari Guna; Yuhai Bao. 2018. "Spatiotemporal Variations of Land Use/Cover Changes in Inner Mongolia (China) during 1980–2015." Sustainability 10, no. 12: 4730.

Conference paper
Published: 20 October 2018 in Proceedings of the 8th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC 2018)
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Qiaofeng Zhang; Guixiang Liu; Hongbo Yu; Shan Yu; Yuhai Bao. Spatiotemporal Characteristics Analysis of Environmental Sensitivity of Drought Disaster in Xilingol Grassland. Proceedings of the 8th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC 2018) 2018, 1 .

AMA Style

Qiaofeng Zhang, Guixiang Liu, Hongbo Yu, Shan Yu, Yuhai Bao. Spatiotemporal Characteristics Analysis of Environmental Sensitivity of Drought Disaster in Xilingol Grassland. Proceedings of the 8th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC 2018). 2018; ():1.

Chicago/Turabian Style

Qiaofeng Zhang; Guixiang Liu; Hongbo Yu; Shan Yu; Yuhai Bao. 2018. "Spatiotemporal Characteristics Analysis of Environmental Sensitivity of Drought Disaster in Xilingol Grassland." Proceedings of the 8th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC 2018) , no. : 1.

Conference paper
Published: 20 October 2018 in Proceedings of the 8th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC 2018)
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Lai Quan; Yulong Bao; Yongbin Bao; Sa Chula; Yuhai Bao. Spatial-temporal Distribution Characteristics of Snow Depth in Mongolian Plateau Based on Reanalysis Data. Proceedings of the 8th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC 2018) 2018, 1 .

AMA Style

Lai Quan, Yulong Bao, Yongbin Bao, Sa Chula, Yuhai Bao. Spatial-temporal Distribution Characteristics of Snow Depth in Mongolian Plateau Based on Reanalysis Data. Proceedings of the 8th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC 2018). 2018; ():1.

Chicago/Turabian Style

Lai Quan; Yulong Bao; Yongbin Bao; Sa Chula; Yuhai Bao. 2018. "Spatial-temporal Distribution Characteristics of Snow Depth in Mongolian Plateau Based on Reanalysis Data." Proceedings of the 8th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC 2018) , no. : 1.

Conference paper
Published: 20 October 2018 in Proceedings of the 8th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC 2018)
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Peiling Li; Xiaojun Huang; Yuhai Bao; Shan Yu. Evaluation of Land Resources Pressure in the districts of Huhhot, Baotou and Erdos Based on GIS Technology. Proceedings of the 8th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC 2018) 2018, 1 .

AMA Style

Peiling Li, Xiaojun Huang, Yuhai Bao, Shan Yu. Evaluation of Land Resources Pressure in the districts of Huhhot, Baotou and Erdos Based on GIS Technology. Proceedings of the 8th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC 2018). 2018; ():1.

Chicago/Turabian Style

Peiling Li; Xiaojun Huang; Yuhai Bao; Shan Yu. 2018. "Evaluation of Land Resources Pressure in the districts of Huhhot, Baotou and Erdos Based on GIS Technology." Proceedings of the 8th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC 2018) , no. : 1.

Conference paper
Published: 20 October 2018 in Proceedings of the 8th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC 2018)
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Yulong Bao; Yuhai Bao; Lai Quan; Yong Mei; Enliang Guo; Zhijuan Bai. A Study of Temporal-spatial Evolution Model of Grassland Fire Behavior and Fire Risk Warning Based on MCD45. Proceedings of the 8th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC 2018) 2018, 1 .

AMA Style

Yulong Bao, Yuhai Bao, Lai Quan, Yong Mei, Enliang Guo, Zhijuan Bai. A Study of Temporal-spatial Evolution Model of Grassland Fire Behavior and Fire Risk Warning Based on MCD45. Proceedings of the 8th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC 2018). 2018; ():1.

Chicago/Turabian Style

Yulong Bao; Yuhai Bao; Lai Quan; Yong Mei; Enliang Guo; Zhijuan Bai. 2018. "A Study of Temporal-spatial Evolution Model of Grassland Fire Behavior and Fire Risk Warning Based on MCD45." Proceedings of the 8th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC 2018) , no. : 1.

Conference paper
Published: 20 October 2018 in Proceedings of the 8th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC 2018)
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ACS Style

Yongbin Bao; Jiquan Zhang; Xingpeng Liu; Na Li; Yuhai Bao; Qing Ma. Effects of Drought and Climate Change on Forest damage and Its Hazard Assessment. Proceedings of the 8th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC 2018) 2018, 1 .

AMA Style

Yongbin Bao, Jiquan Zhang, Xingpeng Liu, Na Li, Yuhai Bao, Qing Ma. Effects of Drought and Climate Change on Forest damage and Its Hazard Assessment. Proceedings of the 8th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC 2018). 2018; ():1.

Chicago/Turabian Style

Yongbin Bao; Jiquan Zhang; Xingpeng Liu; Na Li; Yuhai Bao; Qing Ma. 2018. "Effects of Drought and Climate Change on Forest damage and Its Hazard Assessment." Proceedings of the 8th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC 2018) , no. : 1.