Comprehensive Grassland Degradation Monitoring by Remote Sensing in Xilinhot, Inner Mongolia, China

Xin Lyu, Xiaobing Li, Jirui Gong, Hong Wang, Dongliang Dang, Huashun Dou, Shengkun Li and Siyu Liu
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Xin Lyu: School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Xiaobing Li: School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Jirui Gong: School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Hong Wang: School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Dongliang Dang: School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Huashun Dou: School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Shengkun Li: School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Siyu Liu: School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

Sustainability, 2020, vol. 12, issue 9, 1-18

Abstract: Grassland degradation is a complex process and cannot be thoroughly measured by a single indicator, such as fractional vegetation cover (FVC), aboveground biomass (AGB), or net primary production (NPP), or by a simple combination of these indicators. In this research, we combined measured data with vegetation and soil characteristics to establish a set of standards applicable to the monitoring of regional grassland degradation by remote sensing. We selected indicators and set their thresholds with full consideration given to vegetation structure and function. We optimized the indicator simulation, based on which grassland degradation in the study area during 2014–2018 was comprehensively evaluated. We used the feeding intensity of herbivores to represent the grazing intensity. We analyzed the effects of climate and grazing activities on grassland degradation using the constraint line method. The results showed degradation in approximately 69% of the grassland in the study area and an overall continued recovery of the degraded grassland from 2014 to 2018. We did not identify any significant correlation between temperature and grassland degradation. The increase in precipitation promoted the recovery of degraded grassland, whereas increased grazing may have aggravated degradation. Our findings can not only improve the scientific quality and accuracy of grassland degradation monitoring by remote sensing but also provide clear spatial information and decision-making help in sustainable management of grassland regions.

Keywords: grassland degradation; monitoring standard; climate driving; grazing intensity; constraint line (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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