Quantifying Grazing Intensity Using Remote Sensing in Alpine Meadows on Qinghai-Tibetan Plateau

Qingqing Ma, Linrong Chai, Fujiang Hou, Shenghua Chang, Yushou Ma, Atsushi Tsunekawa and Yunxiang Cheng
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Qingqing Ma: State Key Laboratory of Grassland Agro-ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China
Linrong Chai: State Key Laboratory of Grassland Agro-ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China
Fujiang Hou: State Key Laboratory of Grassland Agro-ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China
Shenghua Chang: State Key Laboratory of Grassland Agro-ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China
Yushou Ma: The Institute of Rangeland Research, Academy of Animal and Veterinary Sciences, Qinghai University, Xining 810016, China
Atsushi Tsunekawa: Arid Land Research Center, Tottori University, 1390 Hamasaka, Tottori 680-0001, Japan
Yunxiang Cheng: State Key Laboratory of Grassland Agro-ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China

Sustainability, 2019, vol. 11, issue 2, 1-14

Abstract: Remote sensing data have been widely used in the study of large-scale vegetation activities, which have important significance in estimating grassland yields, determining grassland carrying capacity, and strengthening the scientific management of grasslands. Remote sensing data are also used for estimating grazing intensity. Unfortunately, the spatial distribution of grazing-induced degradation remains undocumented by field observation, and most previous studies on grazing intensity have been qualitative. In our study, we tried to quantify grazing intensity using remote sensing techniques. To achieve this goal, we conducted field experiments at Gansu Province, China, which included a meadow steppe and a semi-arid region. The correlation between a vegetation index and grazing intensity was simulated, and the results demonstrated that there was a significant negative correlation between NDVI and relative grazing intensity ( p < 0.05). The relative grazing intensity increased with a decrease in NDVI, and when the relative grazing intensity reached a certain level, the response of NDVI to relative grazing intensity was no longer sensitive. This study shows that the NDVI model can illustrate the feasibility of using a vegetation index to monitor the grazing intensity of livestock in free-grazing mode. Notably, it is feasible to use the remote sensing vegetation index to obtain the thresholds of livestock grazing intensity.

Keywords: Alpine meadows; aboveground biomass; grazing intensity; vegetation index; grazing intensity threshold; Forage-livestock balance (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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