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.
Highlights
Livestock grazing is a dominant form of land use globally, as nearly one-quarter of the earth’s land surface is used for grazing (22%) [1]
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The MODIS-NDVI-based linear model could better simulate aboveground biomass in the Maqu alpine meadow, and the NDVI increased with the aboveground biomass, which was consistent with the trend observed for the NDVI and biomass models for grazing pasture [46,47]
Summary
Livestock grazing is a dominant form of land use globally, as nearly one-quarter of the earth’s land surface is used for grazing (22%) [1]. We could conduct research on discipline fusion, using satellite remote sensing data analysis on a large scale and implementing vegetation surveys and experimental analysis on a small scale— integrating large- and small-scale research—as well as conduct research on remote sensing monitoring technology based on the grass–soil–livestock interaction For this reason, we first studied the spatial distribution characteristics of biomass in the study area under different grazing intensities and formulated the following general hypotheses: (1) Grass aboveground biomass stock and grass production are maximized under moderately grazed conditions.
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