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Journal of Applied Remote Sensing • new

Monitoring grazing intensity: an experiment with canopy spectra applied to satellite remote sensing
Author(s): Fei Li; Ying Zhao; Jiajia Zheng; Juhua Luo; Xiaoqiang Zhang
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Paper Abstract

The quantification of grassland grazing intensity (GI) and its detailed spatial distribution are important for grassland management and ecological protection. Remote sensing has great potential in these areas, but its use is still limited. This study analyzed the impacts of grazing on biophysical properties of vegetation and suggested using biomass to quantify GI because of its stability and interpretability. In comparison to a single spectral index, such as the red edge index (REI), combining REI and a cellulose absorption ratio index calculated from hyperspectral data performs better for biomass estimation. Further, an auxiliary spectral index, called the grazing monitoring index (GMI), was developed based on differences in spectral reflectance in the infrared range. Experiments in a grazing area of the Inner Mongolia grassland indicated that GMI can identify GI, with three range intervals (GMI <0, 0–1, and ≥1) used to describe the biomass distribution. The results showed that combining GMI and biomass was more successful than existing approaches for identifying the grassland variability resulting from the spatial heterogeneity of grazing behavior. The thresholds of biomass for four GI levels (ungrazed, lightly grazed, moderately grazed, and heavily grazed) could be determined by the intersections of biomass distributions. In addition, the approach developed at the on-ground canopy scale was extended to remotely sensed Hyperion data. The results showed that the approach could successfully identify the grazing treatments of blocks in the experimental grazing area. Overall, our study provides inspiration and ideas for using satellite remote sensing for evaluating plant production, standing biomass, and livestock impacts.

Paper Details

Date Published: 9 June 2016
PDF: 15 pages
J. Appl. Remote Sens. 10(2) 026032 doi: 10.1117/1.JRS.10.026032
Published in: Journal of Applied Remote Sensing Volume 10, Issue 2
Show Author Affiliations
Fei Li, Nanjing Institute of Geography and Limnology (China)
Ying Zhao, Northwest A&F Univ. (China)
Jiajia Zheng, Nanjing Univ. (China)
Juhua Luo, Nanjing Institute of Geography and Limnology (China)
Xiaoqiang Zhang, Nanjing Institute of Geography and Limnology (China)

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