Remote Sensing Applications and Decision Support

Monitoring grazing intensity: an experiment with canopy spectra applied to satellite remote sensing

[+] Author Affiliations
Fei Li, Juhua Luo, Xiaoqiang Zhang

Nanjing Institute of Geography and Limnology, Key Laboratory of Watershed Geographic Sciences, Chinese Academy of Sciences, Nanjing 210008, China

Ying Zhao

Northwest A&F University, State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Yangling 712100, China

Northwest A&F University, Key Laboratory of Plant Nutrition and the Agri-Environment in Northwest China, Ministry of Agriculture, Yangling 712100, China

Jiajia Zheng

Nanjing University, School of Geographic and Oceanographic Sciences, Nanjing 210093, China

J. Appl. Remote Sens. 10(2), 026032 (Jun 09, 2016). doi:10.1117/1.JRS.10.026032
History: Received March 29, 2016; Accepted May 19, 2016
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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.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Fei Li ; Ying Zhao ; Jiajia Zheng ; Juhua Luo and Xiaoqiang Zhang
"Monitoring grazing intensity: an experiment with canopy spectra applied to satellite remote sensing", J. Appl. Remote Sens. 10(2), 026032 (Jun 09, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.026032


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