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Hyperspectral Remote Sensing of Vegetation Health at the Baiyun Mountain National Forest Park, China

  • Shuisen Chen
  • Weiqi Chen
  • Jia Liu
Chapter

Abstract

Forest pests, wilting disease, wood cutting and phenological changes can affect vegetation health. The traditional method for pigment extraction followed by spectrophotometric determination or high-performance liquid chromatography (HPLC) will have to destroy the measured leaves with high costs and a long processing time. Using hyperspectral EO-1 Hyperion remote sensor imagery and a spectral model of leaf pigment reflectance, we examined the two forest health related important parameters, anthocyanin and carotenoid, in the Baiyun Mountain national forest park, China. The remote sensing-derived outcome was validated through in situ sample analyses of canopy leaves. The result shows that the concentrations of anthocyanin and carotenoid indicating the vegetation stress can be quantified using the reflectance index derived from hyperspectral remote sensor imagery. The index has the potential to indicate the regional forest vegetation health. Furthermore, the forest phenological information can be retrieved when multi-temporal hyperspectral images are available.

Keywords

Vegetation Health Anthocyanin Carotenoid EO-1 hyperion Remote sensing 

Notes

Acknowledgements

This research was partly funded by Guangdong Province’s Science & Technology Plan Project (2016A020223011, 2018B030311059, 2015B070701020), and GDAS’ Special Project of Science and Technology Development (2017GDASCX-0101), Production-Education-Research Collaborative Innovation Major Project of Guangzhou Municipality (201604020117) and Guangzhou Yuexiu District Science and Technology Project (2016-GX-059).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Guangdong Open Laboratory of Geospatial Information Technology and Applications, Guangdong Key Laboratory of Remote Sensing and GIS Technology ApplicationGuangdong Engineering Technology Center for Remote Sensing Big Data Applications, Guangzhou Institute of GeographyGuangzhouChina
  2. 2.Department of Geography and AnthropologyLouisiana State UniversityBaton RougeUSA

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