Using a Vegetation Index to Monitor the Death Process of Chinese Fir Based on Hyperspectral Data

Author:

Tang Xuemei123,Zang Zhuo123,Lin Hui123,Wang Xu4,Wen Zhang123

Affiliation:

1. Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry & Technology, Changsha 410004, China

2. Hunan Provincial Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security, Changsha 410004, China

3. Key Laboratory of National Forestry and Grassland Administration on Forest Resources Management and Monitoring in Southern China, Changsha 410004, China

4. Research Institute of Tropical Forestry, Chinese Academy of Forestry, Guangzhou 510520, China

Abstract

Chinese fir is one of the most widely distributed and extensively planted timber species in China. Therefore, monitoring pests and diseases in Chinese fir plantations is directly related to national timber forest security and forest ecological security. This study aimed to identify appropriate vegetation indices for the early monitoring of pests and diseases in Chinese fir plantations. For this purpose, the researchers used an imaging spectrometer to capture hyperspectral images of both experimental and control groups. The experimental group consisted of Chinese fir trees with two sections of bark stripped off, while the control group consisted of healthy Chinese fir trees. The study then assessed the sensitivity of 11 vegetation indices to the physiological differences between the two groups using the Mann–Whitney U test. The results showed that both the green-to-red region spectral angle index (GRRSGI) and the red edge position index (REP) were able to monitor the difference as early as 16 days after damage. However, GRRSGI performs best in monitoring early death changes in Chinese fir trees because it is less affected by noise and is more stable. The green–red spectral area index (GRSAI) also had high stability, but the monitoring effect was slightly worse than that of GRRSGI and REP. Compared with other indices, GRRSGI and GRSAI can better exploit the advantages of hyperspectral data.

Funder

National Natural Science Foundation of China

Hainan Provincial Natural Science Foundation of China

Publisher

MDPI AG

Subject

Forestry

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