Forest and Grassland Resources Research >
Detection of Discolored Trees Caused by Pine Wilt Disease Based on Vegetation Index Method Using Terrestrial Ecosystem Carbon Inventory Satellite Data
Received date: 2023-06-29
Revised date: 2023-07-28
Online published: 2023-10-16
Pine wilt disease(PWD)is a forestry pest epidemic that poses a major threat to the survival ofpine trees.High-resolution satellite remote sensing is an effective approach for identifying PWD-infected pine trees.The Terrestrial Ecosystem Carbon Inventory Satellite(Goumang Satellite),which was launched on August 4,2022,has a four-band camera with a spatial resolution of more than 2m.The purpose of this paper is to investigate the vegetation index(VI)threshold approach for demonstrating PWD detection using Goumang Satellite multispectral data.Based on the multispectral characteristics of PWD discolored pine trees,an identification strategy based on two biomass-related and pigment-related VIs is proposed and a pine wilt disease index(PWDI)is designed.A test of PWD identification was conducted in Qixia City,Shandong Province.The results demonstrated that the double-VIs identification method outperformed the single-VI strategy,with the combination of PWDI and NDVI having the best PWD recognition capacity,with an overall accuracy of 84.5%,followed by the combinations of NWI-NDVI and NGRDI-NDVI.PWDI outperformed the other five examined VIs in terms of identification performance while using a single-VI method.This research demonstrates that the Goumang Satellite has a high potential for rapid and reliable detection of PWD discolored pine trees,and that the suggested double-VIs identification technique and the new PWDI can serve as a model for other comparable satellites used for PWD monitoring.
Quanjun JIAO , Yanfeng ZHENG , Wenjiang HUANG , Bing ZHANG , Heyi ZHANG , Yimeng SHI , Fayun WU , Anmin FU . Detection of Discolored Trees Caused by Pine Wilt Disease Based on Vegetation Index Method Using Terrestrial Ecosystem Carbon Inventory Satellite Data[J]. Forest and Grassland Resources Research, 2023 , 0(4) : 123 -131 . DOI: 10.13466/j.cnki.lyzygl.2023.04.015
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