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FOREST RESOURCES WANAGEMENT ›› 2022, Vol. 0 ›› Issue (5): 107-117.doi: 10.13466/j.cnki.lyzygl.2022.05.014

• Technical Application • Previous Articles     Next Articles

Tree Level Monitoring of Pine Wilt Disease Based on UAV Multispectral Imagery

WANG Bu1,2(), TAN Wei1,2(), WANG Guilin1,2, PU Xiuqing1,2   

  1. 1. College of Forestry,Guizhou University,Guiyang 550025,China
    2. Research Center of Forestry Information Engineering,Guizhou University,Guiyang 550025,China
  • Received:2022-08-09 Revised:2022-10-26 Online:2022-10-28 Published:2022-12-23
  • Contact: TAN Wei E-mail:wangbu2022@163.com;wtan@gzu.edu.cn

Abstract:

Pine wilt disease(PWD)is one of the most harmful forest diseases,and there is an urgent need to adopt accurate monitoring means to determine the number and location of diseased trees for efficient prevention and control of PWD.In this study,the image of PWD epidemic area in Zhongcheng Town,Rongjiang County,Guizhou Province was obtained by using multi spectral UAV,and the multi spectral UAV and its derived point cloud were used as data sources.Firstly,localization identification and crown profile segmentation of individual trees in the study area were performed by point cloud segmentation algorithm.The spectral features were then extracted in segmentation units and the best feature set was filtered by a combination of random forest and recursive feature elimination(RF-RFE).Finally,random Forest(RF)and support vector machine(SVM)detection models were constructed based on screening feature sets,and the model detection performance was evaluated. At the same time,the RF and the SVM were used to invert the disease susceptibility in the study area and draw the spatial distribution map of PWD.The following key results were obtained:1)The individual trees segmentation based on photogrammetric point clouds was effective,with an overall F-score value of 82.21%.The OA and Kappa of the RF model constructed after feature screening were 84.4% and 0.74,respectively,and the SVM was 76.09% and 0.66.2)The F-score for RF were 78.43%,69.23%,83.33% and 94.12%,SVM were 80.7%,55.81%,70.18% and 84.13% for the four stages of tree health,early,middle,and late detection,respectively.The comprehensive comparison of the detection performance of RF was the best.The study pointed out that it was feasible to use the combination of UAV multispectral image and photographic measurement point cloud for individual trees scale monitoring of PWD.The study aimed to provide a reference for low-cost and accurate remote sensing monitoring of PWD.

Key words: pine wilt disease, UAV multispectral imagery, photogrammetric point cloud, individual tree crown delineation

CLC Number: