Monitoring the Severity of Pantana phyllostachysae Chao Infestation in Moso Bamboo Forests Based on UAV Multi-Spectral Remote Sensing Feature Selection

Forests

Yiwei Zhang¹  Songyang Xiang¹  Zhanghua Xu¹  Yifan Li¹  Xuying Huang¹  Qi Zhang¹  Xin Zhou¹  Qiaosi Li²  Zenglu Li³  Xiong Yao³  Xiaoyu Guo³ 

[1] Academy of Geography and Ecological Environment, Fuzhou University, Fuzhou 350108, China;Department of Earth Sciences, The University of Hong Kong, Hong Kong 999077, China;Fujian Provincial Key Laboratory of Resources and Environment Monitoring & Sustainable Management and Utilisation, Sanming 365004, China;

关键词: UAV multispectral remote sensing;    Moso bamboo forest;    Pantana phyllostachysae Chao;    feature selection;    detection model;   

DOI  :  10.3390/f13030418

来源: DOAJ

【 摘 要 】

In recent years, the rapid development of unmanned aerial vehicle (UAV) remote sensing technology has provided a new means to efficiently monitor forest resources and effectively prevent and control pests and diseases. This study aims to develop a detection model to study the damage caused to Moso bamboo forests by Pantana phyllostachysae Chao (PPC), a major leaf-eating pest, at 5 cm resolution. Damage sensitive features were extracted from multispectral images acquired by UAVs and used to train detection models based on support vector machines (SVM), random forests (RF), and extreme gradient boosting tree (XGBoost) machine learning algorithms. The overall detection accuracy (OA) and Kappa coefficient of SVM, RF, and XGBoost were 81.95%, 0.733, 85.71%, 0.805, and 86.47%, 0.811, respectively. Meanwhile, the detection accuracies of SVM, RF, and XGBoost were 78.26%, 76.19%, and 80.95% for healthy, 75.00%, 83.87%, and 79.17% for mild damage, 83.33%, 86.49%, and 85.00% for moderate damage, and 82.5%, 90.91%, and 93.75% for severe damage Moso bamboo, respectively. Overall, XGBoost exhibited the best detection performance, followed by RF and SVM. Thus, the study findings provide a technical reference for the regional monitoring and control of PPC in Moso bamboo.

【 授权许可】

Unknown