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

Xu, Z. et al. (2022) Monitoring the Severity of Pantana phyllostachysae Chao Infestation in Moso Bamboo Forests Based on UAV Multi-Spectral Remote Sensing Feature Selection. Forests, 13(3), 418. (doi: 10.3390/f13030418)

Abstract

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.

• Item Type: Articles
• Additional Information: This research was funded by the National Natural Science Foundation of China, grant number 42071300; by the National Natural Science Foundation of China, grant number 41501361; by the Fujian Province Natural Science Foundation Project, grant number 2020J01504; and by the China Postdoctoral Science Foundation, grant number 2018M630728.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Li, Dr Qiaosi
• Authors: Xu, Z., Zhang, Q., Xiang, S., Li, Y., Huang, X., Zhang, Y., Zhou, X., Li, Z., Yao, X., Li, Q., and Guo, X.
• College/School: College of Social Sciences > School of Social and Political Sciences > Urban Studies
• Journal Name: Forests
• Publisher: MDPI
• ISSN: 1999-4907
• ISSN (Online): 1999-4907
• Copyright Holders: Copyright © 2022 by the authors
• First Published: First published in Forests 13(3):418
• Publisher Policy: Reproduced under a Creative Commons license