Li, Niwen
- Department of Forest Resource Management, Swedish University of Agricultural Sciences
- Beijing Forestry University
Conference paper2022Peer reviewed
Identifying Nematode-Induced Wilt Using Hyperspectral Drone Images and Assessing the Potential of Early Detection
Li, Niwen; Zhang, Xiaoli; Huo, Langning
Abstract
Since the pine wilt nematode spread and was discovered in China in 1982, it has caused severe damage to the pine forest and has caused a substantial finical loss. Detecting infected trees and removing them from the forest as early as possible is crucial to prevent its spread by the insect vector longhorn-beetles. This study aims at developing methods to detect and map infections using hyperspectral drone images. We inventoried 391 pines in middle east China and recorded them as healthy or early-, middle-, late-stage infected trees. The hyperspectral drone images were obtained with 0.11 m resolution and wavelength from 400 to 1000 nm, covering from red band to near-infrared (NIR). We used the successive projections algorithm (SPA) to select the sensitive bands and the support vector machine (SVM) algorithm to classify trees into different health statuses. The classification resulted in high accuracy during the middle and late-stage infection, while separating healthy and the early -stage infection was challenging. The developed method could map Pine wilt nematode infections and guide the sanitation felling as a crucial disease control measure.
Keywords
support vector machines; Image resolution; Insects; Geoscience and remote sensing; Vegetation; Forestry; Machine learning
Published in
IEEE International Geoscience and Remote Sensing Symposium proceedings
2022, pages: 512-515
Title: IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium : proceedings
Publisher: Institute of Electrical and Electronics Engineers
Conference
IEEE International Geoscience and Remote Sensing Symposium (IGARSS), JUL 17-22, 2022, Kuala Lumpur, MALAYSIA
SLU Authors
Huo, Langning
- Department of Forest Resource Management, Swedish University of Agricultural Sciences
- Department of Forest Resource Management, Swedish University of Agricultural Sciences
Associated SLU-program
SLU Plant Protection Network
SLU Forest Damage Center
UKÄ Subject classification
Remote Sensing
Forest Science
Publication identifier
- DOI: https://doi.org/10.1109/IGARSS46834.2022.9884063
- eISBN: 978-1-6654-2792-0
Permanent link to this page (URI)
https://res.slu.se/id/publ/118883