Abstract

Pine wilt disease (PWD) is a devastating forest disease and has been listed as a quarantine pest in 52 countries around the world. Early identification of the affected trees and timely removal of them from the forest is crucial to control the spread. This study aims to explore the potential of hyperspectral data on early identification of PWD and exhibit the common spectral features, from early- infected tree crowns and needles, and from different species located in different regions. Two types of hyperspectral data were used and compared. One was using drone-based hyperspectral images with a spectral range of 400 - 1 000 nm and a resolution of 0.11 m. The images were analyzed at the individual-tree level. The other was using hyperspectral reflectance from sampled needles with a spectral range of 350 - 2 500 nm. It was used for the analysis at the needle level. We used linear discriminant analysis (LDA) to quantify the separability of spectral reflectance and first-derivative reflectance from the healthy and early-infected samples. The results showed that the red-edge bands were more sensitive than the other bands at both individual-tree and needle levels, and the first-derivative of red-edge bands achieved the best early recognition of the disease with 0.78, 0.72, and 0.85 accuracy at the individual-tree level for Chinese red pine and at the needle level for Japanese pine and Korean pine. We concluded that red-edge bands were the most informative bands with stable sensitivity at different scales and for different species.

Keywords

Pine wilt disease; early detection; hyperspectral; drone; classification

Published in

IEEE International Geoscience and Remote Sensing Symposium proceedings
2023, pages: 7575-7578
Title: Proceedings of IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
ISBN: 979-8-3503-3174-5, eISBN: 979-8-3503-2010-7
Publisher: IEEE
DOI: 10.1109/IGARSS52108.2023.10281997

Conference

IEEE International Geoscience and Remote Sensing Symposium (IGARSS), JUL 16-21, 2023, Pasadena, CA

SLU Authors

• Li, Niwen

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    
  • Beijing Forestry University

• Huo, Langning

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

Associated SLU-program

SLU Plant Protection Network
SLU Forest Damage Center


UKÄ Subject classification

Forest Science
Remote Sensing


Publication identifier

DOI: https://doi.org/10.1109/IGARSS52108.2023.10281997

Permanent link to this page (URI)

https://res.slu.se/id/publ/126618