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CC BY V4.0, Open Access
Journal article
Using hyperspectral imagery to detect an invasive fungal pathogen and symptom severity in Pinus strobiformis seedlings of different genotypes
Remote sensing (Basel, Switzerland), Vol.12(24), Art. 4041
2020
Abstract
Finding trees that are resistant to pathogens is key in preparing for current and future disease threats such as the invasive white pine blister rust. In this study, we analyzed the potential of using hyperspectral imaging to find and diagnose the degree of infection of the non-native white pine blister rust in southwestern white pine seedlings from different seed-source families. A support vector machine was able to automatically detect infection with a classification accuracy of 87% (kappa = 0.75) over 16 image collection dates. Hyperspectral imaging only missed 4% of infected seedlings that were impacted in terms of vigor according to expert's assessments. Classification accuracy per family was highly correlated with mortality rate within a family. Moreover, classifying seedlings into a 'growth vigor' grouping used to identify the degree of impact of the disease was possible with 79.7% (kappa = 0.69) accuracy. We ranked hyperspectral features for their importance in both classification tasks using the following features: 84 vegetation indices, simple ratios, normalized difference indices, and first derivatives. The most informative features were identified using a 'new search algorithm' that combines both the p-value of a 2-sample t-test and the Bhattacharyya distance. We ranked the normalized photochemical reflectance index (PRIn) first for infection detection. This index also had the highest classification accuracy (83.6%). Indices such as PRIn use only a small subset of the reflectance bands. This could be used for future developments of less expensive and more data-parsimonious multispectral cameras.
Details
- Title
- Using hyperspectral imagery to detect an invasive fungal pathogen and symptom severity in Pinus strobiformis seedlings of different genotypes
- Authors/Creators
- M. Haagsma - Oregon State UniversityG. F. M. Page - Murdoch University, College of Environmental and Life SciencesJ. S. Johnson - US Forest Serv, Dorena Genet Resource Ctr, USDA, Cottage Grove, OR 97424 USAC. Still - Oregon State UniversityK. M. Waring - Department of ForestryR. A. Sniezko - US Forest Serv, Dorena Genet Resource Ctr, USDA, Cottage Grove, OR 97424 USAJ. S. Selker - Oregon State University
- Publication Details
- Remote sensing (Basel, Switzerland), Vol.12(24), Art. 4041
- Publisher
- Mdpi
- Number of pages
- 19
- Grant note
- NSF-EAR 1440506; NSF-DEB 1442456-1442597; NSF-CCF 1521687 / National Science Foundation; National Science Foundation (NSF)
- Identifiers
- 991005550568407891
- Copyright
- © 2020 by the authors.
- Murdoch Affiliation
- College of Environmental and Life Sciences; School of Environmental and Conservation Sciences
- Language
- English
- Resource Type
- Journal article
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- Collaboration types
- Domestic collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.169 Remote Sensing
- 4.169.91 Vegetation Mapping
- Web Of Science research areas
- Environmental Sciences
- Geosciences, Multidisciplinary
- Imaging Science & Photographic Technology
- Remote Sensing
- ESI research areas
- Geosciences