Modelling hyperspectral- and thermal-based plant traits for the early detection of Phytophthora-induced symptoms in oak decline

Holm oak decline is a complex phenomenon mainly influenced by the presence of Phytophthora cinnamomi and water stress. Plant functional traits (PTs) are altered during the decline process — initially affecting the physiological condition of the plants with non-visual symptoms and subsequently the le...

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Bibliographic Details
Published in:Remote sensing of environment 2021-09, Vol.263, p.112570, Article 112570
Main Authors: Hornero, A., Zarco-Tejada, P.J., Quero, J.L., North, P.R.J., Ruiz-Gómez, F.J., Sánchez-Cuesta, R., Hernandez-Clemente, R.
Format: Article
Language:English
Subjects:
Anthocyanins
Canopies
Chlorophyll
Classification
Culling
Decline
Dieback
Disease monitoring
Dry matter
Evaluation
Fluorescence
Forest dieback
Heterogeneity
High-resolution imagery
Image resolution
Inspection
Leaf area
Leaf area index
Learning algorithms
Leaves
Machine learning
Oak
Pest outbreaks
Polls & surveys
Radiative transfer
Radiative transfer modelling
Signs and symptoms
Thermal imaging
Three dimensional models
Trees
Water stress
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Summary:Holm oak decline is a complex phenomenon mainly influenced by the presence of Phytophthora cinnamomi and water stress. Plant functional traits (PTs) are altered during the decline process — initially affecting the physiological condition of the plants with non-visual symptoms and subsequently the leaf pigment content and canopy structure — being its quantification critical for the development of scalable detection methods for effective management. This study examines the relationship between spectral-based PTs and oak decline incidence and severity. We evaluate the use of high-resolution hyperspectral and thermal imagery (< 1 m) together with a 3-D radiative transfer model (RTM) to assess a supervised classification model of holm oak decline. Field surveys comprising more than 1100 trees with varying disease incidence and severity were used to train and validate the model and predictions. Declining trees showed decreases of model-based PTs such as water, chlorophyll, carotenoid, and anthocyanin contents, as well as fluorescence and leaf area index, and increases in crown temperature and dry matter content, compared to healthy trees. Our classification model built using different PT indicators showed up to 82% accuracy for decline detection and successfully identified 34% of declining trees that were not detected by visual inspection and confirmed in a re-evaluation 2 years later. Among all variables analysed, canopy temperature was identified as the most important variable in the model, followed by chlorophyll fluorescence. This methodological approach identified spectral plant traits suitable for the detection of pre-symptomatic trees and mapping of oak forest disease outbreaks up to 2 years in advance of identification via field surveys. Early detection can guide management activities such as tree culling and clearance to prevent the spread of dieback processes. Our study demonstrates the utility of 3-D RTM models to untangle the PT alterations produced by oak decline due to its heterogeneity. In particular, we show the combined use of RTM and machine learning classifiers to be an effective method for early detection of oak decline potentially applicable to many other forest diseases worldwide. •High-resolution airborne imagery detected holm oak decline (HOD).•Plant trait quantification enables the interpretation of HOD severity levels.•Temperature and fluorescence were most important for HOD detection.•Plant traits detected HOD in 34% of visually asympt
ISSN:0034-4257
1879-0704
DOI:10.1016/j.rse.2021.112570