Monitoring ash dieback (Hymenoscyphus fraxineus) in British forests using hyperspectral remote sensing

Abstract

Large‐scale dieback of ash trees (Fraxinus spp.) caused by the fungus Hymenoscyphus fraxineus is posing an immense threat to forest health in Europe, requiring effective monitoring at large scales. In this study, a pipeline was created to find ash trees and classify dieback severity using high‐resolution hyperspectral imagery of individual tree crowns (ITCs). Hyperspectral data were collected in four forest sites near Cambridge, UK, where 422 ITCs were manually delineated and labelled using field‐measurements of species and dieback severity (for ash trees). Four algorithms, namely linear discriminant analysis (LDA), principal components analysis coupled with LDA (PCA‐LDA), partial least squares discriminant analysis (PLS‐DA) and random forest (RF), were used to build classification models for species and dieback severity classification. The effect of dark‐pixel filtering on classification accuracy was evaluated. The best performing models were then coupled with automatic ITC segmentation to map species and ash dieback distribution over 16.8 hectares of woodland. We calculated and partitioned the coefficient of variation (CV) of the reflected ash spectra to find variable wavebands associated with dieback. PLS‐DA and LDA were most accurate for classifying ITC species identifies (overall accuracy >90%), whereas RF was most accurate for classifying ash dieback severity (overall accuracy 77%). Dark pixel filtering further increased the accuracy of species classification (+6%), but not disease classification. The reflectances of narrow blue (415 nm), red‐edge (680 nm) and NIR (760 nm) bands had high CV across disease classes and should be included if multispectral imagery were to be used to monitor ash dieback. The study demonstrates the possibility of using remote sensing to forward epidemiological research by monitoring forest pathogens in landscape scales, which would allow temperate forest managers to control pathogen outbreaks, assess associated impacts and restore affected forests much more effectively.

Early Identification of Root Rot Disease by Using Hyperspectral Reflectance: The Case of Pathosystem Grapevine/Armillaria

“…In particular, the high reflectance values from 750 nm up to 951 nm characterized healthy from diseased leaves, with the most relevant peaks in 902 nm, 920 nm, and 889 nm. This conclusion is consistent with the literature, as it is well known that the NIR light is not absorbed by leaf pigments but mostly reflected and transmitted in healthy leaves [32,62,[72][73][74]]. It appears that the values in the NIR are much more valuable than the VIS spectral range in discriminating diseased from healthy plants.…”

“…The classifiers used were the following: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), regularized discriminant analysis (RDA), simple k-nearest neighbor (SkNN), Naïve Bayes (NB), and recursive partitioning regression tree (RPART). The LDA classifier develops a linear boundary by fitting a multivariate normal density with pooled covariance estimates for each class [54,[57][58][59][60][61][62], whereas the QDA is a non-linear model that constructs a non-linear boundary by fitting multivariate normal densities with covariance estimates separated by groups [58,61]. The LDA is a simple model that works better while classifying small sample sizes and requires a shorter computation time, whereas the QDA is better suited for a complex dataset.…”

Early Identification of Root Rot Disease by Using Hyperspectral Reflectance: The Case of Pathosystem Grapevine/Armillaria

“…In particular, the high reflectance values along the NIR spectrum from 750 nm up to 951nm characterized healthy from diseased leaves, with the most relevant peaks in 902nm, 920nm and 889nm. This conclusion is coherent with the literature as it is well known that the NIR light is not absorbed by leaf pigments but rather reflected in healthy leaves [42,[60][61][62][63]. It appears that the values in the NIR are much more valuable than the VIS spectral range to discriminate diseased from healthy plants.…”

Spatial Patterns of ‘Ōhi‘a Mortality Associated with Rapid ‘Ōhi‘a Death and Ungulate Presence

“…It highlights the difficulty in determining the cause of 'ōhi'a mortality from remotely sensed imagery alone, even when using multispectral or hyperspectral data [23,24], especially since symptoms of ROD can be confused with symptoms of drought [21]. Other fungal pathogens affecting forests worldwide, including ash dieback, myrtle rust, and oak wilt, were successfully detected via multispectral and hyperspectral imagery, though misclassification with drought can occur, and early detection remains a challenge [41][42][43][44].…”