*Review Scope and Approach*

Here, we address some important questions that motivate much hyperspectral plant research. Do the taxonomic, structural, or functional characteristics of plant types or species influence the spectral regions that are most important to classification, or are particular spectral regions consistently selected across a diversity of plant or ecological types? A review of selected features from the hyperspectral literature could identify best practices for feature selection methods, as well as detect wave-regions of high-utility, those that best generalize across taxonomic or ecological boundaries.

The search for literature spanned two decades, from January 1996 to December 2018, focusing on peer reviewed journals in the English language. Search was performed with Google Scholar using combinations of the keywords, namely *Hyperspectral, Spectra, Vegetation, Plant, Tree, Species, Identi\*, Discriminat\*, Classif\*, Map, Feature Select\*, Waveband, Band, UAV, Drone*. In order to be included, a study must have performed a feature selection technique on hyperspectral vegetation data with an aim to classify plant samples.

Many studies fulfilled the initial requirement, but did not report selected wavebands with sufficient specificity, and therefore could not be included. Here, we present waveband selections derived from 38 hyperspectral vegetation classification studies. When applicable, studies that included multiple feature selection techniques were broken into sub-studies, increasing the total number of reviewed studies to 61 (Tables 1 and 2). These included studies are from a wide variety of scales (leaf, branch, and canopy), recording methods (lab, field, aerial, satellite), taxonomic units, and bandwidths.

Additionally, a dataset was synthesised from hyperspectral measurements of 22 species of New Zealand plants collected as field spectra from four locations on the North island [20,21]. This dataset was used to examine how study design (number of classes, number of samples, included species, and feature selection method) influenced waveband selection. This was performed with the aim of determining which elements of the study design most contributed to variation seen in selected wavebands.

The remainder of this paper is structured in the following way. Section 2 provides a meta-analysis of the selected wavebands, broken down by spectral region. Section 3 identifies and describes feature selection techniques from these studies, and where possible, highlights their effects on waveband selection. Section 4 examines study design influence on waveband selection, while Sections 5 and 6 present a discussion of the results and conclusions.





 PCA Principal Component Analysis, SDA Stepwise Discriminant Analysis, CFS Correlation-based Feature Selection, SPA Successive Progressions Algorithm, SAM = Spectral Angle Mapper, PLS-DA = Partial Least Squares – Discriminant Analysis (PLS-DA), VIP = Variable Importance Projection.



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**Table**

**2.**

*Cont*.

103


**Table 2.**

*Cont*.



Discriminant Analysis, LS-means = Least Squares means, LL-R2 = Lambda-Lambda R-Squared, SVM = Support Vector Machine, DGVI = Derivative Greenness Vegetation Indices.

#### *Remote Sens.* **2020**, *12*, 113
