
Feature type  Feature (group) name  Characteristic and description  Source/reference 

Spectral band  Spectral band  Multi/hyperspectral bands in digital number or reflectance  Directly selected from optical sensors’ images 
Spectral vegetation index (VI)  Multispectral (MS) VIs  Using 2 or more MS bands to construct a ratio or normalized difference ratio, or other forms of VIs by an arithmetic operation  Typical MS VIs can be found from Table 1 from [132] and Table 2 from [118] 
Hyperspectral (HS) VIs  Using 2 or more HS bands to construct a ratio or normalized difference ratio or other forms of VIs by an arithmetic operation  Typical HS VIs can be found from Table 5.1. Structure (LAI, crown closure, green biomass, species, etc.) from [26] 
Transformed feature  Principal component analysis (PCA)  A linear combination of highdimension raw data to reduce dimensionality and preserve variance contained in raw data as much as possible in the first several PC images. Usually, the 1st several PCs are adopted  [133, 134] 
Minimum noise fraction (MNF)  A linear combination of high dimension raw data to reduce dimensionality and preserve minimal noise or maximal signaltonoise ratio in the first several MNF images. Usually, the 1st several MNFs are adopted  [70, 90] 
Canonical discriminant analysis (CDA)  Search for a linear combination of independent variables to achieve maximum separation of classes (populations). Usually, the 1st 2–5 canonical variables are adopted  [15, 41, 135] 
Wavelet transform (WT)  Decompose spectral signals with scaled and shifted wavelets. The energy feature of decomposition coefficients is computed at each scale and is used to form an energy feature vector that can serve as a feature extraction through a dimension reduction  [75, 76, 136] 
Spatial/textural feature  Textural features  A small area has a wide variation of discrete tonal features, and the dominant property of that area is texture. Texture features are based on 1st and 2ndorder graylevel statistics. The definitions of textural features are slightly different between pixelbased and IObased  Typically, pixelbased 1st order textural features have 5, and 2ndorder textures have 8. This can be found in Table 2 from [137], IObased textural features can be seen in Table 2 from [118]. 
Spatial/geometric feature  A feature describes a shape or geometric form of IO, so a spatial/geometric feature is extracted for IObased analysis only  See definitions of typical spatial/geometric features, extracted from IOs, in Table 1 from [138] 
LiDARderived feature  Vertical/geometric features  Vertical/geometric features related to tree height and tree crown shape, usually extracted from normalized LiDAR data  Typical vertical/geometric features extracted from LiDAR data can be seen in Table 1 from [25] 
Intensity features  The maximum energy of the backscattered echo, representing the reflectivity of every point measured by the laser scanner as an intensity. They are singlechannel and multichannel intensity features derived from LiDAR data  Typical singlechannel/multichannel intensity features derived from LiDAR data can be seen in Tables 2 and 3 from [25] . 
