Journal of Remote Sensing / 2021 / Article / Tab 4

Review Article

Mapping Tree Species Using Advanced Remote Sensing Technologies: A State-of-the-Art Review and Perspective

Table 4

Summary of characteristics of different types of features, extracted from optical and LiDAR sensors’ data and frequently used for tree species mapping.

Feature typeFeature (group) nameCharacteristic and descriptionSource/reference

Spectral bandSpectral bandMulti-/hyperspectral bands in digital number or reflectanceDirectly selected from optical sensors’ images
Spectral vegetation index (VI)Multispectral (MS) VIsUsing 2 or more MS bands to construct a ratio or normalized difference ratio, or other forms of VIs by an arithmetic operationTypical MS VIs can be found from Table 1 from [132] and Table 2 from [118]
Hyperspectral (HS) VIsUsing 2 or more HS bands to construct a ratio or normalized difference ratio or other forms of VIs by an arithmetic operationTypical HS VIs can be found from Table 5.1. Structure (LAI, crown closure, green biomass, species, etc.) from [26]
Transformed featurePrincipal component analysis (PCA)A linear combination of high-dimension 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 signal-to-noise 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 featureTextural featuresA 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 2nd-order gray-level statistics. The definitions of textural features are slightly different between pixel-based and IO-basedTypically, pixel-based 1st order textural features have 5, and 2nd-order textures have 8. This can be found in Table 2 from [137], IO-based textural features can be seen in Table 2 from [118].
Spatial/geometric featureA feature describes a shape or geometric form of IO, so a spatial/geometric feature is extracted for IO-based analysis onlySee definitions of typical spatial/geometric features, extracted from IOs, in Table 1 from [138]
LiDAR-derived featureVertical/geometric featuresVertical/geometric features related to tree height and tree crown shape, usually extracted from normalized LiDAR dataTypical vertical/geometric features extracted from LiDAR data can be seen in Table 1 from [25]
Intensity featuresThe maximum energy of the backscattered echo, representing the reflectivity of every point measured by the laser scanner as an intensity. They are single-channel and multichannel intensity features derived from LiDAR dataTypical single-channel/multichannel intensity features derived from LiDAR data can be seen in Tables 2 and 3 from [25] .