*2.6. Spectral Analysis*

To verify that UAV-HS datasets can differentiate vegetation at the species level in salt marshes, the separability of the spectral signatures of the classifications must be tested. This was analysed through spectral transformations. In addition, the usefulness of new spectral indices for the separation of species is explored, using the relevant wavelengths highlighted by the spectral transformations to generate them.

#### 2.6.1. Continuum Removal and Second-Order Derivative

The differences in absorption and reflection spectra between vegetation species can be very small, making classification difficult. Spectral transformation, such as continuum removal (CR) and derivative spectroscopy, have the potential to amplify small differences [28,42,72]. CR is used to normalize the spectra, and sometimes this is enough to highlight differences in absorption and reflection spectra [73]. The second-order derivative method (2nd derivative from now) emphasises the small differences in absorption peaks associated with biochemical properties, allowing for the identification of different species [28,74]. To enhance the signal-to-noise ratio and extract additional hidden spectral features, the 2nd derivative spectrum is filtered using a boxcar average smoothing. All transformed spectra are analysed in four separated wavelength windows: visible region (VIS, 400–700 nm), near-infrared region (NIR, 700–1000 nm), and two regions of short-infrared (SWIR1, 1000–1800 nm; SWIR2, 1800–2350 nm).
