Species Discrimination of Mangroves using Derivative Spectral Analysis

Abstract

ABSTRACT:Mangroves are salt tolerant trees or shrubs commonly seen in mudflats of intertidal regions of tropical and subtropical coastlines. Recent advances in field spectroscopic techniques enabled the species level discrimination among closely related vegetation types. In this study we have analysed the laboratory spectroscopy data collected from eight species of Rhizophoraceaea family of mangroves. The spectral data ranges between the wavelength of 350 nm and 2500 nm at a very narrow bandwidth of 1 nm. Preprocessing techniques including smoothing were done on the spectra to remove the noise before compiling it to a spectral library. Derivative analysis of the spectra was done and its corresponding first and second derivatives were obtained. Statistical analysis such as parametric and non-parametric tests were implemented on the original processed spectra as well as their respective first and second order derivatives for the identification of significant bands for species discrimination. Results have shown that red edge region (680 nm -720 nm) and water vapour absorption region around 1150 nm and 1400 nm are optimal as they were consistent in discriminating species in reflectance spectra as well as in its first and second derivative spectra. C. decandra species is found to be discriminable from other species while reflectance and its derivative spectra were used. Non-parametric statistical analysis gave better results than that of parametric statistical analysis especially in SWIR 2 spectral region (1831 nm -2500 nm).

SUnSeT: spectral unmixing of hyperspectral images for phenotyping soybean seed traits

“…This influence has been effectively mitigated by employing derivative spectra (Zhang et al. 2006 ; Prasad and Gnanappazham 2014 ). First-order derivative spectra (dR) reveal the peak characteristics of the spectrum, reflecting waveform changes caused by the absorption of light by chlorophyll and other substances in plants (Becker et al.…”

Identifying Mangrove Species Using Field Close-Range Snapshot Hyperspectral Imaging and Machine-Learning Techniques

“…Derivative spectra are commonly employed in hyperspectral investigations of vegetation [45], which can effectively reduce the influence of illumination variations, and eliminate the background signal and systematic errors. Previous studies have reported that the derivative analysis can further enhance the ability of the spectral data to identify tree species [46], and has been developed for mangrove species classification [47]. First-order derivative spectra can reflect the waveform changes caused by the absorption of the light by chlorophyll and other substances in plants, and reveal the peak characteristics of the spectrum [48].…”