Developing a spectral library of mangrove species of Indian east coast using field spectroscopy

Abstract

Development of a spectral library is a prerequisite for the higher order classification of satellite data and hyperspectral image analysis to map any ecosystem with rich diversity. In this study, sampling methodology, collection of field and laboratory spectral signatures and post processing methodologies were investigated for developing an exclusive spectral library of mangrove species using hyperspectral spectroscopic techniques. Canopy level field spectra and leaf level laboratory spectra were collected for 34 species (25 true and 9 associated mangroves) from two different mangrove ecosystems of the Indian east coast.Post processing steps such as removal of water vapour absorption bands, correction of drifts which occur due to the thermal properties of the instrument during data collection and smoothing of spectra for its further utilization were applied on collected spectra. The processed spectra were then compiled as spectral library.

Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning

“…Therefore, it is recommended that spectral smoothing be performed in order to produce a spectral signal that represents the original spectra without the interference of noise [44]. The Savitzky-Golay filter [45] is a common smoothing technique used in hyperspectral remote sensing [43,46,47]. Savitzky-Golay is based on least-squares approximation, which determines smoothing coefficients by applying a polynomial equation of a given degree and cluster size [45].…”

“…The Savitzky-Golay filter has become a popular algorithm for smoothing spectroscopic data (for example, see [43,47,60]). In this study, the filter proved adept at smoothing the hyperspectral signature without significantly altering the originality of the input data.…”

“…The filter is ideal for spectroscopic data as it minimises signal noise whilst preserving the originality and shape of the input spectra. A second order polynomial filter with a filter size of 15 was applied to the spectral samples prior to classification, following the recommendations of [47]. The Savitzky-Golay filter was applied using the 'signal' package [48] in the R statistical software environment [49].…”

Mapping Mangrove Species Using Hyperspectral Data: A Case Study of Pichavaram Mangrove Ecosystem, Tamil Nadu

“…Further, Ramachandran et al (1998) initiated a study to discriminate mangrove species using spectro-radiometer and found similar radiance patterns in mangrove species with different intensity levels. As advancement, Prasad et al (2015) developed spectral library of mangrove species using field and lab spectroscopy. Similar to the studies of Ramachandran et al (1998), Kathiresan (2010) and Prasad et al (2015) in the current study, we tried to map mangrove species of Pichavaram using hyperspectral satellite data and literature-derived species spectrum as ground truth information.…”

“…As advancement, Prasad et al (2015) developed spectral library of mangrove species using field and lab spectroscopy. Similar to the studies of Ramachandran et al (1998), Kathiresan (2010) and Prasad et al (2015) in the current study, we tried to map mangrove species of Pichavaram using hyperspectral satellite data and literature-derived species spectrum as ground truth information. The study is first of its kind to map mangrove species of Pichavaram ecosystems without field survey.…”