Analysis of reflectance spectra of tropical seagrass species and their value for mapping using multispectral satellite images

Wicaksono, Pramaditya and Fauzan, Muhammad Afif and Kumara, Ignatius Salivian Wisnu and Yogyantoro, Rifka Noviaris and Lazuardi, Wahyu and Zhafarina, Zhafirah (2019) Analysis of reflectance spectra of tropical seagrass species and their value for mapping using multispectral satellite images. International Journal of Remote Sensing, 40 (23). 8955 - 8978. ISSN 01431161

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Abstract

Mapping of the distribution of individual seagrass species is essential for any attempts to manage seagrass ecosystems. It is therefore important to understand how the spectra of different seagrass species vary, in order to establish their unique absorption features and how these can be utilised for mapping by making use of remote-sensing images. This paper presents measurements of the reflectance spectra between 400 and 900 nm for nine tropical species of seagrass. Continuum removal and multispectral resampling procedures were applied to the spectra. Dendrogram analysis was carried out to identify species clustering as the basis for a mapping scheme. Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) approaches were employed for the classification of seagrass species using WorldView-2 images and measured spectra as the input endmember. Classification Tree Analysis (CTA) and an image segmentation approach using CTA (Object-Based Image Analysis OBIA) were performed as a means of comparison. The results indicate that the absorption features and overall shape of the spectra for all seagrass species are relatively similar, and implied that the major differences are attributable to the absolute reflectance values. Consequently, SAM and SID produced results of low accuracy (<30), whereas, CTA and OBIA delivered results exhibiting higher accuracy (60-92). The use of a spectral-based classification algorithm was ineffective for the classification and mapping of seagrass species using multispectral images. The utilisation of absolute reflectance values was beneficial for the classification of seagrass species having similar spectral shape. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

Item Type: Article
Additional Information: Cited by: 40
Uncontrolled Keywords: Classification (of information); Image analysis; Image segmentation; Photomapping; Reflection; Remote sensing; Tropics; Absolute reflectance; Classification algorithm; Classification-tree analysis; Multispectral satellite image; Object based image analysis; Remote sensing images; Spectral angle mappers; Spectral information divergences; algorithm; ecosystem management; image analysis; image classification; mapping; remote sensing; satellite imagery; seagrass; Plants (botany)
Subjects: G Geography. Anthropology. Recreation > G Geography (General)
Divisions: Faculty of Geography > Departemen Geografi Lingkungan
Depositing User: Sri JUNANDI
Date Deposited: 05 Aug 2026 06:24
Last Modified: 05 Aug 2026 06:24
URI: https://ir.lib.ugm.ac.id/id/eprint/26929

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