Discrimination of Tropical Mangroves at the Species Level with EO-1 Hyperion Data

Werapong Koedsin,Chaichoke Vaiphasa

doi:10.3390/rs5073562

Werapong Koedsin, Chaichoke Vaiphasa

Open Access

https://doi.org/10.3390/rs5073562

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Abstract

Understanding the dynamics of mangroves at the species level is the key for securing sustainable conservation of mangrove forests around the globe. This study demonstrates the capability of the hyper-dimensional remote sensing data for discriminating diversely-populated tropical mangrove species. It was found that five different tropical mangrove species of Southern Thailand, including Avicennia alba, Avicennia marina, Bruguiera parviflora, Rhizophora apiculata, and Rhizophora mucronata, were correctly classified. The selected data treatment (a well-established spectral band selector) helped improve the overall accuracy from 86% to 92%, despite the remaining confusion between the two members of the Rhizophoraceae family and the pioneer species. It is therefore anticipated that the methodology presented in this study can be used as a practical guideline for detailed mangrove species mapping in other study areas. The next stage of this work will be to exploit the differences between the leaf textures of the two Rhizophoraceae mangroves in order to refine the classification outcome.

Highlights

This study has demonstrated for the first time that the space-borne hyperspectral data with the help of the well-established genetic search algorithm [60,72] is capable of discriminating and mapping diversely populated tropical mangrove species of Southern Thailand
This study is the first to confirm the capability of the hyper-dimensional remote sensing data for discriminating diversely-populated tropical mangrove species
It is found that five different tropical mangrove species of Southern Thailand can be correctly classified

Summary

Introduction

The ecological values of tropical mangroves are recognized in many ways, including: providing carbon sequestration [1,2,3]; reducing shoreline erosion caused by tidal waves, storm surges and tsunamis [1,3,4,5,6,7,8,9,10]; trapping sediments [3,8,9]; acting as biological filters in polluted coastal areas [3,6,8,11]; supporting estuarine food chains [1,3,5,7], and providing habitats for invertebrates and juvenile fish [5,6,8,9]. Mangrove forests around the globe are threatened by the emergence of urban development, the boom in commercial aquaculture and mining, the influence of tidal waves and storm surges, and the various forms of non-renewable exploitation [3,5,6,7,8,12,13,14,15,16]. The conservation of these threatened mangroves becomes a priority for the government and non-government organizations around the world [6,7,17,18,19]. New generation sensors that possess higher spatial and spectral resolutions are needed for a finer level of mangrove studies [17,35,42,43,45,46,47]

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