Hyperspectral Shallow-Water Remote Sensing with an Enhanced Benthic Classifier

Rodrigo Garcia,Zhongping Lee,Eric Hochberg

doi:10.3390/rs10010147

Rodrigo Garcia, Zhongping Lee + Show 1 more

Open Access

https://doi.org/10.3390/rs10010147

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Abstract

Hyperspectral remote sensing inversion models utilize spectral information over optically shallow waters to retrieve optical properties of the water column, bottom depth and reflectance, with the latter used in benthic classification. Accuracy of these retrievals is dependent on the spectral endmember(s) used to model the bottom reflectance during the inversion. Without prior knowledge of these endmember(s) current approaches must iterate through a list of endmember—a computationally demanding task. To address this, a novel lookup table classification approach termed HOPE-LUT was developed for selecting the likely benthic endmembers of any hyperspectral image pixel. HOPE-LUT classifies a pixel as sand, mixture or non-sand, then the latter two are resolved into the three most likely classes. Optimization subsequently selects the class (out of the three) that generated the best fit to the remote sensing reflectance. For a coral reef case, modeling results indicate very high benthic classification accuracy (>90%) for depths less than 4 m of common coral reef benthos. These accuracies decrease substantially with increasing depth due to the loss of bottom information, especially the spectral signatures. We applied this technique to hyperspectral airborne imagery of Heron Reef, Great Barrier Reef and generated benthic habitat maps with higher classification accuracy compared to standard inversion models.

Highlights

Coral reef and other coastal ecosystems provide high socio-economic benefits, they continue to decline due to global impacts such as heightened thermal stress and ocean acidification, as well as from local human induced impacts of over-fishing, destructive fishing, coastal development, influx of pollution, and spread of marine diseases and invasive species [1,2,3]
A primary goal for remote sensing in coral reefs where the water-leaving signal is dominated by the bottom, is the derivation of bottom reflectance that can be subsequently used in benthic classification
We present a four-stage workflow (Section 2.1) for the preselection of the benthic endmembers that are input to a shallow water inversion model for the retrieval of inherent optical properties (IOPs) and depth

Summary

Introduction

Coral reef and other coastal ecosystems provide high socio-economic benefits, they continue to decline due to global impacts such as heightened thermal stress and ocean acidification, as well as from local human induced impacts of over-fishing, destructive fishing, coastal development, influx of pollution, and spread of marine diseases and invasive species [1,2,3]. The benthic signal is reduced by the depth and the absorptive and scattering properties of the overlying water column that synergistically determine the above-water remote sensing reflectance, Rrs [8]—an apparent optical property that can be derived from passive remote sensing platforms. Under such optically complex conditions, approaches that can differentiate the impact of the water column from that of the bottom reflectance solely from Rrs are desired. Research into the benthic classification from multispectral imagery has indicated that higher classification accuracies are obtained from approaches that remove the influences of the water column [9,10]

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