Document Type

Article

Publication Date

12-2015

Publication Title

Remote Sensing

Keywords

Coral reef, Discrete choice model, Multinomial logistic, Remote-sensing data, Landsat TM Imagery, Great Bahama Bank, Logistic-regression, Satellite imagery, Mangrove forests, Bottom-types, Red Sea, Spectral discrimination, Hyperspectral imagery

ISSN

2072-4292

Volume

7

Issue/No.

12

First Page

15894

Last Page

15916

Abstract

As for terrestrial remote sensing, pixel-based classifiers have traditionally been used to map coral reef habitats. For pixel-based classifiers, habitat assignment is based on the spectral or textural properties of each individual pixel in the scene. More recently, however, object-based classifications, those based on information from a set of contiguous pixels with similar properties, have found favor with the reef mapping community and are starting to be extensively deployed. Object-based classifiers have an advantage over pixel-based in that they are less compromised by the inevitable inhomogeneity in per-pixel spectral response caused, primarily, by variations in water depth. One aspect of the object-based classification workflow is the assignment of each image object to a habitat class on the basis of its spectral, textural, or geometric properties. While a skilled image interpreter can achieve this task accurately through manual editing, full or partial automation is desirable for large-scale reef mapping projects of the magnitude which are useful for marine spatial planning. To this end, this paper trials the use of multinomial logistic discrete choice models to classify coral reef habitats identified through object-based segmentation of satellite imagery. Our results suggest that these models can attain assignment accuracies of about 85%, while also reducing the time needed to produce the map, as compared to manual methods. Limitations of this approach include misclassification of image objects at the interface between some habitat types due to the soft gradation in nature between habitats, the robustness of the segmentation algorithm used, and the selection of a strong training dataset. Finally, due to the probabilistic nature of multinomial logistic models, the analyst can estimate a map of uncertainty associated with the habitat classifications. Quantifying uncertainty is important to the end-user when developing marine spatial planning scenarios and populating spatial models from reef habitat maps.

Comments

©2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).

ResearcherID

B-8552-2013

DOI

10.3390/rs71215810

Peer Reviewed

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