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.
Highlights
Coral reefs provide important ecosystem services to many coastal communities and small island developing states located in tropical and sub-tropical climes [1,2,3]
This paper explores the use of multinomial logistic discrete choice models to classify submerged coral reef features identified through object-based segmentation of satellite imagery
This study has shown that some manual editing of key classes might still be desirable, application of a multinomial logistic model to assign the image-objects created by eCognition into habitats can rapidly yield accurate reef maps
Summary
Coral reefs provide important ecosystem services to many coastal communities and small island developing states located in tropical and sub-tropical climes [1,2,3]. The development of coral reef habitat maps is a fundamental component to marine spatial planning and modeling efforts [4,5,6,7,8,9,10,11,12,13]. Habitat maps provide an inventory of habitat types, from which one can understand the range of living marine resources that exist across a coral reef seascape and their location relative to human activities and anthropogenic stressors [14] as well as the evolution of the seascape through time [15,16,17,18]. Coral reefs encompass large, interconnected landscape-level areas, which must be considered in their entirety when developing marine spatial management plans. Large, landscape-level habitat maps must be developed at appropriate scales to properly manage these systems [20,21]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
