Using high spatial resolution hyperspectral imagery to map intertidal habitat structure in Hood Canal, Washington, U.S.A

Ralph J. Garono, Charles A. Simenstad, Rob Robinson, Herb Ripley

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

An understanding of the distribution and structure of discontinuous nearshore habitats is critical to effectively manage estuarine resources, especially migratory and mobile nekton that may depend on the integrity of shallow-water environments. We used 19-band compact airborne spectrographic imager (CASI) imagery, collected at 1.5 m spatial resolution, to map two cover classes of eelgrass (Zostera marina) and six other estuarine habitat classes along 64 km of the intertidal shoreline of Hood Canal in the Pacific Northwest, United States. We used control points derived from digital orthoquads (DOQ) and a differential global positioning system (GPS) to geometrically correct CASI flight lines to within 4.3–23.5 m root mean square error (RMSE). After unsupervised and supervised classification, we found good correspondence between classified eelgrass polygons and field training and ground-truthing data. Although this was not the focus of our habitat mapping, the same was true for most of the other habitat classes, except for oyster beds, which were difficult to separate from the wet sand–gravel–cobble class. We are encouraged by the ability of CASI to produce spatially accurate, high-resolution descriptions of intertidal habitat structure. Results from this study will become the foundation of a broader study to develop a quantitative index of essential habitat quality for migrating juvenile summer chum salmon (Oncorhynchus keta) in Hood Canal. When CASI image processing is complete, fisheries scientists–managers will be able to effectively evaluate estuarine landscape patterns at a spatial scale appropriate for migrating juvenile summer chum salmon.

Original languageEnglish (US)
Pages (from-to)54-63
Number of pages10
JournalCanadian Journal of Remote Sensing
Volume30
Issue number1
DOIs
StatePublished - Jan 1 2004

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