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Remote sensing of submerged aquatic vegetation in lower Chesapeake Bay - A comparison of Landsat MSS to TM imageryLandsat MSS and TM imagery, obtained simultaneously over Guinea Marsh, VA, as analyzed and compares for its ability to detect submerged aquatic vegetation (SAV). An unsupervised clustering algorithm was applied to each image, where the input classification parameters are defined as functions of apparent sensor noise. Class confidence and accuracy were computed for all water areas by comparing the classified images, pixel-by-pixel, to rasterized SAV distributions derived from color aerial photography. To illustrate the effect of water depth on classification error, areas of depth greater than 1.9 m were masked, and class confidence and accuracy recalculated. A single-scattering radiative-transfer model is used to illustrate how percent canopy cover and water depth affect the volume reflectance from a water column containing SAV. For a submerged canopy that is morphologically and optically similar to Zostera marina inhabiting Lower Chesapeake Bay, dense canopies may be isolated by masking optically deep water. For less dense canopies, the effect of increasing water depth is to increase the apparent percent crown cover, which may result in classification error.
Document ID
19870059268
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
Authors
Ackleson, S. G.
(Bigelow Laboratory for Ocean Science Boothbay Harbor, ME, United States)
Klemas, V.
(Delaware, University Newark, United States)
Date Acquired
August 13, 2013
Publication Date
July 1, 1987
Publication Information
Publication: Remote Sensing of Environment
Volume: 22
ISSN: 0034-4257
Subject Category
Earth Resources And Remote Sensing
Accession Number
87A46542
Funding Number(s)
CONTRACT_GRANT: NAS5-27580
CONTRACT_GRANT: NAGW-374
Distribution Limits
Public
Copyright
Other

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