HR: 1400h
AN: OS23B-13    [Abstracts]
TI: Application of color infrared aerial photography to assess macroalgal distribution in an eutrophic estuary, Upper Newport Bay, California
AU: Stein, E D
EM: erics@sccwrp.org
AF: Southern California Coastal Water Research Project, 3535 Harbor Blvd., Suite 110, Costa Mesa, CA 92626, United States
AU: * Nezlin, N P
EM: nikolayn@sccwrp.org
AF: Southern California Coastal Water Research Project, 3535 Harbor Blvd., Suite 110, Costa Mesa, CA 92626, United States
AU: Kamer, K
EM: kkamer@mlml.calstate.edu
AF: Moss Landing Marine Laboratories, 8272 Moss Landing Road, Moss Landing, CA 95039, United States
AB: Newport Bay is a large estuary in southern California that is subject to anthropogenic nutrient loading, eutrophication and hypoxia. Traditional ground-based methods of assessing algal extent for monitoring and management are limited in that they cannot provide a synoptic view of algal distribution over comparatively large areas. The goal of this study was to explore the application of color infrared aerial photography as an alternative for analyzing the changes in the abundance of macroalgae. Three surveys combining remote sensing (false-color infrared aerial photography) and traditional (ground-based quadrats) sampling methods to quantify macroalgal mat coverage were carried out in Upper Newport Bay (UNB) between July and October 2005. Airborne photographs (scale 1:6000) collected during daytime low tides, clear skies and appropriate sun angle were orthorectified, georegistered and combined into three mosaic composite images, one for each survey. During each aerial photography survey, macroalgal percent cover was measured on the ground at ~30 locations randomly scattered throughout the intertidal mudflat area; these ground data were used for calibration of classification schemes developed for each of the composite images. Using a cluster-analysis classification method, ground samples from each survey were classified into three or four classes, based on similarity of their optical signatures. Before classification, each digital image was transformed by the Minimum Noise Fraction Rotation method to remove noise and enhance contrast between the classes. For classification, the Spectral Angle Mapper scheme was used. All pixels in the images were attributed to classes and the areal extent of each class was estimated. For each class, the averaged percent cover by different substrates was estimated from ground sample data. The total coverage by different substrates was calculated by multiplying the within-cluster percent coverage by cluster areas. This analysis showed that remote sensing is an accurate, effective tool for assessing estuarine, intertidal macroalgal coverage. The macroalgal coverage in UNB increased from 50% in July to 75% in September to 84% in October, and during this time Ulva spp. replaced Ceramium spp. as the dominant alga.
DE: 4235 Estuarine processes (0442)
DE: 4275 Remote sensing and electromagnetic processes (0689, 2487, 3285, 4455, 6934)
DE: 4834 Hypoxic environments (0404, 4802)
SC: Ocean Sciences [OS]
MN: 2007 Joint Assembly