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 a nominal ground pixel resolution of 0.20 m. The data collected aerially also were compared to
 ground survey data obtained during the summer of 1997 within one week of the aerial survey.
 We selected 160 reference stations within one of two habitat classes: vegetated and bare
 substrate, situated below the level of the supra-intertidal salt marsh. At each station a 1 m1 area
 was surveyed for the presence/absence of each SAV taxa.  A detailed description of the ground
 survey procedure is presented elsewhere (Young etal., 1998).

 SAV Classification
                               Traditional Photo Interpretation
 A traditional photo interpretation procedure was utilized to detect SAV presence in both CIR and
 TC aerial photographs.  Specifically, each of the 160 reference stations were viewed electronically
 at a scale of 1:500 using ArcView (Version 3.0a) by three of the authors (Young,  Specht, and
 Robbins) who independently determined the presence or absence of SAV from the remote images.

                         Algorithm Development and Image Analysis
 The digital SAV classification of both the CIR and TC orthorectified aerial photographs was
 conducted using a mosaic of 12 digital images within ERMapper (Version 5.5). The development
 of an algorithm to classify SAV from CIR utilized a large (-100 ha) intertidal area with known
 distributions of SAV. Our algorithm, which proved to be highly accurate, had minimal edge
 effects among the mosaic's orthophotographic boundaries, and is similar to a combination of an
 Infrared Percentage Vegetation Index (IPVI) and an Atmospherically Resistant Vegetation Index
 (ARVI) (see a description of thes>e indices in Ray, 1994).  The resulting classified image was
 exported into Arclnfo (Version 7.1.2) Geographic Information System (GIS) and converted into a
 raster grid at the original scale of 0.2 m pixel size.  The grid then was converted into a vector
 polygon coverage and, using an overlay technique in Arclnfo, was compared to a point coverage
 of ground survey data. In the overlay process, a polygon classified as SAV was considered to be
 classified correctly if it included, or was within 1.28 m of, a ground station location representing
 SAV.  The 1.28 m distance function represents the combined spatial accuracy of the  point and
 polygon coverages (RMSE of 0.78 m) plus the nominal distance from the center to the edge of a
 1 m2ground station area (0.5 m).

 The application of our algorithm to TC digital orthophotography was less successful, as were our
 attempts to develop a new algorithm. We didn't succeed because the spectral characteristics of
 macroalgae in the visual range are shared with those of open water, and because the spectral
 characteristics of seagrasses in the visual range are shared with those of bare substrate,
 particularly sandy sediment.

                                  Data Analysis
 To compare the results of our several classifications of SAV versus bare substrate in  the study
 area (Figure 1), we used the error matrix approach described by Congalton (1991). In this
 approach, the percentage of stations with a given classification obtained via the remote sensing
 approach that agree with the reference (ground survey) results is termed Users Accuracy.
 Alternatively, the percentage of stations with a given classification obtained from the ground
survey that are correctly classified by the remote sensing is termed Producers Accuracy. A third
commonly used category, termed Overall Accuracy, also is included.

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Results and Discussion
Our results did not yield a significant difference (p > 0.05; two-tail Student's t-test) between the
CIR and TC Overall Accuracy values (Table 1). Thus the use of either CIR or TC for the
detection and quantification of SAV and bare substrate can be recommended. However, equally
clear is the qualitative indication that the CIR approach is more useful when distributions of
exposed SAV within intertidal flats are digitally classified. Specifically, a visual comparison of
orthorectified aerial photographs of the study area using both CIR and TC film suggests that the
digital classification of the TC film over-represents SAV distributions (Figure 2).  Thus, it appears
that the algorithm we developed for the classification of the CIR images was inadequate when
applied to the TC images. We therefore conclude that our digital classification obtained for
Yaquina Bay using the CIR film is preferable to that obtained from the TC film.

 Acknowledgments
The ground reference survey was conducted by employees of DynCorp/TAI, Inc. stationed  at
Newport,  OR and EPA scientists and staff of the Western Ecology Division's Coastal Ecology
Branch. The aerial photography was conducted by Bergman Photographic, Inc. (Portland, OR);
the orthorectified digital photographs were produced by Photogrammetric Digital Services,  Inc.
(Eugene, OR). Mention of trade names or commercial products does not constitute endorsement
or recommendation for use. This paper has been subjected to EPA's peer review and
administrative review process and has been approved for publication as an EPA document.
References
Congalton, R.G., (1991) A review of assessing the accuracy of classifications of remotely sensed
       data. Remote Sensing of the Environment. 37:35-46.

Dobson, I.E., Bright, E.A., Ferguson, R.L;, Field, D.W., Wood, L.L., Haddad, K.D., Iredale HI,
       H., Jensen, J.R., Kelmas, R.J., Orth, R.J., and Thomas, J.P. (1995) NOAA Coastal
       Change Analysis Program (C-CAP): Guidance for Regional Implementation. NOAA
       Technical Report NMFS 123. U.S. Department of Commerce.

Ray, T.W. (1998) Vegetation in Remote Sensing FAQs (Version 1.0). ERMapper Applications,
       pp. 99-124, San Diego, CA.

Young, D.R, Specht, D.T., Clinton, P.J., and Lee II, H. (1998) Use of color infrared aerial
       photography to map distributions of eelgrass and green macroalgae in a non-urbanized
       estuary of the Pacific Northwest U.S.A.  Proceedings of the Fifth International Conference
       on Remote Sensing for Marine and Coastal Environments, Vol. II, pp. 37-45, ERIM
       International, Inc.,  Ann Arbor, MI.

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Table 1: Accuracy assessment error matrices (%) of SAV versus bare substrate classification by traditional photointerpretation and
digital classification (Congalton, 1991).

Accuracy
Users -CIR
Producers - CIR
Overall - CIR
Users -TC
Producers - TC
Overall -TC
Mean Overall -CIR
Mean Overall -TC
Young
SAV
89.7
78.9
Bare
68.2
83.3
80.5
82.8
90.2
76.8
63.2
81.1
Specht
SAV
85.2
82.0
Bare
68.8
73.6.
79.0
84.1
87.2
73.0
67.7
80.6
Robbins
SAV
87.5
84.2
Bare
72.7
77.8
81.9
78.3
84.2
63.8
54.4
74.1
80.5
78.6
Digital
SAV
92.4
72.9
Bare
64.0
88.9
78.5
77.2
71.4
513
58.8
67.2



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                          Central
                         Yaquina
                          Estuary
Figure 1: Site map showing location of Yaquina Bay on the west coast of Oregon.

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       c.
                         s/  >
D.
                  k. '•    »
                  V '   •;./
figure 2: A comparison of orthorectified aerial photographs using CIR (A) and TC (B) and their

issociated digital classifications (C and D).

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