An indirect assessment of thematic accuracy in the geologic habitat maps of Oregon and Washington
Chris Romsos, Chris Goldfinger, Rondi Robison, and Jason Chaytor
Active Tectonics and Seafloor Mapping Lab, College of Oceanic and
and Atmospheric Sciences, Oregon State University, Corvallis, OR, 97331
What is Thematic Accuracy
and how is it commonly assessed?
Introduction: The surficial geologic habitat maps for Oregon and Washington are thematic maps, they show the distribution of benthic habitat classes over the continental margin of Oregon and Washington.
Traditionally, map accuracy has simply described positional accuracy (e.g. elevations given are + or – 10 meters). A thematic map of marine geologic habitat, the product of an interpretative process, introduces
another type of accuracy termed “Thematic Accuracy”. Thematic Accuracy deals with the misidentification or omission of a habitat class. An assessment of thematic accuracy for the geologic habitat maps is needed
as the maps are implemented in high level decision making processes and modeling efforts. To address this problem a map set of weighted data-density is produced. The map set serves as an estimate of thematic
accuracy based on the assumption that data rich areas yield the highest quality interpretations of habitat classes. The map set portrays, for each of the underlying data types, a continuous “density” surface weighted
according to the unique qualities of the dataset. “Unique” qualities relate to our assessment of the utility of a particular data type for the strict purpose of interpreting the physiographic and lithologic character of
mapped habitats. A final composite weighted density surface or “shadow map” serves as a visual guide among data rich and data poor regions and also as a surrogate to an interpretive quality assessment or
calibration study.
Methods: The weighted data-density
mapping method evaluates the “quality” of
each data type independently on a scale of
one to ten, then in aggregate (final
composite map). Quality ranks for each
data type are determined according to the
nature and shape of density distributions
and to our interpretation as to their utility.
That is, each data type is standardized to a
qualitative assessment of its value for
habitat mapping. This standard ranking
procedure allows combination of disparate
data types in the final assessment of
overall “quality”. Providing a ranked
density map for each data type also allows
map users to reclassify or reassemble final
maps based upon alternate criteria (e.g.
selecting the maximum quality score at any
location instead of the sum of scores).
Results: The weighted data-density maps were specifically designed to be incorporated into the Bayesian Network modeling of Essential Fish Habitat (EFH) by MRAG
Americas, contractors to NOAA Fisheries and the Pacific Fisheries Management Council. There are five individual shadow maps of data density and quality permitting
explorations of dependency at model nodes. The first four maps are each unique to a particular data type or survey technique (bathymetric, samples, seismic
reflection, and sidescan data types). The fifth map is a composite of the principle four created by summing quality scores at each grid cell. The raster data format
permits easy spatial queries. Also provided are the raw data distribution maps used to create the weighted density surfaces (not shown). In this format, the
deliverable product is not a dead-end product. It remains possible to view, re-order, or re-render any map according to the needs of the research question at hand.
Figure 5. Composite map showing the additive weighted datadensity value at each grid cell among all datasets
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Table 1. Data Weighting Schemes:
Soundings Weighted Data-Density
soundings per 100m grid cell
0
1
2–5
5–60
>60
Samples Weighted Data-Density
Data
All Sediment Samples (buffered @ 500m radius)
Seismic Weighted Data-Density
Data
USGS, Corliss Cruise (Twichell, 1998)
MCAR (McCrory, 1998)
OSU (Goldfinger, 1997)
Industry Dataset 1
Industry Dataset 2
Industry Dataset 3 (unpublished)
USGS, Boomer
UW (Palmer, 1998)
Dgicon (Goldfinger, 1992)
Sonne (Flueh, 1996)
Industry Dataset 4
Silver (Silver, 1972)
UW TT79
USGS Open File Report 87-607 (Snavely, 87-607)
Quality/Rank
1
2
3
5
10
Quality/Rank
10
Quality/Rank
10
10
10
10
5
5
5
5
5
5
1
1
1
1
Sidescan Sonar Weighted Data-Density
Survey
Gloria EEZ Survey
High Resolution Deep-Tow Surveys
High Resolution Nearshore Surveys
References:
Quality/Rank
1
10
10
Figure 1. Weighted Sounding Density
Figure 2. Weighted Sample Density
Figure 3. Weighted Seismic Density
Figure 4. Weighted Sidescan Density
Total number of soundings within each
100m gridcell is determined using an
extension of MB system. The resultant
grid is reclassified according to the
scheme at left (Table1.) This scheme
emphasizes the lower portion of the
density range, where small increases in
density correspond to great increases in
bathymetric quality.
Sample points are buffered at a 500m
radius using the Geo-Processing tools of
ArcGIS creating a polygon dataset. The
polygon buffers are each assigned a rank
of 10 (we assume that all sample data is
excellent data, however, its utility
degrades rapidly away from the point).
The polygon dataset is converted to a
100m raster in the final step.
Quality of seismic data for mapping
habitat and predicting rock outcrop
varies greatly among the individual
datasets (Table 1.), a result of varying
acoustic frequencies and varying
technologies employed to collect this
data. Processing steps are similar to
those presented in Figure 2. All seismic
survey lines are buffered at 500m.
Sidescan sonar data is high quality data
with the lone exception of the lowfrequency Gloria EEZ survey. Low
frequency systems are not typically used
to infer surficial geology. All sidescan
datasets are ranked according to the
scheme in Table 1. Cell size of the final
raster is 100m, though the resolution of
sidescan data is typically much higher.
Conclusions/Implications:
• This assessment of thematic accuracy is based solely on the quantity and quality of the input data. Thematic accuracy should be assessed
using traditional methods (reference datasets) as they become available.
• These maps make prioritized continued acoustic and in-situ sampling programs possible, ensuring that additional data be collected over areas
poorly covered at present.
• Crist, P., and R. Deitner. Assessing Land Cover Map Accuracy. Version 2.0.0 (16 February 2000). A handbook for conducting Gap Analysis. Internet WWW page, at URL:
http://www.gap.uidaho.edu/LandCoverAssessment/default.htm
• Romsos, C., Goldfinger, C., Chaytor, J., Mapping Data Density and Quality on the Oregon and Washington Continental Margin, Technical Memorandum, 2003.
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