Conflation of aquatic habitat data for linking stream and

landscape features

Mindi Sheer, NOAA fisheries – Northwest Fisheries Science Center, SeattleBernard Catalinotto – DES, Maryland

What is “GIS Data Conflation?”

Combining attributes and arcs, polygons, or points of two GIS files to create a third, best-case data set.

The first dataset is the “source” The second dataset is the “target” The combination of source + target is the “result”

SOURCE

-GOOD

ATTRIBUTES

TARGETGOODLINEWORK

Conflation

RESULTBEST ATTRIBUTES & LINEWORK

►Automatically match corresponding arc nodes►Automatically match corresponding arcs within user-defined distance►Check and fix errors

Conflation software requires three major steps:

TARGET

SOURCE

ObjectivesObjectives►GIS data conflation

How conflation is applied to hydrographic datasets

►Watershed case study Use of conflation Habitat study results

►Benefits and “caveats” of conflating►Recommendations

GIS Data Conflation - ExampleGIS Data Conflation - Example►US Census Bureau:

Realigning 50 million TIGER file road & hydro arcs, 3200 counties

Target – 1:6,000 & 1:2,000 Target – 1:6,000 & 1:2,000 (photogrammetry(photogrammetry))

Source – 1:100,000 Source – 1:100,000 DIME (1970)DIME (1970)

Why conflate streams?

► Highly variable spatial representation of stream features ► Limitations in positional accuracy, density, and sinuousity of

100k streams, can result in inaccurate results

Multiple methods & sources of stream hydrography

Stream Length

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MF Will Mckenzie Hood River Clackamas Sandy S.Santiam N.Santiam

Molalla

Stream Scale / Watershed

Kilo

met

ers

(100

0 km

2)

100k streams

Stream density

Stream sinuousity

Project Background► The challenge: 1. Stream hydrography & land

cover to correlate landscape & fine-scale stream morphology

2. Validation of DEM-based modeled stream

► Sources: Oregon Dept. of Fish and

Wildlife Surveys (1:100,000)

DEM hydro (1:24,000)

TARGET: DEM-derived 24k reach-segmented streams

►SOURCE: Oregon Department of Fish and Wildlife (ODFW) segmented field data

►All source (survey data) successfully transferred ►Target DEM reaches were subdivided to reflect

relative arc length of the habitat unit►Small amount of stretching of arcs at the unit

scale

Conflation Results

Also…

►10% of the data had “0” arc lengths (dyn segmentation)

►“0” length channels were secondary channels to the main stream (important as salmon rearing habitat)

Channel Complexity

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Individual Survey Streams, S. Santiam

Co

mp

lexi

ty (

sid

ech

ann

el/k

m)

New

Survey

New Update

Habitat Results

► Length differences (+ 9%): 1639 km (New) 1507 km (Survey)

85% of conflated stream units +/- 10 m

New lengths matched calibration info

0-5 m

-5-0 m

Difference in conflated length (m)

Cou

nt

(# a

rcs)

Watershed scale habitat variables

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8

Watershed

Bou

lder

s / m

eter

(#)

Boulder(Survey)

Boulder(conflated)

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590 246 153 118 92 70 14

Length of Target Reach (m)

Str

ea

m G

rad

ien

t (%

)

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6

8

575 319 220 160 126 101 81 62 32

Length of Target Reach (m)

Str

ea

m G

rad

ien

t (%

)

30 per. Mov. Avg.(Net Slope)

30 per. Mov. Avg.(Field Slope)

Model Validation - Gradient

Field slope

Model slope

Molalla

North Santiam

Conclusions► Benefits

►Provides substantial benefits to ecological studies►Allows automated and manual processing►Data was validated effectively►Results had higher confidence than if conflation had not been used

► Costs►Conflation was performed at low cost for major project (80,000

features)

► Recommendations►Recommend researchers consider using conflation on their multi-scale

projects

Feel free to contact Us….

►Mindi Sheer NOAA Mindi.Sheer@noaa.gov 206-860-3428

►Bernard Catalinotto Data Enhancement Services, LLC bcatalinotto@gisdes.com 301-717-1077