Conflation of aquatic habitat data for linking stream and landscape features
description
Transcript of Conflation of aquatic habitat data for linking stream and landscape features
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
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mp
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sid
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ann
el/k
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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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eter
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Boulder(Survey)
Boulder(conflated)
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590 246 153 118 92 70 14
Length of Target Reach (m)
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575 319 220 160 126 101 81 62 32
Length of Target Reach (m)
Str
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m G
rad
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t (%
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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 [email protected] 206-860-3428
►Bernard Catalinotto Data Enhancement Services, LLC [email protected] 301-717-1077