stream mapping using digital elevation models created by
TRANSCRIPT
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The effect of LiDARdigital elevation modelresolution on stream network prediction andcomputational requirements
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Current Stream Layer Inaccuracy
InaccuracyI
DNR Hydro Layer
LiDAR Hillshade
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ProblemProblem Increased protection afforded to stream
networks No clear information about geographic
extent for streams Data quality does not support the
necessary detail for: • • strategic planning• • operational planning• • etc.
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Stream Definition
All segments of natural waters within the
bankfull widths of defined channels
which are either perennial streams
(waters that do not go dry any time of a
year of normal rainfall) or are physically
connected by an aboveabove--ground channel ground channel
system to downstream waters
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Where Streams Begin
(Tarboton 2003)
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Where Streams Begin
Perennial Stream
Start of perennial flow
Source Area
WAC 222-16-030 and 222-16-031 defines perennial flow for our site at 52 acre source area
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Questions•• Does an increase in resolution improve
stream channel determination?
• Can stream types be determined more accurately using LiDAR datasets?
• Should a new algorithm be developedfor identifying perennial streams?
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Research Topics
Flow Directions Utilized
Effects of Resolution on Flow Direction
Flow Direction Comparison
Perennial Stream Classification
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Flow Direction AlgorithmsBlock Centered
1212 1111 99
99 1010 88
1111 77 5
D8
10
MFD
10
Dinf 304 degrees
10
DEMON 304 degrees
1 0 8
7 6
Edge Centered
1 0 8
7 5
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Topography Sources
The Flow Direction Algorithm is tested on:
–– 2m Pixel Size LiDAR Grid
–– 6m Pixel Size LiDAR Grid
–– 10m Pixel Size LiDAR Grid
––10m USGS DEM Grid
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Site of Interest in Red
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8 o8 on DEM ( m )D8 on DEM (2 m)
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D8 on DEMs ( ( 2m –– ueblue, , m 6m - -- red )
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D8 on DEMs (2m -- blue, 6m -- red, 10m -- green)
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Stream s on the 6m LIDAR DEMStreams on the 6m LiDAR DEM
Orange - D8, Red - Dinf Green - MFD
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Flow Direction Convergence
•• D8 and Demon Converge with increasing resolutions
•• Dinf and MFD do not converge with each
Basin Convergence (ha)Demon vs D8
2-m LiDAR DEM 5.06-m LiDAR DEM 6.8
10-m LiDAR DEM 9.110-m USGS DEM 30.4
other or D8 and Demon
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Perrenial Stream
Classification
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Model DevelopmentPerennial Heads
Field Locate Perennial Initiation Points (PIP)Uses D-8 raster flow direction processesBinomial Linear Regression (50 ft spread)
Algorithm uses:– Basin Size
– Percent Slope– Precipitation
Algorithm could not use:– Downstream Gradient, Site Class, Forest Density
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DNR Hy dro LayrDNR Hydro Layer vvs. …‘Perennial, C lass 1Perrenial, Class 1-44'
750 0 750 1,500 2,250 3,000375ft
Green Points are field checked Perennial Stream Heads
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LiDAR DEM (D8 – 66--m) '‘'Perennial, Class 1-44'
Green Points are field checked Perennial Stream Heads
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Potential errors in calculated stream lengths and buffer areas based on DNR and LiDAR-derived stream data
Dataset Length (km) Buffer Area (ha)*
DNR Hydro Layerbased in part using USGS 7.5'
6868
362
240240
LiDAR Streams (6-m) 8 60860
* Uniform 30-m buffer for both datasets for Perennial flow
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Cha
nge
in d
ista
nce
(ft.)
Stream Networks at different Grid Resolutions (D8)
The change in distance between modeled and field-everified stream head location at a given resolution
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Sttream Networks at various flow direction techniques (6-m)
Chh
ange
in d
ista
nce
(ft)
The range in distance between modeled and field-verified stream head location using different models
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Conclusion
• An increase in resolution improved streamchannel determination
•• Stream types can be determined more accurately using LiDAR datasets
• A new algorithm does not need to be developed for identifying perennial streamsbecause computationally demanding flowalgorithms (DEMON) don't vary fromsimple flow algorithms (D8)
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Questions?Questions?
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Road Influence withIncreased Resolution
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Road Effects -- 2-m Uncorrected
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Road Effects -- 2m LiDAR Corrected
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Road Effects -- 6-m LiDAR
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Fish StreamDetermination
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Model DevelopmentFish Location
Cooperative Monitoring, Evaluation, and
Research Group (CMER)
Determines End of Fish Points (EOFP)
D-8 raster flow direction processes
Algorithm uses:
– Elevation
– Down slope gradient
– Basin Size
– Precipitation
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Model DevelopmentFish Location
Physical Constraints on trout distribution in
the Cascade Mountains (2003)
Determines fish habitat location
Based on physical constraints
Algorithm uses:
– Downstream GradientGrade > 13% = fish barrier (100 meters mean)
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Predicted Fish Habitatrs define in the field Barriers define in the field
2500
2600
2700
2800
2900
3000
3100
3200
3300
3400
3500
0 600 1200 1800 2400 3000 3600 4200 4800 5400 6000 6600 7200 7800 8400 9000Distance (ft)
Ele
vatio
n (ft
) LiDAR Stream
Waterfalls
Gradient ModelFish Barriers
DNR HydroEnd of Fish
CMER ModelEnd of Fish
Culvert (Road)
Waterfalls
Waterfall
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Predicted Fish Habitat GradientBarriers define in the field
Gradient Model Fish Barriers
CMER HydroEnd of Fish
DNR HydroEnd of Fish
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Predicted Fish-Bearing Streamsusing different techniques
Dataset for Fish-Bearing Length (km)
DNR Hydro Layer 18.0
CMER Model 22.4
14.5Gradient Model
•Using the Gradient Model, fish-bearing streams decrease 24% from the DNR Hydro layer.
•The CMER Model potentially overestimates fish-bearing streams by 54% when compared to the Gradient Model.
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Acknowledgement I am grateful to Peter Schiess for giving me the opportunity to take
on this project and truck through it as desired and Finn Krogstadfor his ability to handle a bombardment of questions. Thank you to to David Montgomery and Steven Burges for advising me on the ways of hydrobiology and then some; Luke Rogers and Phil Hurvitzfor early GIS advice; Hans-Erik Anderson for LiDAR pre- processing; Bob McGaughey for various advice; David Tarboton and Theodore Endreny for question regarding resolution andmodeling; and Julie Forcier for grammatical help. Can't forgetCapstone 2005 which is composed of Adam Baines, Lou Beck, Ben
C Carlson, Mark Williams, Sara Wilson, Edwin Wong,and AmyH Hawk for their assistance during field collection. The W Washington State DNR provided the financial support for thiss study and then some more.
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Stats on the PIP ModelStats on the PIP Model
The final Linear Regression model for PIP used fewer variables than expected. The final model selected Basin Size using D8, Percent Slope, and Precipitation. Downstream gradient, forest density, elevation, and site class could not be used to create the equation for determining stream head locations based on a 0.05 significance level. The Hosmer-Lemeshow chi-square statistic for this model was 10.262 and the -2 Log likelihood statistic was 80.130. Self-classification accuracies for this model were 77.4% for perennial flow and 88.7% for non-perennial flow.
For further information email me at [email protected]