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A Comparison of Digital Elevation Models to Accurately Predict Stream Channels Spencer Trowbridge Papillion Creek Watershed.

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Presentation on theme: "A Comparison of Digital Elevation Models to Accurately Predict Stream Channels Spencer Trowbridge Papillion Creek Watershed."— Presentation transcript:

1 A Comparison of Digital Elevation Models to Accurately Predict Stream Channels Spencer Trowbridge Papillion Creek Watershed

2 Presentation Outline Research Objectives Methodology Results Summary

3 Research Objective: Determine the resolution of a digital elevation model that best predicts stream channel locations Hypothesis: A finer resolution DEM does not affect its ability to accurately predict a stream channel’s location

4 Significance of Research Offers a new way to evaluate a DEM’s ability to extract stream channels LiDAR Dataset has not been evaluated for this application

5 Determination of Error Distance of derived streams to traced banks was classified as error

6 National Elevation Dataset (NED) Based on USGS topographic maps Continually updated with most recent and accurate methods available – April 2014 using LiDAR data

7 Shuttle Radar Topography Mission (SRTM) Worldwide coverage from 2000 A variety of resolutions available (90 meters for this study)

8 Light Detection and Ranging (LiDAR) Data supplied as raw LAS files High precision elevation data captured and stored as point Flown in 2010 – Same time as aerial photographs used for tracing stream channels

9 Study Area Hilly topography Urbanized areas with channelized streams

10 Named Streams Big Papillion Creek South Papillion Creek West Papillion Creek Little Papillion Creek Papillion Creek Thomas Creek Cole Creek Northwest Branch Walnut Creek Southwest Branch Richter Branch North Branch Mud Creek Leach Branch Hell Creek Falls Branch East Fork Copper Creek Butter Flat Creek Boxelder Creek Boston Branch Big Elk Creek

11 Methodology Data Gathering SRTM, NED, NHD, LiDAR, Orthophotographs LiDAR DEM Creation Stream Extraction ArcGIS Hydrology tools: Fill Flow Direction Flow Accumulation Stream to Feature Stream Tracing Digital Shoreline Analysis System (DSAS) Baseline Drawing, Transect Casting

12 Raster Data NED DEMSRTM DEM 1:800,000 Clipped Data Mosaic of two rasters

13 Raster Data Resolution Differences NED 30 meters per pixelSRTM 90 meters per pixel 1:25,000 Channels clearly visible

14 Stream Locations NED 30 meters per pixelSRTM 90 meters per pixel 1:25,000 Big Papillion Creek Little Papillion Creek Big Papillion Creek Little Papillion Creek 1:25,000

15 Visual Comparison of Stream Locations 1:25,000 Big Papillion Creek Little Papillion Creek

16 LiDAR DEM Creation Suggested workflow for raster creation from LiDAR data according to ESRI help pages. 1. Create LAS Dataset to hold the data 2. Point File 3. LAS to Multipoint 4. Create Terrain 5. Terrain to Raster

17 LiDAR Point File Tool Determination of average point spacing - Should be available in the metadata of the LAS files. If missing, run Point File tool Result: 4.34 meters 1:50,000 LiDAR DEM 4.34 meter cell size LAS to Multipoint tool asks for average point spacing

18 LAS to Multipoint Creates large feature class with elevation point data

19 Create Terrain Tool Creates triangulated surface from points from LAS to Multipoint 1:1,000 1:15,000

20 Terrain to Raster 1:22, meter cell size SRTM NED 90 meters 30 meters Final LiDAR Raster

21 Stream Extraction ArcGIS hydrology tools used Fill Flow Direction Flow Accumulation Raster Calculator Stream to Feature Creates vectors connecting cells from the flow accumulation tool

22 Stream to Feature LiDAR Stream to Feature after Raster Calculator tool 1:300,000 Lower order streams eventually edited out manually 1:12,000

23 NHD Vector Data NHD stream vectors downloaded and used as verification 1:250,000 Papillion Creek Watershed after removal of unneeded streams 1:365,000

24 SRTM DEM 1:250,001:40,000

25 NED DEM 1:40,000

26 LiDAR DEM 1:40,000

27 LiDAR NED SRTM NHD All Streams 1:300,000

28 DSAS ArcGIS extension for collecting shoreline distance measurements Manually drawn baselines Casting transects Raw data collection Quality control

29 Manually Drawn Baselines Baselines follow the general direction of the stream channel

30 Casting Transects Transects cast orthogonally from the baseline Extend far enough to Intersect all extracted streams

31 Stream Tracing Once transects are cast, one knows where they will intersect with the traced stream banks Stream and transect intersection point

32 Stream Tracing Generalized Stream Tracing Precise Stream Tracing at transect crossing Traced streams edited where they intersect transects Transect Stream Bank

33 Raw Data Collection Using the appropriate streams as input, DSAS automatically calculates intersection distances

34 Results Error Distance Calculations Correct Stream Placement Determination Distribution Analysis of Datasets: Normality for ANOVA ANOVA Student-Newman-Keuls Test Results

35 Determination of Error Distance of derived streams to traced banks was classified as error. These values were manually calculated within a spreadsheet and used for analysis.

36 Determination if Streams Predicted Accurately Within a spreadsheet, samples determined if predicted accurately (within the traced stream) If a sample was found to be within the traced banks, a distance value of zero was assigned

37 Box-Cox Transformation LiDAR NED NHD SRTM Bimodal Distribution Normal Distributions

38 ANOVA

39 Student-Newman-Keuls

40 Summary Resolution of DEMs affects their ability to delineate stream channels The finest resolution DEM is not the best at delineating stream channels

41 Conclusions A method was created to evaluate a DEM LiDAR data can be processed (and manipulated) in a variety of ways It may not be necessary to have high resolution LiDAR data depending on the application LiDAR is not meant for huge areas such as this watershed

42 1:40,000 LiDAR NED SRTM NHD Questions All Streams 1:300,000


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