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

Presentation Outline Research Objectives Methodology Results Summary

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

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

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

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

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

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

Study Area Hilly topography Urbanized areas with channelized streams

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

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

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

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

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

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

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

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

LAS to Multipoint Creates large feature class with elevation point data

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

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

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

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

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

SRTM DEM 1:250,001:40,000

NED DEM 1:40,000

LiDAR DEM 1:40,000

LiDAR NED SRTM NHD All Streams 1:300,000

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

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

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

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

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

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

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

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.

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

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

ANOVA

Student-Newman-Keuls

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

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

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