2 foot contours (LiDAR-derived)
|Feature Dataset Name||Stand-Alone|
|Originator||City of Portland|
|Bureau||Bureau of Planning and Sustainability|
|Abstract|| 2' contours derived from 2004, 2005 and 2007 LiDAR data. All contours derived from bare-earth LiDAR point returns. The bare earth returns are a representation of the Earth's surface where all man-made structures and vegetation have been removed. |
|Purpose|| For topographic analysis, surface modeling and mapping. |
|Supplemental Information|| source Information |
2004 LiDAR: Collected by TerraPoint. Covered the extent of the Tryon Creek watershed. Sampling density of 1 point per square meter. Full metadata: http://pugetsoundlidar.ess.washington.edu/lidardata/metadata/pslc2004portland/portland04_all_ascii.h...
2005 LiDAR: Collected by TerraPoint. Flight included Portland's West Hills and the Columbia Slough watershed. Sampling density of 1 point per square meter. Full metadata: http://pugetsoundlidar.ess.washington.edu/lidardata/metadata/pslc2005Columbia/columbia05_dem.htm
2007 LiDAR: Collected by Watershed Sciences. Covered all areas of the Portland Metropolitan region not included in the 2004 and 2005 flights. Sampling density of 8 points per square meter. More information: http://www.oregongeology.org/sub/projects/olc/#what
vertical datum info
Vertical datum: NAVD88 (Geoid 03)
Vertical units: U.S. Survey Feet
|Last Dataset Update||10/07/2014 11:42|
|Last Source Update||12/06/2013 23:46|
|Extent||Clackamas County, Multnomah County, and Washington County, Oregon|
|Shape Type||Poly Line|
|Horizontal Position Accuracy|| 2004/2005 data |
Vertical accuracy of bare-earth LiDAR data is typically 15 to 20cm (~6 inches). Bare-earth vertical accuracy typically decreases as vegetation becomes more dense, the terrain becomes steeper, or impermeable objects (such as buildings) prevent returns from reaching the surface.
A study of the vertical accuracy of the Puget Sound LiDAR Consortium data was performed in May of 2006. This study found an average maximum vertical error of approximately 40cm, with a RMS error of 16.71cm. The complete study can be found online at:
Puget Sound Lidar Consortium evaluates vertical accuracy with two measures: internal consistency and conformance with independent ground control points.
Internal Consistency: Data are split into swaths (separate flightlines), a separate surface is constructed for each flightline, and where surfaces overlap one is subtracted from another. Where both surfaces are planar, this produces a robust measure of the repeatability, or internal consistency, of the survey. The average error calculated by this means, robustly determined from a very large sample, should be a lower bound on the true error of the survey as it doesn't include errors deriving from a number of sources including: 1) inaccurately located base station(s), 2) long-period GPS error, 3) errors in classification of points as ground and not-ground (post-processing), 4) some errors related to interpolation from scattered points to a continous surface (surface generation).
Conformance with independent ground control points: Bare-earth surface models are compared to independently-surveyed ground control points (GCPs) where such GCPs are available. The purpose of the ground control evaluation is to assess that the bare earth DEMs meet the vertical accuracy specification in the PSLC contract with TerraPoint:
"The accuracy specification in the contract between the Puget Sound LiDAR Consortium and TerraPoint is based on a required Root Mean Square Error (RMSE) 'Bare Earth' vertical accuracy of 30 cm for flat areas in the complete data set. This is the required result if all data points in flat areas were evaluated. Because only a small sample of points is evaluated, the required RMSE for the sample set is adjusted downward per the following equation from the FEMA LiDAR specification (adjusted from the 15 cm RMSE in the FEMA specification to 30 cm to accommodate the dense vegetation cover in the Pacific Northwest)."
During this step, the bare earth DEMs were compared with existing survey benchmarks. The differences between the LiDAR bare earth DEMs and the survey points are calculated and the final results are first summarized in a graph that illustrates how the dataset behaves as whole. The graph illustrates how close the DEM elevation values were to the ground control points. The individual results were aggregated and used in the RMSE calculations. The results of the RMSE calculations are the measure that makes the data acceptable for this particular specification in the contract.
Real-time kinematic (RTK) surveys were conducted in multiple locations throughout the study area by the vendor (Watershed Sciences) for quality assurance purposes. The accuracy of the LiDAR data is described as standard deviations of divergence (sigma ~ s) from RTK ground survey points and root mean square error (RMSE) which considers bias (upward or downward). These statistics are calculated cumulatively. For the 2007 study areas, the data have the following accuracy statistics:
No systematic quality checks have yet been performed by the City of Portland.
|Horizontal Position Accuracy Link|
|North Bounding Coordinate||769436.08984375|
|South Bounding Coordinate||494198.99041748|
|East Bounding Coordinate||7785995.15039062|
|West Bounding Coordinate||7497257.08013916|
|Theme Keyword(s)||lidar, elevation, dem, topography, bare earth, bathymetry|
|Theme Keyword Thesaurus|
|Place Keyword(s)||Portland, Multnomah County, Washington County, Clackamas County, Oregon|
|Place Keyword Thesaurus|
|Access Constraints||Available for Public Use|
|Use Constraints|| These data are distributed under the terms of the City of Portland Data Distribution Policy. Care was taken in the creation of this data but it is provided "as is". The City of Portland cannot accept any responsibility for error, omissions, or positional accuracy. . |
|Source Dataset Type||File Geodatabase|
|Distribution|| Grid: State plane coordinate system 1983(91). |
Units: International feet
Datum: North American Datum of 1983/1991 (HPGN)
Liability: The information in this file was derived from digital databases on the City of Portland GIS. Care was taken in the creation of this file. The City cannot accept any responsibility for errors, omissions, or positional accuracy. There are no warranties, expressed or implied.
Format: Shapefile or File Geodatabase Feature Class - Data will be provided via City of Portland FTP Site.
Online Resource: http://portlandmaps.com/opendata
Online Instructions: City of Portland Public/Open Data is distributed via the PortlandMaps Open Data Site - http://portlandmaps.com/opendata. Data not available on the PortlandMaps Open Data site can be requested by contacting the City of Portland CGIS Group - firstname.lastname@example.org
Transfer Size: varies
SPCS Zone Identifier: 5076
| File(s) |
| Contours were derived from multiple LiDAR survey sources using the sametools, but slightly differing methodologies. Watershed Sciences cannot guarantee the accuracy of third party data orthe contours derived from it. |
TerraSolid software tools, operating in the Microstation v.8.01 environment were used for the derivation of 2-foot contour lines. Contours were developed from WatershedSciences 2007 LiDAR survey data by first minimizing contour sinuosity. This was done by smoothing points based on a20 foot search radius and +/- 0.40 foot elevation bounds, followed by athinning operation based on +/- 0.20 foot elevation bounds. Contour confidence information was generatedthrough assessing groundclassified point density. Declaration of “low confidence” and “high confidence”in ground-classified point resolution is based on a threshold value of 0.02points/ft2 (0.22 points/m2). This threshold value is derived from aliterature survey indicating that one point per 4 square meters is adequate forthe description of ground surfaces. Smoothing and thinning operations are responsive to topographic change,maintaining point density in areas of elevation change. Ground-classified point density rasters(3-foot cell size; 6-foot sampling radius) were created, converted to vectorformat, and intersected with the contour lines. Contours with high confidence are coded in the “Confid” field with “1”while contours with low confidence are attributed with “0”. Low ground-classified point densitiesgenerally occur where surface features cannot be penetrated by laser pulses(e.g. dense vegetation canopies, beneath structures such as buildings, bridges,elevated roadways) and over still water.
For LiDAR datasets originating prior to 2007, no thinningor smoothing operations were carried out on the data for contourproduction. These operations are basedon prior knowledge of accuracy statistics and distribution of the nativedatasets in order to make reasonable and supportable decisions regardingthinning parameters. Other LiDAR datasources (prior to 2007 survey data) used in deriving contours within the studyarea were collected at lower pulse rates (a function of the state of thetechnology at the time of acquisition) and hence may not have sufficient groundpoint density to support 2-foot contour resolution. Additionally, from the perspective ofWatershed Sciences, without a record of processing steps and accuracy reports,it is necessary to remain conservative regarding vertical accuracy in thedatasets used. As such, contours derivedfrom pre-2007 data are effectively coded as “low confidence” (the attributefield does not appear in these shapefile datasets).
LiDAR derived contour data should not be used for designand construction. Detailed site-specificground level survey data is required to obtain the precision and confidencenecessary for design and construction projects, regardless of the LiDAR datasource.
|Column Sort||Column Name||Column Alias||Column Type||Column Size||Domain Value(s)||Column Description||1||ELEVATION||ELEVATION||Numeric||8||Elevation in US Survey feet||1||Project||Project||String||50||Specific LiDAR data collection project (and year) that is the source of the contour||1||Subset||Subset||Integer||2||Allows 10', 20', 50' and 100' contours to be mapped seperately or extracted from the dataset (for cartographic purposes)|