Unpublished MaterialLiDAR (Digital Surface Model) Colville NFDigital Image File .IMGAtlanticNone AvailableNone AvailableGeographic Extent: This data set is consisting of LiDAR Point Cloud, Classified LiDAR, Digital Elevation Model, Digital Surface Model, and LiDAR Intensity Images which all pieces encompasses the Colville National Forest project area, approximately 742 square miles. Data set Description: This data set consists of LiDAR point cloud LAS swath files and tiled Las files. Each LAS file contains LiDAR point information, which has been calibrated, controlled, and classified. Each file represents a separate swath of LiDAR.
Ground Conditions: water at normal levels; no unusual inundation; no snow; leaf off
The United States Forest Service (USFS, Government), Colville National Forest (CNF), requested airborne LiDAR services and products for several project areas totalling 742 mi2 throughout the Colville National Forest in NE Washington. The primary goals of this project included providing high accuracy and density (8pls/m2) LiDAR to enhance project planning and implementation, and to provide engineering and timber sale preparation specialists more information for on-the-ground project planning. In addition, these data are used by researchers and scientists to characterize vegetation type and structure as it currently exists on the landscape and to provide a detailed, accurate, and precise benchmark for future change detection work. The data products potentially will also be used for vegetation mapping, hydrologic feature delineation, and landcover characterization applications including a canopy height model, understory vegetation prediction, and other stand metrics. Deliverable products are LAS/ASCII files and DTM/DSMs.All Lidar data was acquired, calibrated, and used in the creation of Lidar derived products by Atlantic.201507080800201507250800ground conditionAs needed-118.512790-117.278985 49.01950248.197070584800
664800
4225400
4141400
NoneElevation DataLidarDigital Surface ModelDSMNoneColvilleWashingtonNo restrictions apply to this data the data represented is the result of data collection and processing per contract specifications and indicates the general existing conditions at the time of the data collection. As such, it is only valid for its intended use, content, time, and accuracy specifications. The user is responsible for the results of any application of the data for other than its intended purpose.
None. However, users should be aware that temporal changes may have occurred since this dataset was collected and that some parts of these data may no longer represent actual surface conditions. Users should not use these data for critical applications without a full awareness of the limitations of the data. Acknowledgement of the United States Forest Service would be appreciated for products derived from these data.The data represented is the result of data collection and processing per contract specifications and indicates the general existing conditions at the time of the data collection. As such, it is only valid for its intended use, content, time, and accuracy specifications. The user is responsible for the results of any application of the data for other than its intended purpose.United States Forest ServiceMark Rileymailing and physical addressPortlandOR97208USA
1220 SW 3rd Ave(503) 808-2989markriley@fs.fed.usMonday through Friday 8:00 AM to 4:00 PM If unable to reach the contact by telephone, please send an email. You should get a response within 24 hours. State NFIP CoordinatorCloudPro 1.2.0 Build 85620; GeoCue Version 2014.1.21.2; Windows 7 Operating System
\\tagwork38\GC_Warehouse_8\80670\46\*.las
1.04 TBMark Riley
United States Forest Service
State NFIP Coordinator
(503) 808-2989
markriley@fs.fed.usNone Available Contact United States Forest ServiceNone Available Contact United States Forest ServiceNone Available Contact United States Forest ServiceDigital Surface Model (DSM) data covers the entire 742 square mile area intended for this project task. All DSM tiles show no edge artifacts or mismatches from tile to tile. Void areas (i.e., areas outside the project boundary but within the tiling scheme) are coded using a unique “NODATA” value. This value is identified in the appropriate location within the file header. Data cover the entire area specified for this project.The raw LAS data files included all data points collected. No points have been removed or excluded. A visual qualitative assessment was performed to ensure data completeness. No void areas or missing data exist. The raw point cloud is of good quality and data passes Fundamental Vertical Accuracy specifications.Void areas (i.e., areas outside the project boundary but within the tiling scheme) are coded using a unique “NODATA” value. This value is identified in the appropriate location within the file header.Vertical Accuracy of the point cloud and bare-earth lidar data was assessed and reported in accordance with the ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014)
One-Hundered and sixteen (116) surveyed points were measured for the purpose of assessing the accuracy of the Colville NF Lidar data. (60 NVA + 56 VVA)15.8 cmThis data set was produced to meet ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014) for a 10.0(cm) RMSEz Vertical Accuracy Class equating to NVA = +/-19.6cm at 95% confidence level and VVA =+/-29.4cm at the 95th percentile.
This data set was tested to meet ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014) for a 10.0(cm) RMSEz Vertical Accuracy Class. Actual NVA accuracy of the point cloud was found to be RMSEz = 8.2 cm, equating to +/-16.0 cm at 95% confidence level. Actual NVA accuracy of the bare-earth was found to be RMSEz = 8.1cm, equating to +/-15.8cm at 95% confidence level. Actual VVA accuracy was found to be +/-14.2cm at the 95th percentile.Horizontal error in lidar derived elevation data is largely a function of positional error as derived from the Global Navigation Satellite System (GNSS), attitude (angular orientation) error (as derived from the INS) and flying altitude; and can be estimated based on these parameters.
14.4 cm.As described in Section 7.5 of the ASPRS Positional Accuracy Standards for Digital Geospatial Data the horizontal errors in lidar data are largely a function of GNSS positional error, INS angular error, and flying altitude. Therefore lidar data collected with GNSS error of 0.05m and the IMU error of 0.03492 degrees at an altitude of 2300m; the expected radial horizontal positional error will be RMSEr = 14.4cm.Unpublished MaterialLiDAR (Digital Surface Model) Colville NFDigital Image File .IMGNone AvailableNone Available None Available0800Atlantic1200Portable Hard Drive201507080800080020150725ground conditionThis data source was used (along with the airborne GPS/IMU Data) to georeferencing of the lidar point cloud data used to create all contracted Lidar deriveted products. Version of the LAS Standard 1.4 / point data record format 6.LiDAR (Digital Surface Model) Colville NFAtlanticAlan Kimbrough, PLSSurvey Managermailing and physical address2223 Drake Ave. SW Suit 200HuntsvilleAL35805United States256-971-9991256-971-1154hakimbrough@theatlgrp.com8:00 - 5:00 Aerial LiDAR Acquisition: Aerial LiDAR was acquired, in sixteen (16) missions, using an ALS070HP SN7225 at an altitude of 2926 MSL to support a 8ppm^2 LiDAR point cloud. Airborne GPS and IMU data was collected during the acquisition and supported by LEICA SR530 GPS stations on NGS monuments K24, G483, and Z264. Data acquisition was completed between 8th of July through 25th of July 2015.
Ground Control Survey: A survey was performed to support the acquisition of LiDAR collection. Ground GPS collection consists of collection NVA and VVA points for LiDAR validation in addition to control point GPS collection for flight support. All field survey observations were conducted between July 5, 2015 through July 25, 2015 using Leica SR530 and Topcon HiperV Dual Frequency GPS equipment, both configured to log data at 1 Hz, and at 10 degrees mask.201507250800AtlanticScott JonesMissinons Operations Managermailing and physical address2223 Drake Ave. SW Suit 200HuntsvilleAL35805United States256-971-9991256-971-1154srjones@theatlgrp.com8:00 - 5:00LiDAR Pre-processing: Airborne GPS and IMU data were merged to develop a Smoothed Best Estimate Trajectory (SBET) of the LiDAR system trajectory for each lift. LiDAR ranging data were initially calibrated using previous best parameters for this instrument and aircraft. Relative calibration was evaluated using advanced plane-matching analysis and parameter corrections derived. This process was repeated interactively until residual errors between overlapping swaths, across all project lifts, was reduced to 2 cm or less. Data were then block adjusted to match surveyed calibration control. Raw data NVA were checked using independently surveyed check points. Swath overage points were identified and tagged within each swath file201507310800AtlanticChris CannonAssociate Vice President Production Managermailing and physical address2223 Drake Ave. SW Suit 200HuntsvilleAL35805United States256-971-9991256-971-1154jccannon@theatlgrp.com8:00 - 5:00Initial processing of the GPS data was processed using Inertial Explorer. The solution file was generated and CloudPro software was used to generate georeferenced laser returns which were then processed in strip form allowing for the QC of the overlap between strips (lines). The data from each line were combined and automated classification routines run to determine the initial surface model. This initial surface model was then verified to the surveyed test points.201508110800AtlanticChris CannonAssociate Vice President Production Managermailing and physical address2223 Drake Ave. SW Suit 200HuntsvilleAL35805United States256-971-9991256-971-1154jccannon@theatlgrp.com8:00 - 5:00LiDAR Post-Processing: The calibrated and controlled LiDAR swaths were processed using automatic point classification routines in TerraSolid software. These routines operate against the entire collection (all swaths, all lifts), eliminating character differences between files. Data were then distributed as virtual tiles to experienced LiDAR analysts for localized automatic classification, manual editing, and peer-based QC checks. Supervisory QC monitoring of work in progress and completed editing ensured consistency of classification character and adherence to project requirements across the entire project. All classification tags are stored in the original swath files. After completion of classification and final QC approval, the NVA and VVA for the project are calculated. Sample areas for each land cover type present in the project were extracted and forwarded to the client, along with the results of the accuracy tests. Upon acceptance, the complete classified LiDAR swath files were delivered to the client.201508160800LiDAR Classification: The calibrated LiDAR data was run through automated classification routines and then manually checked and edited. The data was classified into the following classes:
Class Code:0
Class Item:Never Classified
Class Code:1
Class Item:Undetermined/Unclassified
Class Code:2
Class Item:Bare Earth
Class Code:7
Class Item:Low Noise
Class Code:9
Class Item:Water
Class Code:10
Class Item:Ignored Ground
Class Code:17
Class Item:Bridges
Class Code:18
Class Item:High NoiseAtlanticChris CannonAssociate Vice President Production Managermailing and physical address2223 Drake Ave. SW Suite 200HuntsvilleAL35803United States256-971-9991256-971-1154jccannon@theatlgrp.com8:00 - 5:00201508230800LiDAR Intensity Imagery Creation: All LiDAR Intensity Imagery was created from the final calibrated and classified LiDAR Point Cloud. Intensity Images where produced from all classified points and are posted to a 1 meter cell size. Intensity Images where cut to match the provided United States Forest Service Tile Index and have corresponding names to match tile names.20150823AtlanticChris CannonAssociate Vice President Production Managermailing and physical address2223 Drake Ave. SW Suite 200HuntsvilleAL35805Untied States256-971-9991256-971-1154jccannon@theatlgrp.com8:00 - 5:000800Bare-Earth DEM Creation:Bare-Earth Digital Elevation Models (DEMs) were derived using bare-earth (ground) LiDAR points. All DEMs were created with a grid spacing of 1 meter. The DEMs were cut to 7.5 quads index provided by client.AtlanticChris CannonAssociate Vice President Production Managermailing and physical address2223 Drake Ave. SW Suite 200HuntsvilleAL35805United States256-971-9991256-971-1154jccannon@theatlgrp.com8:00 - 5:00201508250800First Return DSM Creation: First Return Digital Surface Models (DSMs) were derived using first return points from the full point cloud. All DSMs were created with a grid spacing of 0.5 meters. The DSMs were cut to 7.5 quads index provided by client201508250800AtlanticChris CannonAssociate Vice President/ Production Managermailing and physical address2223 Drake Ave. SW Suite 200HuntsvilleAL35805USA256.971.9991256.971.11548:00am to 5:00pm0Required Non-Vegetated Vertical Accuracy (NVA) for the Point Cloud:0.196
Calculated Non-Vegetated Vertical Accuracy (NVA) for the Point Cloud:0.160
Number of Check Points used to calculate the Reported (NVA) for the RAW Point Cloud:60
Calculated (NVA) of the Classified Point Cloud:0.158
Number of Check Points used to calculate the Reported (NVA) for the Classified Point Cloud:60
Required Vegetated Vertical Accuracy (VVA): 0.294
Calculated (VVA) of the Classified Point Cloud:0.142
Number of Check Points used to calculate the Reported (VVA) for the Classified Point Cloud:56RasterPixel2714012893coordinate pair0.0010.001metersAlbers Conical Equal Area43.048.0-120.034.0600000.00.0North American Datum of 1983 Geodetic Reference System 806378137.0298.257222101GCS_North_American_1983R6_AlbersNorth American Vertical Datum of 1988 (Geoid12A)Explicit elevation coordinate included with horizontal coordinates1200metersDigital Surface Model Tiles for Colville NFDigital Surface Model Tiles for Colville NFUnited States Forest ServiceMark Rileymailing and physical address
1220 SW 3rd Ave
PortlandOR97208United States(503) 808-2989markriley@fs.fed.usNFIP Coordinator8:00 am - 5:00 pmNo formal constraints although the present data set does belong to United States Forest Service and more information can be obtained by contacting United States Forest Service at:
Mark Riley
NFIP Coordinator
(503) 808-2989
markriley@fs.fed.usThe data represented is the result of data collection and processing per contract specifications and indicates the general existing conditions at the time of data collection. As such, it is only valid for its intended use, content, time, and accuracy specifications. The user is responsible for the results of any application of the data for other than its intended purpose.No formal constraints although the present data set does belong to United States Forest Service and more information can be obtained by contacting United States Forest Service at:
Mark Riley
NFIP Coordinator
(503) 808-2989
markriley@fs.fed.usContact United States Forest ServiceNo formal constraints although the present data set does belong to United States Forest Service and more information can be obtained by contacting United States Forest Service at:
Mark Riley
NFIP Coordinator
(503) 808-2989
markriley@fs.fed.usContact United States Forest ServiceNo formal constraints although the present data set does belong to United States Forest Service and more information can be obtained by contacting United States Forest Service at:
Mark Riley
NFIP Coordinator
(503) 808-2989
markriley@fs.fed.usNo formal constraints although the present data set does belong to United States Forest Service and more information can be obtained by contacting United States Forest Service at:
Mark Riley
NFIP Coordinator
(503) 808-2989
markriley@fs.fed.us201508312015083120150831United States Forest ServiceMark Rileymailing and physical address
1220 SW 3rd Ave
PortlandOR97208United States(503) 808-2989markriley@fs.fed.usMonday through Friday 8:00 AM to 5:00 PM If unable to reach the contact by telephone, please send an email. You should get a response within 24 hours. NFIP CoordinatorFGDC Content Standards for Digital Geospatial MetadataFGDC-STD-001-1998No formal constraints although the present data set does belong to United States Forest Service and more information can be obtained by contacting United States Forest Service at:
Mark Riley
NFIP Coordinator
(503) 808-2989
markriley@fs.fed.usNo formal constraints although the present data set does belong to United States Forest Service and more information can be obtained by contacting United States Forest Service at:
Mark Riley
NFIP Coordinator
(503) 808-2989
markriley@fs.fed.usNoneUnclassifiedNo formal constraints although the present data set does belong to United States Forest Service and more information can be obtained by contacting United States Forest Service at:
Mark Riley
NFIP Coordinator
(503) 808-2989
markriley@fs.fed.us20150831local time