Fischer - Importance of Quality Control in Using LiDar
0 likes541 views
This document discusses the importance of quality assurance (QA) in light detection and ranging (LiDAR) data collection and applications within watershed districts. It provides examples of how Houston Engineering, Inc. conducted QA on a 45,000 square mile LiDAR collection in the Red River Basin, including checkpoint surveys, visual assessments, and preliminary results. It also outlines several potential uses of LiDAR data within watershed districts, such as hydraulic and hydrologic modeling, permitting, flood control, and more.
![Questions? Presented by: Brian Fischer, CFM GIS Project Manager Houston Engineering, Inc. [email_address] 762-493-4522 June 4, 2009](https://image.slidesharecdn.com/fischer-importanceofqualitycontrolinusinglidar-090618162308-phpapp02/85/Fischer-Importance-of-Quality-Control-in-Using-LiDar-34-320.jpg)
Fischer - Importance of Quality Control in Using LiDar
- 1. Importance of QA in LiDAR Collects and Applications within Watershed Districts Presented to: North Dakota State Water Commission Bismarck, ND Presented by: Brian Fischer, CFM GIS Project Manager Jerry Bents, PE Vice President Mark Deutschman, PE, PhD Vice President June 4, 2009
- 2. Quality Assurance HEI under contract with International Water Institute to QA 45,000 sq. mi. collect in Red River Basin Includes all or Parts of 50 Counties Goal to provide an independent 3 rd party assessment and documentation on the quality of data
- 3. LiDAR Products (Hallock, MN) Bridge RAW Bare Earth Aerial Photo Hybrid Image
- 4. Hybrid Image
- 5. Acceptance Criteria Files are complete for delivery block Raw LAS, Bare Earth LAS, 1m GRID, Hybrid Metadata, Completion Report, Survey Report Root Mean Square Error (RMSE (z) ) of +/- 15 cm No major anomalies or data voids in visual assessment
- 6. Assessment Methodology Systematic check to ensure files are complete (Chain of Custody Form) Comparison between checkpoints and LIDAR product to calculate RMSE (z) Visual Assessment Data Voids Appropriate Classification Anomalies (spikes, penetration problems, etc.)
- 7. Quality Assurance (Checkpoint Survey) Checkpoints (approx. 1500) Minimum of 20 survey check points in dominant land cover class Landuse computed by Block Checkpoints collected in 5 land classes Low grass/bare earth High grass/weeds/crops Brush/low trees Forested Urban
- 8. Quality Assurance (Visual Assessment) Anomalies, Systematic Errors, Human Errors Steps in flight lines, data voids, poor penetration, aggressive classification, misclassification
- 9. Quality Assurance (Visual Assessment)
- 10. Quality Assurance (Visual Assessment)
- 15. Importance of Quality Assurance Gives more confidence in the investment of the data Documents quality of data Serves as an acceptance mechanism for the contract History has shown LiDAR collects need validation IWI has rejected Block A and B for things found in the visual assessment (steps, data voids and missing files)
- 16. Uses of LiDAR within Watershed Districts Preliminary Engineering Design and Planning Hydraulic Modeling Hydrologic Modeling Permit Reviews Risk Assessment Many Others
- 17. Flood Control Impoundment Site Identification County Ditch 18 – Impoundment Example
- 18. Flood Control Impoundment Site Identification County Ditch 18 – Impoundment Example
- 19. Flood Control Impoundment Site Identification Pool at 1192.0
- 20. Flood Control Impoundment Site Identification County Ditch 18 – Impoundment Example Difference between Quad and LiDAR Data Less actual storage at each elevation 2–2.5ft high pool levels and embankment heights required to achieve same storage Approximately 50% increase in required land
- 21. Hydraulic Modeling South Branch of the Wild Rice River – June 2000
- 22. Hydrologic Modeling Drainage Area Delineation Flow Length Time of Concentration Floodplain Storage Volumes Impoundment Site Volumes Wetland Storage Volumes Land Slope
- 25. Watershed Permit Reviews Existing Flow Patterns Assessment Area Contributing Area Slopes
- 26. Floodplain and flood insurance mapping DFIRM – Digital Flood Insurance Rate Map Production Hydraulic Modeling
- 27. Risk Assessment Dam Breach Analysis at Rockwell Dam Storage Volume Inundation Area Topography/Cross-sections Watershed Delineation
- 28. Drainage Complaint Disputes Evidence of historic drainage patterns Complaint of Tree Removal and Filling in Natural Drain
- 29. Diversion Channel Routing Optional Alignment Analysis Quantity Estimates Cost Estimating R/W Requirements Hydraulic Analysis
- 30. Farmstead Ring Dike Design Alignment Quantities Cost Estimates Mitigation Requirements Permitting
- 31. Off-site Wetland Delineation/Restoration Estimates USFWS – Restorable Wetlands NWI – National Wetland Inventory Contours Restoration Opportunities Mitigation Requirements Restoration Opportunities Potential Mitigation
- 32. Lake Level Management Home Lake – Norman County Water Levels from Runoff Potential Outlet Modifications Permitting
- 33. Watershed Applications Opportunities are endless Daily use since collection No more taping maps quad maps together
- 34. Questions? Presented by: Brian Fischer, CFM GIS Project Manager Houston Engineering, Inc. [email_address] 762-493-4522 June 4, 2009