Ontario’s Current LiDAR Acquisition Initiative Ross Kelly Environmental Management Branch Ministry of Agriculture, Food and Rural Affairs May 3, 2017

Introduction The Ontario Ministry of Agriculture, Food and Rural Affairs (OMAFRA) has funded a two year project (2016-2018) to acquire LiDAR data in targeted areas of Ontario to support soil mapping work and other initiatives. OMAFRA is working closely with MNRF on contracting of LiDAR services, data quality control and delivery. Presentation will focus on project cycle followed, some opportunities, challenges and lessons learned to date.

What is LiDAR?  LiDAR (Light Detection and Ranging) A remote sensing method that uses light in the form of a pulsed laser to measure ranges (variable distances) to the Earth light pulses—combined with other data recorded by the airborne system— generate precise, three-dimensional information about the Earth’s surface characteristics. System typically: laser, a scanner, and specialized GPS receiver

LiDAR Project Lifecycle

Acquisition Plan Consider: End use of data and purpose Specifications Budget and timing Resources – staff expertise Our project: 2 years – 2016-2018 3 planned capture areas – leaf off (fall/spring) 2016 and 2017 Cochrane-Hearst, Peterborough and Lake Erie watershed Total planned acquisition area - 30,000 square kilometers.

Planned LiDAR Acquisition Areas Flown - 2016 Flown - 2017 Hearst Cochrane

End Users and Applications Soil Mapping – detailed topography and slopes Agriculture and Precision Farming Flood Risk Management Infrastructure and Construction Management Land-use planning Water Supply and Quality Forest Resources Management Natural Hazards Monitoring Shoreline/nearshore Request for Bids (RFB) - ready to post. LiDAR - Planned data capture – leaf off: spring 2016 (alternate time window fall 2016) and spring 2017. Soil site sampling - Planned site investigations – summer and fall 2016. Additional site investigations planned - summer and fall 2017.

Specifications USGS Version 1.2 Lidar base specification (ver. 1.2, November 2014) Quality Level O Accuracy of 5 cm root mean square error in z (RMSEz) and density of 8 pulses / m2 Aligns with the American Society for Photogrammetry & Remote Sensing (ASPRS) 5 cm vertical accuracy class USGS minimum classification USGS hydro-flattening spec followed Ontario guideline for LiDAR

Classification USGS Version 1.2 Table 6 USGS Lidar base specification minimum classified point cloud classification scheme: Class 1: Processed, but unclassified. Class 2: Bare earth. Class 7: Low noise. Class 9: Water. Class 10: Ignored ground (near a breakline). Class 17: Bridge decks. Class 18: High noise.

Deliverables Deliverables: Raw Point Cloud Classified Point Cloud (minimum classified scheme) Bare-earth Surface (Raster DTM) Metadata Point Cloud DTM

Quality Control Quality control and review of deliverables is important. Spend time to check deliverables, communicate with vendor on regular basis – weekly reports/calls. It may be difficult to meet all quality level requirements as outlined in USGS specification - no fault of vendor Vegetated vertical accuracy Steep terrain, heavy vegetation and bare-ground flattening Striping and point cloud density Point cloud classification – outliers, noise Flight line side lap

Metadata, Naming, Products and Storage Metadata records on the acquisition are critical. Detail specifications used, dates, flight locations Naming of tiles and acquisition files is important. Consider volumes of data, storage, management and use e.g. licensing, IP. Derivative products to be developed?

Challenges and Lessons Learned Learn as much as you can and plan before starting an acquisition – good LiDAR data vs not so good. Weather: Spring and fall seasons can see variable weather conditions that impact acquisition timelines. Narrow time frames with leaf off conditions for large area acquisition – consider area to be covered. What is leaf off exactly? Tolerance for % leaf cover. Minimal snow cover, drifted snow and spring legacy snow (e.g. municipal snow dumps) can impact acquisition. Meeting quality level specifications can be difficult. Large volumes of data to handle. Is there such a thing as too much detail?

Example: Derived Slope Surface

Next Steps Complete QA/QC on 2016 delivered data Complete in-depth QA/QC on select tiles Establish documentation for data, data storage and public dissemination mid-2017 Spring 2017 acquisition underway Acknowledgements: staff of MNRF, GanRCA, Airborne Imaging

Thanks! Ross Kelly Manager, Resource Information and Business Services OMAFRA 1 Stone Road Guelph Ross.kelly@ontario.ca