Ontario’s Current LiDAR
Acquisition Initiative
Ross Kelly
Environmental Management Branch
Ministry of Agriculture, Food and Rural Affairs
May 3, 2017
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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.
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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
•
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LiDAR Project Lifecycle
act Data Life Cycle
Guidelines and
specification,
procurement, flight
mission planning, etc.
Plan
Data integrity (Quality
Control (QC) / Quality
Assurance (QA)).
Acquire
Flight mission and
data acquisition.
Assure
Documentation,
metadata (split off),
user licensing (IP).
Process
Extract, Transform, &
Load (ETL)
Describe
Storage, back-up,
refresh.
Analyze
Classification,
derivative
development (e.g.,
slope, biomass, etc).
Preserve
Share
Data distribution,
cloud computing, web
map services.
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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.
5
Planned LiDAR Acquisition Areas
Flown - 2016
Flown - 2017
•
Hearst
Cochran
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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
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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
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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.
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Deliverables
• Deliverables:
– Raw Point Cloud
– Classified Point Cloud (minimum classified scheme)
– Bare-earth Surface (Raster DTM)
– Metadata
Point Cloud
DTM
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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
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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?
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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?
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Example: Derived Slope Surface
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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
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Thanks!
Ross Kelly
Manager, Resource Information and
Business Services
OMAFRA
1 Stone Road Guelph
[email protected]
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