1. Los Angeles, LARIAC Meeting
By Steve Snow
Industry Specialist

2. Steve Snow
Introduction

3. Agenda
• Introductions
• Challenges
• Users Success Stories
• New Developments
• Resources

4. Types of LiDAR
• Airborne
- Captured from aircraft
- UAS
• Terrestrial and Mobile
- Captured from the surface
- Backpack

5. Lidar Data
First Return
SecondReturn
ThirdReturn
Last Return

6. Lidar Data
First Return
SecondReturn
ThirdReturn
Last Return
DSM

7. Lidar Data
First Return
SecondReturn
ThirdReturn
Last Return
DSM
DTM

8. Lidar Data
First Return
SecondReturn
ThirdReturn
Last Return
DSM
DTM
Breakline

9. Key Lidar Functionality
Surface analysis
- First-return
- Bare-earth
Feature extraction
- Digitizing
- Point classification
Background
information
- Measuring
- Data validation
Sharing
- Serving
- Zip-and-ship

10. Steve Snow
Challenges

11. Lidar Data Challenges
• Huge Volumes
• Multiple Sources
• Multiple Projections
• Varying Accuracy
• Multiple Representations
How to analyze, manage, access?

12. Traditional Workflows
Project based
• Collection – Project based
• Analysis – For specific project

13. Traditional Workflows
Project based
• Collection – Project based
• Analysis – For specific project
• Archive data

14. Traditional Workflows
Project based
• Collection – Project based
• Analysis – For specific project
• Archive data
• Move on to next project

15. Traditional Workflows
?
Project based
• Collection – Project based
• Analysis – For specific project
• Archive data
• Move on to next project
Data not accessible to
• Multiple applications
• Multiple users
Value is lost

16. User Expectations
• Lidar lives beyond project
• Information has to be
- Simple
- Accessible
- Timely
End Users and Decision Makers
Application and data integration is critical

17. The Lidar Workflow
Collect
and Integrate
Manage
Visualize and
Analyze
Share

18. Steve Snow
North Carolina
Emergency
Management

19. • Modernizing statewide LiDAR-derived topography
• After Hurricane Sandy NCEM were informed by
USGS that Disaster Mitigation Funding would
possibly be used for LiDAR-derived topographic
data acquisition in Sandy impacted areas.
• NCEM –established the intent to acquire new QL2
statewide. GTM / FPMP began working with USGS
and other federal and state agencies.
QL2 Topo – In the Beginning….

20. LiDAR Classification
Un-Classified Data
Ground/Bare Earth
Vegetation
Buildings
Roads/Impervious

21. Emergency Management & Prep
• 100% Statewide Coverage
• 12 million + building footprints collected and QA’d

22. 30 Meter Elevation Model
30 Meter Point
Spacing

23. 10 Meter Elevation Model
10 Meter Point
Spacing

24. 3 Meter Elevation Model (2003 NC LiDAR)
3 Meter Point Spacing

25. QL2 Elevation Model
0.3 Meter Point
Spacing

26. 100YR Flood Elevation = 13 ft.
FFE = 5.2 ft.
Building
Footprints
& Finished
Floor
Elevations

27. 30 Meter Elevation Model
*Virtually no agreement with actual survey elevation data
Actual Survey Elevations

28. Actual Survey Elevations
10 Meter Elevation Model
*Very few points matching actual survey data

29. Actual Survey Elevations
3 Meter LiDAR (2003)
*A more defined surface. Lacks true channel topographic definition.
5 ½ Foot Vertical Error in
Stream Bed Elevation

30. Actual Survey Elevations
NC QL2 LiDAR (2014)
*Nearly mirrors existing high precision survey data.
QL2 Profile

31. Actual Survey Elevations
NC QL2 LiDAR (2014)
*Nearly mirrors existing high precision survey data.
QL2 Profile

32. QL2 Benefits
Vegetation Canopy
Detection/Calculations

33. Vegetation Detection

34. Low – Shrubs

35. Medium – Bushes/small trees

36. High – Large trees

37. Total Canopy Detection

38. 3D Volume of Vegetation

39. QL2 Topo - 3D Volume of Vegetation
What can you do with this data?
• Acreage and volumetric calculations
• Detection of vegetation type
• Deciduous vs Evergreen
• Canopy Height Detection
• Forest Ageing
• Wooded Biomass
• Tree Count Density
• Etc…

40. Link to portal http://Rmp.nc.gov/sdd

41. DOGAMI

42. Emergency management
• Potential tsunami inundation: Bandon, Oregon
Data courtesy of DOGAMI
Oregon Department of Geology
and Mineral Industries (DOGAMI)

43. Lidar Data Dissemination
View and download your source data for further uses
http://www.oregongeology.org/dogamilidarviewer/

44. Oso landslide
Washington state

45. Raster data – visualizing the event

46. Transition slide of 2d -> 3d to get feel for magnitude
of disaster
3D maps help much for communicating this
information
2D pre- and post-event 3D pre- and post-event

47. Esri 3D Webscene- A new way to show size of catastrophe

48. San Diego Airport Obstruction Mapping

50. What’s coming
• Tools for Feature Identification
- Footprint identification
- Tree locations
- 3D Building identification
- Data reviewer checks building and tree overlaps

51. Automated Feature Identification

52. Building and tree overlap introduction

53. Easily classify and extract
features from the LIDAR
point cloud
LP360 has several Point
Cloud task routines for
automatic classification and
feature extraction:
Ground and model keypoint
Building classification and
extraction
Stockpile toes
Vegetation Height filter &
polygons
Esri Partner Solution for Lidar Classification

54. Resources
Training courses
www.esri.com/training
Sharing Cached Imagery in ArcGIS
ArcGIS Resources
esriurl.com/ImageManagement
Image Management Workflows
GeoNet Community
geonet.esri.com/groups/esri-training
Tools for lidar processing
esriurl.com/3DLidarTools
LAS Custom GP Tools for ArcGIS 10.2
esriurl.com/EzLAS
LAS Optimizer (v1.2)

55. Esri UC Imaging and Mapping Forum (IMF)
Formerly Esri 3D Mapping Forum
• weekend before Esri UC (June 25-28, 2016) San Diego, CA
• Saturday is a technical bootcamp
• Sunday is user presentations
• Access to Esri UC Monday and Tuesday
• Find out more http://esri.com/3DForum