1. Counting the trees in the forest
A Case Study in Mixed Maritime Forests at Wormsloe State Historic Site
Nancy K. O’Hare and Dr. Thomas R. Jordan
Department of Geography

2. introduction
Can LiDAR collected for ground elevation be used for other purposes?
Spatial scale of tree rather than stand
Can a GPS coordinate in the field allow identification of actual tree in forest from LiDAR?
Insert a picture of one of the geographic features of your country.

3. Study site Wormsloe State Historic Site
Pine
Mixed Hardwood and Pine

4. Problems for Southeastern forests
Pine Plot Profile
Overlapping tree canopy
Spreading canopy
No true leaf-off
Frequently ~1m between canopy maxima & minima
Hardwood Plot Profile

5. Issues USFS fusion
Fusion Manual Page 29 “…works best for conifer trees that are relatively isolated. In dense stands, trees growing in close proximity to one another cannot be separated.”
Default Search Window Width (m) = ht2 or ~3.2 m for 28 m tall tree
LiDAR from 3 pine plots
White = taller
Green = lower

6. Data sources Ground data collection (Jun 2012)
8 plots (4 pine & 4 hardwood)
GPS plots center
eTrex Vista 1,000+ points
Geo6000 post-processed (±1 to 2 m estimated)
Laser range finder to measure distance (±1 cm)
Digital compass to measure azimuth (±0.1⁰)
All trees DBH > 10 cm by strata: canopy, subcanopy, midcanopy
Geometrically calculate X, Y of each tree
Airborne LiDAR (Dec 09 – Feb 10)
Total point density ~2.9 points per sq ft
Intensity; no spectral
X,Y accuracy < 1.2 m; Z accuracy <18 cm
Laser rangefinder and digital compass mounted on tripod over center point

7. Plot size = 25 m radius around GPS point Ground date = Truth
Data Sources
Plot size = 25 m radius around GPS point
Ground date = Truth
Within each plot, error should be same for ALL tree locations
USFS Fusion 3.30 to detect canopy maxima and list of tree X,Y

8. Results Fusion canopy maxima
Example is Pine plot VP21
Lack of correspondence between Fusion derived canopy maxima and ground data on tree location regardless of search window size

9. Results Fusion canopy maxima
Plot
Ground
Smaller Window
 Default
Larger window
Pine
OTPine 59 48
Pine2 20 62 21 41
Pine3 45 82 32 31
VP21 33 92 46
Total Trees
339
121

10. Results Fusion canopy maxima

11. Results lIdar analyst ArcGIS LiDAR Analyst Limited options
Inverse Watershed Algorithm
Relies upon NON-OVERLAP
Cannot specify search window size
Multiple iterations = all poor results
All returns
Returns above certain height
Only first returns
Only first returns above a certain height

12. Results Ground vs lIdar analyst
Raw Data: Number of Canopy Trees
Summary Numbers
Plot
Ground
LiDAR Analyst
Difference
Pine
OTPine 59 5 54
Pine2 20 6 14
Pine3 45 39
VP21 33 3 30
Mixed Hardwood and Pine
VP10 34 4
VP16 47 43
VP2 46 41
VP3 21 1
VP7 8
VP9 26
Total Trees
339
296
Plot
Ground
LiDAR Analyst
Difference
Total Trees
339 43
296
# of Trees
33.9
4.3
Ave Trees/Plot
Pine
157 20
39.3
5.0
Mixed Hardwood and Pine
182 23
30.3
3.8

13. Summary & recommendations
LiDAR collected for ground elevation (density ~2.9 pts/sq sft)
Neither tree counts OR tree locations in forest were accurate
Inverse watershed segmentation used by Fusion and Lidar Analyst inadequate for unmanaged pine or mixed hardwood forests of study area
Recommendations
Algorithm that detects vertical surface of tree trunk
Spectral
Currently, insufficient accuracy and precision in FREE LiDAR data to count the trees in the forest

14. significance Ablility to count trees in the forest Sensor improvements
LiDAR with spectral info
Airborne LiDAR with high point density/penetration
Terrestrial LiDAR
Software improvements
Improve inverse watershed with spectral
Ability to extract vertical surface
Profile from Airborne LiDAR
Profile from Terrestrial LiDAR
Note: profiles of SAME area