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 nkohare@uga.edu

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.

Study site Wormsloe State Historic Site Pine Mixed Hardwood and Pine

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

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) = 2.51503 + 0.00901 ht2 or ~3.2 m for 28 m tall tree LiDAR from 3 pine plots White = taller Green = lower

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

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

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

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

Results Fusion canopy maxima

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

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

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

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