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The Latest In Utilizing
Machine Learning to
Complete Tree Inventories
Josh Behounek
“Solutions Through Innovation and Expertise”
1931
Pen & Paper Computer
1992
GIS-based
2002
Machine learning
2022
Tree Inventory vs Assessment
Assessments
Google Streets
DRG Standard
LiDAR
Current Process
Buffalo, NY Inventory Update
2001 2014 Difference
Sites 124,445 127,080 2,635
Total DBH 871,173” 817,627” -53,546”
Average
DBH
7” 6” -1”
# Species 281 247 -34
# Removals 668 2,707 2,039
# Planting
Sites
48,761 44,619 -4,142
Top 5 Species Still Poor Recommended for Removal
45, 43%
24, 23%
20, 19%
9, 9%
6, 6%
MAPLE, NORWAY
MAPLE, SILVER
LINDEN, LITTLELEAF
HORSECHESTNUT, COMMON
CHERRY, KWANZAN
162 total trees
Condition Change Assessment
2001
Inventory
2014
Inventory
5,698
Poor
3 Dead
145 Poor
11 Fair
0 Good
293 Plant
38,199
Fair
298 Dead
3,445 Poor
26,830 Fair
962 Good
1,365 Plant
25,632
Good
259 Dead
717 Poor
8,952 Fair
9,783 Good
1,878 Plant
3 Types of
Machine Learning &
Tree Inventories
✔ Nothing
✔ Inherited
❏ Current
Machine Learning Process with Google Streets
Geo-localization of tree
canopy (Step 1)
● Aerial imagery is
used to identify
where trees are.
● Canopy pixels are
extracted and
vectorized to define
the boundary called
the tree canopy
zone.
Estimating tree
count (Step 2)
● Within the tree
canopy zone, street
view imagery is used
to find the trees
under street view
Estimating distance
from observer (Step 3)
● A heat map is generated
that defines the distance
of each pixel from the
observer.
● Using this, the average
distance of tree pixels is
calculated within the
bounding box extracted
in step 2
Identifying location of
individual trees (Step 4)
● Observer location and
field of view is projected
in aerial view (the right
angle in blue above)
● Using the distance
calculated in step 3,
individual trees are
placed on aerial image
map (yellow points).
Photo credit - SiteRecon
1. No Idea of Number of Trees to be Inventoried
Scenario 1: No Idea # of Trees – Tucson, AZ
2. Inherited
Inherited - San Diego Results
26% Vacant
Sites
~ 436,770 Total Sites
Updating Missing Trees
Updating Removals
Utilizing Point Data
1. Number of Trees
2. Location of Trees
3. TreeKeeper Software
4. Tree Equity
5. Pruning Cycles
6. Planting Locations
7. Updating
Implementing an
Urban Forestry Mapping &
Monitoring Program
Advantages
Photo credit - greehill
Implementing Tree Monitoring Program
Year 1
Initiate tree monitoring
program
Perform advanced
assessments
Install TreeKeeper 9
Year 2
Implement information
via TreeKeeper 9
Year 3
Perform tree
monitoring data
collection
Perform advanced
assessments of
flagged trees
Perform change
analysis
Update TreeKeeper 9
Year 5
Perform tree
monitoring data
collection
Perform advanced
assessments of
flagged trees
Perform change
analysis
Update TreeKeeper 9
Year 4
Implement information
via TreeKeeper 9
Photo credit - greehill
Initial assessment
greehill drives streets &
parks per contract specs
Data Delivery
Data is delivered into
TreeKeeper 9 with API to
greehill software
Data extraction
Data is processed via
machine learning to provide
information per data specs.
Advanced Assessments
Davey provides Level 2
assessments to flagged trees.
Outlier Trees
Based on results of data,
client goals, & budget a
certain # of trees are
identified for advanced
assessments
Tree Monitoring
Program
Operation workflow
Corridor clearance
Human Thermal Comfort
Machine Learning
Advantages
● Objective
● Repeatable
● Efficient
● Precise
“You do not rise to the level of your
goals, you fall to the level of your
systems.”
Atomic Habits by James Clear
The Latest In Utilizing
Machine Learning to
Complete Tree Inventories
Josh Behounek
573-673-7530
Josh.Behounek@davey.com

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Latest in Machine Learning for Tree Inventories.pdf

  • 1. The Latest In Utilizing Machine Learning to Complete Tree Inventories Josh Behounek
  • 2.
  • 4. 1931 Pen & Paper Computer 1992 GIS-based 2002 Machine learning 2022
  • 5.
  • 6.
  • 7. Tree Inventory vs Assessment Assessments Google Streets DRG Standard LiDAR
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  • 11. Buffalo, NY Inventory Update 2001 2014 Difference Sites 124,445 127,080 2,635 Total DBH 871,173” 817,627” -53,546” Average DBH 7” 6” -1” # Species 281 247 -34 # Removals 668 2,707 2,039 # Planting Sites 48,761 44,619 -4,142
  • 12. Top 5 Species Still Poor Recommended for Removal 45, 43% 24, 23% 20, 19% 9, 9% 6, 6% MAPLE, NORWAY MAPLE, SILVER LINDEN, LITTLELEAF HORSECHESTNUT, COMMON CHERRY, KWANZAN 162 total trees
  • 13. Condition Change Assessment 2001 Inventory 2014 Inventory 5,698 Poor 3 Dead 145 Poor 11 Fair 0 Good 293 Plant 38,199 Fair 298 Dead 3,445 Poor 26,830 Fair 962 Good 1,365 Plant 25,632 Good 259 Dead 717 Poor 8,952 Fair 9,783 Good 1,878 Plant
  • 14. 3 Types of Machine Learning & Tree Inventories ✔ Nothing ✔ Inherited ❏ Current
  • 15. Machine Learning Process with Google Streets Geo-localization of tree canopy (Step 1) ● Aerial imagery is used to identify where trees are. ● Canopy pixels are extracted and vectorized to define the boundary called the tree canopy zone. Estimating tree count (Step 2) ● Within the tree canopy zone, street view imagery is used to find the trees under street view Estimating distance from observer (Step 3) ● A heat map is generated that defines the distance of each pixel from the observer. ● Using this, the average distance of tree pixels is calculated within the bounding box extracted in step 2 Identifying location of individual trees (Step 4) ● Observer location and field of view is projected in aerial view (the right angle in blue above) ● Using the distance calculated in step 3, individual trees are placed on aerial image map (yellow points). Photo credit - SiteRecon
  • 16. 1. No Idea of Number of Trees to be Inventoried
  • 17. Scenario 1: No Idea # of Trees – Tucson, AZ
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  • 23. Inherited - San Diego Results 26% Vacant Sites ~ 436,770 Total Sites
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  • 28. Utilizing Point Data 1. Number of Trees 2. Location of Trees 3. TreeKeeper Software 4. Tree Equity 5. Pruning Cycles 6. Planting Locations 7. Updating
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  • 30. Implementing an Urban Forestry Mapping & Monitoring Program
  • 32. Implementing Tree Monitoring Program Year 1 Initiate tree monitoring program Perform advanced assessments Install TreeKeeper 9 Year 2 Implement information via TreeKeeper 9 Year 3 Perform tree monitoring data collection Perform advanced assessments of flagged trees Perform change analysis Update TreeKeeper 9 Year 5 Perform tree monitoring data collection Perform advanced assessments of flagged trees Perform change analysis Update TreeKeeper 9 Year 4 Implement information via TreeKeeper 9 Photo credit - greehill
  • 33. Initial assessment greehill drives streets & parks per contract specs Data Delivery Data is delivered into TreeKeeper 9 with API to greehill software Data extraction Data is processed via machine learning to provide information per data specs. Advanced Assessments Davey provides Level 2 assessments to flagged trees. Outlier Trees Based on results of data, client goals, & budget a certain # of trees are identified for advanced assessments Tree Monitoring Program Operation workflow
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  • 40. Machine Learning Advantages ● Objective ● Repeatable ● Efficient ● Precise
  • 41. “You do not rise to the level of your goals, you fall to the level of your systems.” Atomic Habits by James Clear
  • 42. The Latest In Utilizing Machine Learning to Complete Tree Inventories Josh Behounek 573-673-7530 Josh.Behounek@davey.com