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Using Machine Learning for
Tree Inventories
Josh Behounek
“Solutions Through Innovation and Expertise”
1931
Pen & Paper Computer
1992
GIS-based
2002
Machine learning
2022
LEVEL ONE
LEVEL TWO
LEVEL THREE
RESEARCH
Assessments
Machine Learning
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
Current Process
San Diego Results
26% Vacant
Sites
~ 436,770 Total Sites
Machine Learning
Advantages
● Objective
● Repeatable
● Efficient
● Precise
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
Data extraction
Data is processed via
machine learning to provide
information per data specs.
Advanced Assessments
Davey provides Level 2 or 3
assessments to flagged trees.
Flagging Trees
Based on results of data,
client goals, & budget a
certain # of trees are
identified for advanced
assessments
Tree Monitoring
Program
Operation workflow
Photo credit - greehill
Using Machine Learning for
Tree Inventories
Josh Behounek
573-673-7530
Josh.Behounek@davey.com
A Scalable Remote Sensing Model for Urban
Forests
From airborne missions to satellites
Jonathan Pando Ocón, UCLA
E. Natasha Stavros, CU Boulder
Thomas W. Gillespie, UCLA
Justin Robertson, LA County
Steven J. Steinberg, LA County
Image: Louis Reed, Unsplash
Urban Los Angeles County
Marina del Rey
East Los Angeles
Altadena
Project Stakeholders
Image: Ev Milee, Unsplash
To ensure our project goals are met, we sought input from stakeholders to identify the
needs and priorities of those that will be using our product in day-to-day operations:
Stakeholder Advisory Group
End users and department managers
Local advocacy and conservation groups
Stakeholder Needs
Image: Henry Perks, Unsplash
 Individual tree species identification
 Canopy cover metrics
 Health assessment
Remote Sensing of Urban Environments
Depending on the mode of image
capture, the spatial and spectral
resolutions of your chosen dataset can
capture a high amount of noise:
High species richness
High density of (in)organic
materials
Mixed-pixels
Existing Approaches to Unmixing Pixels
Spectral Mixture Analysis
Multiple Endmember Spectral Mixture Analysis (MESMA) is used to account for
subpixel spectral mixing
 Computationally expensive
 Complex workflow
Success Stories
Able to untangle spectral confusion relatively well in less complex scenes.
Image: Johannes Mandel, Unsplash
Missing the Mark
Prone to misclassifications in complex urban environments.
Image: Denys Novazhai, Unsplash
Science-led Resources
Science-led projects can deploy the state-of-the-art in remote sensing image analysis using
complicated workflows, computationally expensive models, and often a number of technically savvy
individuals to inform the model.
Uneven distribution of resources
Public and governmental stakeholders may or may not have this.
Stakeholder-led Resources
Adhering to our stakeholder needs informs the research design immensely.
Considerations for processing capacity, and technical know-how are necessary to ensure end-users
can continue to use and update the product.
1. Who will run the model next year? The year after?
2. Are additional image acquisitions needed?
3. What is the best product format for day-to-day operations?
Probabilistic Urban Forest Inventories
High Confidence
Ficus rubiginosa
Low
Confidence
Uncertai
n
No Presence
Condition Assessment
Health
y
Ficus rubiginosa
Needs
Attention
Uncertai
n
Thank you!
Jonathan P. Ocón
Ph.D. Candidate, UCLA
Contact:
jonocon[at]ucla.edu
Image: Louis Reed, Unsplash
Partners in Community Forestry Conference
Nov 16th, 2022
Urban Tree Water Use and Implications for
Stormwater Management
Sarah Ponte, Nancy Sonti, A. Christopher Oishi,
Dexter Locke, Tuana Phillips, and Mitchell Pavao-
Urban development alters the natural hydrologic
cycle
Askarizadeh et al., 2015,
Environ. Sci. Technol.
The role of urban trees on urban hydrology
Image Credit: Jeffrey Milstein
Trees in urban areas vary by
Ecohydrological Landscape
Characteristics (ELCs) (Blood and Day)
Transpiration Rates and Whole-Tree Water Use
by Deciduous Species
Objective:
To quantify the use of trees to
meet stormwater management
requirements.
Single trees over turfgrass
Cluster of trees over turfgrass
Closed canopy
Baltimore City
Montgomery County
Single trees Cluster of trees Closed
Sap flux is a proxy for transpiration rates
Granier-type thermal dissipation sap flux sensors (built by the UMD
Project Development Center)
Different management contexts have significantly
different daily sap flux (Js) distribution
Repeated
measures ANOVA
p < 0.0001
Ponte et al., 2021
Scientific Reports
Single red
maple trees
had nearly
three times the
daily sum of
sap flux density
(Js) of closed
canopy trees
Sap
Flux
Water use among these common, diffuse-
porous, deciduous, eastern species is similar
Sap
Flux
Species differences in sap flux density were
observed at the 24h time-scale
July 19, 2019 – high soil moisture
Aug 30, 2019 – drought conditions
Tulip poplar was the
most sensitive to
drought
Whole-tree water use estimates based on a
predictive model
0
50
100
150
200
250
300
0 10 20 30 40 50 60
L
day
-1
tree
-1
DBH (cm)
A. rubrum - Single
L. styraciflua - Cluster
A. rubrum - Cluster
A. rubrum - Closed Canopy
L. tulipifera - Closed Canopy
L. styraciflua - Closed Canopy
0
100
200
300
400
500
mm
H
2
O Cumulative Plot-Level Transpiration -
900 m2 plots
Single Cluster Closed Canopy
2019
Single: 9% of precipitation
Cluster: 71% of precipitation
Closed Canopy: 52% of precipitation
• Tree water use depends on
management context
• Water use among these diffuse-
porous, deciduous, eastern
species is similar; however,…
• …they do exhibit differences in
response to drought
Main takeaways
This study provides a
foundational framework for
estimating how a proposed
urban tree/forest project
would affect the hydrologic
balance with useful
implications for stormwater
managers
e. sponte@umd.edu
@sarahpcabral
i-Tree is a
Cooperative Initiative
among these partners
Tree Equity:
How i-Tree is Helping
“…enough trees in specific
neighborhoods or
municipalities for everyone
to experience the health,
economic and climate
benefits that trees provide.”
Landscape.itreetools.org
i-Tree is a
Cooperative Initiative
among these partners
Tree Equity:
How i-Tree is Helping
“…enough trees in specific
neighborhoods or
municipalities for everyone
to experience the health,
economic and climate
benefits that trees provide.”
Landscape.itreetools.org
“…a map of tree cover in
America’s cities is too often
a map of income and race.”
i-Tree is a
Cooperative Initiative
among these partners
Tree equity is hard work
• Skeptical
communities
• Overcoming
negative
opinions
• Dealing with tree
mortality
i-Tree is a
Cooperative Initiative
among these partners
How can i-Tree help?
Identify – Find where trees can do
the most good.
Engage – Communicate the
benefits of trees.
Account – Ensure delivery of tree
benefits.
i-Tree is a
Cooperative Initiative
among these partners
 Free software
 Estimate the
benefits of trees
 Based on US
Forest Service
science
 Technical support
i-Tree is a
Cooperative
Initiative
www.itreetools.org
i-Tree: Putting Urban Forest Science into the
Hands of Users
www.itreetools.org
i-Tree is a
Cooperative Initiative
among these partners
The i-Tree Framework
Structur
e
Value
Function
i-Tree is a
Cooperative Initiative
among these partners
Identify: Where to plant
Using i-Tree
Landscape
To prioritize
where tree
planting is
equitable
Landscape.itreetools.org
i-Tree is a
Cooperative Initiative
among these partners
Identify: Where to plant
Using i-Tree
Landscape
To prioritize
where tree
planting is
equitable
Place Priority Index
Darby 100
Camden 91
Millbourne 87
Chester 86
Woodlynne 77
Colwyn 77
Warminster 76
Coatesville 75
East Lansdowne 72
Norristown 71
Upland 67
Yeadon 65
Collingdale 65
Sharon Hill 64
South Coatesville 60
Avondale 58
Lansdowne 58
Clifton Heights 57
Pottstown 56
Bridgeport 56
Oxford 55
Landscape.itreetools.org
i-Tree is a
Cooperative Initiative
among these partners
Identify: Where to plant
i-Tree Design – Energy
Savings
i-Tree Landscape – Heat Island
Landscape.itreetools.org
Design.itreetools.org
i-Tree is a
Cooperative Initiative
among these partners
Engage: i-Tree for Education
MyTree.itreetools.org
i-Tree is a
Cooperative Initiative
among these partners
Account: MyTree Accountability Dashboard
MyTree.itreetools.org
i-Tree is a
Cooperative Initiative
among these partners
Account: Monitor Impact Over Time
2007 2017
Canopy.itreetools.org
i-Tree is a
Cooperative Initiative
among these partners
i-Tree: Make tree equity count
Identify where to plant trees to maximize
benefits.
Engage communities to support
stewardship.
Account for how tree equity efforts benefit
underserved communities.
i-Tree is a
Cooperative Initiative
among these partners
i-Tree: Make tree equity count
Jason Henning PhD
The Davey Institute and
USDA Forest Service, Philadelphia Field Station
jason.henning@davey.com
www.itreetools.org
MyTree Design
Landscape OurTrees
Canopy
Lightning Round: Technology Update

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Lightning Round: Technology Update

  • 1. Using Machine Learning for Tree Inventories Josh Behounek
  • 3. 1931 Pen & Paper Computer 1992 GIS-based 2002 Machine learning 2022
  • 4. LEVEL ONE LEVEL TWO LEVEL THREE RESEARCH Assessments Machine Learning
  • 5. 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
  • 6.
  • 8. San Diego Results 26% Vacant Sites ~ 436,770 Total Sites
  • 9.
  • 10. Machine Learning Advantages ● Objective ● Repeatable ● Efficient ● Precise
  • 12. 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
  • 13. Initial assessment greehill drives streets & parks per contract specs Data Delivery Data is delivered into TreeKeeper 9 Data extraction Data is processed via machine learning to provide information per data specs. Advanced Assessments Davey provides Level 2 or 3 assessments to flagged trees. Flagging Trees Based on results of data, client goals, & budget a certain # of trees are identified for advanced assessments Tree Monitoring Program Operation workflow Photo credit - greehill
  • 14. Using Machine Learning for Tree Inventories Josh Behounek 573-673-7530 Josh.Behounek@davey.com
  • 15.
  • 16. A Scalable Remote Sensing Model for Urban Forests From airborne missions to satellites Jonathan Pando Ocón, UCLA E. Natasha Stavros, CU Boulder Thomas W. Gillespie, UCLA Justin Robertson, LA County Steven J. Steinberg, LA County Image: Louis Reed, Unsplash
  • 17. Urban Los Angeles County Marina del Rey East Los Angeles Altadena
  • 18. Project Stakeholders Image: Ev Milee, Unsplash To ensure our project goals are met, we sought input from stakeholders to identify the needs and priorities of those that will be using our product in day-to-day operations: Stakeholder Advisory Group End users and department managers Local advocacy and conservation groups
  • 19. Stakeholder Needs Image: Henry Perks, Unsplash  Individual tree species identification  Canopy cover metrics  Health assessment
  • 20. Remote Sensing of Urban Environments Depending on the mode of image capture, the spatial and spectral resolutions of your chosen dataset can capture a high amount of noise: High species richness High density of (in)organic materials
  • 22. Existing Approaches to Unmixing Pixels Spectral Mixture Analysis Multiple Endmember Spectral Mixture Analysis (MESMA) is used to account for subpixel spectral mixing  Computationally expensive  Complex workflow
  • 23. Success Stories Able to untangle spectral confusion relatively well in less complex scenes. Image: Johannes Mandel, Unsplash
  • 24. Missing the Mark Prone to misclassifications in complex urban environments. Image: Denys Novazhai, Unsplash
  • 25. Science-led Resources Science-led projects can deploy the state-of-the-art in remote sensing image analysis using complicated workflows, computationally expensive models, and often a number of technically savvy individuals to inform the model. Uneven distribution of resources Public and governmental stakeholders may or may not have this.
  • 26. Stakeholder-led Resources Adhering to our stakeholder needs informs the research design immensely. Considerations for processing capacity, and technical know-how are necessary to ensure end-users can continue to use and update the product. 1. Who will run the model next year? The year after? 2. Are additional image acquisitions needed? 3. What is the best product format for day-to-day operations?
  • 27. Probabilistic Urban Forest Inventories High Confidence Ficus rubiginosa Low Confidence Uncertai n No Presence
  • 29. Thank you! Jonathan P. Ocón Ph.D. Candidate, UCLA Contact: jonocon[at]ucla.edu Image: Louis Reed, Unsplash
  • 30.
  • 31. Partners in Community Forestry Conference Nov 16th, 2022 Urban Tree Water Use and Implications for Stormwater Management Sarah Ponte, Nancy Sonti, A. Christopher Oishi, Dexter Locke, Tuana Phillips, and Mitchell Pavao-
  • 32. Urban development alters the natural hydrologic cycle Askarizadeh et al., 2015, Environ. Sci. Technol.
  • 33. The role of urban trees on urban hydrology
  • 34. Image Credit: Jeffrey Milstein Trees in urban areas vary by Ecohydrological Landscape Characteristics (ELCs) (Blood and Day)
  • 35. Transpiration Rates and Whole-Tree Water Use by Deciduous Species Objective: To quantify the use of trees to meet stormwater management requirements. Single trees over turfgrass Cluster of trees over turfgrass Closed canopy Baltimore City Montgomery County
  • 36. Single trees Cluster of trees Closed
  • 37. Sap flux is a proxy for transpiration rates Granier-type thermal dissipation sap flux sensors (built by the UMD Project Development Center)
  • 38. Different management contexts have significantly different daily sap flux (Js) distribution Repeated measures ANOVA p < 0.0001 Ponte et al., 2021 Scientific Reports Single red maple trees had nearly three times the daily sum of sap flux density (Js) of closed canopy trees Sap Flux
  • 39. Water use among these common, diffuse- porous, deciduous, eastern species is similar Sap Flux
  • 40. Species differences in sap flux density were observed at the 24h time-scale July 19, 2019 – high soil moisture Aug 30, 2019 – drought conditions Tulip poplar was the most sensitive to drought
  • 41. Whole-tree water use estimates based on a predictive model 0 50 100 150 200 250 300 0 10 20 30 40 50 60 L day -1 tree -1 DBH (cm) A. rubrum - Single L. styraciflua - Cluster A. rubrum - Cluster A. rubrum - Closed Canopy L. tulipifera - Closed Canopy L. styraciflua - Closed Canopy
  • 42. 0 100 200 300 400 500 mm H 2 O Cumulative Plot-Level Transpiration - 900 m2 plots Single Cluster Closed Canopy 2019 Single: 9% of precipitation Cluster: 71% of precipitation Closed Canopy: 52% of precipitation
  • 43. • Tree water use depends on management context • Water use among these diffuse- porous, deciduous, eastern species is similar; however,… • …they do exhibit differences in response to drought Main takeaways
  • 44. This study provides a foundational framework for estimating how a proposed urban tree/forest project would affect the hydrologic balance with useful implications for stormwater managers e. sponte@umd.edu @sarahpcabral
  • 45.
  • 46. i-Tree is a Cooperative Initiative among these partners Tree Equity: How i-Tree is Helping “…enough trees in specific neighborhoods or municipalities for everyone to experience the health, economic and climate benefits that trees provide.” Landscape.itreetools.org
  • 47. i-Tree is a Cooperative Initiative among these partners Tree Equity: How i-Tree is Helping “…enough trees in specific neighborhoods or municipalities for everyone to experience the health, economic and climate benefits that trees provide.” Landscape.itreetools.org “…a map of tree cover in America’s cities is too often a map of income and race.”
  • 48. i-Tree is a Cooperative Initiative among these partners Tree equity is hard work • Skeptical communities • Overcoming negative opinions • Dealing with tree mortality
  • 49. i-Tree is a Cooperative Initiative among these partners How can i-Tree help? Identify – Find where trees can do the most good. Engage – Communicate the benefits of trees. Account – Ensure delivery of tree benefits.
  • 50. i-Tree is a Cooperative Initiative among these partners  Free software  Estimate the benefits of trees  Based on US Forest Service science  Technical support i-Tree is a Cooperative Initiative www.itreetools.org i-Tree: Putting Urban Forest Science into the Hands of Users www.itreetools.org
  • 51. i-Tree is a Cooperative Initiative among these partners The i-Tree Framework Structur e Value Function
  • 52. i-Tree is a Cooperative Initiative among these partners Identify: Where to plant Using i-Tree Landscape To prioritize where tree planting is equitable Landscape.itreetools.org
  • 53. i-Tree is a Cooperative Initiative among these partners Identify: Where to plant Using i-Tree Landscape To prioritize where tree planting is equitable Place Priority Index Darby 100 Camden 91 Millbourne 87 Chester 86 Woodlynne 77 Colwyn 77 Warminster 76 Coatesville 75 East Lansdowne 72 Norristown 71 Upland 67 Yeadon 65 Collingdale 65 Sharon Hill 64 South Coatesville 60 Avondale 58 Lansdowne 58 Clifton Heights 57 Pottstown 56 Bridgeport 56 Oxford 55 Landscape.itreetools.org
  • 54. i-Tree is a Cooperative Initiative among these partners Identify: Where to plant i-Tree Design – Energy Savings i-Tree Landscape – Heat Island Landscape.itreetools.org Design.itreetools.org
  • 55. i-Tree is a Cooperative Initiative among these partners Engage: i-Tree for Education MyTree.itreetools.org
  • 56. i-Tree is a Cooperative Initiative among these partners Account: MyTree Accountability Dashboard MyTree.itreetools.org
  • 57. i-Tree is a Cooperative Initiative among these partners Account: Monitor Impact Over Time 2007 2017 Canopy.itreetools.org
  • 58. i-Tree is a Cooperative Initiative among these partners i-Tree: Make tree equity count Identify where to plant trees to maximize benefits. Engage communities to support stewardship. Account for how tree equity efforts benefit underserved communities.
  • 59. i-Tree is a Cooperative Initiative among these partners i-Tree: Make tree equity count Jason Henning PhD The Davey Institute and USDA Forest Service, Philadelphia Field Station jason.henning@davey.com www.itreetools.org MyTree Design Landscape OurTrees Canopy