Does your community have a canopy goal that sounds good but might not be achievable? Are you thinking about setting a goal but are concerned you don't know what it should be? This presentation will demonstrate to attendees how to set an achievable urban tree canopy (UTC) goal that considers parameters like Land Use, Development, Climate, Ordinances, and Possible Planting Spaces. Example of case studies will be shown to demonstrate methodologies for setting achievable canopy goals as well as systems to check on progress of achieving your goals.

1. How To Set Achievable Urban
Tree Canopy Goals

2. How To Set Achievable Urban
Tree Canopy Goals

3. Presenters

4. Establishing an Urban Tree Canopy Goal
Absolute Canopy Change (XA) = ((TPC * N) + TPC + G) - ((TRC * EG) + TRC + M)
Growth Factors
Trees Planted (TP) x Cover (C)= TPC
TP = L+R+P
• Landscape Ordinance Required Plantings per year (L)
• Residential Landowner Planting per year (R)
• Public Land Restoration Plantings per year (P)
Natural Regeneration (N)
Annual Canopy Growth (G)
Decline Factors
Trees Removed (TR) x Cover (C) = TRC
TR = D+S+PT+DT
• Development Clearing per year (D)
• Severe Storms per year (S)
• Pest Threats per year (PT)
• EAB/ALB/Gypsy Moth
• Disease Threats per year (DT)
Existing Canopy Natural Mortality (EG)
Maintenance Loss (M)

5. Establishing an Achievable
Urban Tree Canopy Goal
Current
UTC
Projected
UTC
Potential
UTC
Future
Planting
Canopy
Goal

6. Establishing an Achievable
Urban Tree Canopy Goal
Current
UTC
Projected
UTC
Potential
UTC
Future
Planting
Canopy
Goal
Timeframe?

7. Establishing your
Timeframe
• Trending toward 5 year
• Part of Urban Forest Master
Plan
UTC

14. Primary Methods UTC Assessment
• i-Tree Canopy
• i-Tree Landscape
• High Resolution Multi-Spectral
Imagery

15. Tree Canopy
Recognition
• Starts with leaf-on imagery (aerial
or satellite) or LiDAR point clouds
• Automated feature extraction of
Tree Canopy (and other features)
• Rigorous QC procedures using
highly trained staff
• Accuracy assessment completed
• Verification of classified data

16. Remote Sensing and
Machine Learning
Objects are extracted by algorithms
based on the characteristics of:
• Spectral Reflectance
• Texture
• Shape
• Color
• Intensity
• Height

17. Tree Canopy
Metrics
o Project Boundary
o Parcels
o Watersheds
o Council Districts
o Sewersheds
o Neighborhoods
o Public/Private
o Zoning/Land Use
o Census Tracts
How UTC data is quantified:

18. Custom Tree
Canopy Analysis
Land Cover Classification
LiDAR Analysis
Land Cover Metrics
Ecosystem Benefits Services
Prioritized Planting Plans
Social Equity Analysis
Historical Change Assessment
Tree Canopy Health
Urban Heat Island
Forest Fragmentation
Human Health
Story Maps

19. Case Study
Asheville, NC
High Resolution Multispectral Imagery
Tree Canopy Mapping
Change Assessment
Benefits Losses

20. Asheville
Tree Canopy
2018 Acres:
13,021 (44.5%)
2008 Acres:
13,912 (47.5%)
Change Acres:
- 891 (- 6.4 percent
change; - 3.0 absolute
change)
Benefits Loss:
$109.5M to $100.7M
(-$8.8M or 8% of total
benefits)

21. Establishing an Urban Tree Canopy Goal
Current
UTC
Projected
UTC
Potential
UTC
Future
Planting
Canopy
Goal

23. Identifying Threats
and Trends
• Tree Age
• Disease and Pest Outbreaks
• Future Land Use
• Development Practices
• Ordinances
• Public Perception

24. Data Implementation
TREE
INVENTORY
TREE CANOPY
ASSESSMENT
LAND USE DISEASE AND
PEST MAPS
PREDICTIVE
MODELS

25. Case Study
Charlotte, NC

26. Prediction Models
Model One: Estimate Upcoming Loss
from Aging Canopy
Model Two: Impacts of Development
Model Three: Utilizing Future Land Use
Changes to Estimate Tree Canopy

27. Prediction Results –
Model One: Aging Canopy
Neighborhood Analysis
Street Tree Focus
Older Trees (over 24” DBH)
Loss of Benefit Values
No replenishment

28. Prediction Results –
Model Two: Development Impact
Development (Commercial & Mixed Use)
Determine Tree Loss
Benefits Modeling using i-Tree Eco Forecast
Map Changes in Site Studies

29. Prediction Results –
Model Three: Future Land Use
Comparison of tree canopy
per each current land use
class
How did that compare to
future land use?
Transitional Uses and Mixed
Uses
Loss of Canopy and Benefits

30. Establishing an Urban Tree Canopy Goal
Current
UTC
Projected
UTC
Potential
UTC
Future
Planting
Canopy
Goal

32. Finding Planting Locations
Forest Service provides guidelines and suggestions on this type of
analysis
Possible UTC is simply open areas where tree canopy, hardscape, and
planting restrictions do not occur.
Found by combining our Grass/Low-Lying Vegetation and Bare Soil areas
from land cover analysis.
Where are they?
 Lawns
 Street Trees
 Parks
 Riparian Areas
 Open Space
 Conservation Areas
What is not included?
o Golf Courses
o Airports
o Transmission Rights-of-
Way
o Agricultural Fields
o Cemeteries
o Recreational Fields

33. Prioritizing
Planting Areas
Complete an assessment of
stormwater runoff, urban
heat island, and social equity
Select sites with High and
Very High ranking from
analysis
These sites should be planted
first to receive the greatest
benefit to community

34. Tree Placement
Modeling
Use various placement
models to estimate tree
counts of large,
medium, and small
trees
Models favor large
trees for more benefits
Assign modeled trees
query-able attributes
 Public/Private
 Land Use
 Census Tract
 Neighborhood

35. Case Study Kansas City, MO

36. Development of
the Planting Plan
Data Source:
Base data was 2012 SPOT satellite imagery at
2.5m resolution
Problem:
Data was out of date/Not suitable for analysis
Solution:
Planting site refinement using 2018 NAIP imagery
(machine learning/vegetation algorithms)
Outcome:
More accurate representation of planting areas

37. Deciding
Where to
Plant
Complete an assessment of
stormwater runoff, urban heat island,
and social equity
Select sites with High and Very High
ranking from analysis
These sites should be planted first to
receive the greatest benefit to
community

38. Geospatial Data for
Informed Decision Making
Catalog of planting locations
Prioritized data for increased efficiency
Reduced number of trees 620,000 to
350,000 (45%)

39. Data Integration
• Planting Polygons/Tree Points
• Imported into a software platform for
management
• Planted trees become assets
• Moved into the tree inventory

40. Establishing an Urban Tree Canopy Goal
Current
UTC
Projected
UTC
Potential
UTC
Future
Planting
Canopy
Goal

42. Canopy Scenario Modeling
o Formulate possible scenarios
o No Action
o No Net Loss
o Short-Term Goal
o Long-Term Goal
o Run rough numbers on acres
that need to be added/saved

43. Realizing the
Achievable
Take the napkin math numbers
and dig deeper
Determine the budgets available
for tree planting and maintenance
Start with a “No Net Loss” Goal
Don’t fixate on just a canopy
percentage number (it’s a great
reference but not the whole
picture)

44. No Net Loss
Initiative
I. The best place to start (for
growing and shrinking
urban forests)
II. Different approaches to
address no net loss
III. Review policies and
ordinances

45. Tree Planting for the Future
Check Check your progress
Promote ALWAYS promote the benefits of your trees
Diversify Follow the 10/20/30 rule to increase diversification
Create Create tree palettes that will adapt to climate change
Plan Use i-Tree tools to find suitable tree species (i-Tree Planting/i-Tree Species)

46. Establishing an Urban Tree Canopy Goal
Current
UTC
Projected
UTC
Potential
UTC
Future
Planting
Canopy
Goal

49. Thanks
ISA CEU
DA – 19 - 216
Will Ayersman
William.ayersman@davey.com
330-673-5885 x 8048
Josh Behounek
Josh.Behounek@davey.com
573-673-7530