Smart Solutions for a Resilient Urban Forest.pdf

Demonstrating how technologies can help improve the resiliency of our urban forests.

1. Smart Solutions for Resilient
Urban Forests:
Leveraging Technology for a
Sustainable Urban Forest
Josh Behounek
Davey Resource Group

2. Right
Decision,
at the
Right Time,
on the
Right Tree

4. Feedback Loops

6. • Warming winters &
summer heatwaves
• Zone 8a to 9a
• Heavy floodingwith
intermittent drought
• More hurricanes
Louisiana
Davey.com/climate

8. Software & Hardware Constraints

9. 1900
• mastectomy
1967
• lumpectomy
1980’s
• Chemotherapy
Current
• Targeted
therapy

10. Current Process

11. Unnecessary Data Collection with Standard Fields

12. Reactive

13. Subjective

14. Feedback Loop -Buffalo, NY Inventory Update
Difference 2014 2001
2,635 127,080 124,445 Sites
-53,546” 817,627” 871,173” Total DBH
-1” 6” 7” Average
DBH
-34 247 281 # Species
2,039 2,707 668 # Removals
-4,142 44,619 48,761 # Planting
Sites

15. Feedback Loop -Condition Change Assessment
2001
Inventory
2014
Inventory
5,698
Poor
3 Dead
145 Poor
11 Fair 0 Good
293 Plant
5,246 New
38,199
Fair
298 Dead
3,445 Poor 26,830 Fair
962 Good
1,365 Plant 5,299 New
25,632
Good
259 Dead 717 Poor 8,952 Fair
9,783 Good 1,878 Plant 4,043 New

16. Individual Trees

17. Static Pull Testing

18. Static Pull Testing

19. Static Pull Testing

24. Analysis & Recommendations

25. Analysis & Recommendations

26. Sonic Tomography

27. Technologies Used in Tree Evaluations:
Ground Penetrating Radar
October 5, 2020
Ground Penetrating Radar

28. Analysis &
Recommendations

29. Tree Sensors & 
Internet of Things •Smart Landscape 
•Tilt/Sway
•Watering efficacy
•Clients can view data generated by 
sensors in their landscape
•Measure:
•Soil moisture –2 depths
•Soil temperature –2 depths
•Ambient temperature –for Nature 
Clock
•Tilt
•Notifications, useful links, etc
Photo @banksy

32. Urban Forests

33. The Power of GIS-based Inventories

36. SpeciesRichness

38. Smart Tree
Inventories

40. Tree
Tree
Tree

41. Step 2: Create a 4D Digital Tree Twin of each tree
4D DIGITAL TW IN
Multispectra l Satellite Images
Panoram ic Images
Point Cloud

42. Step 3: We analyze each tree and extract information
Clearance Issues
Live Crow n Ration
% Dieback
Ecological Benefits
Digital Tree Twin
Change Over Time
Cohort Analysis
Size (DBH, Height, etc)
Species
Leaf Area Index
Leaning Angle

43. Step 4: Define outliers Absolute Outliers (cohorts) ●Dead trees
●Too much lean
●Leaf Area compared to Size
●Canopy Width vs Tree Height Relative Outliers (filtering) ●Trees > x”
●Trees in certain neighborhoods
●Certain species of trees
●Trees >40% dieback

44. Step 4: Define outliers Absolute Outliers (cohorts) ●Dead trees
●Too much lean
●Leaf Area compared to Size
●Canopy Width vs Tree Height Relative Outliers (filtering) ●Trees > x”
●Trees in certain neighborhoods
●Certain species of trees
●Trees >40% dieback

45. In Field ~ 25 -100%
Remotely ~ 0 -20%
Step 5: Davey Arborists assess outliers

46. Outlier Assessments

48. Base Information –Species, Size, etc.

49. Health & Vitality

54. Trunk Stability Index (beta)

55. Trunk Stability Index

56. Machine Learning
Advantages
●
Objective
●
Repeatable
●
Efficient
●
Precise

58. Removing Implicit Bias

59. The Future Is Now!
Smart Solutions for Resilient
Urban Forests
Josh Behounek
[email protected]
573-673-7530