Data-Driven Decision Making with Smart Tree Inventories October 2024.pdf
0 likes77 views
The document discusses a data-driven approach to tree inventory management, emphasizing the creation of a digital tree twin for each tree to assess their health and ecological benefits. It presents a five-step process for identifying tree outliers and implementing a smart tree inventory system, which aims to make informed decisions regarding tree management. The use of advanced assessments and machine learning is highlighted as a means to enhance efficiency and objectivity in tree care.
Data-Driven Decision Making with Smart Tree Inventories October 2024.pdf
- 1. Data-Driven Decisions with Smart Tree Inventories Josh Behounek Davey Resource Group
- 4. Right Decision, on the Right Tree, at the Right Time
- 8. Software & Hardware Constraints
- 9. 1900 • mastectomy 1967 • lumpectomy 1980’s • Chemotherapy Current • Targeted therapy
- 11. Current Process
- 12. Unnecessary Data Collection with Standard Fields
- 13. Reactive
- 14. Subjective
- 15. Feedback Loop - 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
- 16. 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
- 18. Ste p 1: We ca p ture cm - a ccura te p oin t cloud a n d a utom a tica lly id e n tify e a ch tre e . Tree Tree Tree
- 19. Step 2: Create a 4D Digital Tree Twin of each tree 4D DIGITAL TWIN Multispectral Satellite Images Panoramic Images Point Cloud
- 20. Step 3: We analyze each tree and extract information Clearance Issues Live Crown Ration % Dieback Ecological Benefits Digital Tree Twin Change Over Time Cohort Analysis Size (DBH, Height, etc) Species Leaf Area Index Leaning Angle
- 21. 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
- 22. 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
- 23. In Field 25 - 100% Remotely 0 - 20% Step 5: Davey Arborists assess outliers
- 25. Make the Right Decision, on the Right Tree, at the Right Time
- 27. 2 Year Cycle
- 28. Smart Tree Inventory Program Year 1 Initiate Smart Tree Inventory Perform advanced assessments Install TreeKeeper 9 Year 2 Implement information via TreeKeeper 9 Year 3 Re-scan smart tree inventory Perform advanced assessments of flagged trees Perform change analysis Update TreeKeeper 9 Year 5 Re-scan smart tree inventory Perform advanced assessments of flagged trees Perform change analysis Update TreeKeeper 9 Year 4 Implement information via TreeKeeper 9 Photo credit - greehill
- 30. Base Information – Species, Size, etc.
- 37. Road Clearance
- 40. Critical Root & Fall Zone
- 42. Trunk Stability Index (beta)
- 44. Machine Learning Advantages ● Objective ● Repeatable ● Efficient ● Precise
- 47. The Future Is Now! Smart Tree Inventories Josh Behounek Josh.Behounek@davey.com 573-673-7530