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Smart Solutions for a Resilient Urban Forest Presentation.pdf
The document discusses leveraging technology to enhance urban forest resilience, focusing on data collection and analysis for tree health management. It emphasizes the use of smart landscape technologies, including sensors and digital twins, to monitor various environmental factors affecting trees. Additionally, the process involves identifying outliers for further assessment to improve overall urban forestry practices.
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Smart Solutions for a Resilient Urban Forest Presentation.pdf
- 1. Smart Solutions forResilient Urban Forests: Leveraging Technology for a Sustainable Urban Forest Josh Behounek Davey Resource Group
- 2. Right Decision, at the Right Time, onthe Right Tree
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- 6. • Warming winters& summer heatwaves • Zone 8a to 9a • Heavy flooding with intermittent drought • More hurricanes Louisiana Davey.com/climate
- 8. Software & HardwareConstraints
- 9. 1900 • mastectomy 1967 • lumpectomy 1980’s •Chemotherapy Current • Targeted therapy
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- 11. Unnecessary Data Collectionwith Standard Fields
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- 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
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- 29. Technologies Used inTree Evaluations: Ground Penetrating Radar October 5, 2020 Ground Penetrating Radar
- 30. Tree Sensors & Internetof Things • Smart Landscape • 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
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- 37. St ep 2:Cr eat e a4D Di gi t alTr ee Twi n ofeach t r ee 4D DI GI TAL TW I N M ul t i spect r alSat el l i t e I m ages Panor am i c I m ages Poi ntCl oud
- 38. St ep 3:W eanal yze each t r ee and ext r acti nf or m at i on Cl ear ance I ssues Li ve Cr own Rat i on % Di eback Ecol ogi calBenef i t s Di gi t alTr ee Twi n Change OverTi m e Cohor tAnal ysi s Si ze ( DBH,Hei ght ,et c) Speci es LeafAr ea I ndex Leani ng Angl e
- 39. St ep 4:Def i ne out l i er s AbsoluteOutliers (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
- 40. St ep 4:Def i ne out l i er s AbsoluteOutliers (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
- 41. In Field 25-100% Remotely0-20% Step 5: Davey Arborists assess Outliers
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- 44. Base Information –Species, Size, etc.
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- 50. Trunk Stability Index(beta)
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- 52. Machine Learning Advantages ● Objective ●Repeatable ● Efficient ● Precise
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- 55. The Future IsNow! Smart Solutions for Resilient Urban Forests Josh Behounek Josh.Behounek@davey.com 573-673-7530