Successfully reported this slideshow.
Your SlideShare is downloading. ×

Lightning Round: Technology Update

Ad
Ad
Ad
Ad
Ad
Ad
Ad
Ad
Ad
Ad
Ad
Upcoming SlideShare
Opening Remarks
Opening Remarks
Loading in …3
×

Check these out next

1 of 60 Ad

More Related Content

More from ColleenSchoch (20)

Recently uploaded (20)

Advertisement

Lightning Round: Technology Update

  1. 1. Using Machine Learning for Tree Inventories Josh Behounek
  2. 2. “Solutions Through Innovation and Expertise”
  3. 3. 1931 Pen & Paper Computer 1992 GIS-based 2002 Machine learning 2022
  4. 4. LEVEL ONE LEVEL TWO LEVEL THREE RESEARCH Assessments Machine Learning
  5. 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. 6. Current Process
  7. 7. San Diego Results 26% Vacant Sites ~ 436,770 Total Sites
  8. 8. Machine Learning Advantages ● Objective ● Repeatable ● Efficient ● Precise
  9. 9. Advantages Photo credit - greehill
  10. 10. 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
  11. 11. 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
  12. 12. Using Machine Learning for Tree Inventories Josh Behounek 573-673-7530 Josh.Behounek@davey.com
  13. 13. 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
  14. 14. Urban Los Angeles County Marina del Rey East Los Angeles Altadena
  15. 15. 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
  16. 16. Stakeholder Needs Image: Henry Perks, Unsplash  Individual tree species identification  Canopy cover metrics  Health assessment
  17. 17. 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
  18. 18. Mixed-pixels
  19. 19. 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
  20. 20. Success Stories Able to untangle spectral confusion relatively well in less complex scenes. Image: Johannes Mandel, Unsplash
  21. 21. Missing the Mark Prone to misclassifications in complex urban environments. Image: Denys Novazhai, Unsplash
  22. 22. 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.
  23. 23. 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?
  24. 24. Probabilistic Urban Forest Inventories High Confidence Ficus rubiginosa Low Confidence Uncertai n No Presence
  25. 25. Condition Assessment Health y Ficus rubiginosa Needs Attention Uncertai n
  26. 26. Thank you! Jonathan P. Ocón Ph.D. Candidate, UCLA Contact: jonocon[at]ucla.edu Image: Louis Reed, Unsplash
  27. 27. 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-
  28. 28. Urban development alters the natural hydrologic cycle Askarizadeh et al., 2015, Environ. Sci. Technol.
  29. 29. The role of urban trees on urban hydrology
  30. 30. Image Credit: Jeffrey Milstein Trees in urban areas vary by Ecohydrological Landscape Characteristics (ELCs) (Blood and Day)
  31. 31. 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
  32. 32. Single trees Cluster of trees Closed
  33. 33. Sap flux is a proxy for transpiration rates Granier-type thermal dissipation sap flux sensors (built by the UMD Project Development Center)
  34. 34. 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
  35. 35. Water use among these common, diffuse- porous, deciduous, eastern species is similar Sap Flux
  36. 36. 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
  37. 37. 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
  38. 38. 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
  39. 39. • 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
  40. 40. 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
  41. 41. 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
  42. 42. 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.”
  43. 43. i-Tree is a Cooperative Initiative among these partners Tree equity is hard work • Skeptical communities • Overcoming negative opinions • Dealing with tree mortality
  44. 44. 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.
  45. 45. 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
  46. 46. i-Tree is a Cooperative Initiative among these partners The i-Tree Framework Structur e Value Function
  47. 47. 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
  48. 48. 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
  49. 49. 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
  50. 50. i-Tree is a Cooperative Initiative among these partners Engage: i-Tree for Education MyTree.itreetools.org
  51. 51. i-Tree is a Cooperative Initiative among these partners Account: MyTree Accountability Dashboard MyTree.itreetools.org
  52. 52. i-Tree is a Cooperative Initiative among these partners Account: Monitor Impact Over Time 2007 2017 Canopy.itreetools.org
  53. 53. 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.
  54. 54. 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

×