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An Application of CITYgreen to the Cities of San Luis Obispo and Lodi

An Application of CITYgreen to the Cities of San Luis Obispo and Lodi. Dr. Brian Dietterick David Yun June 2000 Natural Resources Management Department Cal Poly University funded by the California Department of Forestry and Fire Protection. Overview. Introduction to CITYgreen

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An Application of CITYgreen to the Cities of San Luis Obispo and Lodi

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  1. An Application of CITYgreen to the Cities of San Luis Obispo and Lodi Dr. Brian Dietterick David Yun June 2000 Natural Resources Management Department Cal Poly University funded by the California Department of Forestry and Fire Protection

  2. Overview • Introduction to CITYgreen • Application of CITYgreen for San Luis Obispo and Lodi • Introduction • Data Acquisition • Data Analysis • Summary of Findings • Verification of Findings • Conclusion

  3. CITYgreen background • Developed by American Forests • A planning tool for mapping urban ecosystems and measuring their values • Quantifies the value of urban trees

  4. CITYgreen capabilities • CITYgreen evaluates urban ecosystem values • Summer Energy Savings • Stormwater Management Savings • Carbon Storage and Sequestration • Air Pollution Removal • Urban Wildlife Habitat

  5. What is required to run CITYgreen? • Hardware & software • ArcView 3.1 • Spatial Analyst • Pentium processor • VGA or better graphics • > 32 MB RAM • Orthophotos (color preferred) • Satellite Image • Landuse Map

  6. Application of CITYgreen forSan Luis Obispo & Lodi

  7. SLO background • Located along the Central Coast midway between San Francisco and Los Angeles • Mediterranean climate • mild summers and winters • Average annual rainfall = 24” • morning and evening fog • A coastal valley city • Population: 43,000 • 2% growth rate

  8. Lodi background • Located in the Central Valley midway between Sacramento & Modesto • Mediterranean climate • Hot summers/cool winters • Average annual rainfall = 17” • morning and evening fog • A inland valley city • Population: 55,000 • 1.3% growth rate

  9. Data acquisition • Satellite Image downloaded from TEALE Data Center or U.S. Geological Survey • color infrared images are used to categorize % vegetation • desired data resolution <1 meter • digital image size can be large Large cities may require supplemental storage medium or large hard drive capacity

  10. Data acquisition • Orthophoto Image City/County planning departments or universities may have digital orthophotos Cal Poly supplied the SLO digital orthophoto and the City of Lodi supplied an aerial image of Lodi Digital orthophotos may need to be georeferenced to real world coordinates. In ArcView, world files can be created to georeference the digital orthophoto

  11. Data acquisition • Survey of Trees Trees in sample plots were field checked for: • height • diameter • health • tree conflicts • species • ground cover

  12. Data acquisition • Structure Survey • Structures in sample plots field checked for: • building age • roof & building color • height (story) • material • overhang • location of windows

  13. Data acquisition • Survey of Impervious Surfaces • All impervious surfaces for each • sample plot sampled for: • roads • parking lots • sidewalks

  14. Data acquisition • Survey of Grassland areas • Grass areas in sample plots field checked • A limitation is that CITYgreen 2.0 categorizes • all areas other than impervious, structures, trees, & water to be grassland for calculation purposes. This assumption may result in conservative results. Future versions of CITYgreen are expected to account for other vegetation, such as shrubs

  15. Data acquisition • Survey of water bodies • Streams • Lakes

  16. Data Entry Attribute data collected from the sample plots is entered into the CITYgreen database in preparation for analysis

  17. Data analysis • After data is entered, CITYgreen calculates benefits of urban trees • Tabular results and map are summarized as shown

  18. SLO findings

  19. Lodi findings

  20. Summary of findings SLO findings: • The energy savings per home/year were low due to moderate weather resulting in low energy costs • Larger sample areas resulted in higher runoff volume reduction, peak flow reduction, carbon storage, & carbon sequestration

  21. Summary of findings cont’ Lodi findings: • The energy savings ($/home/year) were higher than SLO due to warmer summer temperatures and higher energy costs • Older residential areas had higher energy savings per home compared to newer residential areas • Growth model resulted in higher canopy coverage in new, but lower energy savings when compared with the older residential areas

  22. Conclusion • Evaluation of CITYgreen still in progress • Dollar savings for cooling are significant for Lodi, but less significant for San Luis Obispo (due to low cooling cost) • Tree placement plays an important role • Model verification and extrapolation of results for the entire city problematic

  23. For more info Contact: Dr. Brian Dietterick bdietter@calpoly.edu 805-756-6155 David Yun Natural Resources Management Department Cal Poly State University San Luis Obispo, CA 93407

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