Assessing Your Local Urban Forest David J. Nowak USDA Forest Service Northern Research Station Syracuse, NY 1
Measurement is Critical Structure Function Value 2
Assessing Urban Forests Top-down approach Aerial-based Bottom-up approach Ground-based 3
Data Collection Aerial Spatial cover data; available space Limited information (cover types; heights) Ground-based Limited geography of data (unless inventory) Statistical sample Same types of data from aerial, plus: Species Condition Sizes Number of trees Local, specific benefits 4
Top-down Approach Cover Data NLCD (30 m) Hi-resolution (~1 m) Photo-interpretation 5
Testing NLCD Tree and Impervious Cover Maps 100% 0% 6
Tree Cover Tree cover: National underestimation = 9.7% Maximum underestimation = 28.4% Underestimation in 64 of 65 zones Impervious cover: National underestimation = 1.4% Maximum underestimation = 5.7% Underestimation in 44 of 65 zones 7
State Reports All reports in publication group for layout and printing State Reports New England - CT, MA, ME, NH, RI, VT Mid Atlantic - NJ, NY, PA South Atlantic - DE, MD, WV, VA, DC, NC, SC, GA, FL East North Central - IL, IN, MI, OH, WI West North Central - IA, KS, MN, MO, NE, ND, SD East South Central - AL, KY, MS, TN (May 2010) West South Central - AR, OK, LA, TX (May 2010) Mountain Region - AZ, CO, ID, MT, NV, NM, UT, WY Pacific Coast - CA, OR, WA Uncorrected cover data Correction factors given http://nrs.fs.fed.us/data/urban/ 8
NLCD Adv: Free wall-to-wall coverage of lower 48 states maps of canopy cover distribution can integrate with GIS Disadv: relatively course resolution tends to underestimate tree cover not designed for local scale (e.g., city) analyses (better for regional analyses) 9
Tree Cover Mapping High resolution (typically sub-meter) cover data 10
High resolution data Adv: high resolution cover map good estimates of cover amount / location Integrates with GIS can locate potentially available spaces for trees Disadv: costly (time and $) cloud cover can be an issue, requiring multi-date images significant effort and time 11
Photo Interpretation 12
PI Mapping 13
Photo Interpretation ret Adv: low cost free through Google Earth quick and easy accuracy increased with more points all cover types (e.g., available planting space, tree, impervious Disadv: does not produce detailed cover map photo-interpreter potential error 14
Ground-based Approach: i-tree www.itreetools.org 15
What is i-tree? A suite of tools to assess urban vegetation and their ecosystem services and values i-tree Eco = UFORE 16
Public-Private Partnership USDA Forest Service Davey Tree Expert Co. National Arbor Day Foundation Society of Municipal Arborists International Society of Arboriculture 17
i-tree Use Eco has been used worldwide in over 60 cities (9 countries) Distributed to over 80 countries 18
Canadian Cities and Towns Ajax, ON Bolton, ON Brampton, ON Caledon, ON Calgary, AB Edmonton, AB Halifax, NS Kelowna, BC London, ON Markham, ON Mississauga, ON Oakville, ON Pickering, ON Toronto, ON Vaughan, ON 19
Assessing Urban Tree Populations i-tree Eco assesses: Structure Function Energy Air pollution Carbon VOC emissions Value Management needs Pest risk Tree health Exotic/invasive spp. 20
The Foundation: Local Data Local Sample or Inventory Local information: Weather Pollution Environmental variables Hourly simulations 21
How is an assessment done? i-tree Step 1 Determine Study Area 22
i-tree Step 2 Determine if street tree or area-based inventory or sample 23
i-tree Step 2a Determine Number of Plots Typically 200 1/10 acre plots 24
i-tree Step 3 Determine what data to collect Required core variables (spp, dbh) Optional variables Crown parameters Tree health Distance to buildings Shrub data Ground cover data 25
i-tree Step 4 Lay sample points Random Pattern Random Grid Pattern Stratified by LU 26
i-tree Step 5 Set up project 27
i-tree Step 6 Train crews and collect field data 28
i-tree Step 7 Enter data and analyze Enter data and analyze 29
i-tree analyses 30
i-tree Step 8 Use data and reports to make a difference Automatic Report Generator 31
DC analysis Data collected by Casey Trees and National Park Service Data collected 2004 and 2009 201 randomly located plots 32
Species Composition American beech 11.8% Callery pear 6.0% Tulip tree 4.8% Boxelder 4.6% other species 52.5% Black cherry 2.4% Red maple 4.6% Other species 4.4% Slippery elm 3.1% Black tupelo 3.0% Tree of heaven 2.8% 2.6 million trees 33
Tree Distribution Number of Trees 1,800,000 1,600,000 1,400,000 1,200,000 1,000,000 800,000 600,000 400,000 200,000 0 Total Trees Per acre 180 160 140 120 100 80 60 40 20 0 Trees per acre 34
Leaf Area 14 12 % of total leaf area % of all trees 10 Percent 8 6 4 2 0 35
Carbon Sequestration Sequestration (tons / year) 2,500 2,000 1,500 1,000 500 0 Carbon Sequestration U.S. Dollars 50,000 45,000 40,000 35,000 30,000 25,000 20,000 15,000 10,000 5,000 0 Value (dollars / year) Total Storage = 595,000 tons ($12.3 million) Total seq. = 19,000 tons/yr ($390,000/yr) 36
Structural Value Structural value (millions of dollars) 800 700 600 500 400 300 200 100 0 Total = $4.0 billion 37
Other Values (2004) Energy Conservation = $2.6 million/yr Air Pollution Removal = 540 tons /yr ($2.5 million/yr) 38
i-tree Tools 39
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i-tree-hydro Separate GIS program Calibrates against stream flow data 48
i-tree Hydro 140 120 Annual Flow Change (%) 100 80 60 40 20 0-20 -40 95 90 80 70 60 50 40 30 20 10 0 20 60 40 95 80 Impervious Cover (%) Tree Cover (%) 0 49
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Population Projectors 51
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Priority Planting Index 53
Pollution Distribution Conc. Vd Temp. Flux 54
Temperature Mapping Heat Island Baltimore, Maryland - - Source: Heisler et al., USFS 55
Heat Island Baltimore, Maryland - - Source: Heisler et al., USFS 56
Heat Island Baltimore, Maryland - - Source: Heisler et al., USFS 57
Questions? dnowak@fs.fed.us nrs.fs.fed.us/units/urbanurba 58