The Science Behind Quantifying Urban Forest …arboriculture.org.au/Uploads/Editor/Doc...The Science...
Transcript of The Science Behind Quantifying Urban Forest …arboriculture.org.au/Uploads/Editor/Doc...The Science...
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The Science Behind Quantifying Urban Forest Ecosystem Services
David J. Nowak
USDA Forest Service
Northern Research Station
Syracuse, NY, USA
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Current Model – Version 3.0
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i-Tree Version 4.0 (March 10, 2011)
5 New or Enhanced Tools
Canopy Pest
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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.
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Data Collection
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i-Tree Eco Methods - Structure
No. trees, species composition, tree sizes, tree condition
Direct measures, statistical estimates with standard errors
Leaf area Formula based on species factors and crown measurements; adjusted based on crown missing
Leaf biomass Converts leaf area to leaf biomass based on species conversion factors
Data can be stratified (e.g., land use)
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i-Tree Eco Methods - Functions Carbon
Biomass equations (spp, dbh, ht)
Adjusted downward for open-grown trees
Annual growth based on dieback, competition and length of growing season
Air Pollution Removal
Leaf area index, canopy cover by evergreen or deciduous; in-leaf season length
Local hourly weather & pollution conc. (C) data
O3, SO2, NO2: multi-layer/big-leaf hybrid model
PM, CO: average deposition velocity (Vd)
Hourly Removal = Vd x C
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i-Tree Eco Methods - Functions
Building Energy Use
Based on work by McPherson and Simpson
Tree size by distance and direction from building
Average effects for region (heating and cooling)
VOC emissions (not reported)
Local hourly weather data
Species leaf biomass
Genera specific emission factors adjusted by NCAR and EPA formulas based on hourly light intensity and temperature (BEIS approach)
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i-Tree Eco Methods - Valuation Air Pollution Removal
National average externality values from literature (updated to 2007)
Converting to EPA BenMap estimates
Carbon Storage and Sequestration Global externality estimates (Fankauser, 1994) = $22.8/metric ton
Energy Use State average electricity and heating fuel costs (oil, wood, natural gas)
Structural Value CTLA formula
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Project Equipment and Costs Crew salary Transportation Project oversight (QA/QC, training) Equipment
Aerial photographs and street map to locate plots Clinometer Diameter tape Clipboard; data sheets, pens/pencils (or digital recorders-PDA) 50/100 ft tape measure (or electronic measuring device) Species ID guide Compass Camera (if taking pictures of plot) Chalk/Flagging (to mark trees that have been measured in plots with many trees)
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Vegetation
Structure
---- Tree locations
Species
Canopy cover
Leaf area
Biomass
Growth
Mortality
Diversity
Health
Site
Sequestration CO2 Released
Reduction in
atmospheric
CO2
Bldg.
data
Meteor. Data Shade
Air temp.
Wind speed
RH
Regional
Climate
Energy Heating
Cooling
Emission
Factors
Avoided
Emissions
Air Quality
Data NOx, SOx, O3,
PM10
Dry
deposition
Air quality
improvement
B.V.O.C. Isoprenes
Monoterpenes
Stormwater
Runoff Interception
Firewise
Landscapes
Other
Aesthetics
Structure Hydrology Air Quality Energy &
CO2 Fire Other
Research Process—Structural Analysis
Data collection –
900 trees, 20 predominant species
age, species, dbh, ht., crown dia., condition, digital photos, foliar biomass samples, etc.
Calculate leaf area and foliar biomass
Regression models predict growth.
Vegetation
Structure
---- Tree locations
Species Canopy cover
Leaf area Biomass Growth Mortality Diversity Health Site
Sequestration CO2 Released
Reduction in
atmospheric
CO2
Bldg.
data
Meteor. Data Shade
Air temp. Wind speed
RH
Regional
Climate
Energy Heating Cooling
Emission
Factors
Avoided
Emissions
Air Quality
Data NOx, SOx, O3,
PM10
Dry
deposition
Air quality
improvement
B.V.O.C. Isoprenes
Monoterpenes
Stormwater
Runoff Interception
Firewise
Landscapes
Other
Aesthetics,
...
Pavement
durability
Structure Hydrology Air Quality Energy &
CO2 Fire Other
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Research Process—Functional Analysis
Models use structural data
– (size at various ages).
To determine magnitude
of annual benefits:
Energy saved
Atmospheric CO2 reduction
Air pollutants removed
Rainfall intercepted
Aesthetics & other
Vegetation
Structure
---- Tree locations
Species Canopy cover
Leaf area Biomass Growth Mortality Diversity Health Site
Sequestration CO2 Released
Reduction in
atmospheric
CO2
Bldg.
data
Meteor. Data Shade
Air temp. Wind speed
RH
Regional
Climate
Energy Heating Cooling
Emission
Factors
Avoided
Emissions
Air Quality
Data NOx, SOx, O3,
PM10
Dry
deposition
Air quality
improvement
B.V.O.C. Isoprenes
Monoterpenes
Stormwater
Runoff Interception
Firewise
Landscapes
Other
Aesthetics,
...
Pavement
durability
Structure Hydrology Air Quality Energy &
CO2 Fire Other
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Research Process—Value Analysis (Net Benefits)
Convert resource units (kWh, lbs) to $
Annual Benefits:
B = Energy + CO2 + AQ + Hydrology + property value
Annual Costs:
C = Plant + Trim + Removal + IPM + Irrigation + Clean-Up + Sidewalk + Liability + Admin + Other
Net Benefits = B – C
Benefits/Costs ratio = B/C
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What Does Species Do?
Ranks tree species based on their environmental
benefits at maturity
Complements existing tree selection programs
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v. 4.0 Improvement
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How Does Species Work?
Utilizes local data
Simple user interface
Produces reports based on function
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Using i-Tree Species
Input location, height, pollutant removal, and other functions
Includes user input of importance values
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UFORE - Hydro Management model designed to be relatively easy to use
Object-oriented, physical based, semi-distributed, topographic model
TOPMODEL theory is used to simulate saturation excess overland flow (for forest area), base flow and ET process
Warm weather, semi-distributed urban soil-vegetation-atmosphere transfer scheme (SVATS)
C++ code with GIS inputs
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UFORE Hydro Strengths
Specifically designed to incorporate urban tree and impervious surface effects on stream flow and water quality
Built to simulate the dynamic forest interception, infiltration and ET processes as well as urban impervious effect on runoff generation.
Calibrated against measure stream flow data
Relatively easy to use
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UFORE Hydro Weaknesses
Lacks capabilities of fully-distributed model
Currently does not allow for specific locational designs of tree cover, impervious cover, or retention/detention ponds (operates on general cover types)
Works on watershed basis (with gauging station)
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Model Inputs Hourly discharge data (USGS)
Digital elevation map (USGS)
Hourly weather and evaporation data
Evaporation data calculated from weather data
Structural information on watershed (NCLD and UFORE data) e.g.,
Tree cover
Impervious cover
Shrub and grass cover
LAI
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Model Calibration
Auto calibrator (DOS Parameter Estimation (PEST) program)
Iterative process
Calibration results
Peak flow weighted (CRF1)
Base flow weighted (CRF2)
Balanced flow (peak and base) (CRF3)
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Model Calculations Topographic index with tree and impervious cover
Interception routine
Canopy parameters (throughfall, storage capacity, daily leaf and trunk area)
Depression storage (impervious)
Evaporation and transpiration from vegetation, soil and water surfaces
Infiltration into soils
Subsurface, overland and impervious runoff
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Model Outputs
For each time step (1 hour for these simulations):
Canopy interception
Depression storage
Infiltration
Evapotranspiration
Surface and subsurface (base flow) runoff
Channel discharge (total runoff)
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Water Quality
Separate program with inputs from UFORE Hydro files
Multiple options that incorporate universal soil loss equation; buildup wash off routines
Currently only using EMC
Many other options need more input data
Dissolved sediment / solid pollutant load
Septic load
Dissolved pollutant concentration
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Baisman Run Watershed Area (m2) 3,844,800
Percent Impervious cover 0.2
Percent Tree Cover 68.7
Percent of Tree Cover over Impervious Area 5
Percent Water Cover 0
Average Tree Leaf Area Index (LAI) 3.5
Percent Shrub Cover 7.8
Percent Grass Cover 20
Percent Evergreen Trees 4.2
Percent Evergreen Shrubs 21
Shrub LAI 3.9
Leaf on Day 80
Leaf off Day 294
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Baisman Run
CRF1 = 0.56 CRF2 = 0.63 CRF3 = 0.70
Red – Observed; Black - Modeled
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100
80
60
40
20
0
020
4060
8095
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
900,000
1,000,000
Runoff
(m3/yr)
Tree Cover
(%)
Impervious Cover (%)
Baisman Run
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Baisman Run
0.0
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20.0
30.0
40.0
50.0
60.0
0 10 20 30 40 50 60 70 80 90 100
Percent Impervious Cover
Perc
ent C
hange in
Annual S
tream
Flo
w .
0.0
2.0
4.0
6.0
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10.0
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Percent Tree Cover
Perc
ent C
hange in
Annual S
tream
Flo
w .
Impervious held at 10% Canopy held at 70%
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Canopy
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i-Tree Canopy (v. 4.0)
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You choose the cover classes
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Classify random points
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Statistics
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Pest
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Pest Detection Protocol
Collect Pest & Disease
Signs
Symptoms
Reports Associated pest & diseases
Trends/patterns
Pest
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I-PED Pest Evaluation & Detection (beta)
IPED Goal- detect
pest and diseases in
urban environments
as soon as possible
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Reporting by species, zone or street
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IPED Online Diagnostic Key
http://wiki.bugwood.org/IPED
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What Does Vue Do?
Utilizes existing land cover data maps for analysis
Provides modeling for future planting scenarios
Analyzes canopy cover
Illustrates ecosystem services
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How Does Vue Work?
Utilizes existing public data sets
Produces simple maps
Can be used at various scales
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Vue Home Page
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Vue Home Page
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Main Navigation Window
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Main Navigation Window
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Analysis Tabs
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Map Output – Carbon Storage
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Save Options
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Analysis Report – Carbon Storage
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Map Output – Canopy Stocking
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Map Output – Canopy Stocking
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Analysis Report – Canopy Stocking
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Street Tree Storm Damage Estimates
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Pre-Storm Sample Survey
PLOT GENERATOR
Post-Storm Survey
Random Plots
Estimating Engine
Final Damage Estimate
The SDAP Process
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How is an assessment done? i-Tree – Step 1
Determine Study Area
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i-Tree – Step 2
Determine if inventory or sample
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Typically 200 1/10 acre plots
i-Tree – Step 2a
Determine Number of Plots
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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
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i-Tree – Step 4 Lay sample points
Random Grid Pattern
Stratified by LU Random Pattern
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Random with no Stratification
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Random with Stratification
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i-Tree – Step 5 Set up project
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i-Tree – Step 6
Train crews and collect field data
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i-Tree – Step 7
Enter data and analyze
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i-Tree analyses
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Automatic Report Generator
i-Tree – Step 8
Use data and reports to make a difference