assessing urban forests

15
Assessing Urban Forests Top-down Bottom- up

Upload: alida

Post on 15-Feb-2016

44 views

Category:

Documents


0 download

DESCRIPTION

Assessing Urban Forests. Top-down. Bottom-up. Assessing Urban Forests. Top-down Produces good cover estimates Can detail and map tree and other cover locations Bottom-up Provides detailed management information - PowerPoint PPT Presentation

TRANSCRIPT

Page 1: Assessing Urban Forests

Assessing Urban Forests

Top-downBottom-up

Page 2: Assessing Urban Forests

Assessing Urban ForestsTop-down

Produces good cover estimatesCan detail and map tree and other cover locations

Bottom-upProvides detailed management information

No. trees, spp. composition, tree sizes and health, tree locations, risk information…

Provides better means to assess and project ecosystem services and values

Air pollution removal, carbon storage…

Page 3: Assessing Urban Forests

Top-down Approaches

30 m resolution imagery (NLCD)

High resolution imagery (UTC)

Photo-interpretation (GIS or iTree Canopy)

Page 4: Assessing Urban Forests

NLCD

Advantages FreeCovers of lower 48 statesData from circa 2011

Disadvantages Coarse resolution Better suited for state or regional analyses Initial analysis - underestimates tree cover

Eg. Syracuse 18% NLCD11 vs. PI 30% vs. UTC10 28%

Page 5: Assessing Urban Forests

UTCAdvantages

Accurate, high-resolution cover mapComplete census of tree canopy locationsIntegrates well with GISAllows the data to be summarized at a broad range of scalesLocates potentially available spaces to plant trees

Disadvantages Can be costly if the data are low quality or incomplete (LiDAR)Requires highly trained personnel along with specialized softwareSignificant effort and time needed to produce quality mapsChange analyses can locate false changes due to map inaccuracies

Page 6: Assessing Urban Forests

Photo-Interpretation i-Tree Canopy

AdvantagesLow cost Accuracy can be easily increased Can produce sub-area analyses

Disadvantages Does not produce detailed cover mapPhoto-interpreters can create errors though misclassificationsLeaf-off imagery can be difficult to interpreti-Tree Canopy interpretation limited to Google imagesResulting data cannot be summarized at multiple, user-defined scales

Page 7: Assessing Urban Forests

N = Total pointsP = no. hits; Q = no. misses (N – P)p = % hits (P/N); q = % misses (Q/N)%cover = P/NSE = sqrt (p • q / N)

Page 8: Assessing Urban Forests

Standard Error

Measure of precision– 68% confidence = +/- 1 SE– 95% confidence = +/- 1.96 SE– 99% confidence = +/- 2.58 SE

E.g., at 95% CI (Margin of Error)– 30% canopy, 220 pts, ME +/- 6%– Between 24% and 36%

Page 9: Assessing Urban Forests

Effect of sample size on precision

0.0

10.0

20.0

30.0

40.0

50.0

60.0

10 30 50 70 90 110 130 150 170 190 210 230 250 270 290 310 330 350 370 390 410 430 450 470 490

Number of Plots

Rel

ativ

e St

anda

rd E

rror

(%)

Page 10: Assessing Urban Forests

Demo

http://www.itreetools.org/canopy/index.php

Page 11: Assessing Urban Forests

NLCD11 18% 3,000 acres

Page 12: Assessing Urban Forests

UTC 2010 28% 4,700 acres

Page 13: Assessing Urban Forests
Page 14: Assessing Urban Forests

iTree Canopy14 30% 4,800 acres

Page 15: Assessing Urban Forests

Questions?