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.

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Presentation transcript:

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 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…

Top-down Approaches 30 m resolution imagery (NLCD) High resolution imagery (UTC) Photo-interpretation (GIS or iTree Canopy)

NLCD Advantages Free Covers of lower 48 states Data 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%

UTC Advantages Accurate, high-resolution cover map Complete census of tree canopy locations Integrates well with GIS Allows the data to be summarized at a broad range of scales Locates 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 software Significant effort and time needed to produce quality maps Change analyses can locate false changes due to map inaccuracies

Photo-Interpretation i-Tree Canopy Advantages Low cost Accuracy can be easily increased Can produce sub-area analyses Disadvantages Does not produce detailed cover map Photo-interpreters can create errors though misclassifications Leaf-off imagery can be difficult to interpret i-Tree Canopy interpretation limited to Google images Resulting data cannot be summarized at multiple, user-defined scales

N = Total points P = no. hits; Q = no. misses (N – P) p = % hits (P/N); q = % misses (Q/N) %cover = P/N SE = sqrt (p q / N)

Standard Error Measure of precision –68% confidence = +/- 1 SE –95% confidence = +/ SE –99% confidence = +/ SE E.g., at 95% CI (Margin of Error) –30% canopy, 220 pts, ME +/- 6% –Between 24% and 36%

Effect of sample size on precision

Demo

NLCD11 18% 3,000 acres

UTC % 4,700 acres

iTree Canopy14 30% 4,800 acres

Questions?