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Urban tree canopy is widely believed to have myriad environmental, social, and
human-health benefits, but a lack of precise canopy estimates has hindered quantification of
these benefits in many municipalities. This problem was addressed for New York City using
object-based image analysis (OBIA) to develop a comprehensive land-cover map, including
tree canopy to the scale of individual trees. Mapping was performed using a rule-based expert
system that relied primarily on high-resolution LIDAR, specifically its capacity for evaluating
the height and texture of aboveground features. Multispectral imagery was also used, but
shadowing and varying temporal conditions limited its utility. Contextual analysis was a key
part of classification, distinguishing trees according to their physical and spectral properties
as well as their relationships to adjacent, nonvegetated features. The automated product
was extensively reviewed and edited via manual interpretation, and overall per-pixel accuracy
of the final map was 96%. Although manual editing had only a marginal effect on accuracy
despite requiring a majority of project effort, it maximized aesthetic quality and ensured the
capture of small, isolated trees. Converting high-resolution LIDAR and imagery into usable
information is a nontrivial exercise, requiring significant processing time and labor, but an expert
system–based combination of OBIA and manual review was an effective method for fine-scale
canopy mapping in a complex urban environment. © 2012 Society of Photo-Optical Instrumentation
Engineers (SPIE). [DOI: 10.1117/1.JRS.6.063567]
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