A Comparison of Geospatial Methods for Tree Canopy Assessment: A Case Study of an Urbanized College Campus

Authors: Won Hoi Hwang*, Virginia Tech, P. Eric Wiseman, Virginia Tech
Topics: Urban Geography, Human-Environment Geography, Cultural Ecology
Keywords: i-Tree Canopy, i-Tree Landscape, urban forestry, geospatial analysis, photo interpretation, image classification, urban trees, remote sensing
Session Type: Paper
Day: 4/3/2019
Start / End Time: 8:00 AM / 9:40 AM
Room: Congressional A, Omni, West
Presentation File: No File Uploaded


Urban tree canopy (UTC) assessment is essential for understanding the structure and function of urban forests and devising management strategies. Geospatial techniques are routinely utilized for UTC assessment, yet their capabilities and limitations may not be apparent to urban forestry practitioners. In this paper, we provide an overview of two primary methods of geospatial UTC assessment: photo interpretation (PI) and computerized image classification (IC). We then evaluate these methods through a case study of an urbanized college campus in the eastern United States. We examined the web-based application i-Tree Canopy as a PI method. Because this method relies on statistical point sampling, we performed independently replicated assessments of our study area at various point sample sizes to examine the effect of sample sizes on accuracy and certainty of the land cover estimates. We further evaluated two IC methods: a proprietary analysis using high-spatial resolution imagery and a low-spatial resolution analysis using the web-based application i-Tree Landscape. Tree cover assessed in our study area (3.58 km2) with i-Tree Canopy began stabilizing around the weighted mean (14.7%) at a sample size of 100 points but required 250 points or more to reach a tolerable standard error for the estimate. By comparison with the proprietary analysis of high-resolution imagery (16.1%, considered the most robust form of assessment), i-Tree Canopy slightly underestimated tree cover (14.7%), and i-Tree Landscape substantially underestimated tree cover (11.3%). Possible causes of variation in estimates amongst the methods and practical considerations for choosing a UTC assessment method are discussed.

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