Comparison between Artificial and Human Estimates in Urban Tree Canopy Assessments

Eden F. Clymire-Stern (), Richard J. Hauer, Deborah R. Hilbert, Andrew K. Koeser, Dan Buckler, Laura Buntrock, Eric Larsen, Nilesh Timilsina and Les P. Werner
Additional contact information
Eden F. Clymire-Stern: College of Natural Resources, University of Wisconsin-Stevens Point, 800 Reserve Street, Stevens Point, WI 54481, USA
Richard J. Hauer: College of Natural Resources, University of Wisconsin-Stevens Point, 800 Reserve Street, Stevens Point, WI 54481, USA
Deborah R. Hilbert: Gulf Coast Research and Education Center, 14625 County Road 672, Wimauma, FL 33598, USA
Andrew K. Koeser: Gulf Coast Research and Education Center, 14625 County Road 672, Wimauma, FL 33598, USA
Dan Buckler: Wisconsin Department of Natural Resources-Urban and Community Forestry, 101 S. Webster Street, P.O. Box 7921, Madison, WI 53707, USA
Laura Buntrock: Wisconsin Department of Natural Resources-Urban and Community Forestry, 101 S. Webster Street, P.O. Box 7921, Madison, WI 53707, USA
Eric Larsen: Department of Geography and Geology, University of Wisconsin-Stevens Point, 2001 Fourth Ave., Stevens Point, WI 54481, USA
Nilesh Timilsina: Department of Forestry and Environmental Conservation, Clemson University, 261 Lehotsky Hall Box 3403317, Clemson, SC 29631, USA
Les P. Werner: College of Natural Resources, University of Wisconsin-Stevens Point, 800 Reserve Street, Stevens Point, WI 54481, USA

Land, 2022, vol. 11, issue 12, 1-13

Abstract: Urban tree canopy (UTC) is commonly used to assess urban forest extent and has traditionally been estimated using photointerpretation and human intelligence (HI). Artificial intelligence (AI) models may provide a less labor-intensive method to estimate urban tree canopy. However, studies on how human intelligence and artificial intelligence estimation methods compare are limited. We investigated how human intelligence and artificial intelligence compare with estimates of urban tree canopy and other landcovers. Change in urban tree canopy between two time periods and an assessment agreement accuracy also occurred. We found a statistically significant ( p < 0.001) difference between the two interpretations for a statewide urban tree canopy estimate (n = 397). Overall, urban tree canopy estimates were higher for human intelligence (31.5%, 0.72 SE) than artificial intelligence (26.0%, 0.51 SE). Artificial intelligence approaches commonly rely on a training data set that is compared against a human decision maker. Within the artificial intelligence training region (n = 21) used for this study, no difference ( p = 0.72) was found between the two methods, suggesting other regional factors are important for training the AI system. Urban tree canopy also increased ( p < 0.001) between two time periods (2013 to 2018) and two assessors could detect the same sample point over 90 % of the time.

Keywords: land cover; tree canopy cover; urban forest cover; urban forestry (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
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