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Comparison between Artificial and Human Estimates in Urban Tree Canopy Assessments

Author

Listed:
  • 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
    Urban Forestry, CN Utility Services, 5930 Grand Ave., West Des Moines, IA 50266, USA)

  • Deborah R. Hilbert

    (Gulf Coast Research and Education Center, 14625 County Road 672, Wimauma, FL 33598, USA
    Many Trees Consulting, LLC, St. Petersburg, FL 33705, 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)

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.

Suggested Citation

  • Eden F. Clymire-Stern & Richard J. Hauer & Deborah R. Hilbert & Andrew K. Koeser & Dan Buckler & Laura Buntrock & Eric Larsen & Nilesh Timilsina & Les P. Werner, 2022. "Comparison between Artificial and Human Estimates in Urban Tree Canopy Assessments," Land, MDPI, vol. 11(12), pages 1-13, December.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:12:p:2325-:d:1007324
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    References listed on IDEAS

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    1. Kirsten Schwarz & Michail Fragkias & Christopher G Boone & Weiqi Zhou & Melissa McHale & J Morgan Grove & Jarlath O’Neil-Dunne & Joseph P McFadden & Geoffrey L Buckley & Dan Childers & Laura Ogden & S, 2015. "Trees Grow on Money: Urban Tree Canopy Cover and Environmental Justice," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-17, April.
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