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Identifying Key Environmental Factors for Paulownia coreana Habitats: Implementing National On-Site Survey and Machine Learning Algorithms

Author

Listed:
  • Yeeun Shin

    (Department of Forestry and Landscape Architecture, Konkuk University, Seoul 05029, Korea)

  • Suyeon Kim

    (Rural Environment & Resource Division, National Institute of Agricultural Sciences, Wanju-gun 55365, Korea)

  • Se-Rin Park

    (Department of Forestry and Landscape Architecture, Konkuk University, Seoul 05029, Korea)

  • Taewoo Yi

    (National Institute of Ecology, Seocheon-gun 33657, Korea)

  • Chulgoo Kim

    (National Institute of Ecology, Seocheon-gun 33657, Korea)

  • Sang-Woo Lee

    (Department of Forestry and Landscape Architecture, Konkuk University, Seoul 05029, Korea)

  • Kyungjin An

    (Department of Forestry and Landscape Architecture, Konkuk University, Seoul 05029, Korea)

Abstract

Monitoring and preserving natural habitats has become an essential activity in many countries today. As a native tree species in Korea, Paulownia coreana has periodically been surveyed in national ecological surveys and was identified as an important target for conservation as well as habitat monitoring and management. This study explores habitat suitability models (HSMs) for Paulownia coreana in conjunction with national ecological survey data and various environmental factors. Together with environmental variables, the national ecological survey data were run through machine learning algorithms such as Artificial Neural Network and Decision Tree & Rules, which were used to identify the impact of individual variables and create HSMs for Paulownia coreana , respectively. Unlike other studies, which used remote sensing data to create HSMs, this study employed periodical on-site survey data for enhanced validity. Moreover, localized environmental resources such as topography, soil, and rainfall were taken into account to project habitat suitability. Among the environment variables used, the study identified critical attributes that affect the habitat conditions of Paulownia coreana . Therefore, the habitat suitability modelling methods employed in this study could play key roles in planning, monitoring, and managing plants species in regional and national levels. Furthermore, it could shed light on existing challenges and future research needs.

Suggested Citation

  • Yeeun Shin & Suyeon Kim & Se-Rin Park & Taewoo Yi & Chulgoo Kim & Sang-Woo Lee & Kyungjin An, 2022. "Identifying Key Environmental Factors for Paulownia coreana Habitats: Implementing National On-Site Survey and Machine Learning Algorithms," Land, MDPI, vol. 11(4), pages 1-16, April.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:4:p:578-:d:794230
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    References listed on IDEAS

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    1. Crisci, C. & Ghattas, B. & Perera, G., 2012. "A review of supervised machine learning algorithms and their applications to ecological data," Ecological Modelling, Elsevier, vol. 240(C), pages 113-122.
    2. Bradley, Bethany A. & Olsson, Aaryn D. & Wang, Ophelia & Dickson, Brett G. & Pelech, Lori & Sesnie, Steven E. & Zachmann, Luke J., 2012. "Species detection vs. habitat suitability: Are we biasing habitat suitability models with remotely sensed data?," Ecological Modelling, Elsevier, vol. 244(C), pages 57-64.
    3. Zohmann, Margit & Pennerstorfer, Josef & Nopp-Mayr, Ursula, 2013. "Modelling habitat suitability for alpine rock ptarmigan (Lagopus muta helvetica) combining object-based classification of IKONOS imagery and Habitat Suitability Index modelling," Ecological Modelling, Elsevier, vol. 254(C), pages 22-32.
    4. Peters, Jan & Baets, Bernard De & Verhoest, Niko E.C. & Samson, Roeland & Degroeve, Sven & Becker, Piet De & Huybrechts, Willy, 2007. "Random forests as a tool for ecohydrological distribution modelling," Ecological Modelling, Elsevier, vol. 207(2), pages 304-318.
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    Cited by:

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