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

Yeeun Shin, Suyeon Kim, Se-Rin Park, Taewoo Yi, Chulgoo Kim, Sang-Woo Lee and Kyungjin An
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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

Land, 2022, vol. 11, issue 4, 1-16

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.

Keywords: typological habitats; habitat monitoring; habitat suitability models; machine learning; Artificial Neural Network (ANN); Decision Tree & Rules (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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