Journal
LAND
Volume 11, Issue 4, Pages -Publisher
MDPI
DOI: 10.3390/land11040578
Keywords
typological habitats; habitat monitoring; habitat suitability models; machine learning; Artificial Neural Network (ANN); Decision Tree & Rules
Categories
Funding
- National Institute of Ecology [NIE-A-2021-01]
- Korea Forest Service (Korea Forestry Promotion Institute) [FTIS 2021331A00-2223-AA01]
- National Research Foundation of Korea (NRF) - Korea government (MSIT) [NRF-2021R1F1A1059444]
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This study explores habitat suitability models for Paulownia coreana using national ecological survey data and various environmental factors. Unlike other studies, this research employed on-site survey data and took into account localized environmental resources to project habitat suitability.
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.
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