@article{ART003075718},
author={Zhiying Jin and Lee, Dong Kun and Eunsub Kim and Jiyoung Choi and Yoonho Jeon},
title={Mapping Mammalian Species Richness Using a Machine Learning Algorithm},
journal={Journal of Environmental Impact Assessment},
issn={1225-7184},
year={2024},
volume={33},
number={2},
pages={53-63}
TY - JOUR
AU - Zhiying Jin
AU - Lee, Dong Kun
AU - Eunsub Kim
AU - Jiyoung Choi
AU - Yoonho Jeon
TI - Mapping Mammalian Species Richness Using a Machine Learning Algorithm
JO - Journal of Environmental Impact Assessment
PY - 2024
VL - 33
IS - 2
PB - Korean Society Of Environmental Impact Assessment
SP - 53
EP - 63
SN - 1225-7184
AB - Biodiversity holds significant importance within the framework of environmental impact assessment, being utilized in site selection for development, understanding the surrounding environment, and assessing the impact on species due to disturbances. The field of environmental impact assessment has seen substantial research exploring new technologies and models to evaluate and predict biodiversity more accurately. While current assessments rely on data from fieldwork and literature surveys to gauge species richness indices, limitations in spatial and temporal coverage underscore the need for high-resolution biodiversity assessments through species richness mapping. In this study, leveraging data from the 4th National Ecosystem Survey and environmental variables, we developed a species distribution model using Random Forest. This model yielded mapping results of 24 mammalian species' distribution, utilizing the species richness index to generate a 100-meter resolution map of species richness. The research findings exhibited a notably high predictive accuracy, with the species distribution model demonstrating an average AUC value of 0.82. In addition, the comparison with National Ecosystem Survey data reveals that the species richness distribution in the high-resolution species richness mapping results conforms to a normal distribution. Hence, it stands as highly reliable foundational data for environmental impact assessment. Such research and analytical outcomes could serve as pivotal new reference materials for future urban development projects, offering insights for biodiversity assessment and habitat preservation endeavors.
KW - Biodiversity;Species distribution model;Mammal;Environmental impact assessment
DO -
UR -
ER -
Zhiying Jin, Lee, Dong Kun, Eunsub Kim, Jiyoung Choi and Yoonho Jeon. (2024). Mapping Mammalian Species Richness Using a Machine Learning Algorithm. Journal of Environmental Impact Assessment, 33(2), 53-63.
Zhiying Jin, Lee, Dong Kun, Eunsub Kim, Jiyoung Choi and Yoonho Jeon. 2024, "Mapping Mammalian Species Richness Using a Machine Learning Algorithm", Journal of Environmental Impact Assessment, vol.33, no.2 pp.53-63.
Zhiying Jin, Lee, Dong Kun, Eunsub Kim, Jiyoung Choi, Yoonho Jeon "Mapping Mammalian Species Richness Using a Machine Learning Algorithm" Journal of Environmental Impact Assessment 33.2 pp.53-63 (2024) : 53.
Zhiying Jin, Lee, Dong Kun, Eunsub Kim, Jiyoung Choi, Yoonho Jeon. Mapping Mammalian Species Richness Using a Machine Learning Algorithm. 2024; 33(2), 53-63.
Zhiying Jin, Lee, Dong Kun, Eunsub Kim, Jiyoung Choi and Yoonho Jeon. "Mapping Mammalian Species Richness Using a Machine Learning Algorithm" Journal of Environmental Impact Assessment 33, no.2 (2024) : 53-63.
Zhiying Jin; Lee, Dong Kun; Eunsub Kim; Jiyoung Choi; Yoonho Jeon. Mapping Mammalian Species Richness Using a Machine Learning Algorithm. Journal of Environmental Impact Assessment, 33(2), 53-63.
Zhiying Jin; Lee, Dong Kun; Eunsub Kim; Jiyoung Choi; Yoonho Jeon. Mapping Mammalian Species Richness Using a Machine Learning Algorithm. Journal of Environmental Impact Assessment. 2024; 33(2) 53-63.
Zhiying Jin, Lee, Dong Kun, Eunsub Kim, Jiyoung Choi, Yoonho Jeon. Mapping Mammalian Species Richness Using a Machine Learning Algorithm. 2024; 33(2), 53-63.
Zhiying Jin, Lee, Dong Kun, Eunsub Kim, Jiyoung Choi and Yoonho Jeon. "Mapping Mammalian Species Richness Using a Machine Learning Algorithm" Journal of Environmental Impact Assessment 33, no.2 (2024) : 53-63.
