Next Article in Journal
Effects of Using Different Sources of Remote Sensing and Geographic Information System Data on Urban Stormwater 2D–1D Modeling
Next Article in Special Issue
Real-Time Transportation Mode Identification Using Artificial Neural Networks Enhanced with Mode Availability Layers: A Case Study in Dubai
Previous Article in Journal
Fabrication and Characterizations of Hot-Melt Extruded Nanocomposites Based on Zinc Sulfate Monohydrate and Soluplus
Previous Article in Special Issue
Impacts of Sample Design for Validation Data on the Accuracy of Feedforward Neural Network Classification
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessFeature PaperArticle
Appl. Sci. 2017, 7(9), 912; doi:10.3390/app7090912

Habitat Potential Mapping of Marten (Martes flavigula) and Leopard Cat (Prionailurus bengalensis) in South Korea Using Artificial Neural Network Machine Learning

1
Geological Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro Yuseong-gu, Daejeon 34132, Korea
2
Korea University of Science and Technology, 217 Gajeong-ro Yuseong-gu, Daejeon 34113, Korea
3
Department of Geoinformatics, University of Seoul, 163 Seoulsiripdaero, Dongdaemun-gu, Seoul 02504, Korea
4
Center for Environmental Assessment Monitoring, Environmental Assessment Group, Korea Environment Institute (KEI), 370 Sicheong-daero, Sejong 30147, Korea
5
Department of Landscape Architecture, Dankook University, 119 Dandae-ro, Dongnam-gu, Cheonan-si, Chungnam 31116, Korea
*
Author to whom correspondence should be addressed.
Received: 15 July 2017 / Revised: 18 August 2017 / Accepted: 31 August 2017 / Published: 5 September 2017
(This article belongs to the Special Issue Application of Artificial Neural Networks in Geoinformatics)
View Full-Text   |   Download PDF [4656 KB, uploaded 6 September 2017]   |  

Abstract

This study developed habitat potential maps for the marten (Martes flavigula) and leopard cat (Prionailurus bengalensis) in South Korea. Both species are registered on the Red List of the International Union for Conservation of Nature, which means that they need to be managed properly. Various factors influencing the habitat distributions of the marten and leopard were identified to create habitat potential maps, including elevation, slope, timber type and age, land cover, and distances from a forest stand, road, or drainage. A spatial database for each species was constructed by preprocessing Geographic Information System (GIS) data, and the spatial relationship between the distribution of leopard cats and environmental factors was analyzed using an artificial neural network (ANN) model. This process used half of the existing habitat location data for the marten and leopard cat for training. Habitat potential maps were then created considering the relationships. Using the remaining half of the habitat location data for each species, the model was validated. The results of the model were relatively successful, predicting approximately 85% for the marten and approximately 87% for the leopard cat. Therefore, the habitat potential maps can be used for monitoring the habitats of both species and managing these habitats effectively. View Full-Text
Keywords: habitat mapping; marten; leopard cat; ANN; South Korea habitat mapping; marten; leopard cat; ANN; South Korea
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Lee, S.; Lee, S.; Song, W.; Lee, M.-J. Habitat Potential Mapping of Marten (Martes flavigula) and Leopard Cat (Prionailurus bengalensis) in South Korea Using Artificial Neural Network Machine Learning. Appl. Sci. 2017, 7, 912.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top