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

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.

Exploration in Mapping Kernel-Based Home Range Models from Remote Sensing Imagery with Conditional Adversarial Networks

“…Let us review the traditional habitat mapping studies [14,29,62], which virtually assume that each pixel in remote sensing images is an independent vector in a multi-dimensional environmental space. They mainly employ traditional classification or regression models in the data mining and machine learning field, to make predictions at the pixel level.…”

“…The experiment demonstrates that our framework could produce visually recognizable and highly accurate results from remote sensing imagery. Our approach could be generalized to map other types of habitat models as well, such as habitat suitability models [73] and habitat potential models [29]. The deep neural network could help to seek the relationship between animal habitat and environmental factors, instead of the GPS data used in these models.…”

“…Compared to its great success in the vegetation field [23,24], the related application to animals is limited by more complicated correspondence [25][26][27]. Some studies [13,14,28,29] leverage the remote sensing imagery to map the quality and extent of wildlife habitats. These studies mostly focus on the specific species and have different source data, mapping models, and final targets.…”

Editorial for Special Issue: “Application of Artificial Neural Networks in Geoinformatics”

“…The first and second papers, authored by Lee, S., Lee, M., Jung, H. [1] and Oh, H., Lee, S. [2], applied GIS and various machine learning algorithms such as artificial neural networks, support vector machines, and boosted tress to map landslide susceptibility. The third paper, authored by Lee, S., Lee, S., Song, W., Lee, M. [3], applied GIS with artificial neural networks to map potential marten and leopard habitats. The fourth paper, authored by Shah, S., Brijs, T., Ahmad, N., Pirdavani, A., Shen, Y., Basheer, M. [4], used data envelopment analysis in GIS with artificial neural networks to evaluate risks related to road safety.…”

Exploiting Time-Series Image-to-Image Translation to Expand the Range of Wildlife Habitat Analysis

“…However, most of these studies can be hardly expected to map general habitat models. That is because they mostly focus on the specific species and map the self-defined habitat category or index (Lee et al 2017). Moreover, their mapping models are mainly the traditional machine learning classifiers or regressors which work on individual pixels (Chegoonian, Mokhtarzade, and Valadan Zoej 2017).…”