| Title |
Mapping Burned Forests Using a k-Nearest Neighbors Classifier in Complex Land Cover |
| Authors |
이한나(Lee, Hanna) ; 윤공현(Yun, Konghyun) ; 김기홍(Kim, Gihong) |
| DOI |
https://doi.org/10.12652/Ksce.2023.43.6.0883 |
| Keywords |
산불; 피해 탐지; k-Nearest Neighbor; 분류; Sentinel-2 Forest fire; Damage detection; k-Nearest neighbor; Classification; Sentinel-2 |
| Abstract |
As human activities in Korea are spread throughout the mountains, forest fires often affect residential areas, infrastructure, and other facilities. Hence, it is necessary to detect fire-damaged areas quickly to enable support and recovery. Remote sensing is the most efficient tool for this purpose. Fire damage detection experiments were conducted on the east coast of Korea. Because this area comprises a mixture of forest and artificial land cover, data with low resolution are not suitable. We used Sentinel-2 multispectral instrument (MSI) data, which provide adequate temporal and spatial resolution, and the k-nearest neighbor (kNN) algorithm in this study. Six bands of Sentinel-2 MSI and two indices of normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as features for kNN classification. The kNN classifier was trained using 2,000 randomly selected samples in the fire-damaged and undamaged areas. Outliers were removed and a forest type map was used to improve classification performance. Numerous experiments for various neighbors for kNN and feature combinations have been conducted using bi-temporal and uni-temporal approaches. The bi-temporal classification performed better than the uni-temporal classification. However, the uni-temporal classification was able to detect severely damaged areas. |