S&M2721 Research Paper of Special Issue
Published in advance: May 28, 2021
Published: November 17, 2021
Application of Maximum Likelihood and Spectral Angle Mapping Classification Techniques to Evaluate Forest Fire Severity from UAV Multi-spectral Images in South Korea [PDF]
Heesung Woo, Mauricio Acuna, Buddhika Madurapperuma, Geonhwi Jung, Choongshik Woo, and Joowon Park
(Received March 17, 2021; Accepted May 17, 2021)
Keywords: forest fire, UAV, remote sensing, accuracy assessment, machine learning
High-resolution unmanned aerial vehicle (UAV) multi-spectral sensor images can provide valuable information for mapping forest areas that have recently been burned. In this study, we investigate the use of multi-spectral images captured with a UAV to evaluate burn severity in areas affected by forest fires in Gumi-si, South Korea. Fire classification was performed using two supervised learning algorithms, maximum likelihood (ML) and spectral angle mapper (SAM). Three spectral indices, namely, normalized difference vegetation index (NDVI), RedEdge NDVI (RE-NDVI), and the visible-band difference vegetation index (VDVI), were used to create burn severity thresholds in ML and SAM classifiers. The classification results indicated that ML has higher overall accuracy (80–89%, Kappa coefficient = 0.8) than SAM (44–52%, Kappa coefficients ~0.27) in identifying fire severity classes. The ML classifier showed higher accuracy for both unburned and crown fire classes, whereas the SAM classifier exhibited moderate accuracy for all classes. Most of the misclassification was identified between the unburned area and the low heat-damaged area. This research revealed that distinguishing between the unburned area and low heat-damaged area is the most challenging task in fire severity classification. Also, further investigation is required to improve the accuracy of fire severity classification from multi-spectral images.Corresponding author: Joowon Park
This work is licensed under a Creative Commons Attribution 4.0 International License.
Cite this article
Heesung Woo, Mauricio Acuna, Buddhika Madurapperuma, Geonhwi Jung, Choongshik Woo, and Joowon Park, Application of Maximum Likelihood and Spectral Angle Mapping Classification Techniques to Evaluate Forest Fire Severity from UAV Multi-spectral Images in South Korea, Sens. Mater., Vol. 33, No. 11, 2021, p. 3745-3760.