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Application of Maximum Likelihood and Spectral Angle Mapping Classification Techniques to Evaluate Forest Fire Severity from UAV Multi-spectral Images in South Korea2.48 MB
Published VersionCC BY V4.0, Open Access
Journal article
Application of Maximum Likelihood and Spectral Angle Mapping Classification Techniques to Evaluate Forest Fire Severity from UAV Multi-spectral Images in South Korea
Sensors and Materials, Vol.33(11), pp.3745-3760
2021
Abstract
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.
Details
- Title
- Application of Maximum Likelihood and Spectral Angle Mapping Classification Techniques to Evaluate Forest Fire Severity from UAV Multi-spectral Images in South Korea
- Authors
- Heesung Woo (Author) - Kangwon National UniversityMauricio Acuna (Author) - University of the Sunshine Coast, Queensland, Forest Industries Research CentreBuddhika Madurapperuma (Author) - Cal Poly HumboldtGeonhwi Jung (Author) - Kyungpook National UniversityChoongshik Woo (Author) - National Institute of Forest ScienceJoowon Park (Author) - Kyungpook National University
- Publication details
- Sensors and Materials, Vol.33(11), pp.3745-3760
- Publisher
- M Y U - Scientific Publishing Division
- Date published
- 2021
- DOI
- 10.18494/SAM.2021.3365
- ISSN
- 2435-0869; 0914-4935
- Copyright note
- This work is licensed under a Creative Commons Attribution 4.0 International License.
- Organisation Unit
- University of the Sunshine Coast, Queensland; Forest Industries Research Centre; Forest Research Institute
- Language
- English
- Record Identifier
- 99541108602621
- Output Type
- Journal article
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- Domestic collaboration
- International collaboration
- Web Of Science research areas
- Instruments & Instrumentation
- Materials Science, Multidisciplinary
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