Application of maximum likelihood and spectral angle mapping classification techniques to evaluate forest fire severity from UAV multi-spectral images in South Korea

Heesung Woo, Mauricio Acuna, Buddhika Madurapperuma, Geonhwi Jung, Choongshik Woo, Joowon Park

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

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.

Original languageEnglish
Pages (from-to)3745-3760
Number of pages16
JournalSensors and Materials
Volume33
Issue number11
DOIs
StatePublished - 2021

Keywords

  • Accuracy assessment
  • Forest fire
  • Machine learning
  • Remote sensing
  • UAV

Fingerprint

Dive into the research topics of 'Application of maximum likelihood and spectral angle mapping classification techniques to evaluate forest fire severity from UAV multi-spectral images in South Korea'. Together they form a unique fingerprint.

Cite this