S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
Award Criteria
Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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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 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




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