An Enhanced Algorithm for Active Fire Detection in Croplands Using Landsat-8 OLI Data

Yizhu Jiang, Jinling Kong (), Yanling Zhong, Qiutong Zhang and Jingya Zhang
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Yizhu Jiang: School of Earth Science and Resources, Chang’an University, 126 Yanta Road, Xi’an 710054, China
Jinling Kong: School of Geological Engineering and Geomatics, Chang’an University, 126 Yanta Road, Xi’an 710054, China
Yanling Zhong: School of Geological Engineering and Geomatics, Chang’an University, 126 Yanta Road, Xi’an 710054, China
Qiutong Zhang: School of Geological Engineering and Geomatics, Chang’an University, 126 Yanta Road, Xi’an 710054, China
Jingya Zhang: School of Geological Engineering and Geomatics, Chang’an University, 126 Yanta Road, Xi’an 710054, China

Land, 2023, vol. 12, issue 6, 1-19

Abstract: Burning biomass exacerbates or directly causes severe air pollution. The traditional active fire detection (AFD) methods are limited by the thresholds of the algorithms and the spatial resolution of remote sensing images, which misclassify some small-scale fires. AFD for burning straw is interfered with by highly reflective buildings around urban and rural areas, resulting in high commission error (CE). To solve these problems, we developed a multicriteria threshold AFD for burning straw (SAFD) based on Landsat-8 imagery in the context of croplands. In solving the problem of the high CE of highly reflective buildings around urban and rural areas, the SAFD algorithm, which was based on the LightGBM machine learning method (SAFD-LightGBM), was proposed to differentiate active fires from highly reflective buildings with a sample dataset of buildings and active fires and an optimal feature combining spectral features and texture features using the ReliefF feature selection method. The results revealed that the SAFD-LightGBM method performed better than the traditional threshold method, with CE and omission error (OE) of 13.2% and 11.5%, respectively. The proposed method could effectively reduce the interference of highly reflective buildings for active fire detection, and it has general applicability and stability for detecting discrete, small-scale fires in urban and rural areas.

Keywords: remote sensing; active fire detection; machine learning; Landsat-8; LightGBM (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
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