A Multiscale Normalization Method of a Mixed-Effects Model for Monitoring Forest Fires Using Multi-Sensor Data

Lanbo Feng, Huashun Xiao, Zhigao Yang and Gui Zhang
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Lanbo Feng: School of Forestry, Central South University of Forestry and Technology, Changsha 410004, China
Huashun Xiao: School of Forestry, Central South University of Forestry and Technology, Changsha 410004, China
Zhigao Yang: National Forest Fire Prevention Virtual Simulation Experimental Teaching Center, Changsha 410004, China
Gui Zhang: Key Laboratory of Digital Dongting Lake of Hunan Province, Changsha 410004, China

Sustainability, 2022, vol. 14, issue 3, 1-16

Abstract: This paper points out the shortcomings of existing normalization methods, and proposes a brightness temperature inversion normalization method for multi-source remote sensing monitoring of forest fires. This method can satisfy both radiation normalization and observation angle normalization, and reduce the discrepancies in forest fire monitoring between multi-source sensors. The study was based on Himawari-8 data; the longitude, latitude, solar zenith angle, solar azimuth angle, emissivity, slope, aspect, elevation, and brightness temperature values were collected as modeling parameters. The mixed-effects brightness temperature inversion normalization (MEMN) model based on FY-4A and Himawari-8 satellite sensors is fitted by multiple stepwise regression and mixed-effects modeling methods. The results show that, when the model is tested by Himawari-8 data, the coefficient of determination ( R 2 ) reaches 0.8418, and when it is tested by FY-4A data, R 2 reaches 0.8045. At the same time, through comparison and analysis, the accuracy of the MEMN method is higher than that of the random forest normalization method (RF) ( R 2 = 0.7318 ), the pseudo-invariant feature method (PIF) ( R 2 = 0.7264 ), and the automatic control scatter regression method (ASCR) ( R 2 = 0.6841 ). The MEMN model can not only reduce the discrepancies in forest fire monitoring owing to different satellite sensors between FY-4A and Himawari-8, but also improve the accuracy and timeliness of forest fire monitoring.

Keywords: Himawari-8; FY-4A; forest fires monitoring; brightness temperature inversion; normalization; mixed-effects model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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