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

Sustainability

Gui Zhang¹  Zhigao Yang²  Lanbo Feng³  Huashun Xiao³ 

[1] Key Laboratory of Digital Dongting Lake of Hunan Province, Changsha 410004, China;National Forest Fire Prevention Virtual Simulation Experimental Teaching Center, Changsha 410004, China;School of Forestry, Central South University of Forestry and Technology, Changsha 410004, China;

关键词: Himawari-8;    FY-4A;    forest fires monitoring;    brightness temperature inversion;    normalization;    mixed-effects model;   

DOI  :  10.3390/su14031139

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown