Amazon Fire Monitoring and Analysis Based on Multi-source Remote Sensing Data

Abstract

In August 2019, a large-scale fire broke out in the Amazon rainforest, bringing serious harm to the ecosystem and human beings. In order to accurately monitor the dynamic change of forest fire in Amazon rainforest and analyse the impact of fire spreading and extinction on the environment, firstly, based on NPP VIIRS data covering the Amazon fire area, the sliding window threshold method is adopted to extract the fire point, and the cause of fire change is monitored and analysed according to the time series. Secondly, based on the time series of CALIPSO data, the vertical distribution changes of atmospheric pollutants in the amazon fire area are analysed, and the comprehensive analysis is carried out by combining NPP VIIRS data. The experimental results show that only NPP VIIRS data is used to predict the fire, and the combination of CALIPSO data can better monitor the forest fire and predict the fire development trend. The combination of optical image and laser radar has greater advantages in dynamic fire monitoring and fire impact analysis. The method described in this paper can provide basic data reference for real-time and accurate prediction of forest fires and provide new ideas for dynamic fire monitoring.