Monitoring of the Fire in Muli County on March 28, 2020, Based on High Temporal-Spatial Resolution Remote Sensing techniques

Abstract Wildfires are serious natural disasters, and fire monitoring has become an important task for the protection of forest resources. Remote sensing technology has become one of the most commonly used tools for wildfire monitoring and fire point identification. However, single-source remote sensing data cannot meet the fire monitoring requirements of temporal resolution and spatial resolution simultaneously, which affects the efficiency and timeliness of monitoring. With the wildfire in Wakeliangzi, Xiangjiao township, Muli county, Liangshan Yi Autonomous Prefecture, Sichuan Province, China, on March 28, 2020 as the study object, fire monitoring was carried out based on a collaboration of multi-source remote sensing data acquired by the Sentinel-2 satellite, Landsat-8 satellite and the GF-4 satellite. Based on the integrated shortwave and infrared bands of the Sentinel-2 satellites, which were sensitive to fire information, the fire patch was analyzed. The normalized differential vegetation indexes (NDVIs) and normalized burn ratios (NBRs) were calculated before and after the fire, and then, the burned area and fire severity were calculated based on the obtained NDVIs and NBRs. We performed time series monitoring of the fire points based on the remote sensing data acquired by the GF-4 satellite with a high temporal resolution. Moreover, according to the remote sensing data in the medium-wave infrared band, which is sensitive to fire information, the wildfire point was located based on the smoke screen and temperature. Lastly, we calculated the vegetation coverage based on the data from the Sentinel-2 satellites and analyzed the relationship between vegetation coverage and fire severity. With the remote sensing data acquired by the Landsat-8 satellites in the integrated shortwave and infrared bands, the spatial distribution of the fire patch was quickly and effectively obtained. The total burned area reached 180.37Km2, 117.24Km2 of which had a low fire severity, 27.05Km2 featured a medium fire severity and 45.09Km2 had a high fire severity. Compared with the results of manual visual interpretation and cross validation of published results, the recognition accuracy of different fire areas is better.The multispectral data and medium-wave infrared band data acquired by the GF-4 satellite enabled us to locate the fire point and monitor the dynamic time series of the fire. The linear regression showed that the vegetation fractional cover (VFC) was significantly correlated with the relative differenced normalized burn ratio (dNBR). By combining the high temporal-spatial resolution remote sensing data acquired by the Sentinel-2 satellites and the GF-4 satellite, wildfires can be dynamically monitored and quantitatively analyzed, thereby achieving precise, macro-level fire monitoring. The findings in this study not only provide information for the Muli fire, but the results also offer a technical reference for wildfire remote sensing monitoring and emergency response.