Wildfire Detection and Burned Area Estimation Based on Multi-source Spatial Data

Forest is an important part of the global ecosystem and is of great significance to the sustainable development of the ecological environment. As the rising temperatures of the earth, forests become increasingly dry and wildfires occur more frequently, which brings great losses to global forest resources and the safety of individuals’ lives. Therefore, wildfire detection is of great significance for environment protection. Recently, thermal infrared remote sensing shows the advantages of wide coverage and long observation time, which provides an important technology for the identification of large-scale and all-weather wildfires. In this paper, we use remote sensing data to detect wildfires by leveraging deep learning algorithm and estimate burned areas. Specifically, We first investigate the characteristics of wildfires in thermal infrared remote sensing imagery and make a dataset, and then train a Faster R-CNN model to detect hotspots. Next, we filter out nonforest areas to discover wildfire by fusing the global forest cover map. Finally, we estimate the burned area through the changes of the Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR) of remote sensing images before and after wildfire. Results show that our method can accurately detect wildfires with F1-Score of 0.881 and error of the burned area estimation is 1.18%, and it is certified on social media.