Fire has many roles around the world. It is a naturally occurring ecological process in fire-prone regions and a tool humans use for land and resource management. However, fires and their emissions continue to have more extreme impacts as human settlements expand, climatic conditions become less predictable, and fire seasons lengthen (Jolly et al. 2015). To better identify and quantify the effects of fires across the globe, it is vital to monitor fires using various methods, including hand-drawn maps, ground-based sensors, GPS tracking, aerial surveys, imagery collection, and satellite-based data (Andela et al. 2019; Archibald et al. 2009; Nogueira et al. 2016; Stinson et al. 2011; Veraverbeke et al. 2014).
Different sources of satellite imagery can be used to visualize fire conditions and progressions, calculate band ratios reflecting disturbance and fire severity, and map burned areas with training data-informed classification algorithms. Many premade fire datasets are readily available for monitoring global fire locations, extents, and progressions. In the case of existing fire datasets available for large spatial and temporal extents, remote sensing scientists apply their robust classification algorithms to satellite imagery and other geospatial data. Earth Engine makes fire monitoring more accessible by sharing multiple data sources in the data catalog so users can easily access and process them to meet their desired objectives.