Abstract
Mapping burned areas using satellite imagery has become a subject of extensive research over the past decades. The availability of high-resolution satellite data allows burned area maps to be produced with great detail. However, their increasing spatial resolution is usually not matched by a similar increase in the temporal domain. Moreover, high-resolution data can be a computational challenge. Existing methods usually require downloading and processing massive volumes of data in order to produce the resulting maps. In this work we propose a method to make this procedure fast and yet accurate by leveraging the use of a coarse resolution burned area product, the computation capabilities of Google Earth Engine to pre-process and download Sentinel-2 10-m resolution data, and a deep learning model trained to map the multispectral satellite data into the burned area maps. For a 1500 ha fire our method can generate a 10-m resolution map in about 5 min, using a computer with an 8-core processor and 8 GB of RAM. An analysis of six important case studies located in Portugal, southern France and Greece shows the detailed computation time for each process and how the resulting maps compare to the input satellite data as well as to independent reference maps produced by Copernicus Emergency Management System. We also analyze the feature importance of each input band to the final burned area map, giving further insight about the differences among these events.
Highlights
Wildfires are a natural hazard with important impacts in ecosystems and human populations (e.g., [1,2])
Mediterranean Europe is regularly affected by wildfires, a trend that is poised to increase according to the range of climate change scenarios [3,4]
This contrasts with the algorithm for near-real-time mapping of burned areas using Sentinel-2 proposed by Pulvirenti et al 2020 ([48]) for Italy where data is processed at a country level
Summary
Wildfires are a natural hazard with important impacts in ecosystems and human populations (e.g., [1,2]). Monitoring the areas burned by wildfires with high resolution is of paramount importance for damage assessment and forest management [5,6], and fire danger and propagation forecasts [7]. Burned area products can be derived based on ground observations or satellite data, often presenting significant differences at the end of the fire season [8,9]. The latter have the advantage of allowing for a consistent analysis over space and time and have been used extensively over the past decades [10]. The importance of satellite observations will keep growing as more satellite data are available with increasingly higher resolution
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