Unsupervised Burned Area Mapping in Greece: Investigating the Impact of Precipitation, Pre- and Post-Processing of Sentinel-1 Data in Google Earth Engine
Abstrakt
Wildfires are one of the most significant threats to ecosystems and are increasing in frequency globally. The aim of this study is to monitor the evolution of selected wildfires in Greece that occurred during August 2021 using Sentinel-1 SAR data and unsupervised k-means clustering in Google Earth Engine.
First, changes in time series after the start of the fire and the influence of precipitation were investigated. In this study, the influence of different speckle filters and post-classification filters on clustering results was tested.
The difference Normalized Burn Ratio Index (dNBR) derived from Sentinel-2 data was used as a validation dataset to assess accuracy using the F1-score, overall accuracy, omission and commission error. The best achieved F1-scores were higher than 0.70 with omission error lower than 35% in all selected areas, where the Lee speckle filter with an 11x11 kernel window size and a 2 ha post-classification filter performed the best.