Tracking Burned Area Progression in an Unsupervised Manner Using Sentinel-1 SAR Data in Google Earth Engine

Abstract

The frequency of wildfires is increasing worldwide, contributing to a third of forest loss over the last two decades. Tracking burned area progression using traditional optical remote sensing is hindered by cloud and smoke coverage. Therefore, this research employs multitemporal synthetic aperture radar (SAR) satellite data, which are not susceptible to atmospheric effects. Focusing on four Greek wildfires in 2021, the research utilizes unsupervised k-means clustering on bitemporal and multitemporal SAR polarimetric features. The impact of input feature smoothing with varying moving kernel window sizes was assessed to improve accuracy. The use of these smoothed features led to a substantial improvement in accuracy across all four areas examined, while a window size of 19 × 19 was chosen as the right balance between preserving fine details and minimizing speckle. Furthermore, adding a filter after clustering to remove areas smaller than 2 ha led to additional improvements in accuracy, especially in commission error. The results using the defined settings revealed F1 scores of 0.75–0.88, overall accuracy of 81%–94%, and omission/commission errors of 33%–16% and 14%–3%, respectively. Challenges were observed in regions characterized by a substantial share of agricultural areas, while terrain effects revealed no substantial effects on the results. The assumption that the SAR will be sensitive mainly to bigger structural changes was proved in the visual validation using high-resolution imagery. In addition, a Google Earth Engine toolbox “Sentinel-1 Burned Area Progression” was developed using the presented methodology and is freely available for the scientific community on GitHub.

Keywords

• Burned area
• Google Earth Engine (GEE)
• Greece
• synthetic aperture radar (SAR)
• Sentinel-1
• unsupervised learning