Spectral angle indices for burned area detection in Chile using Sentinel-2 data and a Random Forest classifier

Every year, fires are more frequent and intense due to weather and vegetation conditions. In January 2017, 114 active fires burned throughout Chile at the same time. These fires spread quickly due to the high temperatures, strong dry winds, and low vegetation water content. The fire events burned more than 570,000 ha, from which 20% of the area was endangered native forest. Almost half of the area burned in Chile 2017 occurred in the Region of Maule.

This study aimed to develop and implement an algorithm for burned area (BA) classification using Sentinel-2 data at 20 meters resolution on Google Earth Engine (GEE) computing platform. GEE allows access to an extensive database of various satellite imagery datasets and a powerful ability of data processing. Because of frequent cloud cover in the region, we computed temporal composites to ensure the analysis of every pixel. The pre and post-fire composites were generated by minimizing the Normalized Burn Ratio spectral index (NBR). This minimization selects burned pixels while dismissing most clouds, cloud shadows, and snow.