Suitability of band angle indices for burned area mapping in the Maule Region (Chile)

Abstract

In January 2017, 114 active fires burned throughout Chile at the same time. These fires spread quickly due to high temperatures, fast 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. Timely and accurate burned area mapping is crucial for the evaluation of damages and management of the affected areas. As Chile is a diverse country with many types of ecosystems and vegetation, the use of novel spectral indices may improve the accuracy of satellite data-based burned area mapping algorithms. In this study, we explored the contribution of band angle indices (BAnI) to burned area mapping. The BAnI are based on trigonometric equations that proved to be sensitive to moisture conditions. Then, we aimed to test their sensitivity to the burned area spectral signature. We used Sentinel-2 data at 20 m resolution to calibrate and implement a random forest (RF) classifier in Google Earth Engine (GEE) computing platform. We ran the RF classifier with and without BAnI to evaluate their potential to identify burned areas and performed two accuracy assessments comparing the results with visually digitized fire perimeters from (1) WorldView 3 (WV3) images, and (2) Sentinel at 10 m resolution. We determined that both BA classifications were more accurate than the perimeters created by the Chilean National Forest Corporation (CONAF), which overestimates the area burnt. The overestimation of CONAF perimeters is produced by considering as burned the inner unburned areas and omitting some small, burned areas. The first assessment showed no significant differences between the two RF classifications. However, the second validation showed lower omission and commission errors for the RF classifier with the BAnI (5 and 17.8%, respectively). On the other hand, comparing both BA classifications with and without BAnI, we observed differences in the spatial distribution of the errors. However, the RF classification with BAnI offered fewer commission errors located in agricultural areas. The burned area algorithms developed in GEE showed their potential to map the fire-affected area quickly, efficiently, and accurately, accounting for all the areas burned in the season, including the small and agricultural fires the official perimeters did not consider.

In January 2017, 114 active fires burned throughout Chile at the same time. These fires spread quickly due to high temperatures, fast 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. Timely and accurate burned area mapping is crucial for the evaluation of damages and management of the affected areas. As Chile is a diverse country with many types of ecosystems and vegetation, the use of novel spectral indices may improve the accuracy of satellite data-based burned area mapping algorithms. In this study, we explored the contribution of band angle indices (BAnI) to burned area mapping. The BAnI are based on trigonometric equations that proved to be sensitive to moisture conditions. Then, we aimed to test their sensitivity to the burned area spectral signature. We used Sentinel-2 data at 20 m resolution to calibrate and implement a random forest (RF) classifier in Google Earth Engine (GEE) computing platform. We ran the RF classifier with and without BAnI to evaluate their potential to identify burned areas and performed two accuracy assessments comparing the results with visually digitized fire perimeters from (1) WorldView 3 (WV3) images, and (2) Sentinel at 10 m resolution. We determined that both BA classifications were more accurate than the perimeters created by the Chilean National Forest Corporation (CONAF), which overestimates the area burnt. The overestimation of CONAF perimeters is produced by considering as burned the inner unburned areas and omitting some small, burned areas. The first assessment showed no significant differences between the two RF classifications. However, the second validation showed lower omission and commission errors for the RF classifier with the BAnI (5 and 17.8%, respectively). On the other hand, comparing both BA classifications with and without BAnI, we observed differences in the spatial distribution of the errors. However, the RF classification with BAnI offered fewer commission errors located in agricultural areas. The burned area algorithms developed in GEE showed their potential to map the fire-affected area quickly, efficiently, and accurately, accounting for all the areas burned in the season, including the small and agricultural fires the official perimeters did not consider.