Burned Area Detection in the Brazilian Amazon using Spectral Indices and GEOBIA

• Published in: Revista brasileira de cartografia Vol. 72; no. 2; pp. 253 - 269
• Main Authors: Penha, Thales Vaz, Körting, Thales Sehn, Fonseca, Leila Maria Garcia, Silva Júnior, Celso Henrique Leite, Pletsch, Mikhaela Aloísia Jessie Santos, Anderson, Liana Oighenstein, Morelli, Fabiano
• Format: Journal Article
• Language: English
• Published: Universidade Federal de Uberlândia 30-06-2020
• Subjects: fires mapping; landsat-8 oli; sentinel-2a msi; tropical forest
• ISSN: 0560-4613; 1808-0936
• DOI: 10.14393/rbcv72n2-48726

Mapping refined burned areas (BA) in the Brazilian Amazon is still a challenge. The main difficulty of BA detection in large areas is the presence of cloud cover and water bodies. The use of different data sources of medium spatial resolution satellite images can provide a higher availability of cloud-free images. Besides that, it may decrease the uncertainties associated with coarse spatial resolution data (>250m), which can under or overestimate BA and hinder the detection of small BA patches (<0.1km²). In this study, we propose an innovative methodology based on spectral indices and geographic object-based image analysis (GEOBIA), using medium spatial resolution images to improve BA detection in the Brazilian Amazon region. Firstly, we assessed the performance of nine spectral indices in two study areas, derived from Landsat-8 OLI and Sentinel-2A MSI data to identify the most suitable index for BA detection in this region. Then, we refined this data through the GEOBIA-based model. The results showed that the Burned Area Index (BAI) was the most suitable index for BA mapping (M index >1.5) for both sensors. Our model allowed detecting more than 80% of small BA and also presented high Dice coefficient values (~0.70) with low omission and commission errors (0.22 and 0.32, respectively). Such combined approach corresponds to a novel contribution to the BA detection in the Brazilian Amazon region and for enhancing the operational product generation.