Mapping Burned Areas with Multitemporal-Multispectral Data and Probabilistic Unsupervised Learning

Autor(es)

Negri, Rogerio G. ; Luz, Andrea E. O. ; Frery, Alejandro C. ; Casaca, Wallace

• Tipo de documento: Artigo Científico
• Fonte: REMOTE SENSING; v. 14, n. 21, p. 20-pg., 2022-11-01.

Resumo

The occurrence of forest fires has increased significantly in recent years across the planet. Events of this nature have resulted in the leveraging of new automated methodologies to identify and map burned areas. In this paper, we introduce a unified data-driven framework capable of mapping areas damaged by fire by integrating time series of remotely sensed multispectral images, statistical modeling, and unsupervised classification. We collect and analyze multiple remote-sensing images acquired by the Landsat-8, Sentinel-2, and Terra satellites between August-October 2020, validating our proposal with three case studies in Brazil and Bolivia whose affected regions have suffered from recurrent forest fires. Besides providing less noisy mappings, our methodology outperforms other evaluated methods in terms of average scores of 90%, 0.71, and 0.65 for overall accuracy, F1-score, and kappa coefficient, respectively. The proposed method provides spatial-adherence mappings of the burned areas whose segments match the estimates reported by the MODIS Burn Area product. (AU)

• Processo FAPESP: 21/01305-6 - Avanços teóricos em detecção de anomalias e construção de sistemas de monitoramento ambiental
• Beneficiário: Rogério Galante Negri
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 21/03328-3 - Desenvolvimento de novas metodologias e soluções tecnológicas inteligentes em segmentação de imagens digitais e enfrentamento da COVID-19
• Beneficiário: Wallace Correa de Oliveira Casaca
• Modalidade de apoio: Auxílio à Pesquisa - Regular