| Full text | |
| Author(s): |
Negri, Rogerio G.
;
Luz, Andrea E. O.
;
Frery, Alejandro C.
;
Casaca, Wallace
Total Authors: 4
|
| Document type: | Journal article |
| Source: | REMOTE SENSING; v. 14, n. 21, p. 20-pg., 2022-11-01. |
| Abstract | |
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) | |
| FAPESP's process: | 21/01305-6 - Theoretical advances on anomaly detection and environmental monitoring systems building |
| Grantee: | Rogério Galante Negri |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 21/03328-3 - Development of new methodologies and machine intelligence-based technological solutions for digital image segmentation and COVID-19 pandemic response |
| Grantee: | Wallace Correa de Oliveira Casaca |
| Support Opportunities: | Regular Research Grants |