A New Method (MINDED-BA) for Automatic Detection of Burned Areas Using Remote Sensing

This work presents a change detection method (MINDED-BA) for determining burned extents from multispectral remote sensing imagery. It consists of a development of a previous model (MINDED), originally created to estimate flood extents, combining a multi-index image-differencing approach and the analysis of magnitudes of the image-differencing statistics. The method was implemented, using Landsat and Sentinel-2 data, to estimate yearly burn extents within a study area located in northwest central Portugal, from 2000-2019. The modelling workflow includes several innovations, such as preprocessing steps to address some of the most important sources of error mentioned in the literature, and an optimal bin number selection procedure, the latter being the basis for the threshold selection for the classification of burn-related changes. The results of the model have been compared to an official yearly-burn-extent database and allow verifying the significant improvements introduced by both the pre-processing procedures and the multi-index approach. The high overall accuracies of the model (ca. 97%) and its levels of automatization (through open-source software) indicate potential for being a reliable method for systematic unsupervised classification of burned areas.

Automated summary

This study introduces a change detection method (MINDED-BA) for determining burned extents from multispectral remote sensing imagery, utilizing a multi-index image-differencing approach and magnitude analysis. The model, developed from a previous model (MINDED) initially for flood extents estimation, achieved high overall accuracies and automation levels, demonstrating its potential for reliable systematic unsupervised classification of burned areas. The innovative modelling workflow includes preprocessing steps to address important error sources and an optimal bin number selection procedure for thresholding in burn-related changes classification.