An Automatic Processing Chain for Near Real-Time Mapping of Burned Forest Areas Using Sentinel-2 Data

Luca Pulvirenti,Giuseppe Squicciarino,Luca Ferraris,Elisabetta Fiori,Massimiliano Severino,Dario Negro,Paolo Fiorucci,Andrea Gollini,Silvia Puca

doi:10.3390/rs12040674

Abstract

A fully automated processing chain for near real-time mapping of burned forest areas using Sentinel-2 multispectral data is presented. The acronym AUTOBAM (AUTOmatic Burned Areas Mapper) is used to denote it. AUTOBAM is conceived to work daily at a national scale for the Italian territory to support the Italian Civil Protection Department in the management of one of the major natural hazards, which affects the territory. The processing chain includes a Sentinel-2 data procurement component, an image processing algorithm, and the delivery of the map to the end-user. The data procurement component searches every day for the most updated products into different archives. The image processing part represents the core of AUTOBAM and implements an algorithm for burned forest areas mapping that uses, as fundamental parameters, the relativized form of the delta normalized burn ratio and the normalized difference vegetation index. The minimum mapping unit is 1 ha. The algorithm implemented in the image processing block is validated off-line using maps of burned areas produced by the Copernicus Emergency Management Service. The results of the validation shows an overall accuracy (considering the classes of burned and unburned areas) larger than 95% and a kappa coefficient larger than 80%. For what concerns the class of burned areas, the commission error is around 1%−3%, except for one case where it reaches 25%, while the omission error ranges between 6% and 25%.

Highlights

Forest fires and, more generally, wildfires represent one of the major causes of ecosystem disturbance and ecological damage
A confusion matrix was computed for each case study, and the results are presented in Table 2, where columns represent Copernicus Emergency Management Service (CEMS) classes, while rows represent the AUTOBAM predictions
This paper presents a processing chain for near real-time mapping of burned forest areas using S2 data

Summary

Introduction

More generally, wildfires represent one of the major causes of ecosystem disturbance and ecological damage. The difference between the spectral responses of healthy vegetation and burned areas reaches a peak in these bands because a significant reduction of the NIR reflectance (ρNIR) and an increase of the SWIR reflectance (ρSWIR) occur after burning. The former effect is mainly due to the sensitivity of the NIR band to the chlorophyll content of healthy vegetation, while the latter effect is related to the influence of the water content of soil and vegetation on the SWIR reflectance [4,5,6,7]

Methods

Results

Conclusion