Geodaysit 2023

The damages generated by fire events on vegetation structure and its evolution and the economic impacts on human activity, life and infrastructures have led the scientific interest to develop tools and algorithms able to support the detection and monitoring of burned areas (BA).
The possibility of monitoring the fire evolution and mapping the BA has been strongly supported in last decades by the opportunity to use a significant quantity of satellite observations. The freely and timely availability of remote sensing data has grown so faster in the last years as well as a higher spatial resolution that makes the earth observation derived data the key component in supporting both government agencies and local decision-makers in monitoring natural disasters such as wildfire or floods.
The Copernicus Sentinel-2 with 20-m spatial resolution and a 5-day return period is a great candidate for near real-time (NRT) applications of change detection based on spectral indices. An automatic near-real time (NRT) burned area (BA) mapping approach designed to map BA using Sentinel-2 (S2) data was proposed in [1] and recently updated in [2]. The AUTOmatic Burned Areas Mapper (AUTOBAM) tool was originally designed to respond the need of the Italian Department of Civil Protection in monitoring spatial distribution and numerousness of BA during the fire season (June- September) over the Italian territory. The atmospherically corrected Level-2A(L2A) surface reflectance products from S2 are used: the automatic chain downloads and processes the most updated L2A products available on Copernicus Open Access Hub over the studied area. At the three spectral indices estimated (Normalized Burn Ratio, the Normalized Burned Ratio 2, and the Mid-Infrared Burned Index) a change detection approach is applied. AUTOBAM compares the values of these indices acquired at current time with the values derived from the most recent cloud-free S2 data. The procedure for BA mapping is based on different sequential image processing techniques such as clustering, automatic thresholding, region growing that conduce to a final BAs map with grid pixel size of 20m. Finally, a quality flag is included for each AUTOMAB BAs to certify a temporal and spatial correspondence with ancillary data, such as derived active fire detections from MODIS, VIIRS and national fire notifications.
The daily run of AUTOBAM allowed us to produce a burned area database for Italy. To evaluate the quality of the database, the AUTOBAM-derived BAs have been compared with the burn perimeters compiled by Carabinieri Command of Units for Forestry, Environmental and Agri-food protection. These perimeters represent the official burned area data for Italy. A validation procedure based of both a pixel-based confusion matrix and a set object-based accuracy metrics has been set up considering the whole Italian territory and years 2019-2021. Good results have been obtained by AUTOBAM in terms of detection capability (the Correctness parameter) and overlap factor (both larger than 60%). However, quite high values of the commission error were obtained, especially in 2019. Through a per land cover analysis, it was found that this error mostly occurred in cultivated land. Excluding the latter target, the commission error was always less than 35%, the omission error was less than 27% and the Dice Coefficient was larger than 69%. Moreover, starting from 2021, the Lazio region is providing AUTOBAM with accurate fire notifications derived from its SOUP (Italian acronym of Permanent Unified Operations Room). An experimental activity has been performed to verify whether these notifications can be used as trigger for the burned area mapping algorithm to reduce the number of false positives.

References:

[1] L. Pulvirenti et al., “An automatic processing chain for near real-time mapping of burned forest areas using sentinel-2 data,” Remote Sens., vol. 12, p. 674, 2020.
[2] L. Pulvirenti, G. Squicciarino, E. Fiori, D. Negro, A. Gollini, and S. Puca, “Near real-time generation of a country-level burned area database for Italy from Sentinel-2 data and active fire detections,” Remote Sens. Appl. Soc. Environ., vol. 29, 2023.