Exploitation of Sentinel-2 Time Series to Map Burned Areas at the National Level: A Case Study on the 2017 Italy Wildfires

Abstract

Satellite data play a major role in supporting knowledge about fire severity by delivering rapid information to map fire-damaged areas in a precise and prompt way. The high availability of free medium-high spatial resolution optical satellite data, offered by the Copernicus Programme, has enabled the development of more detailed post-fire mapping. This research study deals with the exploitation of Sentinel-2 time series to map burned areas, taking advantages from the high revisit frequency and improved spatial and spectral resolution of the MSI optical sensor. A novel procedure is here presented to produce medium-high spatial resolution burned area mapping using dense Sentinel-2 time series with no a priori knowledge about wildfire occurrence or burned areas spatial distribution. The proposed methodology is founded on a threshold-based classification based on empirical observations that discovers wildfire fingerprints on vegetation cover by means of an abrupt change detection procedure. Effectiveness of the procedure in mapping medium-high spatial resolution burned areas at the national level was demonstrated for a case study on the 2017 Italy wildfires. Thematic maps generated under the Copernicus Emergency Management Service were used as reference products to assess the accuracy of the results. Multitemporal series of three different spectral indices, describing wildfire disturbance, were used to identify burned areas and compared to identify their performances in terms of spectral separability. Result showed a total burned area for the Italian country in the year 2017 of around 1400 km2, with the proposed methodology generating a commission error of around 25% and an omission error of around 40%. Results demonstrate how the proposed procedure allows for the medium-high resolution mapping of burned areas, offering a benchmark for the development of new operational downstreaming services at the national level based on Copernicus data for the systematic monitoring of wildfires.

Effectiveness of Sentinel-2 in Multi-Temporal Post-Fire Monitoring When Compared with UAV Imagery

“…In this specific context, Sentinel-2 MSI data were used for exploring spectral indices of burn severity discrimination [30][31][32][33][34], as well as to assess burn severity in combination with Landsat data [35,36]. They were also used to take into account the available multi-temporal data in order to evaluate burned areas at a national level [37] and to assess post-fire vegetation recovery mapping of an island [38].…”

Assessing Spatio-Temporal Variability of Wildfires and their Impact on Sub-Saharan Ecosystems and Air Quality Using Multisource Remotely Sensed Data and Trend Analysis

“…Fortunately, BA detection and mapping is well established and has been studied since the dawn of satellite imagery [5] with most recent studies focusing on (a) the development and improvement of detection and mapping techniques [6][7][8], (b) enhancement of existing global products both in detection accuracy and spatial detail [9,10] and (c) the inter-comparison and validation in different environmental settings and regions [11]. Additionally, the availability of operational satellite-based products, such as land cover, temperature, rainfall, tree cover, etc., provide prospects for assessing and quantifying the impact of wildfires on the ecosystems and biodiversity.…”

Observing Post-Fire Vegetation Regeneration Dynamics Exploiting High-Resolution Sentinel-2 Data

“…Sentinel-2 L2A data, atmospherically corrected using the MACCS-ATCOR Joint Algorithm (MAJA) [2] and distributed by Theia in MUSCATE format, were downloaded and used for the analysis. All the spectral bands contained in the Sentinel-2 L2A product were first masked from cloud contaminated data and successively resampled to a 20 m spatial resolution according to the procedure described in [3]. Later, the biophysical processor [4], available in ESA SNAP software, was used to compute LAI and multitemporal LAI observations stacked in a multidimensional datacube after applying an image coregistration step [3].…”

“…All the spectral bands contained in the Sentinel-2 L2A product were first masked from cloud contaminated data and successively resampled to a 20 m spatial resolution according to the procedure described in [3]. Later, the biophysical processor [4], available in ESA SNAP software, was used to compute LAI and multitemporal LAI observations stacked in a multidimensional datacube after applying an image coregistration step [3]. Finally, LAI time series were first smoothed using a Whittaker approach [5] to avoid the residual noise rate affecting time series due to cloud contamination, and secondly, masked using a reference burned area map [3].…”