*2.2. Data and Image Processing*

In this work, the following multisensor satellite images fully covering the study area, including Landsat-8, Sentinel-2, and Terra, as well as their spectral bands, were selected and used to discriminate the area that is affected by the fire in the pixel distribution format on the digital number (DN) scale:

(i) A Landsat-8 scene acquired on 1 August 2019 by the OLI sensor(LC08\_L1TP\_203033\_ 20190801\_20190801, orbit/point: 203/033) with a spatial resolution of 30 m obtained from the Earth Resources Observation and Science Center of the US Geological Survey (USGS) [98]. This is a product of level 1T (corrected terrain) and adjusted with the solar angle with the processing steps described in [99].

(ii) A Sentinel-2 scene acquired on 3 August 2019 through the cloudless MSI sensor (S2A\_MSIL1C\_20190803T112121\_N0208\_R037\_T29SND\_20190803T132806) with 20 m spatial resolution obtained from the European Space Agency (Copernicus Open Access Hub). It is a Level 1C Top of Atmosphere (TOA) Reflectance product, which includes radiometric and geometric corrections (UTM projection with Geodetic Reference System WGS84), together with orthorectification [100].

(iii) For Terra satellite, one scene acquired on 25 July 2019 by the ASTER sensor. It is a cloud-free 1T level product with 15 m spatial resolution obtained from the USGS EROS Center [98]. For ASTER, unfortunately, shortwave infrared (SWIR) bands were not available for the study region, as they are no longer usable since 2008.

(iv) Additionally, for Terra satellite, one scene was acquired on 28 July 2019 by the MODIS sensor using the surface reflectance product (product MOD09A1). We also used the MODIS Terra MOD09A1 (Version 6) product from the Oak Ridge National Laboratory's Distributed Active Archive Center (ORNL DAAC) (Global Subset Tool: MODIS/VIIRS Land Products: https://modis.ornl.gov/cgi-bin/MODIS/global/subset.pl (accessed on 14 February 2021)). This product, with 500 m spatial resolution, provides spectral surface reflectance of the MODIS 1–7 Terra bands corrected for atmospheric conditions (for example, gases, aerosols, and Rayleigh scattering) at eight-days interval. For each pixel, a value is selected from all acquisitions within the eight-day compounding period. The criteria for choosing the pixel include cloud and solar zenith. When several acquisitions meet the criteria, the pixel with the minimum value of channel 3 (blue) is used [101].

Table 1 summarizes the bands that were used in this study for the different sensors. In the case of MSI, an image composition with all bands (10 and 20 m) was performed, resulting in a product of 20 m of Ground Sampling Distance (GSD).

**Table 1.** Landsat-8/Operational Land Imager (OLI), Sentinel-2/MultiSpectral Instrument (MSI), Terra/Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Terra/Moderate Resolution Image Spectroradiometer (MODIS) spectral band numbers, wavelength ranges (λ), and spatial resolutions used in this study.


2.2.1. Flowchart

Figure 2 summarizes the classification scheme and analysis followed in this work. Fire area classification methods using kNN and RF algorithms were used to explain the effects of different satellite images on both classifiers.

**Figure 2.** The flowchart of the methodology used in this study.

The workflow for the supervised classification of burned vegetation using kNN and RF algorithms was implemented with multispectral images from Landsat 8/OLI, Sentinel-2/MSI, and Terra (ASTER/MODIS) through training samples using photointerpretation features. The classification accuracy was determined making use of validation data and the results obtained from the analysis of the classification parameters using the generated confusion matrices. After image composition, the procedure includes the following steps: training samples, spectral separability analysis, classification with kNN and RF algorithms, validation, and accuracy analysis.
