*3.5. Classification Errors*

Figure 7 shows the spatial distribution of the OE and CE for the classification of burned and unburned areas from OLI, MSI, ASTER, and MODIS sensors using the kNN and RF algorithms.

**Figure 7.** Spatial distributions of Omission Error (OE) and Commission Error (CE) for each classification. (**a**) OLI with kNN, (**b**) OLI with RF, (**c**) MSI with kNN, (**d**) MSI with RF, (**e**) ASTER with kNN, (**f**) ASTER with RF, (**g**) MODIS with kNN, and (**h**) MODIS with RF.

It is observed that, in general, all of the classifications have low CE more frequently within the perimeter that is affected by the fire, although, for ASTER, there is a significant presence of missing mixing pixels and CE outside the burned area (Figure 7e,f).

For ASTER images, the classifications present the smallest OE, with a spatial distribution of 8.73 km2 of areas with missing pixels for kNN and 8.19 km2 for RF. In contrast, despite the lower spatial resolution of MODIS, there was a moderate frequency of missing pixels within the burned area when compared to the other sensors, which decreased the sensors OE reaching ~13–14 km2. It is more evident in the upper border, as shown in Figure 7g,h, the place of transition between burned and unburned areas, which, in turn, is more susceptible to errors that are caused by low spatial resolution.

## *3.6. Overall Accuracy (OA)*

The differences in areas that were classified as burned in our classifications and the reference map were the lowest for ASTER (4 km2) and the highest for MODIS (47 km2).

This result is consistent for the images with better spatial resolution and greater proximity to the date of the reference product, such as OLI, MSI, and ASTER, resulting in a stable thematic quality. When the time interval between the data is too long, it is difficult to know exactly what period the pixel finally extracted from the image refers to. This statement is disconnected from the results that were obtained by the MODIS sensor, which, despite the proximity of the day of the burning occurrence, its spatial resolution, and its eight-days compaction form, was an important factor as mentioned above.

In terms of algorithms, RF was the classification method that presented the smallest error in the total burned area in relation to the ICFN reference area with values of the order of 4 to 17 km<sup>2</sup> for the finer spatial resolution sensors (Table 4) and good estimates of OA and DC, as can be seen in Table 5.

**Table 5.** Values of OA and Dice Coefficient (DC) for the products generated by kNN and RF classifiers in the different sensors used.


The results show that the classification based on kNN and RF for the different sensors mapped the burned area with a very high accuracy (OA > 89% and DC > 0.8) and without significant variations in the computed OA and DC values for all of the sensors.
