Satellite data have been used to systematically monitor fire globally at coarse spatial resolution, using algorithms that detect the location of active fires at the time of satellite overpass, and using burned area mapping algorithms that map the spatial extent of the areas affected by fires [1
]. There is a need for moderate spatial resolution burned area products at regional to global scale that to date has only been partially met [8
]. The launch of Sentinel-2A and the forthcoming launch of the Sentinel-2B satellite that carry the Multi Spectral Instrument (MSI) [9
] provide the opportunity for moderate spatial resolution burned area mapping. Combined, the Sentinel-2A and -2B systems will provide multi-spectral global coverage up to every 5 days. The MSI has 13 spectral bands ranging from 0.433 μm to 2.19 μm; four 10 m visible and near-infrared bands, six 20 m red edge, near-infrared (NIR) and short wave infrared (SWIR) bands, and three 60 m bands for characterizing aerosols, water vapor and cirrus clouds [9
]. The MSI bands are similar to the Landsat-8, SPOT-6 and SPOT-7 bands and so include wavelengths that are suitable for burned area discrimination. Burned areas are characterized by deposits of charcoal and ash, and also by the removal of vegetation and alteration of the vegetation structure that sometimes reveal the vegetation understory and/or soil [10
]. The fire behavior controls the severity of fire effects including the degree and parts of the vegetation structure that burned, the amount of charcoal and ash deposition, the combustion completeness, and the size and spatial distribution of the burning [13
]. The persistence of the charcoal and ash signal depends on rates of dissipation by wind and rain and may be controlled by the unburned fuel load and structure [15
]. Post-fire the vegetation may regrow, at rates dependent on the site primary productivity and environmental factors and have a phenological signal that complicates burned area detection [17
]. Consequently, the reflectance over a burned area may have considerable variation in space and time as the spectral characteristics of unburned and burned areas change.
In general, NIR and shortwave-infrared wavelengths have been found to provide stronger burned area discrimination than visible wavelengths, and most burned area mapping algorithms are based on detecting decreased reflectance at these wavelengths. However, in some cases reflectance can increase due to the exposure of highly reflective soil and white ash deposition [12
]. In addition, some surface changes not associated with fire, such as shadows or agricultural harvesting, may induce similar spectral changes and depending on the algorithm and wavelengths used may cause false burned area detection [2
]. Visible wavelengths are not generally suitable for burned area detection because they are more sensitive to atmospheric contamination than longer wavelength bands and in particular are sensitive to smoke aerosols that are difficult to atmospherically correct [24
]. A large number of researchers have used spectral indices to map burned areas. Ideally, if a spectral index is appropriate to detect the physical change of interest, then there is a simple relationship between the change and the direction of the change displacement in spectral feature space [26
]. However, burned area spectral indices have often been designed quite empirically, for example, by considering every possible combination of bands and a variety of non-linear band transformations [27
]. Although spectral indices may produce good burned area discrimination for a particular location and time they may not perform well elsewhere. Commonly, indices based on spectral band ratios, usually of NIR and SWIR wavelengths that are less sensitive to atmospheric contamination, are used. The ratio formulation reduces first order bi-directional reflectance and solar zenith induced reflectance variations [32
]. Notably, the normalized burn ratio (NBR), computed as the difference between NIR and SWIR reflectance divided by their sum has been used widely for burned area mapping [33
The purpose of this research is to investigate which Sentinel-2A MSI bands provide greater burned area discrimination. Pairs of largely cloud-free Sentinel-2A images, which were sensed 10 or 20 days apart were selected before and after visually identified fire events that produced evident burned areas at sites in tropical, sub-tropical, and boreal regions are examined. Both top of atmosphere and surface reflectance MSI data are examined. Parametric and non-parametric statistical techniques are used to quantify the spectral burned to unburned separability for different MSI bands and a select number of spectral indices.
illustrates the mean reflectance and spectral index values of the unburned (first image acquisition) and burned (second image acquisition) pixel samples at each site. As discussed in the introduction, the impact of burning is predominantly to reduce NIR and SWIR reflectance. Among the spectral indices the NBR index derived from bands 8a and 12 (denoted b8a/12 in Figure 1
) has the greatest mean unburned to burned reduction. Among the four NDVI band variants the greatest reductions occur for the 10m NDVI band (denoted 8/4 in Figure 1
). These results should be treated with some caution as although a large number of pixels were considered (Table 4
, middle column) the burned and unburned data were not normally distributed. This is illustrated in Figure 2
which shows the results of the Shapiro-Wilk normality tests. Only the unburned NDVI red edge band values (denoted 8a/6 and 8a/7 in Figure 2
) for the Australia and Canada sites were significantly normally distributed, and only the burned NDVI red edge band derived from bands 8a and 7 for the Australia and Cambodia sites were significantly normally distributed.
The pattern of results in Figure 1
and Figure 2
was similar for the atmospherically corrected (i.e., surface reflectance) data. To illustrate the effect of atmospheric correction, and provide context for the separability results, Figure 3
shows TOA and surface reflectance for the second image acquisition near Fort McMurray, Alberta, Canada, illustrating true color 10 m (b04, b03, b02) and false color 20 m (b12, b11, b8a) TOA and surface reflectance. The impact of the atmospheric correction is most apparent comparing the true color TOA reflectance (a) and surface reflectance (b). The atmospheric correction increases the true color image spatial contrast and reduces the blue appearance, which is expected from comparable Landsat atmospheric correction experiments [64
]. The distinct smoke plume is not well corrected, which as noted earlier is a problem for most atmospheric correction methods. The impact of the atmospheric correction is visually less apparent in the longer wavelength false color bands, i.e., comparing (c) and (d), which is expected as atmospheric effects are smaller at longer wavelengths [65
]. The false color longer wavelength bands are more sensitive to the effects of fire on vegetation and consequently the extensive burned area in the east side of the image has more contrast with the surrounding unburned vegetation than is apparent in the true color images. These qualitative results illustrate the need for careful band selection for burned area mapping.
illustrates the TD separability values for each of the TOA band reflectances and spectral indices. The blue bars illustrate the control unburned-unburned TD values and the red bars illustrate the unburned-burned TD values. Figure 5
shows the same results for the SEN2COR atmospherically corrected data. The impact of the atmosphere at visible wavelengths, i.e., for b02, b03 and b04, is apparent with greater differences between the TOA control (blue) and the unburned-burned (red) separability values (Figure 4
) than for the equivalent TD surface (Figure 5
) values. This reduction is much less apparent for the longer wavelength bands, because, as illustrated in Figure 3
, the atmosphere has less effect at longer wavelengths. Residual atmospheric contamination may remain in the SEN2COR generated surface reflectance, particularly at shorter wavelengths [66
With regard to the unburned to unburned control pixels, the separability for each band and spectral index is generally quite low. This is expected because the images were acquired 10 or 20 days apart and so change in the surface condition (e.g., vegetation state and soil moisture changes) at the unburned pixel locations is likely to be small. With regard to the unburned to burned pixels, a clear TD separability pattern among the different bands and indices is evident. This pattern is similar to the decision tree classification results. As noted previously, the TD is indicative of class separability if probability distribution classification approaches (i.e., maximum likelihood) are used and if the data are normally distributed. As the data are not normally distributed (Figure 2
), the decision tree kappa and overall accuracy results provide a more precise depiction of class separability, and these are discussed below.
and Figure 7
illustrate the decision tree separability results for the TOA and the SEN2COR atmospherically corrected data respectively. In these two figures the overall accuracy and kappa values are very similar (across the ten bands and six indices the values are highly correlated with r > 0.98 for both the illustrated TOA and surface reflectance results). The surface unburned-burned kappa and overall accuracy values are always higher than the control unburned-unburned equivalent values (Figure 7
) which is expected. This is also the case for the TOA results (Figure 6
) except for the blue band over Australia, due to strong atmospheric effects in the shortest wavelength blue band.
Considering the ten different MSI bands, the visible bands (b02, b03, b04) have the lowest unburned-burned separability (both overall accuracy and kappa) for all the sites (Figure 6
and Figure 7
). The longest wavelength MSI band (b12: 2190 nm) also has low separability, except for the Canadian site TOA and surface reflectance where the burned reflectance was much higher than the unburned vegetation which has been observed by others for boreal and temperate forested regions [17
]. High overall accuracy (>0.8) and kappa (>0.8) values are found for the red-edge (b05, b06), NIR (b07, b08, b08a) and the SWIR (b11: 1610 nm) bands. It is well established that the NIR has high burned-unburned separability due primarily to the greater NIR reflectance of green and dry vegetation compared to black char [15
]. The high SWIR band burned-unburned separability is less established, and, for example, has been found to vary geographically [23
] and is suggested by some researchers as being related to the removal of water-retaining vegetation post-fire that leads to an increase in SWIR reflectance [70
]. The high red-edge band burned-unburned separability, particularly evident for b06 (740 nm), is of great interest as this band is not present on most spaceborne sensors. The red-edge bands are included on the MSI primarily because they have potential for vegetation chlorophyll content retrieval [73
]. For all five sites, fire resulted in a decrease in the red-edge band reflectance values, perhaps due to the removal of vegetation, and so reduced canopy chlorophyll content, in addition to decreased reflectance due to black char deposition.
The four NDVI spectral indices provide variable among-site separability. This is expected and, for example, NDVI is observed to provide poor burned-unburned discrimination over dry senescent vegetation but reasonable discrimination over boreal forest [23
]. The TOA and surface NDVI separability values are particularly low over the Guinea site where the predominant vegetation type is woody savanna. We note that the Guinea acquisitions were in the local dry season, and other researchers have observed that the NDVI of unburned and burned dry senescent savanna vegetation is quite similar [75
]. For the non-Guinea sites moderate to high separability values are found for the 10 m NDVI (denoted 8/4 in Figure 6
and Figure 7
) and for the 20 m NDVI derived from b8a and b05 (denoted 8a/5 in Figure 6
and Figure 7
). The other two NDVI implementations are defined at 20 m (b8a/6 and b8a/7) and have relatively lower separability, perhaps because they have the closest wavelength separation between their red and NIR bands. Among all the sites there is no clear pattern comparing the TOA and surface reflectance derived NDVI separability values. This in part is because the impact of the atmosphere on NDVI varies as function of the background red and NIR reflectance; with typically a greater increase in NDVI when imagery is atmospherically corrected over vegetated rather than over soil dominated surfaces [65
]. The two 20 m NBR indices (denoted 8A/11 and 8A/12 in Figure 4
, Figure 5
, Figure 6
and Figure 7
), like the NDVI, have variable among site separability values. However, for all the sites, the NBR implementation derived from b08 and b12 provides comparable or higher separability than the NDVI values and supports the broad adoption of the NBR for burned area mapping.
This study provides an evaluation of the effectiveness of individual Sentinel-2A MSI bands and spectral indices for discriminating burned areas. It supports the findings of previous studies that the NIR provides high burned-unburned discrimination in a variety of ecosystems including, for example, boreal forest [23
], savanna [23
] and temperate forest [36
]. As observed in other studies, the visible bands provide low burned-unburned discrimination [23
]. This is primarily because the visible reflectance spectra of non-photosynthetic vegetation is similar to the spectra of burned vegetation and also to soil that may be revealed post-fire [12
]. Few studies have considered the red-edge wavelength region for burned area mapping because it is not present on sensors commonly used for fire monitoring. The two MSI red-edge bands demonstrated relatively high burned-unburned discrimination and this was previously observed using Medium Resolution Imaging Spectroradiometer (MERIS) data [84
]. Given the performance of the Sentinel-2 MSI red edge bands, which will also be present on the Sentinel-3 Ocean and Land Colour Instrument (OLCI) sensor [74
], the results of this study suggest they could be combined, for example, by an efficient model inversion [85
The performance of the NDVI and NBR spectral indices considered in this study was not generally comparable with the better performing spectral bands in the sense that the spectral index separabilities were more variable among the five sites. The majority of previous studies concerning the suitability of spectral indices for burned area mapping have been specific to a given biome or land cover type. As stated in [71
] (boreal forest), [77
] (savanna), [87
] (grass-shrub), the performance of NDVI is limited by vegetation senescence or growth, and the NDVI has been shown to provide poor discrimination in savannas [75
]. The NBR was developed originally for burned area mapping [33
], and not for satellite-based assessments of fire/burn severity although it is used widely for this purpose [88
]. The MSI NBR provided comparable or higher separability than the NDVI and the NBR has been observed by others to provide high burned-unburned discriminative capability, e.g., [33
] (mediterranean forest), [34
] (boreal forest), [80
] (savanna), [92
] (heathland). The reported separabilities for the spectral indices were likely more variable among the five sites than for the individual bands because the spectral indices combine bands from spectral regions with different sensitivities to factors including the vegetation type and condition, the soil background, atmospheric contamination, and the degree of char and ash deposition [14
]. Therefore, caution in the interpretation of the exact cause of the different separability results is necessary, particularly given the complexity of the post-fire surface trajectory discussed in the introduction.
This study only considered the effectiveness of individual Sentinel-2A MSI bands and spectral indices for discriminating burned areas in a radiometric sense. Further work to consider the relative advantages of the 10 m bands, particularly the 10 m NIR band, for improved spatial resolution burned area mapping is recommended, although we note that the 20 m and 10 m bands have different wavelengths and so are not directly comparable. Burned area mapping algorithms usually take advantage of the discriminative power of multiple spectral bands, for example, by thresholding spectral indices. In this study, we reported separability analyses in the conventional manner with respect to single indices and bands. This provides meaningful insights into which individual indices and bands are appropriate for burned area mapping. The effective use of multiple bands and/or indices for burned area mapping is dependent on the algorithm used. The design of algorithm-specific experiments is beyond the scope of this study, but is a recommended subject for future research.
The unburned and burned MSI reflectance and derived spectral index values were generally not normally distributed indicating that parametric separability measures, such as the transformed divergence or the Jeffries-Matusita distance [52
], are less appropriate for analyses of the sort reported in this study. The decision tree based non-parametric separability approach that we used is straightforward and easily implementable. However, issues related to the unburned and burned sample data collection, the atmospheric correction, and the global representativeness of the image data used, may reduce the generality of the reported findings. The sample data were collected through interactive visual interpretation of two date image pairs. Only unambiguously burned and unburned pixels were collected. Albeit unlikely, it is not possible to exclude the presence of misinterpreted samples. This is difficult to avoid and quantify, but given the large sample sizes this issue is not expected to be significant. The sample data were collected in images unaffected by strong atmospheric contamination. Furthermore, the SEN2COR atmospheric correction may have its own limitations. Therefore, the findings are less likely to be representative of particularly smoke or atmospherically contaminated Sentinel-2 MSI data. Finally, the collection of moderate resolution image pairs in a statistically robust way that represent global fire conditions is subject to ongoing research [51
]. It is unknown if the study findings would be significantly different if the analysis was undertaken using more sites and image pairs acquired at different times in the local fire season.