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remote sensing
Article
Burned Area Detection and Mapping:
Intercomparison of Sentinel-1 and Sentinel-2 Based
Algorithms over Tropical Africa
Mihai A. Tanase 1, * , Miguel A. Belenguer-Plomer 1, Ekhi Roteta 2, Aitor Bastarrika 2,
James Wheeler 3,Ángel Fernández-Carrillo 1, Kevin Tansey 3, Werner Wiedemann 4,
Peter Navratil 5, Sandra Lohberger 4, Florian Siegert 4and Emilio Chuvieco 1
1Environmental Remote Sensing Research Group, Department of Geology, Geography and Environment,
University of Alcala, 28801 Alcala de Henares, Spain; miguel.belenguer@uah.es (M.A.B.-P.);
angel.fernandezcarri@uah.es (Á.F.-C.); emilio.chuvieco@uah.es (E.C.)
2Department of Mining and Metallurgical Engineering and Materials Science, School of Engineering of
Vitoria-Gasteiz, University of the Basque Country UPV/EHU, 01006 Vitoria-Gasteiz, Spain;
ekhi.roteta@ehu.eus (E.R.); aitor.bastarrika@ehu.eus (A.B.)
3
Centre for Landscape and Climate Research, School of Geography, Geology and Environment, University of
Leicester, Leicester LE1 7RH, UK; jemw3@leicester.ac.uk (J.W.); kjt7@leicester.ac.uk (K.T.)
4Remote Sensing Solutions GmbH, 81673 Munich, Germany; wiedemann@rssgmbh.de (W.W.);
lohberger@rssgmbh.de (S.L.); siegert@rssgmbh.de (F.S.)
5GAF AG, 80634 Munich, Germany; navratil@rssgmbh.de
*Correspondence: mihai@tma.ro
Received: 14 November 2019; Accepted: 14 January 2020; Published: 20 January 2020
Abstract:
This study provides a comparative analysis of two Sentinel-1 and one Sentinel-2 burned area
(BA) detection and mapping algorithms over 10 test sites (100
×
100 km) in tropical and sub-tropical
Africa. Depending on the site, the burned area was mapped at different time points during the
2015–2016 fire seasons. The algorithms relied on diverse burned area (BA) mapping strategies
regarding the data used (i.e., surface reflectance, backscatter coefficient, interferometric coherence)
and the detection method. Algorithm performance was compared by evaluating the detected BA
agreement with reference fire perimeters independently derived from medium resolution optical
imagery (i.e., Landsat 8, Sentinel-2). The commission (CE) and omission errors (OE), as well as the
Dice coefficient (DC) for burned pixels, were compared. The mean OE and CE were 33% and 31% for
the optical-based Sentinel-2 time-series algorithm and increased to 66% and 36%, respectively, for the
radar backscatter coefficient-based algorithm. For the coherence based radar algorithm, OE and CE
reached 72% and 57%, respectively. When considering all tiles, the optical-based algorithm provided
a significant increase in agreement over the Synthetic Aperture Radar (SAR) based algorithms that
might have been boosted by the use of optical datasets when generating the reference fire perimeters.
The analysis suggested that optical-based algorithms provide for a significant increase in accuracy
over the radar-based algorithms. However, in regions with persistent cloud cover, the radar sensors
may provide a complementary data source for wall to wall BA detection.
Keywords:
burned area; backscatter coefficient; interferometric coherence; time series; Sentinel-1;
C-band; Sentinel-2
1. Introduction
Fire has a key ecological role in a large part of the Earth’s surface since it affects global bio-chemical
cycles, carbon sequestration, soil properties, water supply, and biodiversity. Due to its contribution to
Remote Sens. 2020,12, 334; doi:10.3390/rs12020334 www.mdpi.com/journal/remotesensing
Remote Sens. 2020,12, 334 2 of 15
the global climate system, fire disturbance is considered an Essential Climatic Variable (ECV) [
1
] with
global burned area (BA) products being routinely derived from coarse resolution sensors (>250 m)
such as the Medium Resolution Imaging Spectrometer (MERIS), the Moderate Resolution Imaging
Spectroradiometer (MODIS), or SPOT Vegetation [
2
–
10
]. A comparison analysis done with reference
data from 2008 found that NASA’s MCD64A1 was the most accurate global BA product of the existing
global BA products [
11
] with commission (CE) and omission errors (OE) reaching 42% and 68%,
respectively. More recent analyses, carried out with reference data from 2003 to 2014 [
9
] and 2014 to
2015 [
12
], found average commission errors of 35% and 40.2%, respectively, while omission errors
reached 62% and 72.6%, respectively. With these values, global BA products are far from achieving
the accuracy requirements needed for climate modelling, i.e., omission and commission errors below
20%, [
13
], in part due to the low spatial resolution, which results in small fires being overlooked [
14
].
In fact, the likelihood of detecting small burns (i.e., <100 ha) in coarse resolution products is low due
to the frequent omission errors [
6
,
11
,
14
]. A recent study suggested that contribution of small fires
may be significant as BA products based on MODIS imagery (500 m) estimated 80% less burned area
than those based on Sentinel-2 (20 m) in sub-Saharan Africa when compared to products based on
medium resolution imagery [
15
]. Small fires have a large impact on emissions in most ecosystems [
16
].
Apart from small fire size, BA uncertainties were also related to satellite instrument calibration and
viewing geometry, the temporal sampling related to the persistence of the burn signal, and the
detection algorithm design [
17
]. In addition, for atmospheric modelling, ancillary information becomes
important, including the detection date, type of burned land (generally relying on external land
cover products with their own uncertainties), and combustion completeness and fraction burned
area, i.e., critical parameters for atmospheric emission estimations [
18
]. To overcome some of these
limitations (e.g., small fire size, burned fraction), medium resolution optical sensors have been used
for BA detection and mapping with early algorithms being mostly based on Landsat imagery [
19
–
23
].
However, detection from Landsat imagery is challenging because of the low temporal resolution of this
data (16 days), particularly over areas with persistent cloud cover and short post-fire signal persistence
(e.g., savannah fires). The launches of Sentinel-1 (-with microwave) and Sentinel-2 (optical) sensors
have incentivised the development of novel BA detection and mapping algorithms as the availability
of relatively dense time-series (3–5 days when different sensors or Synthetic Aperture Radar (SAR)
viewing geometries are combined) may significantly reduce omission errors for regions where small
fires account for a large proportion of total BA or where the earth surface is obscured by persistent cloud
cover. However, most such algorithms have been developed and tested for rather small areas [
24
,
25
]
or few fire events [
26
–
30
] except for the global BA prototype product with Landsat data for a single
year [
23
], and the wall-to-wall mapping of Sub-Saharan Africa performed with Sentinel-2 images for
2016 [15].
Locally developed algorithms are difficult to transfer to other regions as changes in surface
reflectance or backscatter coefficient are dependent on a range of factors, including vegetation type and
structure, fire regimes, and climatic variables. To improve the characterisation of small fires over large
areas, the European Space Agency (ESA) Climate Change Initiative (CCI) programme developed BA
algorithms and products from medium resolution sensors for the generation of a small fire database
(SFD) at regional to continental scales. The CCI programme aims to obtain information on different
ECVs using remote sensing data to help improving climate modelling [
31
,
32
]. The SFD was initially
focused on sub-Saharan Africa, the most burned continent worldwide [
9
,
33
], and was largely based on
optical sensors (O) and time series (ts) analysis (Ots) [
15
]. Over areas with persistent cloud cover (daily
mean cloud cover above 50%), the SFD was complemented by burned perimeters detected from time
series of Sentinel-1 C-band radar (R) interferometric coherence (RtsC).
The SFD was complemented by regional BA products in tropical South-East Asia (1.5 million km
2
)
and South America (7 million km
2
). To cope with the persistent cloud cover in these regions, the BA
detection and mapping algorithms were based on synthetic aperture radar (SAR) backscatter coefficient
acquired by the Sentinel-1 C-band sensor [
34
,
35
]. The BA product for tropical South America was
Remote Sens. 2020,12, 334 3 of 15
based on radar temporal series of the backscatter (B) coefficient (RtsB) while the product for South-East
Asia used a multi-temporal approach based on radar backscatter analysis at the beginning and the end
of the fire season. The different components of the SFD are available for download from the Fire CCI
website (https://www.esa-fire-cci.org/).
While the algorithms were individually validated [
15
,
35
,
36
], such validation was carried out for
different areas and time periods. Since the algorithms used to generate the SFD components were
based on different input datasets (e.g., optical, SAR) and detection strategies, the objective of this study
was to intercompare the algorithms, assessing them over the same areas and with the same reference
datasets. However, as the algorithm developed for South-East Asia [
34
] was designed specifically for
fires in low laying (<15
◦
slope) peat swamp forests, its evaluation outside such conditions was not
feasible. Therefore, this study only compared the three time-series algorithms (Ots, RtsB, and RtsC)
designed to work over a variety of fire regimes and vegetation types.
2. Materials and Methods
2.1. Reference Datasets
Ten Military Reference Grid System (MGRS) tiles (100
×
100 km) were used to analyse burned area
agreement with reference fire perimeters derived from optical images within the 2015 and 2016 fire
seasons (Figure 1and Table 1). The tiles were selected along an east–west transect in areas with high
fire activity and daily mean cloud cover below 75% to allow for the deployment of the optical-based
algorithm (Figure 1). Four tiles were dominated (>75% cover) by forests. In the remaining tiles, the
land cover was a mix of cropping areas, savannas, grasslands, shrublands, and forests (Table 1). The
reference datasets were formed by (1) fire perimeters derived from Landsat 8 imagery (four tiles) and
(2) fire perimeters derived from Sentinel-2 imagery (six tiles). Tiles with reference fire perimeters
derived from Landsat imagery were selected to assess the effect of temporal discrepancy between BA
detection and validation dates.
Remote Sens. 2020, 12, x FOR PEER REVIEW 4 of 14
not corresponding to burned areas were removed manually, with most being caused by cloud
shadows and crop harvest.
Figure 1. Tiles used for inter-comparison together with the mean daily cloud cover (2015) and the
total burned (2016), according to the FireCCI5.1 global product. The source of the optical imagery
used to generate the reference fire perimeters for each of the analysed tiles is also shown.
Table 1. Military Grid Reference System (MGRS) tiles used for inter-comparison. The main land cover
type (from CCI Land Cover product, 2015) and the total burned area. The sensor used to generate the
reference fire perimeters was labelled S for Sentinel-2 and L for Landsat-8.
MGRS Main Land Cover (%) Analysed Total No. of Patches
Tile Crops
Grasslands
/Shrubs Forests Fire Period
(dd.mm.yyyy)
Burned
Area (km2)
Small
(<25 ha)
Large
(>25 ha)
28PETS 18 14 54 11.01.2016–11.03.2016 1205.6 2717 525
29NNJS 57 24 18 02.01.2016–02.03.2016 1139.4 9736 278
30NWPS 58 13 28 27.12.2015–16.01.2016 543.0 2469 182
31NEJS 61 26 10 18.12.2015–07.01.2016 909.4 24957 544
32NNPS 50 32 16 22.12.2015–10.02.2016 3609.1 37868 1144
33NTGL 0 10 89 15.01.2016–16.02.2016 456.0 4104 274
33NUGL 0 28 72 22.10.2016–25.12.2016 271.4 4255 193
33NWES 2 10 87 02.01.2016–22.01.2016 859.5 7212 674
35NQGL 0 0 99 01.11.2016–19.12.2016 3529.1 4538 658
36NXPL 5 53 42 09.09.2016–12.11.2016 11.6 41 12
All 25.1 21 51.5 - 12534.1 97897 4484
2.2. SFD Algorithms Description in Brief
2.2.1. Optical Time Series
The optical-based detection algorithm (Ots) uses time-series of atmospherically corrected
Sentinel-2 Multi Spectral Instrument (MSI) reflectance measurements from the L2A product [15]. The
algorithm detects the burned areas using spectral changes in three bands, near-infrared (NIR) and
short and long short shortwave infrared bands (SWIRS, SWIRL) at the original spatial resolution of
20 m. These bands are used to calculate two spectral indices the Mid-Infrared Burned Index (MIRBI)
and the Normalized Burned Ratio 2 (NBR2) as in Equations (1) and (2). The algorithm compares two
consecutive Sentinel-2 images using the multitemporal difference and the post-fire values of the
MIRBI, NBR2 spectral indices, and the NIR reflectance. The algorithm applies fixed thresholds to
obtain an Initial Burned/Not Burned area which is cross-checked against the existence of MODIS
Figure 1.
Tiles used for inter-comparison together with the mean daily cloud cover (2015) and the total
burned (2016), according to the FireCCI5.1 global product. The source of the optical imagery used to
generate the reference fire perimeters for each of the analysed tiles is also shown.
The Landsat based reference fire perimeters were generated from consecutive image pairs. The
time length between consecutive observations was limited (e.g., ideally one satellite cycle) to avoid
confusion due to fire-unrelated land cover changes or misinterpretation of the fire signal over areas
with rapid vegetation growth. However, in areas with frequent cloud cover, the interval was increased
(e.g., up to 32 days) to allow formation of additional sampling pairs. The use of a larger interval was
Remote Sens. 2020,12, 334 4 of 15
reasonable as vegetation usually needs longer time to recuperate after fire. Indeed, the global median
persistence time of fire scars visible on an optical dataset was estimated as 29 days [
37
]. For efficiency,
the surface reflectance of each individual image pair was reformatted into a raster file containing six
bands, the shortwave infra-red (SWIR), near infra-red (NIR), and RED bands of each image forming
the pair. An independent interpreter digitised training polygons for burned and unburned areas and
clouds. The polygons were subsequently used to train a Random Forest classifier [
38
,
39
], taking as
input variables the pre- and post-reflectance and the multitemporal differenced Normalized Burn
Ratio (dNBR) index [
40
]. The burned area classification was based on an iterative process of visual
inspection, delineation of additional training polygons, and classification. When classification errors
were spotted, the classification was improved by digitizing missed areas or removing incorrectly
labelled areas. Within post-processing, fire perimeters below 0.1 ha were removed.
Table 1.
Military Grid Reference System (MGRS) tiles used for inter-comparison. The main land cover
type (from CCI Land Cover product, 2015) and the total burned area. The sensor used to generate the
reference fire perimeters was labelled S for Sentinel-2 and L for Landsat-8.
MGRS Main Land Cover (%) Analysed Total No. of Patches
Tile Crops Grasslands/Shrubs Forests Fire Period
(dd.mm.yyyy)
Burned Area
(km2)
Small
(<25 ha)
Large
(>25 ha)
28PETS18 14 54
11.01.2016–11.03.2016
1205.6 2717 525
29NNJS57 24 18
02.01.2016–02.03.2016
1139.4 9736 278
30NWPS58 13 28
27.12.2015–16.01.2016
543.0 2469 182
31NEJS61 26 10
18.12.2015–07.01.2016
909.4 24957 544
32NNPS50 32 16
22.12.2015–10.02.2016
3609.1 37868 1144
33NTGL0 10 89
15.01.2016–16.02.2016
456.0 4104 274
33NUGL0 28 72
22.10.2016–25.12.2016
271.4 4255 193
33NWES2 10 87
02.01.2016–22.01.2016
859.5 7212 674
35NQGL0 0 99
01.11.2016–19.12.2016
3529.1 4538 658
36NXPL5 53 42
09.09.2016–12.11.2016
11.6 41 12
All 25.1 21 51.5 - 12534.1 97897 4484
The Sentinel-2 based reference fire perimeters were selected from an available pool of validation
tiles and provided for more diverse land cover types. In each validation tile, two Sentinel-2 cloud-free
images with a temporal difference as short as possible were selected in a period where fires were
visually observed. The minimum temporal difference was the revisit period of 10 days, which was
extended in some cases up to 1 or 2 months (30 or 60 days) due to high cloud coverage or low image
availability, particularly in February 2016. The Sentinel-2 L1C products were processed to surface
reflectance [
41
]. Reference burned perimeters were generated from the pre- and post-fire reflectance
images using the Burned Area Mapping Software (BAMS) [
42
]. BAMS also uses training polygons to
classify burned areas. The classification results were subsequently analysed by a trained operator. The
training polygons were modified until acceptable classification results were obtained. Polygons not
corresponding to burned areas were removed manually, with most being caused by cloud shadows
and crop harvest.
2.2. SFD Algorithms Description in Brief
2.2.1. Optical Time Series
The optical-based detection algorithm (Ots) uses time-series of atmospherically corrected Sentinel-2
Multi Spectral Instrument (MSI) reflectance measurements from the L2A product [
15
]. The algorithm
detects the burned areas using spectral changes in three bands, near-infrared (NIR) and short and long
short shortwave infrared bands (SWIRS, SWIRL) at the original spatial resolution of 20 m. These bands
are used to calculate two spectral indices the Mid-Infrared Burned Index (MIRBI) and the Normalized
Burned Ratio 2 (NBR2) as in Equations (1) and (2). The algorithm compares two consecutive Sentinel-2
images using the multitemporal difference and the post-fire values of the MIRBI, NBR2 spectral indices,
Remote Sens. 2020,12, 334 5 of 15
and the NIR reflectance. The algorithm applies fixed thresholds to obtain an Initial Burned/Not Burned
area which is cross-checked against the existence of MODIS active fire hotspots. When hotspots exist,
the Initial Burned/Not Burned is confirmed (IBC). IBC areas are used to select burned seeds, which
are subsequently used to derive a burned membership function, where the minimum and maximum
values are extracted from unburned background and burned areas. The algorithm is locally adapted as
IBC existence is tested, and subsequent membership functions are derived for each MGRS tile and
Sentinel-2 image pair analysed. As the membership functions are derived at tile level, the algorithm
assumes a certain spectral homogeneity for different burned land cover classes. The algorithm uses
consecutive Sentinel-2 acquisitions to derive BA maps at 20 m spacing. Unobserved areas (affected by
clouds and cloud shadows) are gap-filled by using up to four pre-fire images to reduce omission errors.
In areas where Sentinel-2A data were recorded at the nominal frequency and in the absence of heavy
cloud cover (>95% cloud free), the BA maps temporal frequency is ten days. A detailed description of
the algorithm is found in Roteta et al. (2019).
MIRBI =10 ∗ρSWIRL −9.8 ∗ρSWIRS +2 (1)
NBR2=ρSWIRS −ρSW IRL
ρSWIRs +ρSWIRL
(2)
where
ρSWIRL
and
ρSWIRS
are, respectively, short wave infrared long reflectance (SWIRL) and short
wave infrared short reflectance (SWIRS).
2.2.2. SAR Coherence Time Series
To complement the optical-based burned area in regions affected by persistent cloud cover, a BA
detection and mapping algorithm was developed based on time-series of Sentinel-1 interferometric
coherence (RtsC). The algorithm needs four consecutive Sentinel-1 acquisitions (12 days apart) to
identify the burned areas over the period of interest. For each period of interest, the algorithm generates
three interferometric products corresponding to pre-, fire, and post-fire epochs. The post-fire epoch
is used to cross-check that burns have indeed taken place. The burned area detection and mapping
algorithm uses pre-trained machine learning algorithms such as Random Forests (RF). The training
is based on manually selected polygons over four land cover classes (no data, burned, unburned,
and water). The training polygons were selected and digitised for each biome in independent tiles
distributed over Sub-Saharan Africa using pre- and post-fire Sentinel-2 observations of burn events.
Biome specific pre-trained models are used to map burned area at 30 m spacing every 12 days.
2.2.3. SAR Backscatter Coefficient Time Series
An algorithm based on temporal time-series of Sentinel-1 backscatter coefficient (RtsB) was
also developed to identify anomalous changes in the radar signal and associate them with biomass
burning [
35
]. The algorithm considers multi-temporal changes of incoherent SAR-based metrics (e.g.,
backscatter intensities) together with ancillary information on land cover and active fires (hotspots).
The Reed-Xiaoli detector [
43
] is used to distinguish areas affected by anomalous changes (AC) with
respect to a reference state provided by Sentinel-1 dataset acquired before the period of interest. When
hotspots coincide in space and time with ACs, these areas are labelled as burned. The remaining
ACs are labelled as burned/unburned using machine learning (i.e., RF). The RFs are trained locally
(within 100
×
100 tiles) by main land cover types (e.g., crops, forests). RF training samples are
obtained from ACs overlapping hotspots after applying a series of filtering mechanisms to ensure high
burned/unburned probabilities of the selected samples. As for the Ots algorithm, the RtsB algorithm is
self-calibrating over each MGRS tile and detection period. The algorithm uses consecutive Sentinel-1
acquisitions to derive BA maps at 40 m spacing. The temporal resolution may vary between six days
to one month depending on Sentinel-1 actual acquisition frequency. A detailed description of the
algorithm is found in Belenguer-Plomer et al. [35] while its further validation was detailed in [36].
Remote Sens. 2020,12, 334 6 of 15
2.3. Burned Area Detection Accuracy Assessment
The three algorithms were deployed to detect, and map burned area over the time periods with
available reference fire perimeters. As inference processes (models) are affected by errors, there is
an element of uncertainty regarding the results produced using remote sensing data. Therefore, the
quality of remote sensing data and the derived products need to be characterised quantitatively to
facilitate critical information on product reliability to end users. The accuracy of the results is usually
characterised through cross-tabulation against reference datasets by accounting for the spatio-temporal
coincidences and disagreements. The approach is widely used in BA mapping projects [
6
,
11
,
44
–
50
].
However, one should also bear in mind that cross-tabulation based on ancillary reference datasets
derived from remote sensing data acquired by other sensors, largely indicates the agreement between
different BA products as the accuracy of the reference dataset is often not known.
For each tile, the agreement between the reference and the detected burned areas was computed
through cross-tabulation [
51
,
52
]. The result of the cross tabulation can be represented by the error matrix
(Table 2), which express the amount of agreement between a product and a reference classification.
From the confusion matrix, commission (CE, Equation (1)), omission errors (OE, Equation (2)), and the
Dice coefficient (DC, Equation (3)) were computed. DC summarises both commission and omission
errors showing the global accuracy for the target category (Padilla et al., 2014).
Table 2. Sampled error matrix on a sampling unit *.
Product
Reference
Total
Burned Unburned
Burned e11 e12 e1+
Unburned e21 e22 e2+
Total e+1e+2
* eij express the agreements (diagonal cells) or disagreements (offdiagonal) between the BA product (map) and the
reference data.
The detected BA products were matched to the reference BA perimeters dates as closely as possible.
However, due to the different satellite orbital cycles, the detection and validation periods often differed
except for the Ots algorithm when deployed over tiles where reference burn perimeters were derived
from Sentinel-2 images (i.e., the same sensor was used when generating both reference fire perimeter
and the BA product). The validation metrics were computed over each tile, over all tiles, and by main
land cover classes (e.g., crops, grasslands, shrubs, and tropical forests). The CCI Land Cover product
v2.0.7 for the year 2015 [
53
] was used to segment the area by main land cover types. Cloud affected
areas, no data areas, and Sentinel 1/2 pre-processing errors were masked out and were not used when
computing the accuracy metrics. The pixel-wise spatial agreement between the optical (Ots) and
radar (RtsB) based algorithms was also computed. The RtsB algorithm was used as it showed higher
accuracies when compared to the RtsC algorithm.
Ce =e12
e1+
(3)
Oe =e21
e+1
(4)
DC =2e11
2e11 +e12+e21
(5)
3. Results
Although reference validation periods were common, the detection periods varied slightly
depending on the algorithm input data (e.g., Sentinel-1, Sentinel-2), thus limiting a like for like
Remote Sens. 2020,12, 334 7 of 15
comparison. However, general trends could be distinguished (Table 3). The Ots Sentinel-2 algorithm
provided superior agreement with the reference fire perimeters when compared to the Sentinel-1
algorithms for all tiles except 33NUG, 33NQG, and 36NXP. Considering all tiles, the Ots algorithm
showed OE and CE around 30% while for the radar backscatter coefficient-based algorithm (RtsB),
the OE and CE were 66% and 36%, respectively. For the coherence-based algorithm (RtsC), slightly
higher OE (67%) and CE (55%) errors were observed. Over all tiles, the largest difference between the
three algorithms was observed for the omission errors, which were twice as much for the radar-based
algorithms (66 vs. 33%). One should notice that much higher accuracies were obtained for the Ots
algorithm over tiles where the validation data were generated from Sentinel-2 images (Table 3) when
compared to tiles where the validation data was generated from Landsat-8 images. For the later
tiles, the Ots algorithm showed OE and CE estimates in the same range as those observed for the
RtsB algorithm.
Table 3.
Agreement between reference and detected BA by algorithm and tile. L-validation data from
Landsat 8 images; S-validation data from Sentinel-2 images. ‘All tiles’ shows values (in bold) computed
for a confusion matrix formed by all pixels from the 10 tiles analysed.
Algorithm
Detected Area (Rounded) with Respect to Reference (in Parenthesis) by Land
Cover Classes (km2)
Accuracy Metrics
(Tile Level)
Total Crops Grasslands Shrubs Tropical Forests OE CE DC
Ots
28PETS795 (1206) 43 (64) 3 (4) 53 (84) 679 (1029) 0.37 0.03 0.76
29NNJS1753 (1139) 923 (696) 65 (13) 266 (234) 487 (194) 0.09 0.33 0.77
30NWPS575 (543) 133 (116) 0 (0) 80 (87) 360 (340) 0.16 0.04 0.89
31NEJS867 (909) 427 (454) 5 (5) 268 (280) 165 (168) 0.17 0.12 0.85
32NNPS3430 (3609) 1351 (1434) 25 (27) 1281 (1347) 763 (791) 0.14 0.09 0.89
33NTGL442 (456) 0 (0) 0 (0) 74 (83) 366 (372) 0.5 0.43 0.53
33NUGL19 (271) 0 (2) 0 (0) 3 (66) 16 (202) 0.98 0.51 0.04
33NWES834 (860) 19 (22) 12 (13) 122 (130) 681 (694) 0.22 0.03 0.86
35NQGL3901 (3529) 0 (0) 0 (0) 17 (13) 3880 (3514) 0.61 0.64 0.37
36NXPL216 (12) 4 (1) 0 (0) 92 (10) 118 (0) 0.57 0.93 0.12
All tiles 12831 (12534) 2901 (2789) 109 (63) 2256 (2335) 7516 (7304) 0.33 0.31 0.68
RtsB
28PETS426 (1206) 16 (64) 1 (4) 17 (84) 379 (1029) 0.71 0.16 0.44
29NNJS935 (1139) 574 (696) 10 (13) 195 (234) 156 (194) 0.35 0.19 0.72
30NWPS1707 (543) 723 (116) 0 (0) 204 (87) 780 (340) 0.5 0.52 0.49
31NEJS262 (909) 123 (454) 1 (5) 82 (280) 57 (168) 0.89 0.63 0.16
32NNPS1090 (3609) 408 (1434) 8 (27) 412 (1347) 251 (791) 0.8 0.32 0.31
33NTGL343 (456) 0 (0) 0 (0) 63 (83) 279 (372) 0.61 0.43 0.46
33NUGL335 (271) 3 (2) 0 (0) 75 (66) 257 (202) 0.52 0.41 0.53
33NWES726 (860) 13 (22) 8 (13) 104 (130) 602 (694) 0.47 0.28 0.61
35NQGL2235 (3529) 1 (0) 0 (0) 8 (13) 2225 (3514) 0.63 0.41 0.46
36NXPL101 (12) 3 (1) 0 (0) 56 (10) 42 (0) 0.18 0.74 0.39
All tiles 8161 (12534) 1864 (2789) 29 (63) 1215 (2335) 5026 (7304) 0.66 0.36 0.45
RtsC
28PETS310 (1206) 12 (64) 1 (4) 34 (84) 242 (1029) 0.87 0.47 0.2
29NNJS143 (1139) 78 (696) 3 (13) 29 (234) 32 (194) 0.91 0.26 0.15
30NWPS344 (543) 27 (116) 0 (0) 37 (87) 279 (340) 0.76 0.48 0.33
31NEJS244 (909) 127 (454) 1 (5) 74 (280) 41 (168) 0.98 0.91 0.04
32NNPS3933 (3609) 2230 (1434) 31 (27) 1202 (1347) 466 (791) 0.58 0.61 0.41
33NTGL537 (456) 0 (0) 0 (0) 99 (83) 437 (372) 0.72 0.85 0.19
33NUGL19 (271) 0 (2) 0 (0) 5 (66) 14 (202) 0.96 0.19 0.08
33NWES172 (860) 3 (22) 2 (13) 24 (130) 144 (694) 0.93 0.54 0.11
35NQGL2321 (3529) 0 (0) 0 (0) 12 (13) 2308 (3514) 0.14 0.38 0.72
36NXPL17 (12) 0 (1) 1 (0) 9 (10) 7 (0) 0.93 0.62 0.12
All tiles 8040 (12534) 2477 (2789) 38 (63) 1524 (2335) 3969 (7304) 0.67 0.55 0.38
Remote Sens. 2020,12, 334 8 of 15
By land cover type, the Ots algorithm showed small (<20%) OE and CE over cropping areas,
and shrublands, which increased for savanna (17–50%) and forest (42–44%) vegetation. For the RtsB
algorithm, the errors were largely similar over most classes with OE around 70% and CE between 30
and 40% (Table 4). For the coherence-based algorithm (RtsC), the OE and CE varied within higher
intervals 60–80% and 45% to 70%, respectively. The smallest difference between the optical and the
SAR BA mapping accuracy was observed for the forest class (DC 57% vs. 46%) and the RtsB algorithm.
Table 4.
Agreement between reference and detected burned areas by land cover type using a common
confusion matrix formed by all pixels from the 10 tiles analysed.
Algorithm Ots RtsB RtsC
Land Cover (%) OE CE DC OE CE DC OE CE DC
Crops 0.15 0.16 0.85 0.69 0.30 0.43 0.72 0.69 0.29
Grasslands 0.17 0.50 0.62 0.72 0.36 0.39 0.77 0.61 0.29
Shrubs 0.19 0.13 0.84 0.71 0.33 0.41 0.72 0.56 0.34
Tropical forest 0.44 0.42 0.57 0.63 0.38 0.46 0.61 0.46 0.45
The pixel-wise spatial agreement for the total burned area detected by the optical-based Ots and
the radar-based RtsB algorithm ranged between 7% and 65% (Table 5) except for tile 33NUG where
the Ots algorithm performed poorly (Table 3). For unburned areas, the spatial agreement between
the two algorithms was above 91% (Table 5) except for tile 35NQG where both algorithms had large
(>60%) omission errors (Table 3and Figure 2). Commission errors usually occurred over different
areas for the two algorithms as the spatial agreement was below 1% for six tiles. For tiles 35NQG and
36NXP, CE agreement was much higher (around 20%), which may be related with the low mean daily
cloud cover (<30%) or different landscapes in eastern Africa. For the omission errors, the agreement
between the algorithms reached over 30% in four tiles and over 10% in nine tiles (Table 5). Over tiles
dominated by low vegetation and high small fires activity (31NEJ, 32NNP), the agreement between the
two algorithms was low (<10%) due to the high omission errors observed for the Sentinel-1 algorithm
(Figure 2). The maximum agreement of the detected burned area (nearly 65%) was observed over tile
29NNJ (Table 5and Figure 2). Notice that for unburned areas, the percentage spatial agreement in
Table 5was computed using the reference unburned area as the reference. For burned areas, OE and
CE, the spatial agreement was computed using as reference the burned area. BA detection accuracy
(i.e., DC), as well as the size of the detected burned area, was positively related to the number of large
size fire patches (Figure 3).
Table 5.
Spatial agreement (%) between detections from Sentinel-1 (RtsB) and Sentinel-2 (Ots) based
algorithms (NB–not burned, BA–burned area, CE–commission errors, OE–omission errors).
Tile 28PET 29NNJ 30NWP 31NEJ 32NNP
NB S-1 & S-2 98.6 91.4 95.7 98.0 93.7
BA Sl-1 & S-2 23.3 64.7 37.3 6.7 7.1
CE S-1 & S-2 0.2 2.2 0.9 0.4 0.7
OE S-1 & S-2 30.9 3.6 12.4 16.3 12.9
Tile 33NTG 33NUG 33NWE 35NQG 36NXP
NB S-1 & S-2 96.7 98.0 96.0 66.6 98.1
BA Sl-1 & S-2 21.5 1.0 41.8 18.4 36.0
CE S-1 & S-2 5.7 0.3 0.4 18.6 23.1
OE S-1 & S-2 32.9 53.7 10.6 41.8 10.7
Remote Sens. 2020,12, 334 9 of 15
Remote Sens. 2020, 12, x FOR PEER REVIEW 9 of 14
32NNPS 3933 (3609) 2230 (1434) 31 (27) 1202 (1347) 466 (791) 0.58 0.61 0.41
33NTGL 537 (456) 0 (0) 0 (0) 99 (83) 437 (372) 0.72 0.85 0.19
33NUGL 19 (271) 0 (2) 0 (0) 5 (66) 14 (202) 0.96 0.19 0.08
33NWES 172 (860) 3 (22) 2 (13) 24 (130) 144 (694) 0.93 0.54 0.11
35NQGL 2321 (3529) 0 (0) 0 (0) 12 (13) 2308 (3514) 0.14 0.38 0.72
36NXPL 17 (12) 0 (1) 1 (0) 9 (10) 7 (0) 0.93 0.62 0.12
All tiles 8040 (12534) 2477 (2789) 38 (63) 1524 (2335) 3969 (7304) 0.67 0.55 0.38
Table 4. Agreement between reference and detected burned areas by land cover type using a common
confusion matrix formed by all pixels from the 10 tiles analysed.
Algorithm Ots RtsB RtsC
Land Cover (%) OE CE DC OE CE DC OE CE DC
Crops 0.15 0.16 0.85 0.69 0.30 0.43 0.72 0.69 0.29
Grasslands 0.17 0.50 0.62 0.72 0.36 0.39 0.77 0.61 0.29
Shrubs 0.19 0.13 0.84 0.71 0.33 0.41 0.72 0.56 0.34
Tropical forest 0.44 0.42 0.57 0.63 0.38 0.46 0.61 0.46 0.45
Table 5. Spatial agreement (%) between detections from Sentinel-1 (RtsB) and Sentinel-2 (Ots) based
algorithms (NB–not burned, BA–burned area, CE–commission errors, OE–omission errors).
Tile 28PET 29NNJ 30NWP 31NEJ 32NNP
NB S-1 & S-2 98.6 91.4 95.7 98.0 93.7
BA Sl-1 & S-2 23.3 64.7 37.3 6.7 7.1
CE S-1 & S-2 0.2 2.2 0.9 0.4 0.7
OE S-1 & S-2 30.9 3.6 12.4 16.3 12.9
Tile 33NTG 33NUG 33NWE 35NQG 36NXP
NB S-1 & S-2 96.7 98.0 96.0 66.6 98.1
BA Sl-1 & S-2 21.5 1.0 41.8 18.4 36.0
CE S-1 & S-2 5.7 0.3 0.4 18.6 23.1
OE S-1 & S-2 32.9 53.7 10.6 41.8 10.7
Remote Sens. 2020, 12, x FOR PEER REVIEW 10 of 14
Figure 2. Pixel-wise agreement between reference and burned areas detected by the Sentinel-2 (i.e.,
Ots) and Sentinel-1 (i.e., RtsB) algorithms.
Figure 3. The relationship between detection accuracy (left) and detected area (right) for the optical-
based (Ots) and interferometric coherence based (RtsC) algorithms for the 10 tiles analysed.
Of the three algorithms, only the RtsB algorithm was previously validated over other study areas
including 44 sites (20 × 30 km each) in the Amazon basin [36] and 15 MGRS tiles globally distributed
[35]. In the Amazon basin, the RtsB algorithm provided more accurate (DC 0.63 vs. 0.45) overall
results [36]. The increased accuracy may be related to the different fire regime for the Amazon
grasslands (few repeat burns, large fire patches), the large number of sites dominated by forests (25
out of 44), and the use of temporally long sampling units over the Amazon sites [50]. When compared
to globally distributed MGRS tiles, the accuracy observed in Africa for the RtsB algorithm was
generally lower except for the Australian grasslands and shrublands [35]. Such spatial differences
suggest that the radar-based algorithm may need further refinement to better adapt to the local
conditions (fire regime, vegetation type, environmental factors).
The optical-based Ots algorithm showed higher variation of the DC by land cover type with
values ranging from 57% to 85%. In particular, OE and CE for tropical forests were more than double
Figure 2.
Pixel-wise agreement between reference and burned areas detected by the Sentinel-2 (i.e.,
Ots) and Sentinel-1 (i.e., RtsB) algorithms.
Remote Sens. 2020,12, 334 10 of 15
Remote Sens. 2020, 12, x FOR PEER REVIEW 10 of 14
Figure 2. Pixel-wise agreement between reference and burned areas detected by the Sentinel-2 (i.e.,
Ots) and Sentinel-1 (i.e., RtsB) algorithms.
Figure 3. The relationship between detection accuracy (left) and detected area (right) for the optical-
based (Ots) and interferometric coherence based (RtsC) algorithms for the 10 tiles analysed.
Of the three algorithms, only the RtsB algorithm was previously validated over other study areas
including 44 sites (20 × 30 km each) in the Amazon basin [36] and 15 MGRS tiles globally distributed
[35]. In the Amazon basin, the RtsB algorithm provided more accurate (DC 0.63 vs. 0.45) overall
results [36]. The increased accuracy may be related to the different fire regime for the Amazon
grasslands (few repeat burns, large fire patches), the large number of sites dominated by forests (25
out of 44), and the use of temporally long sampling units over the Amazon sites [50]. When compared
to globally distributed MGRS tiles, the accuracy observed in Africa for the RtsB algorithm was
generally lower except for the Australian grasslands and shrublands [35]. Such spatial differences
suggest that the radar-based algorithm may need further refinement to better adapt to the local
conditions (fire regime, vegetation type, environmental factors).
The optical-based Ots algorithm showed higher variation of the DC by land cover type with
values ranging from 57% to 85%. In particular, OE and CE for tropical forests were more than double
Figure 3.
The relationship between detection accuracy (
left
) and detected area (
right
) for the
optical-based (Ots) and interferometric coherence based (RtsC) algorithms for the 10 tiles analysed.
The classification rate (classified vs. not classified) was also analysed. Not classified pixels
corresponded to areas of clouds/shadows in optical images (Ots) or pre-processing errors (e.g.,
difficulties in deriving coherence estimates) in the radar-based algorithms. The average classification
rates for the Ots, RtsB, and RtsC algorithms were 83%, 100%, and 85%, respectively.
4. Discussion
The algorithms compared here were developed using diverse input datasets (optical, radar
backscatter, and interferometric coherence) and change detection strategies. Considering all tiles, the
use of an optical-based algorithm (Ots) increased the accuracy of burned area detection by about
23% when compared to the most accurate radar-based (RtsB) algorithm (DC 0.68 vs. 0.45). When
compared to reference fire perimeters derived from independent sensors (i.e., Landsat-8), the RtsB
algorithm provided more accurate detection for three of the four tiles analysed (33NUG 35NQG,
36NXP). However, the particularly high errors observed over the 33NUG tile for the Ots algorithm
seemed to be related to the few cloud-free observations of the Sentinel-2 sensor. Over tiles where
Sentinel-2 images were used to derive the validation perimeters, the BA detection accuracy improved
noticeably (OE 19% and CE 11%, average values) for the Ots algorithm (also based on Sentinel-2 images)
when compared to tiles where reference perimeters were derived from Landsat images. The increased
agreement with the reference data was attributed to (i) exact matching of validation and detection
periods thus avoiding errors due to missing or extra days, (ii) the same spatial resolution (20 m) at
which validation and detection were carried out and thus lower errors along fire borders, (iii) the use of
the same spectral information (Sentinel-2 bands) and similarities between the BAMS algorithm (used
for validation perimeters) and the Ots algorithm as both use a two step mapping strategy. However,
accuracy metrics for the Ots algorithm over Sub-Saharan Africa were higher (DC =0.77, OE =0.27, CE
=0.19) when using a fully statistical validation approach [15], which included unmatched validation
periods (from Landsat-7 and 8) and temporally long sample units. This suggests that the tiles selected
for algorithms inter-comparison may not represent average burn conditions over all of Africa and that
temporally short sample units may underestimate BA detection accuracy. Such an assumption is also
supported by the higher accuracy observed for the RtsC coherence algorithm, (DC =0.48, OE =0.53,
CE =0.57) when assessed over a significantly larger area in the same region (Sub-Saharan Africa) [
54
].
Over the studied tiles, the time series coherence-based algorithm (RtsC) showed larger errors when
compared to the time-series backscatter-based algorithm (RtsB) over all areas as well as lower detection
rates (85% vs. 100%). This suggests that the coherent scattering properties of the vegetation before
and after the fire event may not be changing sufficiently in the post-fire image to indicate a fire event
may have been present. It was also noticed that the interferometric coherence was very dynamic in
Sub-Saharan Africa, possibly as a result of fire-unrelated vegetation structure and moisture changes as
a result of the on-going dry season that coincides with the fire season.
Remote Sens. 2020,12, 334 11 of 15
Of the three algorithms, only the RtsB algorithm was previously validated over other study
areas including 44 sites (20
×
30 km each) in the Amazon basin [
36
] and 15 MGRS tiles globally
distributed [
35
]. In the Amazon basin, the RtsB algorithm provided more accurate (DC 0.63 vs. 0.45)
overall results [
36
]. The increased accuracy may be related to the different fire regime for the Amazon
grasslands (few repeat burns, large fire patches), the large number of sites dominated by forests (25 out
of 44), and the use of temporally long sampling units over the Amazon sites [
50
]. When compared to
globally distributed MGRS tiles, the accuracy observed in Africa for the RtsB algorithm was generally
lower except for the Australian grasslands and shrublands [
35
]. Such spatial differences suggest that
the radar-based algorithm may need further refinement to better adapt to the local conditions (fire
regime, vegetation type, environmental factors).
The optical-based Ots algorithm showed higher variation of the DC by land cover type with
values ranging from 57% to 85%. In particular, OE and CE for tropical forests were more than double
when compared to other land covers. Such large errors were attributed to the poor performance of the
Ots algorithm over the heavily burned 35NQG tile (dominated by forest cover). The large OE (61%)
and CE (64%) errors in tile 35NQG seemed unrelated to the daily mean cloud cover (15–30%) or the
number of small fire patches as for tile 33NWE (also dominated by forest cover) the OE and CE were
much lower (0.22 and 0.03) despite increased mean daily cloud cover (50–75%), and a similar number
of small fire patches. Instead, higher errors in tiles dominated by forest vegetation (33NTG, 35NUG,
35NQG) seemed to be related to mismatches between detection and validation periods due to the use
of Landsat imagery when generating the reference fire perimeters. Indeed, for forest dominated tiles
(28PET, 33NWE) where reference fire perimeters were generated from Sentinel-2 imagery (i.e., exact
matching of detection and validation dates), the observed OE and CE where much lower.
The accuracy of the radar-based algorithms was more stable with DC differences below 8% and
16% for the RtsB and RtsC, respectively. The Ots and RtsB algorithms showed a consistent pattern in
regard to the location of commission errors. Such errors largely occurred over different areas (spatial
agreement <6% for most tiles), suggesting potential synergies with a combined radar-optical detection
method. The spatial agreement of the burned area detected by the Ots and RtsB algorithms did not
depend on the total burned area but was negatively correlated with the number of large fire patches
particularly when tile 33NUG was discarded from the analysis (Pearson’s r =0.55). Notice that the
Ots algorithm showed poor results (DC =0.04) over tile 33NUG as explained above. Correlation of
DC values with the number of large fire patches reached 0.52,
−
0.30 and 0.43 for Ots, RtsB, and RtsC
respectively suggesting more accurate results with an increasing number of large fire patches for the
Ots and RtsC algorithms and the opposite for the RtsB algorithm although the relationship for the
latter was weak. In addition, strong correlations (>0.7) were observed between the total burned area
detected by the Ots and RtsC algorithms and the number of large fire patches.
The validation of medium resolution (10–30 m) BA products is a known issue due to the difficulty
in obtaining reference datasets from high spatial resolution optical sensors (e.g., WorldView, GeoEye,
Planet) over large areas and temporal spans. This study was limited by the lack of reference fire
perimeters from such high spatial resolution sensors. However, the rather small difference between the
spatial resolution at which the burned area was detected 20–40 m and the imagery used to derive the
reference fire perimeters (20–30 m) should not impede obtaining at least some preliminary conclusions
as shown in this study.
5. Conclusions
The analysis suggested that optical-based algorithms may provide for a significant increase in
accuracy over SAR based algorithms, particularly over regions where persistent cloud cover is not an
issue as detection rates may otherwise drop considerably. However, BA detection from SAR time series
is in its infancy when compared to the decades-long research based on optical sensors. Therefore, more
mature SAR-based BA detection algorithms may provide accuracy metrics like those obtained from
high resolution optical data (e.g., Sentinel-2) by taking advantage of combined backscatter-coherence
Remote Sens. 2020,12, 334 12 of 15
information. Further work is needed to better understand the relationship between interferometric
SAR coherence from C-band and the process of vegetation burning in terms of the influence of scatters
and their post-fire stability. Furthermore, locally adaptive SAR-based algorithms that use information
from different polarisations depending on the land cover type, topography, and post-fire backscatter
change direction (increase vs. decrease) may improve burned area detection particularly over grass
and shrub dominated areas. Nevertheless, the added complexity of SAR data interpretation and the
massive amount of data generated by interferometric SAR processing may only be justified over areas
of persistent cloud cover where optical-based algorithms struggle.
Future work should also assess the consequences of the high BA, OE, and CE for greenhouse
gases emissions, mitigation actions, restoration work, non-carbon ecosystem services, etc., as such
analysis was beyond the scope of this study.
Author Contributions:
Conceptualization, M.A.T. and E.C.; methodology, M.A.T. and M.A.B.-P.; software,
M.A.B.-P.; formal analysis, M.A.T. and M.A.B.-P.; investigation, M.A.B.-P. and M.A.T.; resources, M.A.B.-P.,
Á
.F.-C.,
E.R., A.B., J.W., W.W., S.L., and P.N.; data curation, M.A.B.-P.,
Á
.F.-C., E.R., A.B., J.W., W.W., S.L., and P.N.;
writing—original draft preparation, M.A.T. and M.A.B.-P.; writing—review and editing, M.A.T., M.A.B.-P., E.R.,
A.B., J.W., W.W., K.T., S.L., P.N., F.S., and E.C.; visualization, M.A.B.-P.; supervision, M.A.T. and E.C.; funding
acquisition, M.A.T. and E.C. All authors have read and agreed to the published version of the manuscript.
Funding:
This research has been financed by the European Space Agency (ESA) through the Fire_cci (Climate
Change Initiative) project (Contract 4000126706/19/I-NB) and by the Spanish Ministry of Science, Innovation, and
Universities through a Formacion Profesorado Universitario (FPU) doctoral fellowship (FPU16/01645).
Conflicts of Interest: The authors declare no conflict of interest.
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