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Exploring PRISMA Scene for Fire Detection: Case Study of 2019 Bushfires in Ben Halls Gap National Park, NSW, Australia

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Exploring PRISMA Scene for Fire Detection: Case Study of 2019 Bushfires in Ben Halls Gap National Park, NSW, Australia

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Precursore IperSpettrale della Missione Applicativa (Hyperspectral Precursor of the Application Mission, PRISMA) is a new hyperspectral mission by the ASI (Agenzia Spaziale Italiana, Italian Space Agency) mission launched in 2019 to measure the unique spectral features of diverse materials including vegetation and forest disturbances. In this study, we explored the potential use of this new sensor PRISMA for active wildfire characterization. We used the PRISMA hypercube acquired during the Australian bushfires of 2019 in New South Wales to test three detection techniques that take advantage of the unique spectral features of biomass burning in the spectral range measured by PRISMA. The three methods—the CO2-CIBR (continuum interpolated band ratio), HFDI (hyperspectral fire detection index) and AKBD (advanced K band difference)—were adapted to the PRISMA sensor’s characteristics and evaluated in terms of performance. Classification techniques based on machine learning algorithms (support vector machine, SVM) were used in combination with the visual interpretation of a panchromatic sharpened PRISMA image for validation. Preliminary analysis showed a good overall performance of the instrument in terms of radiance. We observed that the presence of the striping effect in the data can influence the performance of the indices. Both the CIBR and HFDI adapted for PRISMA were able to produce a detection rate spanning between 0.13561 and 0.81598 for CO2-CIBR and that between 0.36171 and 0.88431 depending on the chosen band combination. The potassium emission index turned out to be inadequate for locating flaming in our data, possibly due to multiple factors such as striping noise and the spectral resolution (12 nm) of the PRISMA band centered at the potassium emission.
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remote sensing
Technical Note
Exploring PRISMA Scene for Fire Detection: Case Study of 2019
Bushfires in Ben Halls Gap National Park, NSW, Australia
Stefania Amici * and Alessandro Piscini


Citation: Amici, S.; Piscini, A.
Exploring PRISMA Scene for Fire
Detection: Case Study of 2019
Bushfires in Ben Halls Gap National
Park, NSW, Australia. Remote Sens.
2021,13, 1410. https://doi.org/
10.3390/rs13081410
Academic Editor: Ioannis Gitas
Received: 4 February 2021
Accepted: 2 April 2021
Published: 7 April 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
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iations.
Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Istituto Nazionale di Geofisica e Vulcanologia, Sezione ONT, 00143 Rome, Italy; alessandro.piscini@ingv.it
*Correspondence: stefania.amici@ingv.it
Abstract:
Precursore IperSpettrale della Missione Applicativa (Hyperspectral Precursor of the Appli-
cation Mission, PRISMA) is a new hyperspectral mission by the ASI (Agenzia Spaziale Italiana, Italian
Space Agency) mission launched in 2019 to measure the unique spectral features of diverse materials
including vegetation and forest disturbances. In this study, we explored the potential use of this
new sensor PRISMA for active wildfire characterization. We used the PRISMA hypercube acquired
during the Australian bushfires of 2019 in New South Wales to test three detection techniques that
take advantage of the unique spectral features of biomass burning in the spectral range measured by
PRISMA. The three methods—the CO
2
-CIBR (continuum interpolated band ratio), HFDI (hyperspec-
tral fire detection index) and AKBD (advanced K band difference)—were adapted to the PRISMA
sensor’s characteristics and evaluated in terms of performance. Classification techniques based
on machine learning algorithms (support vector machine, SVM) were used in combination with
the visual interpretation of a panchromatic sharpened PRISMA image for validation. Preliminary
analysis showed a good overall performance of the instrument in terms of radiance. We observed
that the presence of the striping effect in the data can influence the performance of the indices. Both
the CIBR and HFDI adapted for PRISMA were able to produce a detection rate spanning between
0.13561 and 0.81598 for CO
2
-CIBR and that between 0.36171 and 0.88431 depending on the chosen
band combination. The potassium emission index turned out to be inadequate for locating flaming in
our data, possibly due to multiple factors such as striping noise and the spectral resolution (12 nm)
of the PRISMA band centered at the potassium emission.
Keywords: PRISMA; HFDI; CIBR; Australia; bushfires; fire detection; classification
1. Introduction
Active wildfires are complex phenomena that involve heat transfer and high tem-
peratures, peaking at around 1000 K for flaming and around 600 K for the smoldering
phase [
1
,
2
]. Because of the high contrast with the ambient background temperature (300 K),
most active fire remote-sensing applications rely on measurements of the Plankian thermal
emission signature in the middle infrared (MIR) atmospheric window (3–5
µ
m), where
solar radiation is lower than in the short-wave infrared (SWIR) window. Long-wavelength
infrared (LWIR: 8–14 µm) regions can also be used to improve fire detection methods. [3].
Among other techniques, imaging spectroscopy in the visible and near-infrared (VNIR)
and SWIR spectral ranges has been proved to have great potential for characterizing the fire
disturbance continuum, which includes three phases: (1) pre-fire (fuel type and condition),
(2) active fires (fire spread, intensity and emission) and (3) post-fire (burn severity) [4].
Several methods have been explored and tested for studying the pre- and post-fire
phases [
4
] by using reflectance spectra, which highlight unique characteristics linked to
the state of vegetation. For active fire characterization in the VNIR–SWIR hyperspectral
range, [
4
,
5
] diverse aspects need to be considered. Because the data are generally acquired
in the day time [
4
], to accurately detect a fire, the emitted radiance and reflected solar
radiance background need to be properly separated. The spatial resolution of satellite
Remote Sens. 2021,13, 1410. https://doi.org/10.3390/rs13081410 https://www.mdpi.com/journal/remotesensing
Remote Sens. 2021,13, 1410 2 of 15
hyperspectral sensors (typical 30 m) means that a fire can occupy a fraction of the pixel,
weakening the radiance measured at the sensor down to a level similar to that of the solar
radiance background [
6
]. Hyperspectral techniques developed for active fire characteriza-
tion focus on fire detection [
2
,
7
9
] and temperature retrieval [
6
,
10
]. While great potential
is reported in the literature concerning the use of airborne sensors, the exploitation [
6
,
7
]
of imaging spectroscopy from space has been limited to sensors on just one satellite,
EO1-Hyperion, operating between 2000 and 2017. A new generation of hyperspectral
space missions such as the new Italian Precursore IperSpettrale della Missione Applicativa
(PRISMA) mission and another planned (e.g., Environmental Mapping and Analysis Pro-
gram -EnMap [
11
] and Copernicus Hyperspectral Imaging Mission-CHIME [
12
]) offer the
opportunity to improve and exploit these methods.
This study analyzed, for the first time, the scene of a wildfire provided by the new
PRISMA mission to test a range of fire detection indices. By using an approach simi-
lar to that developed by Waigl [
10
], we examined three diverse fire detection methods
(CO
2
- continuum interpolated band ratio (CIBR), hyperspectral fire detection index (HFDI)
and advanced K band difference (AKBD) and explored PRISMA’s ability to characterize
biomass burning. The metrics were originally developed for airborne sensors [
2
,
5
,
6
] and
subsequently tested on data from space [10]. Because the detection performance depends
on diverse aspects such as scene characteristics (e.g., the aspect and slope) [
2
,
4
,
10
] and
sensor characteristics (e.g., the spectral resolution, central band positions, signal-to-noise
ratio (SNR) and atmospheric effects), the metrics need to be adapted to the sensor. The
PRISMA image (hypercube) was acquired within the New South Wales (NSW) region
during the Australian bushfire. Bushfires are seasonal events in Australia caused by a com-
bination of factors such as extreme heat, drought, natural climate variability and human
activities [13,14].
Australia has experienced mega fires in the past, and a few in the current century—
the Canberra bushfires (2003) and Black Saturday (2009) [
15
,
16
]—however, the 2019–20
bushfire season was the most widespread, extreme and catastrophic that Australia has
ever experienced since European settlement [
16
]. Prolonged drought in conjunction with
high maximum temperatures and strong winds [
17
,
18
] and the hydroclimatic variables and
geomorphic characteristics (the surface soil moisture, wind speed, relative humidity, heat
waves, dead and live fuel moisture and certain land cover types) [
16
] created the optimal
conditions for the bushfires of 2019–2020 to ignite and spread. December 2019 was the
warmest in 110 years of record for NSW and the monthly accumulated Forest Fire Danger
Index [19] was the highest on record over most of the Australia including NSW [20].
The fires covered extensive areas of the eastern Australian seaboard, including ap-
proximately 7% (5.37 million hectares) of the land in New South Wales within the Rural
Fire Service (RFS) fire ground perimeter (at 3 February 2020) [21,22].
The work reports the first results of active fire characterization over a study area in
NSW affected by the Australian bushfires of 2019, realized by using PRISMA data on
a selected case study. Detection maps were retrieved by adapting the three detection
metrics to PRISMA spectral bands. The preliminary results, obtained for the Ben Halls
Gap National Park case study, suggest, in accordance with the literature, that the HFDI
performs better than CO
2
-CIBR, while the results for the K-emission-based method for this
case study results underperforming.
2. Materials and Methods
2.1. Case Study
To select the PRISMA hypercube, we searched the PRISMA catalogue [
23
] for images
acquired in the eastern Australian seaboard between October 2019 and February 2020. We
then selected the hypercube, acquired on 27 December 2020, in a cloud-free condition. The
study was conducted on a subarea (region of interest (ROI)) of the hypercube with a size of
140
×
115 pixels and 212 bands depicting an active fire (Figure 1b) in New South Wales
about 250 km north of Sydney in the Ben Halls Gap National Park (BHGNP) which covers
Remote Sens. 2021,13, 1410 3 of 15
2500 ha, and is situated 60 km south-east of Tamworth and 10 km from the township of
Nundle. The park is located at a comparatively high altitude that results in generally cool
temperatures and high rainfall. Due to these characteristics, as reported in the BHGNP
plan of management [
24
], wildfires were considered an unusual event at the time that plan
was written; however, the simultaneous occurrence of high temperatures and wind speeds
and low relative humidity that is required for high-intensity fire behavior, conditions rare
for this region, occurred in 2019.
Remote Sens. 2021, 13, x FOR PEER REVIEW 3 of 15
2. Materials and Methods
2.1. Case Study
To select the PRISMA hypercube, we searched the PRISMA catalogue [23] for images
acquired in the eastern Australian seaboard between October 2019 and February 2020. We
then selected the hypercube, acquired on 27 December 2020, in a cloud-free condition. The
study was conducted on a subarea (region of interest (ROI)) of the hypercube with a size
of 140 × 115 pixels and 212 bands depicting an active fire (Figure 1b) in New South Wales
about 250 km north of Sydney in the Ben Halls Gap National Park (BHGNP) which covers
2500 ha, and is situated 60 km south-east of Tamworth and 10 km from the township of
Nundle. The park is located at a comparatively high altitude that results in generally cool
temperatures and high rainfall. Due to these characteristics, as reported in the BHGNP
plan of management [24], wildfires were considered an unusual event at the time that plan
was written; however, the simultaneous occurrence of high temperatures and wind
speeds and low relative humidity that is required for high-intensity fire behavior, condi-
tions rare for this region, occurred in 2019.
(a) (b)
Figure 1. (a) Active fire/burned area map [25] for bushfires in New South Wales and Victoria on 31 December 2019. The
green square in the map corresponds to Precursore IperSpettrale della Missione Applicativa (PRISMA) imaged area; (b)
PRISMA subarea hypercube acquired on 27 December 2019. Red, Green, Blue (RGB), false color band composition with
central wavelengths located at 1646.9205 nm, 8,548,984 nm and 579.1278 nm shows a derived fire front extension of about
5 km.
2.2. PRISMA Sensor
The Hyperspectral Precursor of the Application Mission, PRISMA, was launched on
22 March 2019 on board the(Vettore Europeo di Generazione Avanzata) VEGA rocket.
The mission has a capacity to acquire up to 223 images per day with a scene of 30 km × 30
km. Generally, the acquisition is based on a priority list and on-demand acquisition re-
quest.
The PRISMA imaging payload includes an imaging spectrometer (hyperspectral
camera) with VNIR (visible and near-infrared) and SWIR (shortwave infrared) detectors
and a panchromatic (PAN) camera detector. The imaging spectrometer provides a contin-
uum of spectral bands spanning between 400 nm and 2500 nm at a ground-sampling dis-
tance (GSD) of 30 m/pixel (Table 1). The PAN acquires the same area at 5 m/pixel.
The PRISMA hyperspectral instrument differs from previous space-imaging spec-
trometers because it uses an optical design based on a prism, rather than a grating, to
obtain the dispersion of incoming radiation on a 2D focal plane. The advantages of prism-
based spectrometers are their compact design and high efficiency [26]; the main disad-
vantage is the low dispersion [26]. The “instantaneous” spectral and spatial dimensions
(across the track) of the PRISMA hyperspectral cube are directly determined by the 2D
Figure 1.
(
a
) Active fire/burned area map [
25
] for bushfires in New South Wales and Victoria on 31 December 2019.
The green square in the map corresponds to Precursore IperSpettrale della Missione Applicativa (PRISMA) imaged area;
(b) PRISMA
subarea hypercube acquired on 27 December 2019. Red, Green, Blue (RGB), false color band composition with
central wavelengths located at 1646.9205 nm, 8,548,984 nm and 579.1278 nm shows a derived fire front extension of about
5 km.
2.2. PRISMA Sensor
The Hyperspectral Precursor of the Application Mission, PRISMA, was launched on
22 March 2019 on board the(Vettore Europeo di Generazione Avanzata) VEGA rocket. The
mission has a capacity to acquire up to 223 images per day with a scene of 30 km
×
30 km.
Generally, the acquisition is based on a priority list and on-demand acquisition request.
The PRISMA imaging payload includes an imaging spectrometer (hyperspectral cam-
era) with VNIR (visible and near-infrared) and SWIR (shortwave infrared) detectors and a
panchromatic (PAN) camera detector. The imaging spectrometer provides a continuum
of spectral bands spanning between 400 nm and 2500 nm at a ground-sampling distance
(GSD) of 30 m/pixel (Table 1). The PAN acquires the same area at 5 m/pixel.
The PRISMA hyperspectral instrument differs from previous space-imaging spectrom-
eters because it uses an optical design based on a prism, rather than a grating, to obtain
the dispersion of incoming radiation on a 2D focal plane. The advantages of prism-based
spectrometers are their compact design and high efficiency [
26
]; the main disadvantage
is the low dispersion [
26
]. The “instantaneous” spectral and spatial dimensions (across
the track) of the PRISMA hyperspectral cube are directly determined by the 2D detectors,
while the third dimension (along the track) is determined by the satellite motion (push
broom) [27], to provide the 30 ×30 km scene.
Remote Sens. 2021,13, 1410 4 of 15
Table 1. PRISMA instrument characteristics [27].
Description Value Unit
Scene size 30 ×30 Km
Pixel size nadir 30 ×30 M
FOV 2.4 Degrees
Spectral range—VNIR 400–1010 Nm
Spectral range—SWIR 920–2505 Nm
Spectral range—PAN 400–700 Nm
Spectral resolution—VNIR 13 Nm
Spectral resolution—SWIR 14.5 Nm
Spectral resolution—PAN 13.5 Nm
Number of spectral bands—VNIR
66 -
Number of spectral bands—SWIR
174 -
Spatial resolution—VNIR-SWIR 30 m/px
Spatial resolution—PAN 5 m/px
SNR—VNIR >160 (>450 at 650 nm) -
SNR—SWIR >100 (>360 at 1550 nm) -
SNR—PAN >240 -
Absolute radiometric accuracy Better than 5% -
2.3. PRISMA Scene
The PRISMA data are made available for free by the Italian Space Agency (ASI) [
23
,
28
].
The data are delivered in the HDF5 format with four options: (1) Level 1 (Hyperspec-
tral/PAN) is radiometrically corrected and calibrated TOA (top of atmosphere) data;
(2) Level
2B, the Geolocated at Ground Spectral Radiance Product (hyperspectral/PAN);
(3) Level 2C, the Geolocated At-surface Reflectance Product (hyperspectral/PAN); and
(4) Level
2D, a Geocoded version of the Level 2C product (hyperspectral/PAN). We used
the LC1-PRISMA product acquired on 27 December 2019. The image processing was imple-
mented by using the ENVI 5.5 (Harris Geospatial Solution, USA) software and consisted
of three steps: (1) applying the scale factor 100 to convert the digital numbers of the L1
product to radiance expressed in Wm
2
sr
1µ
m
1
[
23
,
28
]; (2) removing noisy bands and
building the hyperspectral cube, by stacking the visible and SWIR data; (3) implementing
the atmospheric correction and obtaining a reflectance spectrum; (4) producing spectral
detection maps and implementing the code (in Matlab 2020b) for the semi-automatic pro-
cessing of the CO
2
-CIBR and HDFI. The hypercube was visually explored to identify the
noisy bands affecting the spectra; specifically, the bands within two VNIR spectral intervals
(402.2041–426.7687 nm and 972.3630–959.4313 nm) and two SWIR intervals (942.8875–
969.7985 nm and 2456.2781–2496.8254 nm) were removed. Because, in our data, the L2
PRISMA reflectance product showed several artifacts in the SWIR that would affect the
classification, we preferred to derive the reflectance by applying atmospheric correction to
the L1 PRISMA product.
The atmospheric correction was realized by using the QUAC (quick atmospheric
correction) available in ENVI 5.5 and applied to the whole PRISMA scene. The QUAC
was chosen for several reasons: (a) the image is cloud-free and no cloud shadows are
in the scene, and it works best with scenes that contain diverse materials such as soil,
vegetation and manmade structures [
29
], as in our case; (b) the full set of metadata that
would be used to implement first-principles radiative-transfer modelling (i.e., fast line-of-
sight atmospheric analysis of hypercubes, FLAASH) could not be found in our data; (c) [
30
]
generally found that the results for QUAC were within
±
15% of those for FLAASH. The
QUAC returns apparent reflectance integer data, with pixel values ranging from 0 to 10,000
(representing 0 to 100% reflectance) [
29
]. Due to the PRISMA spectral characteristics, the
near-shortwave infrared (NIR-SWIR) option available for QUAC was selected, and the pixel
values were divided by 10,000 to obtain values spanning between 0 and 1.0 [
29
].
Figure 2a
shows PRISMA single-pixel reflectance spectra corresponding to diverse categories in
the imagery.
Remote Sens. 2021,13, 1410 5 of 15
Remote Sens. 2021, 13, x FOR PEER REVIEW 5 of 15
QUAC returns apparent reflectance integer data, with pixel values ranging from 0 to
10,000 (representing 0 to 100% reflectance) [29]. Due to the PRISMA spectral characteris-
tics, the near-shortwave infrared (NIR-SWIR) option available for QUAC was selected,
and the pixel values were divided by 10,000 to obtain values spanning between 0 and 1.0
[29]. Figure 2a shows PRISMA single-pixel reflectance spectra corresponding to diverse
categories in the imagery.
2.4. Fire Detection Algorithms
2.4.1. Carbon Dioxide Continuum-Interpolated Band Ratio (CO
2-
CIBR)
The continuum-interpolated band ratio (CIBR) [2,31] can be used to separate the
dominantly emitted radiance from the dominantly reflected radiance and is described by
Metric (1).
CIBR
=L
λm
(w2 ∗ L
λ2
+w3∗L
λ3
)
(1)
where λ
m
is the wavelength at which the absorption is the strongest, λ
2
and λ
3
are the
“shoulder” wavelengths with no absorption, and w2 and w3 are weighting factors, where
w2 + w3 = 1 and w2 is given by (2).
w2  (λ
3
λ
m
)
(λ
3
λ
2
)
(2)
For the carbon dioxide absorption features at 2010 nm were used by [2,6,10] to de-
velop the CO
2
-CIBR metric, which turned out to be successful with both airborne (airborne
visible/infrared imaging spectrometer, AVIRIS) and EO1-Hyperion data. The PRISMA
fire radiance spectra were explored (an example of the spectra is shown in Figure 2b) to
select the wavelengths to be used to calculate the CIBRs. The minimum absorption (λ
m
)
was found for bands centered at 2001.79 nm, 2010.36 nm, 2052.70 nm and 2061.09 nm. For
the shoulders, the PRISMA bands centered at 1984.49 nm and 2035.94 nm were selected
as λ
2
and λ
3
,
and those at 2035.94 nm and 2086.04 nm, as λ
2
and λ
3
.
(a) (b)
Figure 2. (a) PRISMA reflectance spectra corresponding to five categories: vegetation, fire, smoke, bare soil and freshly
burned. The black bracket number 1 indicates very near-shortwave infrared (VNIR-SWIR) bands overlapping; the brackets
number 2 and 3 indicate atmospheric attenuation from water vapor that can occur at 1400 nm and 1900 nm respectively;
the bracket number 4 indicates the CO
2
absorption bands around 2000 nm. (b) Two PRISMA fire radiance spectra against
the background (no fire) radiance show absorption bands around 2000 nm and 2010 nm (λ
m
) and around 2050 nm and
2060 nm (λ
m
), where CO
2
absorption bands are expected. The positions of the wavelength corresponding to the “shoul-
ders” are indicated as λ
2
(shoulder at the left of λ
m
)
and λ
3
(shoulder at the right of λ
m
), respectively.
The PRISMA central wavelengths and corresponding weighting factors that were
used to calculate the CO
2
-CIBR are reported in Table 2.
Figure 2.
(
a
) PRISMA reflectance spectra corresponding to five categories: vegetation, fire, smoke, bare soil and freshly
burned. The black bracket number 1 indicates very near-shortwave infrared (VNIR-SWIR) bands overlapping; the brackets
number 2 and 3 indicate atmospheric attenuation from water vapor that can occur at 1400 nm and 1900 nm respectively; the
bracket number 4 indicates the CO
2
absorption bands around 2000 nm. (
b
) Two PRISMA fire radiance spectra against the
background (no fire) radiance show absorption bands around 2000 nm and 2010 nm (
λm
) and around 2050 nm and 2060 nm
(
λm
), where CO
2
absorption bands are expected. The positions of the wavelength corresponding to the “shoulders” are
indicated as λ2(shoulder at the left of λm) and λ3(shoulder at the right of λm), respectively.
2.4. Fire Detection Algorithms
2.4.1. Carbon Dioxide Continuum-Interpolated Band Ratio (CO2-CIBR)
The continuum-interpolated band ratio (CIBR) [
2
,
31
] can be used to separate the
dominantly emitted radiance from the dominantly reflected radiance and is described by
Metric (1).
CIBR =Lλm
(w2 Lλ2+w3 Lλ3(1)
where
λm
is the wavelength at which the absorption is the strongest,
λ2
and
λ3
are the
“shoulder” wavelengths with no absorption, and w2 and w3 are weighting factors, where
w2 + w3 = 1 and w2 is given by (2).
w2 =(λ3λm)
(λ3λ2)(2)
For the carbon dioxide absorption features at 2010 nm were used by [
2
,
6
,
10
] to develop
the CO
2
-CIBR metric, which turned out to be successful with both airborne (airborne
visible/infrared imaging spectrometer, AVIRIS) and EO1-Hyperion data. The PRISMA fire
radiance spectra were explored (an example of the spectra is shown in Figure 2b) to select
the wavelengths to be used to calculate the CIBRs. The minimum absorption (
λm
) was
found for bands centered at 2001.79 nm, 2010.36 nm, 2052.70 nm and 2061.09 nm. For the
shoulders, the PRISMA bands centered at 1984.49 nm and 2035.94 nm were selected as λ2
and λ3, and those at 2035.94 nm and 2086.04 nm, as λ2and λ3.
The PRISMA central wavelengths and corresponding weighting factors that were
used to calculate the CO2-CIBR are reported in Table 2.
2.4.2. HFDI
The authors of [
6
,
10
] reported fire detection more accurate than that realized with the
CIBR by using the hyperspectral fire detection index (HFDI), a normalized difference index
defined as
HFDI =Lλ1Lλ2
(Lλ1+Lλ2(3)
Remote Sens. 2021,13, 1410 6 of 15
where L
λ1
and L
λ2
are the spectral radiances at two SWIR bands around 2430 nm and
2060 nm
expressed in Wm
2
sr
1µ
m
1
. These two bands provided better performance
when using AVIRIS spectral data [
6
]. In addition, [
10
] highlighted that this metric needs to
be adapted to the sensor noise level around the spectral range 2400–2500 nm. Because the
PRISMA sensor is very new and its derived products are under continuous evaluation, we
derived the HFDI for a series of band combinations following an approach similar to that
used by [10] for EO1-Hyperion to identify the best combination.
Table 2.
PRISMA wavelengths to be used to derive the diverse CO
2
-continuum interpolated band
ratio (CIBR)PRISMA.
λmλ2λ3 W2 W3
2001.79 1984.49 2035.94 0.6640 0.3360
2010.36 1984.49 2035.94 0.4972 0.5028
2052.70 2035.94 2086.04 0.66538 0.33462
2061.09 2035.94 2086.04 0.49807 0.50193
2.4.3. Potassium Emission
Trace elements such as potassium, sodium and phosphorus [
32
34
] present in the
biomass ionized at flaming temperatures emit signals at specific wavelengths that can be
detected in the spectrum measured by a spectrometer [
5
,
7
,
10
]. These signals have been
used to locate wildfire flaming phases using laboratory and airborne sensors [
5
,
7
]; their
ability to be used to detect flaming from space [
2
,
5
,
10
] depending on a range of factors
warrants further exploration [
10
]. Because the K emission lines occur at two specific
wavelengths (766.5 nm and 769.9 nm) [
7
], two metrics were developed: (1) the K emission
index [
6
,
10
], defined by the ratio of the radiances measured at the K emission wavelength
(770 nm) and at the proximal band (780 nm), not influenced by the emission and acting
as the background; (2) the AKBD metric, a normalized difference between the K emission
bands and the background bands [5,10].
2.5. Fire Reference for Comparison
In order to compare the detection performance of the metrics, the fire pixels need to
be discriminated from non-fire pixels. To do this, we applied a supervised classification
to the reflectance PRISMA scene, to select endmembers (the truth to be used to train the
classification algorithm). Specifically, we selected 48 pixels representing smoke, 48 pixels
for the bare soil class (bare soil/road/anthropic), 36 pixels for vegetation (high vegetation,
bushes and low vegetation), 12 pixels for freshly burned areas, and 48 pixels for active fires.
Of the ground truth, 83% was used for the training, while the remaining 17% was selected
for the classification validation. The pixels were selected by exploring the false color
composite (Figure 1b) and looking at the PRISMA spectral profile, which was comparable
with the corresponding classes that can be found in the literature [
10
,
35
]. In addition, pan
sharpening was attempted to fuse a panchromatic-camera, 5 m-resolution PRISMA product
and L2C PRISMA (VNIR-SWIR) projected product to obtain higher-resolution images,
to be used for fire pixel validation. The data were fused using the Gram–Schmidt pan
sharpening function available in ENVI 5.5, and although the L2D data were relatively noisy,
they were used to visually confirm the burning areas. The classification was implemented
by using the support vector machine (SVM) [
36
,
37
]. The SVM is considered, along with the
random forest classifier and artificial neural networks, one of the most effective supervised
classification methods [
38
] for multidimensional, large-scale image data. The SVM method
algorithms perform well on noisy data and small numbers of training pixels [
38
,
39
], are
suitable for anomaly detection [
40
] and are usually more accurate than other classification
algorithms [37,41].
The kernel type was set as polynomial, with the value set to 2 and the penalty param-
eter set to 200. The result is showed in Figure 3b.
Remote Sens. 2021,13, 1410 7 of 15
Remote Sens. 2021, 13, x FOR PEER REVIEW 7 of 15
was implemented by using the support vector machine (SVM) [36,37]. The SVM is consid-
ered, along with the random forest classifier and artificial neural networks, one of the
most effective supervised classification methods [38] for multidimensional, large-scale im-
age data. The SVM method algorithms perform well on noisy data and small numbers of
training pixels [38,39], are suitable for anomaly detection [40] and are usually more accu-
rate than other classification algorithms [37,41].
The kernel type was set as polynomial, with the value set to 2 and the penalty param-
eter set to 200. The result is showed in Figure 3b.
The overall classification accuracy was 96.87%, with a K coefficient of 0.96. The class
fire, which is was of interest in this study, resulted in commission (false positive) and
omission (false negative) error of 0%).
(a) (b)
Figure 3. (a) RGB visible composition of PRISMA region of interest shows a dense fire smoke orig-
inating from diverse locations; (b) classification result corresponding to the selected region of in-
terest (ROI) by using the complete VNIR–SWIR PRISMA spectral range. The yellow pixels corre-
spond to active fire; light blue corresponds to the smoke class; brown, to freshly burned; green is
the vegetation; and light pink, bare soil.
Determining if there was good agreement with the other classes regarding this result
was beyond the scope of this study. The classification was used to create a mask, with
values equal to 1 corresponding to fire pixels and those equal to 0, the background (not
burning pixels), for use in assessing the performance of the detection indices.
3. Results
Fire Detection Analysis
The fire localization analysis was conducted for the three fire detection indices
adapted to the PRISMA band features.
The detection rate was calculated by linear regression between the fire pixels derived
by the specific indices and the ones resulting from the reference (the SVM-derived fire
mask). A thresholding approach was used to identify the detected fire pixels. The cut-off
ranges were obtained by automatically computing the minimum and maximum values
for each index considering only pixels inside the “fire” class. (1) The cut-off minimum was
obtained by subtracting two standard deviations (2σ) from the mean, and (2) the cut-off
maximum, by adding one standard deviation (1σ) to the mean. The cut-off intervals were
used to discriminate fire from background for each index frame.
The first index to be analyzed was the CO2-CIBR. Figure 4 shows the CO2-CIBR maps
for the four band combinations. We can observe that, despite the striping noise, the front
fire is recognizable. Specifically, it was better delineated when the absorption bands cen-
tered at 2001.79 nm and 2010.36 nm were used.
Figure 3.
(
a
) RGB visible composition of PRISMA region of interest shows a dense fire smoke originating from diverse
locations; (
b
) classification result corresponding to the selected region of interest (ROI) by using the complete VNIR–SWIR
PRISMA spectral range. The yellow pixels correspond to active fire; light blue corresponds to the smoke class; brown, to
freshly burned; green is the vegetation; and light pink, bare soil.
The overall classification accuracy was 96.87%, with a K coefficient of 0.96. The class
fire, which is was of interest in this study, resulted in commission (false positive) and
omission (false negative) error of 0%).
Determining if there was good agreement with the other classes regarding this result
was beyond the scope of this study. The classification was used to create a mask, with
values equal to 1 corresponding to fire pixels and those equal to 0, the background (not
burning pixels), for use in assessing the performance of the detection indices.
3. Results
Fire Detection Analysis
The fire localization analysis was conducted for the three fire detection indices adapted
to the PRISMA band features.
The detection rate was calculated by linear regression between the fire pixels derived
by the specific indices and the ones resulting from the reference (the SVM-derived fire
mask). A thresholding approach was used to identify the detected fire pixels. The cut-off
ranges were obtained by automatically computing the minimum and maximum values for
each index considering only pixels inside the “fire” class. (1) The cut-off minimum was
obtained by subtracting two standard deviations (2
σ
) from the mean, and (2) the cut-off
maximum, by adding one standard deviation (1
σ
) to the mean. The cut-off intervals were
used to discriminate fire from background for each index frame.
The first index to be analyzed was the CO
2
-CIBR. Figure 4shows the CO
2
-CIBR
maps for the four band combinations. We can observe that, despite the striping noise, the
front fire is recognizable. Specifically, it was better delineated when the absorption bands
centered at 2001.79 nm and 2010.36 nm were used.
Table 3shows the results obtained in terms of the detection rate. A detection rate of
0.81 and a cut-off ranging between 0.550 and 0.74674 were determined for the CO
2
-CIBR
having the minimum absorption band centered at 2001.36 nm, in agreement with the visual
interpretation in Figure 4a.
Remote Sens. 2021,13, 1410 8 of 15
Remote Sens. 2021, 13, x FOR PEER REVIEW 8 of 15
(a) (b)
(c) (d)
Figure 4. CO
2
-continuum interpolated band ratio (CIBR) maps. (a) CO
2
-CIBR derived for mini-
mum absorption band centered at 2001.79 nm; (b) CO
2
-CIBR derived for minimum absorption
band centered at 2010.36 nm; (c) CO
2
-CIBR derived for minimum absorption band centered at
2052.71 nm; (d) CO
2
-CIBR derived for minimum absorption band centered at 2061.09 nm.
Table 3 shows the results obtained in terms of the detection rate. A detection rate of
0.81 and a cut-off ranging between 0.550 and 0.74674 were determined for the CO
2
-CIBR
having the minimum absorption band centered at 2001.36 nm, in agreement with the vis-
ual interpretation in Figure 4a.
Instead detection rate of 0.29 was obtained for the CO
2-
CIBR centered at 2010.36 nm
very low compared to what was suggested by visual interpretation.
A low detection rate, consistent with the visual interpretation, was obtained for the
other two CO
2
-CIBRs (Figure 4c,d). In these cases, the striping noise was relatively uni-
formly distributed and comparable with the signal.
Table 3. CO
2
-CIBR detection rate.
Band 1 Band 2 Band 3 λm
(nm)
λ2
(nm)
λ3
(nm)
Cut-off
Min
Cut-off
Max
Detection
Rate
169 167 173 2001.79 1984.49 2035.94 0.55094 0.74674 0.81598
179 167 173 2010.36 1984.49 2035.94 0.32421 0.85531 0.29205
175 173 179 2052.71 2035.94 2086.04 0.78086 0.89774 0.25083
176 173 179 2061.09 2035.94 2086.04 0.75938 0.93708 0.13561
To mitigate the effect of the noise on the single bands [10], the mean value of the CO
2
-
CIBR indices was calculated. The result (Figure 5) provides a cut-off of 0.7741 ± 0.1091,
well separated from the mean background value (Figure 5).
Figure 4.
CO
2
-continuum interpolated band ratio (CIBR) maps. (
a
) CO
2
-CIBR derived for minimum
absorption band centered at 2001.79 nm; (
b
) CO
2
-CIBR derived for minimum absorption band
centered at 2010.36 nm; (
c
) CO
2
-CIBR derived for minimum absorption band centered at 2052.71 nm;
(d) CO2-CIBR derived for minimum absorption band centered at 2061.09 nm.
Table 3. CO2-CIBR detection rate.
Band 1 Band 2 Band 3 λm
(nm)
λ2
(nm)
λ3
(nm)
Cut-off
Min
Cut-off
Max
Detection
Rate
169 167 173 2001.79 1984.49 2035.94 0.55094 0.74674 0.81598
179 167 173 2010.36 1984.49 2035.94 0.32421 0.85531 0.29205
175 173 179 2052.71 2035.94 2086.04 0.78086 0.89774 0.25083
176 173 179 2061.09 2035.94 2086.04 0.75938 0.93708 0.13561
Instead detection rate of 0.29 was obtained for the CO
2
-CIBR centered at 2010.36 nm
very low compared to what was suggested by visual interpretation.
A low detection rate, consistent with the visual interpretation, was obtained for
the other two CO
2
-CIBRs (Figure 4c,d). In these cases, the striping noise was relatively
uniformly distributed and comparable with the signal.
To mitigate the effect of the noise on the single bands [
10
], the mean value of the
CO
2
-CIBR indices was calculated. The result (Figure 5) provides a cut-off of 0.7741
±
0.1091, well separated from the mean background value (Figure 5).
Remote Sens. 2021,13, 1410 9 of 15
Remote Sens. 2021, 13, x FOR PEER REVIEW 9 of 15
Figure 5. Separability of Ben Halls Gap National Park fire indices; mean value of the four CO
2
-
CIBRs is 0.7741 ± 0.1091, and mean value of the background is 0.9664 ± 0.0477.
The HFDI was retrieved by applying Equation (2) for the 24 possible band combina-
tions (Figure 6).
Figure 6. Hyperspectral fire detection index (HFDI) maps for the twenty-four combinations.
Table 4 reports the cut-off ranges and detection rates obtained for the 24 band com-
binations.
In this case, a bias value of 0.05 or 0.08 (Table 4) was added to the cut-off min in order
to discriminate the “fire” from background. The results of the linear regression in terms
of the detection rate are reported in Table 4. We can observe that nine HFDIs out of the 24
resulted in detection rates better than 0.76. The best performance was obtained by using
the bands 2312.85 nm and 2061.08 nm, with a detection rate of 0.88. Twelve detection rates
resulted in a “not a number” output.
Figure 5.
Separability of Ben Halls Gap National Park fire indices; mean value of the four CO
2
-CIBRs
is 0.7741 ±0.1091, and mean value of the background is 0.9664 ±0.0477.
The HFDI was retrieved by applying Equation (2) for the 24 possible band combina-
tions (Figure 6).
Remote Sens. 2021, 13, x FOR PEER REVIEW 9 of 15
Figure 5. Separability of Ben Halls Gap National Park fire indices; mean value of the four CO
2
-
CIBRs is 0.7741 ± 0.1091, and mean value of the background is 0.9664 ± 0.0477.
The HFDI was retrieved by applying Equation (2) for the 24 possible band combina-
tions (Figure 6).
Figure 6. Hyperspectral fire detection index (HFDI) maps for the twenty-four combinations.
Table 4 reports the cut-off ranges and detection rates obtained for the 24 band com-
binations.
In this case, a bias value of 0.05 or 0.08 (Table 4) was added to the cut-off min in order
to discriminate the “fire” from background. The results of the linear regression in terms
of the detection rate are reported in Table 4. We can observe that nine HFDIs out of the 24
resulted in detection rates better than 0.76. The best performance was obtained by using
the bands 2312.85 nm and 2061.08 nm, with a detection rate of 0.88. Twelve detection rates
resulted in a “not a number” output.
Figure 6. Hyperspectral fire detection index (HFDI) maps for the twenty-four combinations.
Table 4reports the cut-off ranges and detection rates obtained for the
24 band combinations
.
Remote Sens. 2021,13, 1410 10 of 15
Table 4.
This table reports the PRISMA bands and corresponding wavelengths used to derive the
HFDI, the used cut-off and the detection rate.
Band 1 Band 2 Central λ1
(nm)
Central λ2
(nm)
Cut-Off
Min
Cut-Off
Max
Detection
Rate
208 175 2312.80 2052.70 0.08 0.19192 0.8223
209 175 2320.58 2052.71 0.08 0.19111 0.75852
210 175 2327.54 2052.72 0.08 0.21888 0.83235
208 176 2312.85 2061.08 0.05 0.17544 0.88431
209 176 2320.59 2061.09 0.08 0.17544 0.67228
210 176 2327.55 2061.10 0.08 0.20339 0.84063
208 177 2312.86 2069.49 0.05 0.16256 0.79732
209 177 2320.60 2069.50 0.05 0.15021 0.83295
210 177 2327.56 2069.51 0.08 0.17842 0.76144
208 178 2312.87 2077.70 0.05 0.092593 0.50731
209 178 2320.61 2077.71 0.05 0.080645 0.36171
210 178 2327.57 2077.72 0.05 0.10938 0.64754
208 179 2312.88 2086.04 0.05 0.075472 NaN 1
209 179 2320.62 2086.05 0.05 0.059289 NaN
210 179 2327.58 2086.06 0.05 0.088123 NaN
208 180 2312.89 2094.36 0.05 0.066667 NaN
209 180 2320.63 2094.37 0.05 0.058824 NaN
210 180 2327.59 2094.38 0.05 0.083969 NaN
208 181 2312.90 2102.49 0.05 0.065421 NaN
209 181 2320.64 2102.50 0.05 0.050000 NaN
210 181 2320.59 2102.51 0.05 0.073684 NaN
208 182 2327.55 2110.77 0.05 0.064516 NaN
209 182 2312.86 2110.78 0.05 0.058824 NaN
210 182 2320.60 2110.79 0.05 0.076923 NaN
1NaN (not a number).
In this case, a bias value of 0.05 or 0.08 (Table 4) was added to the cut-off min in order
to discriminate the “fire” from background. The results of the linear regression in terms
of the detection rate are reported in Table 4. We can observe that nine HFDIs out of the
24 resulted
in detection rates better than 0.76. The best performance was obtained by using
the bands 2312.85 nm and 2061.08 nm, with a detection rate of 0.88. Twelve detection rates
resulted in a “not a number” output.
To highlight the fire front, a mask was applied to the background (Figure 7).
Figure 7.
HFDI maps for all twenty-four combinations masked by support vector machine (SVM) classification to evidence
fire front.
Remote Sens. 2021,13, 1410 11 of 15
The mean value of the HFDIs was calculated. The result provides a cut-off of
0.0778 ±0.0265, well separated from the mean background value (Figure 8).
Remote Sens. 2021, 13, x FOR PEER REVIEW 11 of 15
Figure 7. HFDI maps for all twenty-four combinations masked by support vector machine (SVM)
classification to evidence fire front.
Figure 8. Separability of Ben Halls Gap National Park fire indices HFDI mean value over the
twenty-four combinations is 0.0778 ± 0.0265, and the background is 0.0117 ± 0.0080.
Figure 8.
Separability of Ben Halls Gap National Park fire indices HFDI mean value over the
twenty-four combinations is 0.0778 ±0.0265, and the background is 0.0117 ±0.0080.
Finally, we calculated the potassium emission index by using the PRISMA band 44,
centered at 770.25 nm (for the emission, Figure 9a) and the band 45 centered at 780.63 nm
(for the background). The scatter plot obtained for the two bands shows a high correlation
between the bands (Figure 9b). The results for the two indices are shown in Figure 9c,d. We
can observe the prevalence of the striping effect over the signal. The fire is not recognizable
when using the band ratio, and it is blurred and difficult to delineate when using the
AKBD metric.
Remote Sens. 2021, 13, x FOR PEER REVIEW 12 of 15
(a) (b)
(c) (d)
Figure 9. (a) Gray color PRISMA imagery at 770.25 nm; (b) scatter plot for PRISMA bands at
770.25 nm vs. 780.64 nm, showing high correlation between band where potassium emission is
expected and that for the background; (c) potassium emission band ratio; (d) advance K band dif-
ference (AKBD) normalized index map.
4. Discussion
We analyzed the three fire detection indices based on daytime acquisition by the new
PRISMA sensor over the bushfires that occurred in Ben Halls Gap National Park in 2019.
In our PRISMA subscene, both the CO2-CIBR and HFDI indices were able to discriminate
the fire front. Regarding the CO2-CIBR, the best performance was obtained by using the
band centered at 2001.79 nm (absorption) and those at 1984.49 nm and 2035.94 nm (the
“shoulders”). However, when we look at the CO2-CIBR calculated with the PRISMA
bands (2010.36 nm, 1984.49 nm and 2035.94 nm) very close to the AVIRIS [10] ones (2010
nm, 1990 nm and 2040 nm), the obtained map (Figure 4b) and the detection rate (Table 3)
look very different. The lower detection rate is due to the presence of a stripe whose values
are comparable to those of fire. This needs to be further investigated in a range of PRISMA
scenes.
The higher detection rate of the PRISMA CO2-CIBR with a band combination (2001.79
nm, 1984.49 nm and 2035.94 nm) comparable to that (1981.86 nm, 2032.35 nm 2002.06 nm)
used by [10] for the CO2CIBR for EO1-Hyperion is interesting found.
The results obtained with the HFDI agree with the literature [10], which indicates
that the HFDI should perform better than the CO2-CIBR. The best band combination
turned out to be 2312.85 nm and 2061.08 nm, resulting in a detection rate of 0.88; this is
very close to the AVIRIS one (2430 nm and 2061 nm) reported in [10]. When comparing
the best-performing EO1-Hyperion combinations used in [10] with the “almost” corre-
sponding PRISMA combination, we obtained NaN. In Table 4, it is possible to observe
how the cut-off ranges for these combinations are too narrow to permit a distinction be-
tween fire and background. Even looking at the single HFDI images in Figure 6, it is evi-
dent that, from the thirteenth combination onward, the front of the fire blends into the
background.
The potassium (K)-emission-based index underperformed compared to the other two
indices. Diverse aspects need to be considered to interpret this result. The full width half
Figure 9.
(
a
) Gray color PRISMA imagery at 770.25 nm; (
b
) scatter plot for PRISMA bands at
770.25 nm
vs. 780.64 nm, showing high correlation between band where potassium emission is
expected and that for the background; (
c
) potassium emission band ratio; (
d
) advance K band
difference (AKBD) normalized index map.
Remote Sens. 2021,13, 1410 12 of 15
4. Discussion
We analyzed the three fire detection indices based on daytime acquisition by the new
PRISMA sensor over the bushfires that occurred in Ben Halls Gap National Park in 2019.
In our PRISMA subscene, both the CO
2
-CIBR and HFDI indices were able to discriminate
the fire front. Regarding the CO
2
-CIBR, the best performance was obtained by using the
band centered at 2001.79 nm (absorption) and those at 1984.49 nm and 2035.94 nm (the
“shoulders”). However, when we look at the CO
2
-CIBR calculated with the PRISMA bands
(2010.36 nm, 1984.49 nm and 2035.94 nm) very close to the AVIRIS [
10
] ones (
2010 nm
,
1990 nm and 2040 nm), the obtained map (Figure 4b) and the detection rate (
Table 3
)
look very different. The lower detection rate is due to the presence of a stripe whose
values are comparable to those of fire. This needs to be further investigated in a range of
PRISMA scenes.
The higher detection rate of the PRISMA CO
2
-CIBR with a band combination (
2001.79 nm
,
1984.49 nm and 2035.94 nm) comparable to that (1981.86 nm, 2032.35 nm 2002.06 nm) used
by [10] for the CO2-CIBR for EO1-Hyperion is interesting found.
The results obtained with the HFDI agree with the literature [
10
], which indicates that
the HFDI should perform better than the CO
2
-CIBR. The best band combination turned
out to be 2312.85 nm and 2061.08 nm, resulting in a detection rate of 0.88; this is very close
to the AVIRIS one (2430 nm and 2061 nm) reported in [
10
]. When comparing the best-
performing EO1-Hyperion combinations used in [
10
] with the “almost” corresponding
PRISMA combination, we obtained NaN. In Table 4, it is possible to observe how the
cut-off ranges for these combinations are too narrow to permit a distinction between fire
and background. Even looking at the single HFDI images in Figure 6, it is evident that,
from the thirteenth combination onward, the front of the fire blends into the background.
The potassium (K)-emission-based index underperformed compared to the other two
indices. Diverse aspects need to be considered to interpret this result. The full width
half maximum (FWHM) of PRISMA in the used bands is 12.7 nm, so the separation is
challenged compared to that for the
10 nm FWHM of EO1-Hyperion or AVIRIS [
10
].
Another effect to take into account is the striping effect present in our data that affects
each band in a different way. This effect is stronger or weaker depending on the signal
present in the image. For example, in an absorption band, all the signal is absorbed and the
striping is imaged. The K emission band is very close to the O
2
absorption band centered
at 765 nm, and it can be weakened by the two combined effects. Furthermore, the type of
combustion (i.e., crown) as well as the vegetation type can contribute, as diverse vegetation
species contain various amounts of K [
33
]. Finally, the presence of smoke can attenuate
the signal [
10
] as well as the spatial resolution (30 m/px) [
9
] if only a fraction of the pixel
is flaming.
In agreement with the literature for the EO1-Hyperion, our first results show that the
HFDI performs better, in terms of fire detection, than the CO
2
-CIBR and AKBD. This is
promising considering the difference in spectral resolution between PRISMA (~13 nm) and
EO1-Hyperion (10 nm).
Future efforts should focus on the analysis of fires in diverse ecosystems (present in
the scene or found in other PRISMA data), to determine if a common threshold can be
found for retrieving the HFDI.
5. Conclusions
This study concerns a multimethod analysis for studying active land fires in New
South West Australia that occurred during the 2019 bushfire season using hyperspectral
techniques from space. We report the first case study of active fire characterization by using
data from the new PRISMA space mission by the Italian Space Agency. The fire occurred in
the Ben Halls Gap National Park during the bushfire season of 2019/2020 in Australia and
was imaged by the PRISMA sensor. Firstly, we wanted to verify the ability of the PRISMA
sensor to provide spectral features useful for characterizing phenomena, such as wildfires,
different from those considered in its primary mission objectives; secondly, we tested three
Remote Sens. 2021,13, 1410 13 of 15
fire detection indices that relate the measured radiance to the intrinsic characteristics of
the phenomena, such as the ionization of trace elements (AKBD index), carbon dioxide
emissions (the CO2-CIBR index) and radiance values in the SWIR bands (HFDI).
The indices were adapted to PRISMA spectral features. Atmospheric correction was
implemented to transform radiance into reflectance; a supervised classification, using the
SVM approach, was used to retrieve the detection rate.
The obtained reflectance spectra are representative of diverse classes present in the
scene. The overall performance of the instrument in terms of the radiance in our data
was good. However, the presence of the striping effect can influence the performance of
the indices.
In terms of the detection performance, the results agree with the literature to date [
10
].
In addition, PRISMA resulted in improved CO
2
-CIBR and HFDI performance compared to
the EO1-Hyperion satellite [10].
The AKBD data resulted in underperformance. This is possibly due to multiple
factors including the spectral resolution in the potassium band (12 nm), the striping noise,
attenuation from the atmosphere and the spatial resolution.
The results are promising in terms of using PRISMA for active fire characterization
using SWIR bands.
To close, it is relevant to mention that, due to the complexity of the hyperspectral
sensors and the large number of data they collects, there is a gap between case studies and
operational applications. In this context, synergies between future missions (i.e., EnMap,
Chime, Surface Biology and Geology -SBG and Spaceborne Hyperspectral Applicative
Land and Ocean Mission-SHALOM) and knowledge sharing will be very important for
providing complementary information.
Author Contributions:
Conceptualization, S.A.; methodology, S.A. and A.P.; software, A.P. and S.A.;
validation, S.A. and A.P.; formal analysis, S.A. and A.P.; investigation, S.A.; resources, S.A.; data
curation, S.A. and A.P.; writing—original draft preparation, S.A.; writing—review and editing, S.A.
and A.P.; visualization, S.A. and A.P. All authors have read and agreed to the published version of
the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not Applicable.
Data Availability Statement:
PRISMA L1 and L2 data are available free of charge under PRISMA
data license policy and can be accessed online at the official Italian Space Agency website www.
prisma.asi.it, accessed on 4 February 2021. The added value dataset generated during the current
study is available from the corresponding author upon reasonable request.
Acknowledgments:
We thank the Italian Space Agency for the PRISMA data and permitting its free
use. The Ettore Lopinto (Mission Director-ASI) is thanked for helping us to resolve any technical
issues we encountered. We thank ENVI-Harris Geospatial Solution in the person of Cristina Squarzoni
for the support.
Conflicts of Interest: The authors declare no conflict of interest.
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