remote sensing
Article
Integrated Satellite System for Fire Detection and Prioritization
1 Institute of Methodologies for Environmental Analysis (IMAA), National Research Council (CNR), Tito Scalo,
85050 Potenza, Italy; giuseppe.mazzeo@imaa.cnr.it (G.M.); fortunato.desantis@imaa.cnr.it (F.D.S.);
alfredo.falconieri@imaa.cnr.it (A.F.); teodosio.lacava@imaa.cnr.it (T.L.); antonio.lanorte@imaa.cnr.it (A.L.);
francesco.marchese@imaa.cnr.it (F.M.); gabriele.nole@imaa.cnr.it (G.N.); nicola.pergola@imaa.cnr.it (N.P.);
and characterizing fire events as support to fire management activities. On the other hand, up
to now, only a few satellite-based platforms provide immediately and easily usable information
about events in progress, in terms of both hotspots, which identify and localize active fires, and
the danger conditions of the affected area. However, this kind of information is usually provided
through separated layers, without any synthetic indicator which, indeed, could be helpful, if timely
provided, for planning the priority of the intervention of firefighting resources in case of concurrent
fires. In this study, we try to fill these gaps by presenting an Integrated Satellite System (ISS) for fire
detection and prioritization, mainly based on the Robust Satellite Techniques (RST), and the Fire
Danger Dynamic Index (FDDI), an original re-structuration of the Índice Combinado de Risco de
Incêndio Florestal (ICRIF), for the first time presented here. The system, using Moderate Resolution
Imaging Spectroradiometer (MODIS), Advanced Very High Resolution Radiometer (AVHRR), and
Spinning Enhanced Visible and InfraRed Imager (SEVIRI) data, provides near real-time integrated
information about both the fire presence and danger over the affected area. These satellite-based
products are generated in common formats, ready to be ingested in Geographic Information System
(GIS) technologies. Results shown and discussed here, on the occasion of concurrent winter and
summer fires in Italy, in agreement with information from independent sources, demonstrate that
the ISS system, operating at a regional/national scale, may provide an important contribution to fire
prioritization. This may result in the mitigation of fire impact in populated areas, infrastructures, and
the environment.
Keywords: satellite; early fire detection; fire danger; automatic near real-time system
- Introduction
Wildfires are one of the most important causes of ecosystem degradation because of
their strong impact on flora, fauna, and soils [ 1–3 ]. On a local scale, fires influence the soil
structure, plant nutrition, composition, and competition among species. Burned areas are
particularly sensitive to leaching nutrients and soil erosion, because of ensuing changes
affecting hydrological processes [ 2]. Nowadays, wildfires represent a huge problem on
a global scale. A large increase in the fire rate has been recorded globally, with social
impacts including the loss of human life, and economic effects, such as damage to houses
and infrastructures as well as impacts on the climate. Examples are the devastating fires
occurring in 2019–2020 in Siberia [4–6], Australia [6–10], and South America [6,11–13].
The huge economic and environmental damage directly or indirectly related to fires
has constantly pushed the international space agencies (i.e., EUMETSAT, NASA) to better
Remote Sens. 2022, 14, 335. https://doi.org/10.3390/rs14020335 https://www.mdpi.com/journal/remotesensing
Remote Sens. 2022, 14, 335 2 of 25
exploit satellite observations to detect fires, especially through sensors such as SEVIRI
(Spinning Enhanced Visible and InfraRed Imager), AVHRR (Advanced Very High Resolu-
tion Radiometer), MODIS (Moderate Resolution Imaging Spectroradiometer), and VIIRS
(Visible Infrared Imaging Radiometer Suite). The performance of those systems relies on the
accuracy of algorithms used to detect thermal anomalies with a low false-positive rate. The
pros and cons of the algorithms and systems used, including their potential in promptly
alerting for fires, have been analyzed in recent studies (e.g., [14,15]).
In addition, satellite remote sensing may provide information about a number of
variables suited to evaluate fire danger ratings [16 – 18]. Danger estimations can be generally
approached at two temporal scales: short-term and long-term (e.g., [ 19 – 21]). Long-term
fire danger estimations provide information useful for management activities, and to plan
prevention strategies. Short-term estimations are suited for operational activities during the
fire emergency phase, for the management of fire-fighting resources, early detection, and
timely attack on the flame front (for a state-of-art review see [19 – 21]). Those estimations
require dynamic information updated daily (or even hourly) on the moisture content of
the fuel, and additional meteorological parameters, such as temperature, relative humidity,
wind, and precipitation. Moreover, fire danger is typically evaluated using static indices
based on topography, fuel type, and load, which may be also derived from remote sensing
data [21–23].
However, up to now, only a few fire-monitoring systems provide additional infor-
mation about fires and relative flame conditions as separated layers (e.g., meteorological
fire danger maps, weather forecasts, fire perimeters, and susceptibility maps). Among
these, it is worth mentioning some systems such as the European Forest Fire Informa-
tion System (EFFIS, [ 24 ]), the Instituto Nacional de Pesquisas Espaciais (INPE, Brazilian
Institute for Space Research [25 ]), the Advanced Fire Information System (AFIS, [ 26 , 27 ]),
the FSI Fire Alerts System (FAST, [28 ,29 ]). The above-mentioned systems are generally
modular web-based platforms at the global (AFIS), continental (INPE over South America,
EFFIS over Europe), or sub-continental (FAST on India) scale. They allow end users to
fully monitor forest fire activity by means of a series of separate modules, which aim at
managing different aspects of fire contrast actions. The active fire detection is the basic
module of these systems and may include hotspots only from polar satellite data (e.g.,
in EFFIS and FAST) or both polar and geostationary satellite data (such as in INPE and
AFIS). The availability of other modules (e.g., fire danger forecast, fire spread modeling,
rapid damage assessment) may vary or differ in terms of spatial resolution according to
the specific system. In all cases, end users may have considerable useful information in
separated layers which may or may not be displayed, but there is no synthetic indicator for
priority action against simultaneous fires. Apart from a few pioneering studies performing
fire prioritization, which required assessment in the field (e.g., [ 30 ]), there are no systems
combining/integrating satellite hotspot products and fire danger information to define
priority levels, in operational fire contrast action. On the other hand, the above-mentioned
systems are not generally useful for supporting firefighter activity which needs fire prod-
ucts in real-time mode, i.e., frequently updated and delivered in a timely manner. This
happens, for example, when hotspots are only provided from low-frequency overpass
satellite sensors (e.g., polar orbiting) and/or fire danger products are only furnished daily.
This issue is particularly significant in highly populated areas like the Italian territory.
In this study, we fill these gaps by presenting a satellite-based system called the
Integrated Satellite System (ISS), which combines fire detections in near real-time from
satellite, through the RST-FIRES (Robust Satellite Techniques for FIRES detection and
monitoring) algorithm [ 15 ], to an innovative index used to flag the most vulnerable areas,
called the Fire Dynamic Danger Index (FDDI), developed for the prioritization of fire events
in near real-time. In particular, FDDI is structured similarly to the Forest Fire Risk Combined
Index (Índice Combinado de Risco de Incêndio Florestal–ICRIF, [31 ]), based on the Fire
Weather Index (FWI) [32], which is updated every 24 h. FDDI, instead, is based on the
Fuel Moisture Index (FMI) [33] which, unlike FWI, can be updated hourly. Moreover, two
Remote Sens. 2022, 14, 335 3 of 25
synthetic Priority Indicators (PINGEO and PINLEO) are proposed to immediately estimate
the priority order of intervention. This system has been designed for rapid identification,
localization, and danger characterization of active fires over the Italian territory, to support
firefighting activities and fire management. - Study Area
The ISS system has been developed to perform over the entire Italian territory. Italy
has two fire regimes: the winter regime, affecting the northern part of the country; and
the summer regime which typically affects the central and southern regions. Drought and
vegetative rest in regions characterized by the Mediterranean climate are the first factors
affecting summer fires, while dry winds (i.e., Föen), combined with a general dryness of the
vegetation are responsible for winter fires in alpine or alpine-like climate regions (e.g., [ 34 ]).
Italian local regional authorities are responsible for extinguishing forest fires with the
support of ground teams (Regional Forest Corps, National Fire Corps, volunteers, etc.),
exploiting aerial regional resources (light and medium helicopters). Moreover, the national
forest firefighting air fleet (Canadair aircraft and heavy helicopters) is operative through
the Unified Air Operational Center (COAU), managed by the National Fire Corps [35]. In
this work, we test the proposed system in the case of summer as well as winter fires, which
always represent a challenge for satellite fire detection and monitoring due, for example, to
critical weather conditions (i.e., frequent and dense cloud cover) [36]. - Data and Methods
The final goal of the ISS system is to establish a suitable priority order of intervention
in the case of concurrent fire events at large scale (e.g., for the Italian territory). To this
end, different data, products, and information are integrated together as shown in Figure 1
All these resources as well as the methods used for their analysis and integration will
be described in the following sub-sections. In more detail, satellite data and products
(Section 3.1.1) are automatically processed through the RST-FIRES (Section 3.2.1) algorithm
to identify active fires. Weather forecasts (Section 3.1.2) are used to produce two dynamic
indices, the Fire Danger Dynamic Index (FDDI, Section 3.2.2), depending on the health
status of the vegetation, and the Wind Intensity (WI, Section 3.2.4), which is calculated for
the same area. Geomorphology data (Section 3.1.3) are used to generate the Morphological
Danger Index (MDI) (Section 3.2.3). Both WI and MDI are produced for polar-orbiting
satellite data only (i.e., AVHRR and MODIS) offering data at higher spatial resolution
(about 1 km) than those provided by geostationary platforms (i.e., SEVIRI). Two synthetic
indicators, PINGEO and PINLEO (Section 6), are then built from the above-mentioned indices.
In the following section, we describe the system inputs and the methodologies used.
3.1. Data
3.1.1. Satellit
- e Data and Products
The ISS system has been developed for a prompt fire identification, exploiting high
temporal resolution data, which are able to detect fires at their early stage and continu-
ously monitor their evolution. ISS analyzes satellite data acquired in the Middle, Thermal
InfraRed, and Visible bands (MIR, TIR, VIS) for this purpose. The system elaborates all
96 daily observations from SEVIRI, aboard Meteosat Second Generation (MSG) geostation-
ary satellites. SEVIRI provides data with a very high temporal sampling (15 min), but also
with a rough spatial resolution (around 13–15 km2 at the considered latitudes). The ISS sys-
tem also uses satellite data at a higher spatial resolution (1 km2), but with a lower frequency
of observation (from 3 to 6 h) from AVHRR and MODIS, respectively, onboard the National
Oceanic and Atmospheric Administration (NOAA)/Meteorological Operational Satellites
(MetOP) and the Earth Observing System (EOS). ISS runs operationally using satellite data
directly acquired at the multi-mission receiving stations (EUMETCast and L+X-band direct
readout antenna) operational at the Institute of Methodologies for Environmental Analysis
Remote Sens. 2022, 14, 335 4 of 25
of National Research Council (IMAA-CNR) and the School of Engineering of University of
Basilicata (SI-UNIBAS).Remote Sens. 2022, 13, x FOR PEER REVIEW 4 of
Figure 1. Integrated Satellite System scheme.
3.1. Data
3.1.1. Satellite Data and Products
The ISS system has been developed for a prompt fire identification, exploiting hi
temporal resolution data, which are able to detect fires at their early stage a
continuously monitor their evolution. ISS analyzes satellite data acquired in the Midd
Thermal InfraRed, and Visible bands (MIR, TIR, VIS) for this purpose. The syste
elaborates all 96 daily observations from SEVIRI, aboard Meteosat Second Generati
(MSG) geostationary satellites. SEVIRI provides data with a very high temporal sampli
(15 min), but also with a rough spatial resolution (around 13–15 km2 at the consider
latitudes). The ISS system also uses satellite data at a higher spatial resolution (1 km2 ), b
with a lower frequency of observation (from 3 to 6 h) from AVHRR and MOD
respectively, onboard the National Oceanic and Atmospheric Administrati
(NOAA)/Meteorological Operational Satellites (MetOP) and the Earth Observing Syste
(EOS). ISS runs operationally using satellite data directly acquired at the multi-missi
receiving stations (EUMETCast and L+X-band direct readout antenna) operational at t
Institute of Methodologies for Environmental Analysis of National Research Coun
(IMAA-CNR) and the School of Engineering of University of Basilicata (SI-UNIBAS).
The system analyzes fire danger in terms of the health status of the vegetation. F
this purpose, it implements the EUMETSAT NDVD product (ND VI Decadal, [37]) f
SEVIRI, and the Copernicus 10-day NDVI product for MODIS and AVHRR data [38]. T
NDVD is a decadal aggregated product based on the daily SEVIRI NDVI. Aggregation
made for days 1 to 10, 11 to 20, and 21 up to the last day of each month. This produ
widely used to characterize vegetation density and vigor as well as to identify vegetati
stress and drought, is distributed by EUMETSAT at the SEVIRI pixel spatial resolutio
together with the raw data. For AVHRR and MODIS, ISS uses the Copernicus Glob
Land Service NDVI product. This product is based on the Maximum Value Compos
(MVC) of NDVI, computed over a 10-day period, considering the SPOT/VEGETATIO
C3 and PROBA-V C1 satellite data, which are atmospherically corrected and cloud-fr
The used product is distributed at the 1 km resolution within three days of the end of t
Figure 1. Integrated Satellite System scheme.
The system analyzes fire danger in terms of the health status of the vegetation. For
this purpose, it implements the EUMETSAT NDVD product (NDVI Decadal, [37]) for
SEVIRI, and the Copernicus 10-day NDVI product for MODIS and AVHRR data [38 ]. The
NDVD is a decadal aggregated product based on the daily SEVIRI NDVI. Aggregation
is made for days 1 to 10, 11 to 20, and 21 up to the last day of each month. This product,
widely used to characterize vegetation density and vigor as well as to identify vegetation
stress and drought, is distributed by EUMETSAT at the SEVIRI pixel spatial resolution,
together with the raw data. For AVHRR and MODIS, ISS uses the Copernicus Global Land
Service NDVI product. This product is based on the Maximum Value Composite (MVC)
of NDVI, computed over a 10-day period, considering the SPOT/VEGETATION C3 and
PROBA-V C1 satellite data, which are atmospherically corrected and cloud-free. The used
product is distributed at the 1 km resolution within three days of the end of the aggregation
period. Moreover, to characterize fire danger in terms of fuel danger (FD), level three of the
CORINE (Coordination of Information on the Environment) Land Cover 2012 (CLC2012)
map is used.
3.1.2. Weather Forecast
Air temperature, humidity, and wind are key parameters for fire predisposition condi-
tions. They are considered using weather forecasts generated by the COSMO (Consortium
for Small-scale Modeling) meteorological model [39]. We included the forecast from the
LAMI application (Limited Area Model Italy) in the system. As part of the LAMI agree-
ment, Cosmo 5M operational chain provides numerical forecasts on the Mediterranean
area, with a grid of 5 km. The model processes twice a day, at 00 and 12 GMT, and makes
forecasts for a time horizon of three days (72 h), although only 25 h are stored. The two
sets of weather forecasts are always available for each hour of the day. ISS integrates
only the 00 GMT set of forecasts (very similar to 12 GMT as reported in Figure 2), storing
12 GMT as a backup solution, for time computation saving. These products are distributed
in GRIB (GRIdded Binary) format and contain 17 information layers (regarding pressure,
geopotential, temperature, wind, albedo, precipitation, snow). To determine the FDDI,
the ISS system automatically extracts both temperature (T2m) and dew point temperature
Remote Sens. 2022, 14, 335 5 of 25
(Td2m) generated at 2 m above ground, and the u and v components of the wind, provided
at 10 m above the ground.GMT, and makes forecasts for a time horizon of three days (72 h), although only 25 h are
stored. The two sets of weather forecasts are always available for each hour of the day.
ISS integrates only the 00 GMT set of forecasts (very similar to 12 GMT as reported in
Figure 2), storing 12 GMT as a backup solution, for time computation saving. These
products are distributed in GRIB (GRIdded Binary) format and contain 17 information
layers (regarding pressure, geopotential, temperature, wind, albedo, precipitation,
snow). To determine the FDDI, the ISS system automatically extracts both temperature
(T2m) and dew point temperature (Td 2m) generated at 2 m above ground, and the u and v
components of the wind, provided at 10 m above the ground.
Figure 2. (a) Transect AB of about 120 km. (b) Temperature trend observed along with the transect
AB for 25 October 2018, 13:00 GMT forecast at 00 GMT (red line) and 12 GMT (green line). It is
worth noting that the trend of both the curves is similar; the difference never exceeds about 1.5 °C.
3.1.3. Geomorphology Data
To consider the possible “fire accelerating factors” due to the morphology of the
territory, ISS uses a Digital Terrain Model (DTM) with a resolution of 20 m. Although this
layer (in terms of slope and aspect) has an immediate impact on fire dynamics, it was
upscaled only at the spatial resolution of MODIS and AVHRR (1 km). Indeed, no
upscaling procedure was performed for SEVIRI due to its low spatial resolution, and
consequently, no MDI was generated.
3.2. Methods Implemented and Indices Developed
3.2.1. The RST-FIRES Methodology
The ISS system implements the RST-FIRES algorithm [15] to detect and monitor
fires. This approach was successfully used to detect thermal anomalies related to both
winter (e.g., [36,40]) and summer fires (e.g., [15,41–44]), in different operational contexts.
RST-FIRES is a specific configuration of the RST multi-temporal approach [45–47], which
requires: (i) characterization of the satellite signal under unperturbed conditions; and (ii)
identification of anomalous signals through an automatic change detection scheme. In
particular, RST considers “anomalous” a signal that significantly diverges from the
“normal” condition (i.e., expected value), which is typical of the site and observation
time. The normal unperturbed condition can be defined using multi-annual time series
of satellite records. The ALICE (Absolutely Llocal Index of Change of the Environment,
[45–47]) index detects perturbing events:
Figure 2. (a) Transect AB of about 120 km. (b) Temperature trend observed along with the transect
AB for 25 October 2018, 13:00 GMT forecast at 00 GMT (red line) and 12 GMT (green line). It is worth
noting that the trend of both the curves is similar; the difference never exceeds about 1.5 ◦C.
3.1.3. Geomorphology Data
To consider the possible “fire accelerating factors” due to the morphology of the
territory, ISS uses a Digital Terrain Model (DTM) with a resolution of 20 m. Although
this layer (in terms of slope and aspect) has an immediate impact on fire dynamics, it was
upscaled only at the spatial resolution of MODIS and AVHRR (1 km). Indeed, no upscaling
procedure was performed for SEVIRI due to its low spatial resolution, and consequently,
no MDI was generated.
3.2. Methods Implemented and Indices Developed
3.2.1. The RST-FIRES Methodology
The ISS system implements the RST-FIRES algorithm [ 15 ] to detect and monitor
fires. This approach was successfully used to detect thermal anomalies related to both
winter (e.g., [36, 40]) and summer fires (e.g., [15 ,41 – 44 ]), in different operational contexts.
RST-FIRES is a specific configuration of the RST multi-temporal approach [ 45 –47 ], which
requires: (i) characterization of the satellite signal under unperturbed conditions; and
(ii) identification of anomalous signals through an automatic change detection scheme.
In particular, RST considers “anomalous” a signal that significantly diverges from the
“normal” condition (i.e., expected value), which is typical of the site and observation time.
The normal unperturbed condition can be defined using multi-annual time series of satellite
records. The ALICE (Absolutely Llocal Index of Change of the Environment, [ 45 – 47]) index
detects perturbing events:
⊗V (x, y, t) = [V(x, y, t) − μV(x, y)]
σV(x, y) (1)
where V(x,y,t) is the signal observed at time t in a single spectral band or a band combination
at the pixel (x,y); μV(x,y) is the expected value (temporal mean) of the signal, while σV(x,y)
is the temporal standard deviation. Those terms are computed by analyzing homogenous
cloud-free satellite records acquired at the same time of day and period of the year (same
month). The frequency/intensity of fires in the used time-series may possibly contaminate
μV(x,y) and σV(x,y) values; a kσ-clipping procedure such as that described in [15, 45] is
applied to exclude corresponding V(x,y,t) values from the reference field computation.
Therefore, the ALICE index quantifies the signal excess of V(x,y,t) in reference to
the mean value μch(x,y), weighted by its natural variability σch(x,y). Since fires emit
the maximum of thermal radiation in the MIR region (3–5 μm), the ALICEMIR index
Remote Sens. 2022, 14, 335 6 of 25
analyzing the brightness temperature (BT) measured in the MIR channel of used sensors
(V(x,y,t) = BTMIR(x,y,t)) is computed by RST-FIRES to detect fires. A wide description of
the RST-FIRES methodology, including the other indices that are used to timely detect fires
at an early stage, can be found in previous studies (e.g., [15,36,40]).
3.2.2. The Fire Danger Dynamic Index
The Fire Danger Dynamic Index is an indicator based on a conceptual multiscale
model, which combines satellite-derived indices. FDDI, whose performance is evaluated in
Appendix A, is a dynamic fire hazard index, which analyzes the fuel characteristics and
state of the vegetation. This index is updated hourly, to analyze changes in the weather
parameters during the day. It is calculated for each pixel of satellite imagery and integrates
both structural and meteorological indices:
• Fuel Moisture Index (FMI), derived from meteorological forecast data;
• NDVD index (decadal value of NDVI);
• Fuel Danger (FD).
Figure 3 shows a synthetic scheme.Therefore, the ALICE index quantifies the signal excess of V(x,y,t) in reference to
mean value μ ch(x,y), weighted by its natural variability σch(x,y). Since fires emit
maximum of thermal radiation in the MIR region (3–5 μm), the ALICE MIR index analyz
the brightness temperature (BT) measured in the MIR channel of used sensors (V(x,y,
BTMIR(x,y,t)) is computed by RST-FIRES to detect fires. A wide description of
RST-FIRES methodology, including the other indices that are used to timely detect fi
at an early stage, can be found in previous studies (e.g., [15,36,40]).
3.2.2. The Fire Danger Dynamic Index
The Fire Danger Dynamic Index is an indicator based on a conceptual multisc
model, which combines satellite-derived indices. FDDI, whose performance is evalua
in Appendix A, is a dynamic fire hazard index, which analyzes the fuel characterist
and state of the vegetation. This index is updated hourly, to analyze changes in
weather parameters during the day. It is calculated for each pixel of satellite imagery a
integrates both structural and meteorological indices:
• Fuel Moisture Index (FMI), derived from meteorological forecast data;
• NDVD index (decadal value of NDVI);
• Fuel Danger (FD).
Figure 3 shows a synthetic scheme.
Figure 3. Fire Danger Dynamic Index scheme.
The latter is derived from CLC2012 and resampled for each satellite sensor (AVHR
MODIS, and SEVIRI). Its values are indicative of the hazard associated with each kind
Figure 3. Fire Danger Dynamic Index scheme.
The latter is derived from CLC2012 and resampled for each satellite sensor (AVHRR,
MODIS, and SEVIRI). Its values are indicative of the hazard associated with each kind of
land cover and can be updated based on the latest available land use maps. Each pixel
is represented by a vector with 38 elements indicating the fraction area occupied by each
CLC class (Ak). The fuel map was generated by associating a hazard level to each class
(Dk, [31]), as reported in Table 1.
Remote Sens. 2022, 14, 335 7 of 25
Table 1. Level 3 of the CLC classes that are associated with the hazard level Dk.
K CLC CODE CLC CLASS (Ak) Hazard Level (Dk)
1 111 Continuous urban fabric 1
2 112 Discontinuous urban fabric 1
3 121 Industrial or commercial units 1
4 122 Road and rail networks and associated land 1
5 123 Port areas 1
6 124 Airports 1
7 131 Mineral extraction sites 1
8 132 Dump sites 1
9 133 Construction sites 1
10 141 Green urban areas 1
11 142 Sport and leisure facilities 1
12 211 Non-irrigated arable land 10
13 212 Permanently irrigated land 5
14 213 Rice fields 1
15 221 Vineyards 1
16 222 Fruit trees and berry plantations 1
17 223 Olive groves 5
18 231 Pastures 10
19 241 Annual crops associated with permanent crops 7
20 242 Complex cultivation patterns 10
21 243 Land principally occupied by agriculture, with significant