Integrated Satellite System for Fire Detection and Prioritization

Abstract

Several studies have shown the relevance of satellite systems in detecting, monitoring, 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.

1. Introduction

Wildfires are one of the most important causes of ecosystem degradation because of their strong impact on flora, fauna, and soils [1,2,3]. 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 [4,5,6], Australia [6,7,8,9,10], and South America [6,11,12,13].

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 exploit satellite observations to detect fires, especially through sensors such as SEVIRI (Spinning Enhanced Visible and InfraRed Imager), AVHRR (Advanced Very High Resolution 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 [16,17,18]. Danger estimations can be generally approached at two temporal scales: short-term and long-term (e.g., [19,20,21]). 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 [19,20,21]). 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 [21,22,23].

However, up to now, only a few fire-monitoring systems provide additional information 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 Information 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 products 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 synthetic Priority Indicators (PIN_GEO and PIN_LEO) 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.

2. 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].

3. 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, PIN_GEO and PIN_LEO (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. Satellite 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 continuously 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) geostationary 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 system 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 of National Research Council (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. 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 conditions. 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 agreement, 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 (T_2m) 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.

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., [15,41,42,43,44]), in different operational contexts. RST-FIRES is a specific configuration of the RST multi-temporal approach [45,46,47], 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, [45,46,47]) index detects perturbing events:

$${\otimes }_{\mathrm{V}}(\mathrm{x},\mathrm{y},\mathrm{t})=\frac{\left[{\mathrm{V}(\mathrm{x},\mathrm{y},\mathrm{t})-\mathsf{\mu }}_{\mathrm{V}}(\mathrm{x},\mathrm{y})\right]}{{\mathsf{\sigma }}_{\mathrm{V}}(\mathrm{x},\mathrm{y})}$$

(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); µ_V(x,y) is the expected value (temporal mean) of the signal, while σ_V(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 μ_V(x,y) and σ_V(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 µ_ch(x,y), weighted by its natural variability σ_ch(x,y). Since fires emit the maximum of thermal radiation in the MIR region (3–5 µm), the ALICE_MIR index analyzing the brightness temperature (BT) measured in the MIR channel of used sensors (V(x,y,t) = BT_MIR(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.

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 (A_k). The fuel map was generated by associating a hazard level to each class (D_k, [31]), as reported in

Table 1. Level 3 of the CLC classes that are associated with the hazard level D_k.

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 areas of natural vegetation	10
22	244	Agro-forestry areas	15
23	311	Broad-leaved forest	25
24	312	Coniferous forest	70
25	313	Mixed forest	30
26	321	Natural grasslands	15
27	322	Moors and heathland	50
28	323	Sclerophyllous vegetation	50
29	324	Transitional woodland-shrub	100
30	331	Beaches, dunes, sands	1
31	332	Bare rocks	1
32	333	Sparsely vegetated areas	5
33	335	Glaciers and perpetual snow	0
34	411	Inland marshes	10
35	412	Peat bogs	10
36	421	Salt marshes	5
37	422	Salines	1
38	511	Water courses	1

.

The final structural index is a combination of the corresponding area-weighted hazard:

$$\mathrm{FD}\left(\mathrm{x},\mathrm{y}\right)={\displaystyle \sum }_{\mathrm{k}=1}^{\mathrm{k}=38}\left({\mathrm{A}}_{\mathrm{k}}{\ast \mathrm{D}}_{\mathrm{k}}\right)$$

(2)

The Fuel Moisture Index is a dynamic dimensionless parameter that can be considered as a direct estimate of the moisture content of the necromass (dead vegetable organic substance). It is updated hourly, and calculated using a well-known index for assessing fire danger rating [33]:

FMI = 10 − 0.25 × (T_2m − RH)

(3)

where T_2m and RH are the temperature (in Celsius) at 2 m above ground level and the relative humidity (in %), respectively. RH is calculated from actual vapor pressure (E) and saturation vapor pressure (Es), assuming standard atmospheric conditions:

RH = (E/Es) × 100

(4)

where:

Es = 6.11 × 10 (7.5 × T_2m/(237.7 + T_2m))

(5)

E = 6.11 × 10 (7.5 × Td_2m/(237.7 + Td_2m))

(6)

Temperature (T_2m) and dew point temperature (Td_2m) at 2 m above ground level are based on the weather forecast. The FDDI is then defined by the following equation:

$$\mathrm{FDDI}(\mathrm{x},\mathrm{y})=\frac{\left(100-\mathrm{NDVD}(\mathrm{x},\mathrm{y})\right)\times \mathrm{FD}(\mathrm{x},\mathrm{y})}{\mathrm{FMI}(\mathrm{x},\mathrm{y})}$$

(7)

Figure 4 shows an example of an FDDI map generated for Italy for a particular day and hour.

3.2.3. The Morphological Danger Index

The Morphological Danger Index (MDI) is derived by combining two relevant topographic elements (slope and aspect) used for estimating the fire hazard:

MDI = (Slope_DV × 0.7) + (Aspect_DV × 0.3)

(8)

where DV is Danger Value. We have used a Multi-Criterial Decision Analysis (MCDA) to attribute appropriate weights.

Table 2. Topographical factors and corresponding weights.

Slope (in °) (Weight 70%)		Aspect (in °) (Weight 30%)
Classes	DV	Classes	DV
0–5 5–10 10–15 15–20 20–25 25–30 30–35 >35	1 2 3 4 5 6 7 8	0–22.5 and 337.5–360 22.5–67.5 292.5–337.5 67.5–112.5 247.5–292.5 112.5–157.5 202.5–247.5 157.5–202.5	1 2 3 4 5 6 7 8

shows the classes relating to the identified topographic static factors and the attribution of relative weights.

These values were defined, based on the following theoretical considerations:

• the slope determines an increase in the speed of propagation, and thus the danger correlated to the event. The inclination of the slopes affects the pre-heating capacity of the fuels by accelerating the combustion process and so the rate of spread of the fire;
• the slope affects the formation of an angle between the surface and the sun’s rays: the closer this is to 90°, the greater the calorific value of the sun’s rays on the ground. Experimental observations [48] estimate that under the same wind conditions, inclinations of up to about 16° increase fire speed by two times, and at inclinations of up to about 30°, by four times;
• the aspect influences the duration of exposure to the solar irradiation, the type of wind, and the temperature and humidity. SW exposures, for instance, suffer from more irradiation than others, heating up more, and thus fuels suffer from greater relative humidity losses. Consequently, the types of vegetation that are present on the slopes most exposed will be more combustible than others.

Figure 5 shows the MDI resampled for MODIS data. The figure shows that the index assumes values from 1 to 8, with the highest values corresponding to mountainous areas.

3.2.4. The Wind Intensity

Starting from the values of u and v components of wind contained in the COSMO-5M weather forecast, we generated the values of the indicator of wind intensity (WI) according to the following relationship:

WI = √ (u² + v²)

(9)

Figure 6 shows an example of elaboration over Italy (10 December 2019 at 13:00 GMT).

4. Synthetic Indicators

Within operational conflicts, and mainly in the presence of concurrent events, some parameters, such as the number of thermal anomalies (i.e., RST-FIRES detections) and the relative intensity index (in terms of ALICE_MIR) should be considered and monitored in real-time to better evaluate the space-time dynamics of each fire, and timely action against the most dangerous. Furthermore, in addition to the above-mentioned factors, the danger evaluation (FDDI) of an area gradually affected by fires should be considered to assess the actual situation and better prioritize firefighter actions. In addition, the time persistence of thermal anomalies should be considered when high temporal resolution data are used.

Finally, a synthetic indicator, Priority Index (PIN), which combines the above-mentioned significant parameters and gives an immediate danger overview as well as a measure for comparing simultaneous fires, can be defined starting from the different needs of the end-users and data availability. Thus, the higher the PIN value, the higher the intervention priority should be.

A potential PIN for geostationary satellites (PIN_GEO) is presented for SEVIRI data in equation (10). The index is calculated for each time slot, considering all anomalous pixels detected by RST-FIRES related to the event within an area of interest (AI).

$${\mathrm{PIN}}_{\mathrm{GEO}}\left(\mathrm{tcurr}, \mathrm{AI}\right)={\displaystyle \sum }_{\mathrm{tu}=\mathrm{t}0}^{\mathrm{tcrr}}[{\displaystyle \sum }_{(\mathrm{x},\mathrm{y}) \in \mathrm{AI}}{(\mathrm{k}}_{\mathrm{RST}}{*\mathrm{ALICE}}_{\mathrm{MIR}}{(\mathrm{x},\mathrm{y},\mathrm{t})+\mathrm{k}}_{\mathrm{FDDI}}{*\mathrm{FDDI} (\mathrm{x},\mathrm{y},\mathrm{t}))+\mathrm{k}}_{\mathrm{PT}}{*\mathrm{Num}}_{\mathrm{PT}}{\left(\mathrm{t}\right)+\mathrm{k}}_{\mathrm{ES}}{*\mathrm{Num}}_{\mathrm{Anom}}\left(\mathrm{t}\right)]$$

(10)

where:

• t₀ is the first time slot when thermal anomalies are detected;
• t_curr is the current time slot;
• AI is the area affected by the fire;
• k_RST is a weighting factor of the ALICE_MIR index;
• ALICE_MIR(x,y,t) is the value of the ALICE_MIR index, at time t, relating to each SEVIRI anomalous pixel of coordinates (x,y);
• k_FDDI is a weighting factor of the FDDI index;
• FDDI(x,y,t) is the value of the FDDI index, at time t, relating to each SEVIRI anomalous pixel of coordinates (x,y);
• Num_PT (t) is the number of time slots in which SEVIRI thermal anomalies related to the event are detected from the beginning (t₀) up to time t, on the area AI;
• k_PT is a weighting factor related to the temporal persistence of SEVIRI thermal anomalies on the area under observation;
• Num_Anom(t) is the number of SEVIRI thermal anomalies related to the event from the beginning (t₀) to t, over the area AI;
• k_ES is a weighting factor that accounts for the spatial extension of the thermal anomalies in the area affected by the fire.

In Equation (11), a similar index (PIN_LEO) is proposed for polar satellite data (MODIS and AVHRR), giving up temporal persistence for their worse temporal resolution than SEVIRI, but exploiting useful information about wind (WI) and geomorphology (MDI), thanks to their greater spatial resolution.

$${\mathrm{PIN}}_{\mathrm{LEO}}\left(\mathrm{tcurr}, \mathrm{AI}\right)={\displaystyle \sum }_{\mathrm{t}=\mathrm{t}0}^{\mathrm{tcurr}}[ {\displaystyle \sum }_{\left(\mathrm{x},\mathrm{y}\right)\in \mathrm{AI}}\left({\mathrm{k}}_{\mathrm{RST}}{*\mathrm{ALICE}}_{\mathrm{MIR}}\left(\mathrm{x},\mathrm{y},\mathrm{t}\right){+\mathrm{k}}_{\mathrm{FDDI}}*\mathrm{FDDI} \left(\mathrm{x},\mathrm{y},\mathrm{t}\right){+\mathrm{k}}_{\mathrm{MDI}}*\mathrm{MDI}\left(\mathrm{x},\mathrm{y}\right){+\mathrm{k}}_{\mathrm{WI}}*\mathrm{WI}\left(\mathrm{x},\mathrm{y},\mathrm{t}\right)\right){+\mathrm{k}}_{\mathrm{ES}}{*\mathrm{Num}}_{\mathrm{Anom}}\left(\mathrm{t}\right)]$$

(11)

where t₀, t_curr, AI, k_RST, ALICE_MIR(x,y,t), k_FDD, FDDI(x,y,t), Num_PT (t), k_PT, Num_Anom(t), k_ES have the same meaning as in relation (10), and

• k_MDI is a weighting factor of the MDI index;
• MDI (x,y) is the value of the MDI index for each anomalous AVHRR or MODIS pixel (x,y);
• k_WI is a weighting factor of the WI index;
• WI (x,y,t) is the value of the WI index for each anomalous AVHRR or MODIS pixel (x,y), at time t.

PIN_GEO, ALICE_MIR, FDDI, and Num_PT are weighted respectively by the coefficients k_RST, k_FDDI, k_PT (equal to 0.3) to consider them at the same importance level; Num_Anom is weighted by the constant k_ES (equal to 0.1) to take account of possible errors due to duplication effects, rather than the actual size of a fire. In PIN_LEO, the parameter related to the temporal persistence of thermal anomalies is replaced by MDI and WI. These two indicators could be utilized to characterize an area affected by an event when used with AVHRR and MODIS data (the weight constants k_MDI and k_WI are both set equal to 0.15).

5. Results

In this section, we show some preliminary results of the ISS system experimentation. To perform a first assessment of the system performance, we analyzed some concurrent fires occurring during the summer and winter seasons. In the first case, the fires analyzed occurred in central and southern Italy, in the Abruzzo and Sicily regions (shown in blue in Figure 7) at the end of July 2020. In 2020, the Sicily region registered the largest total burned area at the national level (more than 23,000 ha, equal to 40% of the total). The Abruzzo region registered a burned area roughly equal to 27.5 ha, the second largest at the national level after Sicily (40.8 ha) [49].

In the second case, some fires occurring in Northern Italy at the end of February 2019 were considered. In that period and area, both the number of fires and the area burned were particularly significant [35]. Liguria and Tuscany (shown in yellow in Figure 7) were among the Italian regions which were most affected by fires, due to dry wind from the north, high temperatures, and sparse rainfall [35].

Table 3. Winter forest fires occurring during February 2019 in Northern Italy, and relative results from the ISS system in terms of fire detections.

Place and Data DD/MM/YY	Burned Area (ha)	SEVIRI		AVHRR		MODIS		ISS
		ALICEMIR (Min–Max)	No. RST Thermal Anomalies	ALICEMIR (Min–Max)	No. RST Thermal Anomalies	ALICEMIR (Min–Max)	No. RST Thermal Anomalies	ALICEMIR (Min–Max)	Total No. RST Thermal Anomalies
Careggine (LU) 24 February 2019	20	1.6–13.2	22	2.8–6.3	3	-	-	1.6–13.2	25
Passo della Bocchetta (GE) 24–25 February 2019	10	2.1–4.8	3	4.7–11.6	4	-	-	2.1–11.6	7
Sillano (LU) 25–26 February 2019	100	2.4–18.3	9	2.7–10.7	17	3.8	1	2.4–18.3	27
Vicopisano (PI) 25–26 February 2019	230	4.3–29.8	133	3.9–16.8	7	6.9–7.2	2	3.9–29.8	142
Sesta Godano (SP) 25 February 2019	5	4.7	1	-	-	-	-	4.7	1

summarizes the fire events occurring between 24 and 26 February 2019, extracted from the EFFIS fire news section and showing differences in burned areas. In addition, the table also reports the results from the ISS system in terms of the number of detected thermal anomalies (referred to as each active event and aggregated in space and time) and relative intensity levels (ALICE_MIR min-max), for both single (i.e., only AVHRR, MODIS or SEVIRI observations) and integrated satellite systems.

As can be seen in Table 3, the Vicopisano event was the largest one, affecting about 230 hectares. The images in Figure 8 show the area of the Vicopisano fire during (b) and after (c) the event, in comparison with a pre-event image (a). In addition, Figure 8d shows the temporal trend of the MIR signal (in red) detected by SEVIRI in correspondence with the same event. The abrupt increment of the thermal signal, correctly detected by ISS at 09:15 GMT, is in line with the information of local newspapers which report 9:30 GMT as the start of the event [50,51].

In correspondence with the Vicopisano event, as can be seen in Table 3, the values of thermal signal intensity, in terms of the ALICE_MIR index, are the highest observed by all satellites (29.8 in case of SEVIRI data, 16.8 in case of AVHRR, 7.2 in case of MODIS). Moreover, the event is characterized by the highest number of detections (142). The same can be observed for the other large fire here analyzed occurring near Sillano (about 100 hectares of total burned area), which generated a high number of thermal anomalies (27) as well as high values of the ALICE_MIR index (18.3).

Each satellite sensor contributed differently to the ISS results, especially in the presence of fires just starting and/or short-lived events, which were, in general, better detected using SEVIRI (e.g., Sesta Godano, Careggine, and Passo della Bocchetta). This analysis reveals a good correlation between total burned area and analyzed parameters (i.e., number of thermal anomalies, ALICE_MIR values), despite the possible impact of clouds/smoke on results of thermal anomaly identification. By using the PIN_GEO and PIN_LEO indicators, it was then possible to analyze concurrent fires and their space-time dynamics. Here, we show some examples of PIN_GEO and PIN_LEO applications for the same events in Table 3. Figure 9 shows the temporal trend of PIN_GEO for the concurrent fire events:

• Careggine and Passo della Bocchetta during 24 February 2019 (Figure 9a);
• Vicopisano, Sillano, and Sesta Godano during 25 and 26 February 2019 (Figure 9b).

It can be noted how the above-mentioned events were characterized by different PIN_GEO values, which were derived at 15 min time intervals by exploiting the high temporal resolution of SEVIRI data. This analysis provides a first overview of the active fires and makes available a smart indicator that allows us to compare simultaneous fire events, promptly evaluating the priority of fire contrast action in operational contexts.

In Figure 9a with more detail, the first PIN_GEO value (57.3) for Careggine (12:45 GMT) is greater than the last value (55.5) retrieved at Passo della Bocchetta (11:15 GMT). This trend was also confirmed in the following hours when the PIN_GEO value at Careggine increased up to 87.3 (13:00 GMT). This value is even higher than the PIN_GEO derived at Passo della Bocchetta (81.8) fifteen minutes later (i.e., at 13:15 GMT). Those differences suggest the priority of intervention at Careggine, in agreement with information provided by local sources, reporting the use of a higher number of ground and aerial resources at Careggine [52,53] to extinguish flames.

Figure 9b shows another example of the indicator developed in reference to the concurrent fire events of Vicopisano, Sillano, and Sesta Godano, at around 13:45 GMT. Comparing the PIN_GEO values, the Vicopisano fire is clearly the event to be looked at with greater attention. Indeed, the value of the index analyzed is from 10 to 36 times greater than Sillano and Sesta Godano, respectively. Moreover, it is worth noting that already about four hours before, at 09:30 GMT, the value of the Vicopisano PIN_GEO is the greatest observed. The different number of ground and aerial firefighting resources used to extinguish flames, from local newspapers, confirms the greater danger posed by the Vicopisano event. In particular, online information records an evident difference in the deployment of resources: 22 ground teams, 1 helicopter, and 3 Canadair aircraft employed at Vicopisano [50], in place of 5 ground teams, 2 helicopters, and 1 Canadair at Sillano [50] and 1 ground team at Sesta Godano [54].

Similar findings are derived from PIN_LEO for the same aforementioned fire events. Figure 10 shows the values of PIN_LEO retrieved during the early morning of 26 February 2019, with forest fires simultaneously occurring near Sillano and Vicopisano, whose results are detectable using AVHRR and/or MODIS data. As for PIN_GEO, the higher values of the index recorded at Vicopisano indicate that this fire event required continuous monitoring and prompt intervention.

Analogous outcomes can be found considering the case of two concurrent summer fires (

Table 4. Summer forest fires occurring in central and southern Italy during 31 July 2020 and relative results from the ISS system in terms of fire detections.

Place and Data DD/MM/YY	SEVIRI		MODIS		ISS
	ALICEMIR (Min–Max)	No. RST Thermal Anomalies	ALICEMIR (Min–Max)	No. RST Thermal Anomalies	ALICEMIR (Min–Max)	Total No. RST Thermal Anomalies
Gran Sasso (AQ) 31 July 2020	1.8–12.9	88	4.2–21.7	17	1.8–21.7	105
Petralia Sottana (PA) 31 July 2020	2.3–5.8	5	8.0–14.0	3	2.3–14.0	8

), occurring in central and southern Italy on 31 July 2020. July 2020 saw a high percentage of warm days, mainly in the center-south of Italy, as compared with the 1981–2010 reference period [55]. Abruzzo and Sicily were two of the Italian regions affected by fires on 31 July 2020 and, in particular, an awful event started around 11:00 GMT in the Gran Sasso area (L’Aquila Province, Abruzzo), while another fire was active in Petralia Sottana (Palermo Province, Sicily). Regional Civil Protection Departments had declared a medium fire susceptibility for L’Aquila Province for that day (Figure 11a, [56]) and a medium fire danger level (Figure 11b, [57]) for Palermo Province. The Petralia Sottana fire was identified by RST-FIRES starting from 08:15 GMT, while the first identification of the Gran Sasso fire was at 10:15 GMT. The total number of RST thermal anomalies identified by SEVIRI and MODIS gives an idea of the differences in the spatial extension and intensity of the two events at the end of the day. However, the PIN value trends allow us to highlight different space-time dynamics during fire evolution.

Figure 12 shows, in particular, the integrated use of PIN_GEO and PIN_LEO. As described above, the two PINs are based on different weighted indices and therefore their values cannot be directly compared. For this reason, the vertical axis on the left of the graph refers to the PIN_LEO values, while the vertical axis on the right is related to the PIN_GEO values. Comparison of the same PIN for the two events clearly shows that greater attention should be given to the Gran Sasso fire. In detail, the Gran Sasso PIN_GEO value turns out to be 178.4 at the first RST-based detection (10:15 GMT); therefore, it is greater than the last available Petralia Sottana PIN_GEO (98.0 at 08:15 GMT).

Similarly, the first PIN_LEO value for Petralia Sottana is 61.1 at 09:59 GMT; during the following MODIS overpass (at 11:39 GMT), the Petralia Sottana PIN_LEO value increases (133.1), but at that time the Gran Sasso PIN_LEO value (754.7) is five times greater than Petralia Sottana PIN_LEO. Therefore, PIN_LEO comparison also confirms the need for primary intervention against the Gran Sasso event.

On the other hand, although PIN_GEO and PIN_LEO are not directly comparable and PIN_LEO can only be computed during polar satellite passages, the information from PIN_LEO can strengthen the PIN_GEO information.

Suggestions from PIN_GEO and PIN_LEO are corroborated by online news, which indicates the intervention of several teams of volunteers, an army helicopter, and five Canadair aircraft against the Gran Sasso fire [58], while the Petralia Sottana fire required a more limited mobilization of fire-fighting teams and vehicles [59].

Moreover, it should be highlighted that FDDI values foreseen in correspondence with these two fires fall in the ‘very high’ class (Figure 13). Concerning that point, it is also worth noting that:

• the FDDI index used in ISS is able to give information on a local scale and with a 1-h update, unlike the forecasts that are used in operational mode by Civil Protection Departments (daily, at a Province level, see Figure 11);
• the use of the FDDI index alone is not enough to establish prioritization if it is not coupled with other indices that are at the basis of the PINs.

6. Discussion

Preliminary results presented in previous sections have shown the potential of the ISS system in providing information about fire monitoring and prioritization. Like other satellite-based systems, however, ISS suffers from some limitations due to the use of optical band data for fire detection. Both clouds and smoke obscuring the areas affected by fires may cause significant omission errors or lead to the underestimation of fire events (e.g., [14]).

In this study, we retrieved information required to assess the alert from the ISS system from some of the summer fires as well as the winter fires, aiming at testing the system in different conditions. The test included fires occurring in the summertime, when meteorological conditions are generally favorable for satellite fire surveillance, as well as the events occurring in the winter season, when dense and persistent cloud coverage is more frequent and capable of obscuring active fires, making them more difficult to be continuously monitored. On the other hand, the reliability of fire detection algorithms in the winter season is severely tested due to the possibility of having a false alarm proliferation mainly related to a high MIR signal, observed for example in correspondence with cloud edges.

It is then evident that the capacity of the ISS system for forest fires prioritization needs to be verified in further cases.

Moreover, accurate validation of fire products, including those provided by the ISS system, is not a trivial task. This analysis not only requires detailed information about fires (e.g., starting and end time, duration, size) which may sometimes be derived from local sources (as for this study), but also information about a number of other important factors, such as fire propagation speed, space-time dynamics, flame intensity, and front height and length. This information is usually not available, particularly during the evolution of fire events. Hence, the results of this study give a first indication of the potential of the proposed system in providing information of great relevance for firefighting activities regarding the possibility of better managing available resources.

Regarding the advantages of the ISS system, although it has been developed to perform over the Italian territory, it can be easily scaled to a local (i.e., regional territory) and continental scale. This is possible thanks to the use of an efficient fire detection algorithm (RST-FIRES) exportable to different geographic areas, which may be applied to data from different sensors (both polar and geostationary).

Clearly, ancillary data (i.e., weather forecasts) are required to implement the ISS system operationally.

The availability of high-resolution and/or updated layers also at the regional level (i.e., maps of land use) could further improve the performance of the system developed. It is customizable to different end-users. Additionally, both PIN_GEO and PIN_LEO developed here are synthetic indicators that can use different weights for the parameters considered. Specific end-users could then require a different set of PIN constants, based on their own needs.

7. Conclusions

Fires are a complex environmental risk generally addressed with a multiplicity of systems at all stages of their development: hazard evaluation, monitoring, and damage assessment.

In this paper, we have presented an automatic satellite-based system for fire detection and prioritization that, for the first time, integrates two different approaches. The first one is focused on early warning and monitoring, while the second one is devoted to an assessment of danger and damage reduction. The ISS system integrates the following modules:

• hot spot real-time detection by using the RST-FIRES technique, implemented with multi-mission satellite data (AVHRR, MODIS, SEVIRI);
• evaluation of fire ignition/propagation, for each detected hot spot, by using a simplified index, FDDI, to consider the state of vegetation, the wind intensity (WI), and geomorphological characteristics (MDI) for the area affected by the event;
• construction of synthetic priority indicators, PIN_GEO and PIN_LEO, for an immediate (and continuously updated) overview of the situation.

ISS extends the performance of the RST-FIRES system, which was successfully used in operational contexts [15] by implementing ancillary information to evaluate, pixel by pixel, the predisposition and the possibility of the fire occurrence. The system is then capable of early fire detection, characterizing some crucial features of the territory potentially affected by flames. Moreover, it may represent a decision support tool to prioritize active concurrent fires driving priorities for intervention, by optimizing the use of available ground and aerial resources to extinguish fires. To perform a first assessment of the developed system, we analyzed a number of winter fires of different sizes and intensity levels occurring in northern Italy, exploiting the information from the EFFIS fire news section and local online newspapers. In addition, the case of two concurrent summer fires was analyzed.

In both cases, the priority synthetic indices, PIN_GEO and PIN_LEO, were then calculated for satellite imagery acquired during the fire events demonstrating their usefulness to fire prioritization.

Although further analyses are required to fully evaluate the performance of the ISS system, it may support civil protection, firefighters, volunteers, and other operators involved in better managing active fires, minimizing the impact of dangerous and highly destructive events on populations and the environment, in agreement with European legislation [60]. Finally, the ISS system is also in line with the key objectives (i.e., reduction of the “incidence and extent of forest fires”) of the EU forest strategy, described in the European Green Deal [61].