Characterization of the July 2007 Swaziland fire disaster using satellite remote sensing and GIS

Applied Geography 29 (2009) 299–307

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Characterization of the July 2007 Swaziland fire disaster using satellite remote sensing and GIS Wisdom M. Dlamini* Swaziland National Trust Commission, P.O. Box 100, Lobamba, Swaziland

a b s t r a c t Keywords: Disaster Fire GIS MODIS MSG-SEVIRI Swaziland

Data from the NASA’S MODIS (Aqua and Terra) and EUMETSAT’S MSG-SEVIRI satellite sensors is analysed to characterise the geographic and temporal (including diurnal) evolution of the July 2007 fire disaster in the Kingdom of Swaziland using a geographic information system (GIS). Significant fire activity was observed during a three-day period beginning on the 27th July 2007. A total of 1358 and 4365 active fire hotpots were detected by MODIS and MSG-SEVIRI, respectively, mainly concentrated in the Highveld (70.91% for MODIS, 89.89% for MSG) and Middleveld (11.27% for MODIS, 5.23% for MSG) with MSG/MODIS active fire count ratio ranging from a high of 3.69 in the Highveld to a low of 0.06 in the Lubombo Plateau. The results indicate complex differences in spatial fire distribution, behaviour and risk within the country and the effect of sensor differences. A pronounced fire diurnal cycle with a broad afternoon peak centred on 14:00 local time is observed, in general agreement with observations from the region. Despite their limitations, the study demonstrates the importance and usefulness of remotely sensed data and GIS technology for fire disaster and risk assessment for a developing country, where fire monitoring resources are scarce. Ó 2008 Elsevier Ltd. All rights reserved.

Introduction Scientific evidence exists to suggest that savannah ecosystems evolved and adapted with fire as an agent of ecological change (Bond & Keeley, 2005). However, human activities have altered many natural landscapes and placed human beings in direct contact with fire or fire sources. Fires are known to cause loss of human life and personal property, and economic upsets, whilst heat, smoke and aerosol particles are known to cause disturbances in regional and global atmospheric composition and chemistry, and ultimately on climate (Andreae & Merlet, 2001; van der Werf et al., 2004). Research and ˜ o cycle (Randerson et al., 2005; Rian ˜ o, Moreno results from several studies also suggest that fire activity is linked to the El Nin Ruiz, Baro´n Martı´nez, & Ustin, 2007; van der Werf et al., 2006). Since such conditions are likely to become more frequent in the future, the magnitude and frequency of fires may increase (Lavorel, Flannigan, Lambin, & Scholes, 2007). Fires, therefore, have become an area of interest for scientists dealing with climate change and this makes fire detection also useful for climatologists. The three elements necessary for combustion, namely heat, oxygen and fuel (Trollope, De Ronde, & Geldenhuys, 2004) form what is commonly known as the fire triangle. Other conditions such as topography, fuels and weather, collectively called the fire environment, can perpetuate extreme wildfire behaviour (Perry, 1998). Drought conditions, such as those that have

* Tel.: þ268 6024716; fax: þ268 4161875. E-mail address: [email protected] 0143-6228/$ – see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.apgeog.2008.10.007

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been prevalent in Swaziland and the region over the past few years, trigger fire and, coupled with windy conditions, make a small fire potentially disastrous. Hot, dry, and windy conditions are generally ideal for the rapid growth and spread of wildfires and steeper slopes tend to further increase the rate of fire spread. These conditions often persist over Swaziland ˜ o Southern during the months of July/August more especially in western half of the country and in association with the El Nin Oscillation (ENSO) warm phase (Dlamini, 2007). As the synoptic weather pattern changes within the climatic conditions, periods of critical fire weather and fire behaviour can develop, requiring constant and consistent monitoring. The continuous monitoring of fire involves observation of fires and fire-causing processes, fire risk assessment, land cover dynamics analysis, and emission estimation, among others and countries with significant wildfire activity, especially developed countries, have developed ground and air-based monitoring networks (San-Miguel-Ayanz, Ravail, Kelha, & Ollero, 2005). However, due to technological and financial difficulties, developing countries are still lagging behind in taking advantage of such technologies (Flasse, Ceccato, Downey, Raimadoya, & Navarro, 1997). The emergence of satellite remote sensing provides opportunities for continuous, large-scale monitoring, which may overcome the logistical and financial constraints of ground-based, and air-/satellite-borne observations. Remote sensing instruments on polar-orbiting and geostationary satellites allow fire observations at a broad range of spatial and temporal scales. These satellite-based fire and thermal anomaly detection systems are indispensable for both research and operational use. Satellite instruments that can simultaneously utilise 3.9 mm and 11 mm channels can be used for fire detection due to the 3.9 mm channel’s strong thermal sensitivity even if only a small portion of the pixel is covered by fire (Matson & Dozier, 1981). For a long time, including the present, operational systems have been using data from the NOAA Advanced Very High Resolution Radiometer (AVHRR) and GOES (Geostationary Operational Environmental Satellite), including a few others such as the DMSP-OLS (San-Miguel-Ayanz et al., 2005). Significant progress was made with the launch in 1999 and 2002 of the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on board the morning descending Terra and afternoon ascending Aqua polar-orbiting earth observation satellites thus offering the opportunity to observe fire activity both day and night. MODIS Terra scans the Southern African region between 10:00–11:30 am and at night around 22:00pm whereas MODIS Aqua scans in the afternoons between 14:00–15:30pm and also in the early morning at 03:00 am (Giglio, Csiszar, & Justice, 2006). Validation results for MODIS indicates that a minimum detectable fire size, flaming at between 800 and 1000 K and typically detectable at 50% probability, is of the order of 100 m2 (Frost & Vosloo, 2006). The MODIS sensor also includes bands specifically selected for fire and cloud detection and allows the retrieval of sub-pixel fire area and temperature. Although the capabilities of current geostationary satellites are limited, they can provide valuable local, regional and global fire products in near real time, and are critical for fire detection and monitoring in remote locations and developing countries. Under ideal conditions, the performance of these sensors is somewhat satisfactory and such conditions occur when a fire is observed at (or near) nadir on a fairly homogeneous surface, or when no other significant fires are nearby, or when the scene is free of clouds, heavy smoke or sun glint. In these circumstances, the smallest flaming fire that can be routinely detected (i.e. near 100% probability of detection) is approximately 50 m2 in size. (Giglio, 2007). The accuracy of the fire detection by MODIS has been observed to reach up to 90% and even higher (Wang, Zhou, & Wang, 2003). In 2002, EUMETSAT launched Meteosat-8 or Meteosat Second Generation (MSG), which possesses enhanced spectral and temporal capabilities with respect to the then existing generation of Meteosat spacecraft. Its geostationary orbit allows MSG, through its Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument, to take images of the Earth every 15 min, at a spatial resolution of 3 km at nadir for the short wave infrared (SWIR) and thermal infrared (TIR) bands (Jahjah, Laneve, & Marzoli, 2002). The high temporal resolution makes it is very useful for detecting continuously changing phenomena like active fires detection as also seen by Schmetz et al. (2002). The coarse resolution, however, limits the detection of small fires and validation of the minimum detectable fire size for MSG-SEVIRI in southern Africa is still under investigation (Frost & Vosloo, 2006). Validation in the USA on similar satellites have shown detectable sizes in the region of 500 m2, depending on factors such as scan angles, biome, sun position, land surface temperature, cloud cover, amount of smoke and wind direction (Prins, Schmetz, Flynn, Hillger, & Feltz, 2001). Laneve, Castronuovo, and Cadau (2006) report a minimum detectable fire size of 1000 m2 in the Mediterranean. High potential for a unique contribution was recognised, given the specific Sun-target sensor geometric conditions, the high revisit frequency, and good spectral resolution of the instrument. Fire-related processes were clearly identified in the list of applications that can be derived from MSG-SEVIRI data. However, all satellite-based fire detection systems have their advantages and shortcomings. One of the common shortcomings is the lower limits for fire sizes that space-borne instruments can detect and the incapability of detecting fires through clouds or thick smoke. Polar-orbiting satellite, on one hand, cannot see short-duration fires that take place between the satellite overpasses; on the other hand, geostationary satellites have better temporal resolution, but worse spatial resolution and have difficulties when scanning at high viewing angles. San-Miguel-Ayanz et al. (2005) provide an overview of capabilities and limitations of remotely sensed data applications for fire emergency management. The main purpose of this study is to describe the geographical occurrence and temporal (daily and diurnal) variation of the July 2007 fires as recorded by the two satellite-based fire detection systems, MODIS and MSG-SEVIRI. The Swaziland fire disaster During the month of July 2007, a series of devastating fires blazed throughout Swaziland resulting in widespread destruction of property and even the loss of lives. Propelled by high wind speeds and a prolonged dry season, the fires

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resulted in damage to thousands of hectares of forest and other vegetation, and direct economic losses estimated in the tens of millions of US dollars. This culminated in the declaration of these fires by the country’s Prime Minister as a national disaster on the 1st August 2007. A report by the Ministry of Regional Affairs and Youth Affairs, which also houses the Disaster Management Agency, puts the number of homesteads destroyed at 169 and the total number of affected people at 934 with two casualties (MORDYA, 2007). Smoke pollution was evident as the sky was darkened by plumes from the same fires. Various media reports suggest acts of arson and negligence. Whilst these may not be ignored, it is critical to understand the underlying eco-climatic factors which may have accelerated and exacerbated the impact of these fires. Typically in Swaziland, the driest months of the year occur in the winter, i.e. from around April to September (Goudie & Price-Williams, 1983). When the ENSO warm phase is established, an anomalous dry and warm pattern can be significantly amplified, as it has been in the past 2–3 years (Dlamini, 2007). The country’s topography, especially the higher altitude areas, can often be the catalyst for extreme weather conditions which may amplify fire behaviour such as was observed during the disaster. Methods Study area The Kingdom of Swaziland, located in southern Africa, is 17 364 km2 in extent straddling latitudes 25 400 and 27 200 South and longitudes 30 400 and 32100 East (Fig. 1). The country is sandwiched between South Africa to the north, west and south, and Mozambique to the east. It is also endowed with tremendous natural diversity and complex topography with elevation that decreases from an average of 1400 m above sea level on the west to below 100 m on the eastern part of the country giving rise to four major eco-climatic (commonly referred to as agro-ecological) regions, namely the Highveld, Middleveld, Lowveld and the Lubombo Plateau (Gibbons, 1981, Goudie & Price-Williams, 1983). Goudie and Price-Williams (1983) and Remmelzwaal (1993) further subdivide the Middleveld into two zones - Middleveld grassveld or Upper Middleveld on the west and Middleveld bushveld or Lower Middleveld on the east; similarly, the Lowveld is partitioned into the Western and Eastern Lowveld (Fig. 1). Climatic variations within the country are largely controlled by topography and within a year there are four seasons with December being mid-summer and June mid-winter. Mean annual rainfall also varies extensively from above 2000 mm per annum in the Highveld to below 500 mm in the Lowveld. Variations in temperature also follow the altitudinal gradients, the Highveld being temperate and seldom hot while the semi-arid Lowveld can record temperatures of up to 40  C during summer. Satellite data analysis Two active fire datasets, MODIS and MSG-SEVIRI, from 1 to 31 July 2007 were collected and collated. The MODIS dataset is based on the version 4 contextual fire detection algorithm from the MODIS Rapid Response System (Web Fire Mapper, http:// maps.geog.umd.edu/) at a spatial resolution of 1 km (Justice et al., 2002). The MODIS active fire data is based on Giglio, Descloitres, Justice, and Kaufman’s (2003) enhanced contextual fire detection algorithm which uses the 4 mm and 11 mm bands and classifies every pixel as missing data, cloud, water, non-fire, fire, or unknown. A full description of the algorithm is given by Giglio et al. (2003). The MODIS active fire product contains information about the detected fire pixels including location, observed brightness temperature, pixel size, and fire confidence. The fire confidence is calculated by a system of equations within the algorithm and is expressed as a percentage (Giglio, 2007). The confidence levels reported in MODIS active fires during the period under investigation were explored and it was found that even pixels in areas where the fires were intense (plantations) were given a zero confidence, most likely due to the obscuration by the thick smoke cover over almost the whole country. From the MOD14A1 (MODIS active fire) data, the smoke was found to have been classified as ‘‘nonfire clear land’’. Therefore, MODIS fire pixels with a confidence level of 0% were also considered as individual fire counts. The 15-min temporal resolution MSG-SEVIRI active fire data was obtained from the Council for Scientific and Industrial Research (CSIR) – Satellite Application Centre (SAC)/Eskom’s Advanced Fire Information System (AFIS). The MSG-SEVIRI system currently uses a contextual algorithm-based on a threshold technique and statistical analysis of each potential fire pixel developed by Flasse and Ceccato (1996) for the AVHRR. An improved approach using a Kalman filter model of the normal diurnal cycle is being developed (Van Den Bergh & Frost, 2005). The focus of this analysis is on the July 2007 fires during which a majority of the fires were large-sized fires, big enough to be detected even by the low spatial resolution MSG-SEVIRI sensor. However, due to the low spatial resolution (w4 km) and the spatial scale of the analysis, the MSG-SEVIRI data was found inappropriate for separate geographical analysis of fire distribution. The active fire hotspots from both the MODIS and MSG-SEVIRI datasets were obtained in the form of ASCII files which were imported into the geographic information system (GIS) software ArcGISÔ 9.2 (ESRI, 2006) for spatial and temporal analysis. ArcGISÔ was used for its versatility and its ability to simultaneously handle both spatial and temporal data. To answer some of the important ecological questions it was necessary to relate fire activity information to the country’s eco-climatic zones so as to uncover knowledge on which ecosystems or landscapes were most affected. MODIS active fire hotspots were then counted for each eco-climatic zone as an indicator of the spatial or geographic distribution of fire activity. Basic analysis of the active fires detected was undertaken through the calculation of a basic ratio of MSG-SEVIRI to MODIS active fires for each ecoclimatic zone. The ratio was calculated to provide information on the behaviour of fires where a large MSG/MODIS ratio is used as a proxy of a high degree of large short-duration and intense fires that were detected by the geostationary MSG-SEVIRI

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Fig. 1. Location, eco-climatic zones and elevation of the Kingdom of Swaziland.

that could have been missed by the polar-orbiting MODIS. A ratio of less than unity, on the other hand, indicates the presence of smaller fires that are undetected by MSG. A ratio of unity is supposed to indicate the occurrence of fires that are most likely simultaneously or equally detected by both sensors. Active fires detected by both the MODIS and MSG-SEVIRI during the month of July were counted for each day of the month to ascertain the temporal evolution of the fire activity and to confirm the days of intense fire activity and relating these to actual observations on the ground. The diurnal fire cycle, or the systematic variation in fire activity with respect to time of the day, has become a very important temporal metric in fire analysis. The high temporal resolution MSG-SEVIRI dataset was used to study the diurnal cycle of fire activity for each eco-climatic zone in Swaziland. These were binned into 1-hour intervals to provide more meaningful interpretation. Results and discussion Geographic distribution A total of 1358 hotspots were detected by MODIS over the period under investigation whilst MSG-SEVIRI detected a total of 3926 hotspots. The smoke plumes from these fires were spread throughout almost the whole country with most of the fires

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detected in the Highveld followed by the Middleveld regions, the eastern zones recording the least number of fires (Fig. 2). MODIS detected 71% and 11% of all active fires in the Highveld and Upper Middleveld, respectively. Thus, the most affected eco-climatic zone was the Highveld, consisting mainly of plantation forests and grasslands, confirming Dlamini’s (2005) observations that this zone is most prone to fires. The Highveld and Upper Middleveld are the areas where hundreds of hectares of plantation forests, natural vegetation (mainly grass) and homesteads were burnt (Dlamini, 2007; MORDYA, 2007). The most evident cluster of fires is in the north-western part of the country, located in the Highveld, where the Piggs Peak Timber plantations company reportedly lost tens of millions of US dollars worth of property and an estimated 80% of the total plantation forest area in addition to significant destruction of other plantations in the south-western part of the country. This geographic distribution of the fires illustrates the spatial pattern of the current burning practices, land use and the landscape in the country. Although the exact cause or ignition point of these fires has not yet been ascertained, fires in Swaziland are generally used by people to facilitate pasture regeneration and in clearing vegetation for farming and settlements whilst commercial/industrial plantation forests are typically designed with networks of firebreaks that are annually burnt in June and July to provide clean belts around the plantation compartments (Dlamini, 2005). Csiszar et al. (2004) observe that the increased risk of accidental fires is also due to landscape fragmentation and land cover change which exposes forests and woodlands to fires. The steep and rugged topography of the country, more especially the Highveld and Middleveld (Remmelzwvaal, 1993), can often generate the hot, dry, and windy environment needed for extreme fire behaviour and accelerated fire spread rates. During the period of the fires, gusty conditions were prevalent in the country creating perfect conditions for the fires. The observations, therefore, indicate that these eco-climatic zones are riskiest in terms of wildfires under such conditions and as such fire management and control should pay particular attention to these areas.

Fig. 2. MODIS active fire hotspots during July 2007 in Swaziland.

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Table 1 MODIS and MSG-SEVIRI fire hotspots by eco-climatic zone (percentage of total indicated in brackets). Eco-climatic zone

MODIS

MSG-SEVIRI

MSG/MODIS ratio

Highveld Upper Middleveld Lower Middleveld Western Lowveld Eastern Lowveld Lubombo Plateau

963 (70.91) 153 (11.27) 98 (7.22) 56 (4.12) 37 (2.72) 51 (3.76)

3529 (89.89) 221 (5.63) 113 (2.88) 18 (0.46) 42 (1.07) 3 (0.08)

3.66 1.44 1.15 0.32 1.13 0.06

Total

1358

3926

2.89

The ratios of MSG-SEVIRI/MODIS active fires for all the eco-climatic zones are shown in Table 1. The ratios indicate that MSG-SEVIRI detected more of the destructive and intense large fires of the Highveld and Middleveld, whilst MODIS was also effective in detecting smaller fires in the Western Lowveld and Lubombo Plateau. However, only fires actively burning at the time of the polar-orbiting MODIS satellite overpass could be detected. The findings indicate that the MSG-SEVIRI detections were the fast-spreading fires of the Highveld and Upper Middleveld. It is, however, possible that for numerous small fires detected as one fire pixel by MSG-SEVIRI fire product, MODIS detected these as separate fires due to its smaller spatial resolution. Calle et al. (2005) provide a very good comparative analysis of MSG and MODIS active fire performances and found false alarm rates of as high as 70% and as low as 10% within the hottest zones of fires. Van den Berg and Frost (2005) also observe MSG-SEVIRI/MODIS active fire ratios varying from 7 to 1.9 depending on the size and characteristics of the fire. However, the false alarm rate decreases with increasing fire size reaching zero with fire sizes of the order of 0.35 ha while the probability of detection increases with fire size reaching 100% with fires greater than 15 ha (Costantini, Zavagli, Cisbani, & Greco, 2006). Accordingly, the fires in the country, particularly in the Highveld, were very large and intense and so were their detection probability, thereby minimising omission errors especially from MODIS. The MSG-SEVIRI/MODIS ratios also give credibility to a proposition that the size, duration and intensity of the intense fires, coupled with differences in sensor characteristics and algorithms, is the main reason for the differences in detected fires. MSG-SEVIRI Cloud or smoke cover, and fire location on the topography are some of the factors that may have limited the ability of the satellites, especially MODIS, to detect fires. Of particular note is the prevalence of thick plumes around the country which might have also hindered or limited the detection of fires beneath the smoke. Temporal distribution Temporal analysis reveals that a majority of the fires were detected from the period 27–29 July 2007 (Fig. 3). Actually, MODIS detected 76.73% of the July 2007 fires during the three-day period whilst MSG-SEVIRI detected 88.79% during the same period indicating high spread rates during this time. A notable signal of fire activity was first detected on 24 July followed by a dramatic upsurge on 27 July. Fire suppression efforts were first made on 24 July 2007 and hopes were elevated among the plantation forest companies that the fire had been successfully put out. However, the fires were rekindled aided by strong winds and dry conditions resulting, as Fig. 3 indicates, in the three-day period of intense fire activity. The prevalent strong winds most likely caused spotting, i.e. the start of a new fire ahead of a main fire by an airborne firebrand (Trollope, 1993), which typically occurs during high speed winds exceeding 11 km/h and in crown fires within highly flammable vegetation such as plantation forests (Trollope & Potgieter, 1985). While MSG-SEVIRI was still detecting an increase in fires on

Fig. 3. MODIS and MSG-SEVIRI active fire counts for 1–31 July 2007 in Swaziland.

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28 July, MODIS identified the beginning of a gradual decline. This could be attributed to the fact that most of the fires were intense and fast due to the windy conditions and could have been missed by MODIS due to its orbital characteristics. Another reason, as stated in the previous section, is the effect of the dense smoke plumes which could have obscured a majority of the fires mainly burning intensely within the plantation forests. The number of active fires decreased after 29 July 2007 after the large areas of vegetation (mainly plantation forests and grass) biomass and other material had been quickly consumed. The usefulness of satellite fire observation in studying temporal fire evolution is clearly demonstrated in these observations. Diurnal-scale analysis of biomass burning is very important because in the tropics, terrestrial carbon sinks have also been observed to have a strong diurnal cycle (Kauffman, Steele, Cummings, & Jaramillo, 2003; Prins, Feltz, Menzel, & Ward, 1998; WRAP, 2005), which is of particular interest for short-lived trace gases and aerosols in the lower troposphere. Diurnal fire behaviour information is also important for applications in support of fire suppression and for emergency preparedness purposes. In this study, the MSG-SEVIRI (Fig. 4) data reveals a strong and interesting diurnal cycle with most fires flaring strongly from late morning (11:00) to the afternoon (17:00) and again at late night (22:00) to early morning (04:00) with an overall peak in fire activity centred around 14:00 local time. This indicates that the fires were spreading faster and burning more intensely, thereby indicating that risk is high and suppression efforts should be increased during these times. Low fire activity is observed in the mornings and in the early evening, typical of human managed ecosystems (Zoumas, Eva, Gregoire, & Stibig, 2006). The observed diurnal cycle patterns are comparable to the observations by Eck et al. (2003) during southern African biomass burning seasons. Giglio et al. (2006) and Peck, Rice, Tressel, Lee-Wagner, and Oshika (2000) also found a strong southern African diurnal fire cycle with a peak around 14:00 local time, at which time fires tend to be burning in larger numbers and with greater intensity. Long (2006), in a study of extreme fire events in Victoria (Australia), found peak fire danger at 15:00. The diurnal pattern in this study is more or less similar for all the eco-climatic zones with minor differences that could be attributed to differences in fuel (vegetation and land cover) types and the possible influence of elevation and prevailing weather conditions. Cheney (1981) and Zoumas et al. (2006) attribute the broad morning to afternoon peak to the peak fire season whilst the switch from afternoon to night time fires follows the rainfall reduction gradient and land cover type. Since July is near the peak of the fire season in the country (Dlamini, 2005), the burning likely became long lasting and persistent through the night particularly in the Highveld. These fires were aided by the prevalent and fire-favourable climate conditions of long and persistent drought experienced in the country in the past few years. The diurnal variations also indicate the influence of diurnally driven processes of boundary layer mixing or fast changing conditions such as frontal passages and windy conditions (Trollope, 1993). Surface meteorological conditions, coupled with certain synoptic weather patterns, have been shown to be important factors affecting fire occurrence probability (Long, 2006; Van Wilgen & Scholes, 1997). Temperature has also an important role in diurnal fire behaviour because in the cooler higher altitudes (such as the Middleveld and Highveld), fire occurrence is delayed and confined during the warmer periods of the day. The results, analogous to Beck and Armitage (2004) and Zoumas et al. (2006), are indicative of the response of fuel moisture content throughout the day typically reaching minimum in the afternoon, increasing rapidly to an overnight maximum that is maintained until the late evening/early morning. Similarly, Everson, Smith, and Everson (1985) found that the moisture content of the upper grass layer declined from 33% in the morning to 11% in the afternoon in the montane grasslands of Natal (South Africa) thus influencing the diurnal fire activity patterns. However, the behaviour of the Highveld

Fig. 4. Diurnal variation in MSG-SEVIRI active fire counts in Swaziland.

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and Upper Middleveld fires produced a relatively uniform graph indicating little variation due to the intensity and size of the fires. The findings point to the geostationary sensor’s possible usefulness for evaluating diurnal fire evolution, detecting shortduration fires and for resolving the diurnal behaviour of large fires. The geostationary MSG-SEVIRI sensor offers a great opportunity to develop a robust early warning system capable of timely identification of forest fires and to monitor them in real time thus minimising damage. However, caution needs to be taken and considered with satellite observations because the daytime radiance signal at 3.7 mm may be a combination of both emitted thermal and a reflected solar radiation which may elevate the signal to produce false fire alarms (Gao, Xiong, & Li, 2006). Similarly, daytime thermal radiation from hot background surfaces and/or increased solar reflection can also saturate the 3.7 mm channel (Di Bisceglie, Episcopo, Galdi, & Ullo, 2005). Active fires from polar-orbiting satellites, on the contrary, represent a limited temporal sample due to the satellites’ restricted overpass frequency, coupled with the diurnal fire cycle (Hyer, Kasischke, & Allen, 2007). Conclusion and recommendations The usefulness, including the unique strengths and limitations, of remotely sensed active fire data from both polar-orbiting and geostationary satellites in understanding the geographic and temporal characteristics of fire is demonstrated in this study. Despite the observed limitations, satellite monitoring of fires proves to be useful for a resource-constrained developing country such as Swaziland. There is an observed distinctive spatial distribution pattern in the fire activity during the month of the Swaziland fire disaster. In general, the highest proportion of fire hotspots were detected in the Highveld and Middleveld, where a majority of the devastating fires raged through the country’s plantation forests thus indicating the high fire risk in these areas. The high temporal resolution MSG-SEVIRI detected more fires in the Highveld and Upper Middleveld and reveals a distinct diurnal variation in fire activity where peak activity is observed mainly in the afternoon and late evening/early morning during when fires are most likely to burn. The diurnal fire cycle slightly varies between the eco-climatic zones since most of the large fires were concentrated in the Highveld and Upper Middleveld and also points to the influence of land cover/ land use, elevation and the prevalent weather characteristics in line with observation from the region ad other parts of the world. The burning patterns presented in this study could be used as input information for the further analysis of current and future fire regimes and fire risk according to local and global change. This study also helps to understand active fire detection differences that can be explained by differences in orbits, fire algorithms, and fire pixel characteristics. Ultimately, integration of multi-sensor data can benefit from high temporal resolution (MSG-SEVIRI) data and medium spatial resolution (MODIS) fire products to provide more information than either product could provide alone. Increasing observational and technological skill and experience at recognizing dangerous fire activity will offer significant lead time to anticipate future disasters and minimize losses and environmental impacts from such disasters. 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