RSE-08000; No of Pages 13
Remote Sensing of Environment xxx (2011) xxx–xxx
Contents lists available at ScienceDirect
Remote Sensing of Environment
j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / r s e
Global night-time fire season timing and fire count trends using the ATSR
instrument series
Olivier Arino a, Stefano Casadio b,⁎, Danilo Serpe b
a
b
European Space Agency, Earth Observation Directorate, ESA-ESRIN, Via Galileo Galilei, 00044, Frascati, Italy
Serco, c/o ESA-ESRIN, Via Galileo Galilei, 00044, Frascati, Italy
a r t i c l e
i n f o
Article history:
Received 30 October 2009
Received in revised form 19 April 2011
Accepted 7 May 2011
Available online xxxx
Keywords:
Fires
Satellite
Global trends
a b s t r a c t
Global night-time fire counts for the years from 1995 to 2009 have been obtained by using the latest version of
Along Track Scanning Radiometer Top of Atmosphere radiance products (level 1B), and related trends have
been estimated. Possible biases due to cloud coverage variations have been assumed to be negligible. The
sampling number (acquisition frequency) has also been analysed in detail and proved not to influence our
results. The new ATSR World Fire Atlas (WFA) product continuity has been tested by comparing the partially
overlapping fire counts time series from the ATSR-2 (on board ERS-2) and the AATSR (on board ENVISAT)
missions which showed negligible offsets. The ATSR-WFA products show very good correlation with
the TRMM-VIRS and MODIS-Aqua/Terra monthly night-time fire counts. Global night-time fire trends have
been evaluated by inspecting the time series of hot spots aggregated a) at 2° × 2° scale; b) at district/country/
region/continent scales, and c) globally. The statistical significance of the estimated trend parameters has
been verified by means of the Mann–Kendall test. Results indicate that no trends in the absolute number of
fire counts can be identified at the global scale, that there has been no appreciable shift in the fire season
during the last 14 years, and that statistically significant positive and negative trends are only found when
data are aggregated at smaller scales.
© 2011 Elsevier Inc. All rights reserved.
1. Introduction
Biomass burning has been recognised to play an important role in
regional and global climate change (Harden et al., 2000; Hoffmann et
al., 2003; Kasischke et al., 1995), and also to affect weather on much
shorter scales (Andreae, 1991; Rosenfeld, 1999). Investigation of the
impact of climate change on wild fire activity at the regional scale has
been recently attempted, showing that by the year 2050 the expected
increases in surface temperature might cause a drastic increase in the
annual mean burned area (Spracklen et al., 2009). However, our
capability of assessing risks related to possible changes in fire regimes
at the global scale is impaired by the poor representations of fires in
global models (Bowman et al., 2009).
Globally fires can be of different nature, but in general three main
categories can be identified: forest fires, agricultural fires and
industrial fires. Forest fires are mainly attributed to human behaviour,
e.g. in the Mediterranean region, while an ignition factor of natural
origin (e.g. lightning) is relevant in the Boreal region. In agriculture
fire has been used for thousands of years (Clement & Horn, 2001;
Delcourt et al., 1998; Goldammer, 1988). As to the industrial
⁎ Corresponding author. Tel.: + 39 0694180594.
E-mail address: [email protected] (S. Casadio).
component, gas flaring activity related to oil-gas extraction appears
to be the dominant factor (Marland et al., 2008).
The most effective tools for global fire analysis are space-borne
instruments, as they are capable of detecting flames at relatively high
space and time resolution, even in remote areas not accessible with
other means. Rigorous processing of such measurements allows the
construction of a statistically significant data set that shows no
sampling bias.
Global fire time series derived from different space-borne sensors
are by now long enough (i.e. more than 14 years) to allow the analysis
of long term trends at the global scale with adequate accuracy. The
long history of fire detection from space started with the pioneering
works of Dozier (1981) and Matson and Dozier (1981), in which the
thermal response of the Advanced Very High Resolution Radiometer
(AVHRR) sensor (on board the NOAA satellites) was analysed. In
particular, Dozier theoretically approached the study of the sub-pixel
temperature fields by using the 3.7 and 10.6 μm channels of AVHRR.
For more than twenty years space-borne detection of active fires has
been conducted making use of sensors operating at visible to infrared
wavelengths, with a 1–3 km nadir spatial resolution and a spectral
channel in the middle infrared atmospheric window, i.e. 3.4 to 4.2 μm.
The most effective sensors in this class have been NOAA-AVHRR
(Martin et al., 1999), VAS/GOES (Prins & Menzel, 1994), ERS and
ENVISAT ATSR (Arino & Melinotte, 1998), TRMM-VIRS (Visible and
Infrared Scanner) (Giglio et al., 2003a), and MODIS (Moderate
0034-4257/$ – see front matter © 2011 Elsevier Inc. All rights reserved.
doi:10.1016/j.rse.2011.05.025
Please cite this article as: Arino, O., et al., Global night-time fire season timing and fire count trends using the ATSR instrument series, Remote
Sensing of Environment (2011), doi:10.1016/j.rse.2011.05.025
2
O. Arino et al. / Remote Sensing of Environment xxx (2011) xxx–xxx
Resolution Imaging Spectroradiometer) on the EOS Terra and Aqua
satellites (Giglio et al., 2003b). The middle-infrared (MIR) channels of
these instruments (with the exception of MODIS) saturate at
relatively low pixel-averaged brightness temperatures, and this
saturation impairs the quantitative characterisation of large fires.
Such a limitation originates from the design of these instruments,
which were not intended for fire detection. In order to overcome the
problem, a relatively recent experiment with a prototype small
satellite, the Bi-spectral InfraRed Detection (BIRD) experimental small
satellite (Zhukov et al., 2006), was entirely dedicated to fire detection
and characterisation from space. With its 380 m ground pixel size and
its spectral channels in MIR (3.4 to 4.2 μm) and TIR (8.5 to 9.3 μm) the
BIRD instrument successfully operated for more than 28 months, thus
allowing for the characterisation of fires which had previously been
not detectable with the above mentioned sensors. Besides such low
Earth orbiting (LEO) satellite sensors, fire detection has exploited
instruments onboard geostationary (GEO) platforms (Calle et al.,
2006; Cisbani et al., 2002; Costantini et al., 2008; Prins et al., 1998;
Roberts et al., 2005). Despite the very high revisit time frequency
(data acquired every 15 to 30 min) that could allow a detailed analysis
of daily fire life cycle, the main limitations to fire detection for these
type of sensors are the relatively low spatial resolution, ranging from 3
to 6 km, which results in a low signal magnification over the
background when compared to higher spatial resolution LEO imagers.
In addition, GEO platforms are not suitable for fire detection over mid
latitudes and polar regions due to the extreme observation viewing
angles. Hence, the geostationary satellites represent an improvement
over tropical regions but have difficulties monitoring fires in high
latitude regions. The various instruments flying on LEO platforms are
characterised by very different geographic coverage, in terms of both
overpass frequency and sampling time. The overpass local time for the
sensors which are relevant in this context is such that different
sensors investigate the same areas at different local times, so that the
information gathered from a single sensor should be used in synergy
with the others to fully characterise the fire daily cycle. In addition,
the overpass frequency significantly varies too, from a few hours (e.g.
TRMM), to one day (e.g. MODIS), to 5 days (e.g. ATSR for night time
measurements at equator).
The European Space Agency Along Track Scanning Radiometer
World Fire Atlas (ATSR-WFA) project provides a global fire monitoring service by using data acquired by the ATSR-2 and AATSR sensors
from the European Space Agency satellites (Arino & Plummer, 2001;
Arino et al., 2005, 2007a; Goloub & Arino, 2000; Grégoire et al., 2001;
Jenkins et al., 1997). The World Fire Atlas consists of a global collection
of “hot spots”, i.e. the Earth locations from which emitted radiances
exceed predefined thresholds, detected by analysing the measurements of the Along Track Scanning Radiometer (ATSR-2), onboard
ERS-2 platform from November 1995 to December 2002, and
extended onward by using the Advanced ATSR (AATSR), onboard
ENVISAT, since 1st January 2003 to present. The ATSR-WFA now
includes almost 14 years of consistent data thus representing a valid
support to studies focusing on the long term behaviour of climate
parameters (Kasischke et al., 2003; Le Page et al., 2007). The ATSR
radiance products used in this work have been produced by the latest
version of the level 0 to 1 processing chain (version 6.0.1) (Procter,
2009). They cover the three ATSR missions and have been available to
users since February 2009. The new level 1 products are characterised
by several radiometric and geolocation processing improvements and,
most relevant in the context of this work, several orbit gaps have been
filled in order to provide a consistent dataset spanning from 1991 to
present. The overall ATSR-WFA processing chain is aimed at detecting
hot spots in the thermal bands of the ATSR family instruments for
night-time scenarios. In particular, the MIR band centred at 3.7 μm
shows the highest sensitivity to surface temperature variations and
two simple fixed threshold algorithms have been developed to fully
exploit this signal. In this MIR band, anomalous night-time Brightness
Temperature (BT) hot spots are detected according to the following
criteria:
• ALGO1: BT saturated (i.e. BT ≥ 312 K)
• ALGO2: BT ≥ 308 K
In the following only ALGO1 products, i.e. counts of saturated
pixels, will be used for the trend analysis, as the ALGO2 products,
while increasing the number of detections, are characterised by small
but noticeable differences between the ERS-2 and ENVISAT WFA
products. Such discrepancies are related to a 30 minute sensing time
difference between the two satellites which causes the background
temperatures at 3.7 μm to rise above the 308 K level more often in the
case of ENVISAT than for ERS-2 due to a slight change in the
background temperature, as reported by Arino and Casadio (2008).
We also considered that it was preferable to have omissions rather
than commissions of fire for the purpose of trends analysis.
2. ATSR-WFA dataset coverage and self consistency
The monthly record generated by the ATSR WFA processing
provides the detection date and time, and the pixel's coordinates with
a ± 1 km geolocation accuracy for each hot spot. The ATSR revisit
frequency for night-time measurements is about once every 5 days at
equator, rapidly increasing with latitude. The monthly zonal mean of
ATSR (version 6.0.1) night-time number of overpasses for the same
ground location is shown in Fig. 1, where different colours indicate the
number of satellite overpasses per month. From Fig. 1 it can be
deduced that: a) the number of overpasses at each latitude is
consistent between the ATSR-2 and AATSR missions; b) a data gap
between January and June 1996 is present due to ATSR-2 operations
(the instrument was turned off during this period); c) two periods of
reduced coverage are found in early 2001 (January to April) and early
2002 when the number of overpasses in the tropical regions is slightly
reduced.
The continuous coverage extends from 70°N to 75°S, while higher
latitudes are only covered during local winters: this is due to our
choice to analyse top-of-atmosphere (TOA) radiances for which the
related solar zenith angle (‘sza’) is less than 10°. In the previous
version of the WFA products only data in the 70°S–70°N latitude range
were considered, independently of the season.
The zonal mean number of overpasses is a good indicator of the
data coverage. However it doesn't give any hint on how overpasses
are spatially distributed, whether uniformly or not. As an example,
Fig. 2 shows the global map of ATSR-2 overpasses for March 2001
(upper panel) and February 2002 (lower panel). Although the zonal
mean gives similar numbers for both months, it is evident that the
impact on fire counts is totally different. In fact, during March 2001
the total orbit coverage was 82% (i.e. 18% of the orbits were missing)
but only the American continent was not covered while the rest of the
world was regularly sampled. This implies that only the American
night-time fires are missing in the related monthly fire count. On the
other hand, the January 2002 overpass map (orbit coverage 72%)
shows that the missing orbits are spread worldwide and hence the
number and distribution of detected fires is significantly impacted.
One of the major achievements of the latest ATSR reprocessing is
the recovery of a large number of ATSR-2 orbits that were not fully
processed in the past. In fact the eclipsed portion of many ATSR-2
orbits passing over Western Africa and the Eastern Pacific was not
analysed in the period spanning from December 1995 to June 1999, so
that the Western US was only partially covered, which is relevant for
night-time fire detection. The ATSR-2 global monthly overpass
frequency relative to the earlier product version is shown in the
upper panel of Fig. 3 while the one relative to the new version is
shown in the lower panel. The data shown in Fig. 3 have been
obtained by averaging the overpass information relative to the entire
duration of the ATSR-2 mission (1995–2003).
Please cite this article as: Arino, O., et al., Global night-time fire season timing and fire count trends using the ATSR instrument series, Remote
Sensing of Environment (2011), doi:10.1016/j.rse.2011.05.025
O. Arino et al. / Remote Sensing of Environment xxx (2011) xxx–xxx
3
Fig. 1. Time evolution of the ATSR-2 and AATSR zonal mean number of overpasses per month (night-time only). (For interpretation of the references to colour in this figure legend,
the reader is referred to the web version of this article.)
These important data gaps were not highlighted by any ATSR user
until late 2007, when the reprocessing activity took place. It should be
noted that the most affected area is also relevant for SST retrievals
from ATSR night-time radiances and, even in this case, no ATSR user
lamented this enormous data gap (which is now filled). The presence
of an additional data gap was pointed out by Mota et al. (2005) who
reported a lack of ATSR-2 measurements above 60°N for a period
spanning from February to August 1997: this gap has been filled too,
as it can be clearly seen in Fig. 1, where the zonal data monthly
coverage is reported.
The ATSR-WFA historical and NRT Monthly data sets are available
as ASCII files at the European Space Agency (ESA) ATSR WFA website
(http://due.esrin.esa.int/wfa/), while the new products described
(and used) in this work will be made available to the public in the
future.
The results shown in this paper have been obtained with the
following strategy. First, the 14 years of the new WFA ALGO1 monthly
products were pre-processed in order to provide a classification of hot
spot pixels according to country/continent geographic information,
and to GlobCover land use classification (Arino et al., 2007b). We then
developed a tool for the selection/extraction of significant data
according to our choice of relevant parameters. Finally, the extracted
time series was analysed in order to retrieve statistical information
and trend parameters (plus the related uncertainties) for each
selected category.
In order to correctly estimate trends we decided to limit the period
of interest to the July 1996/June 2009 time window, i.e. thirteen full
years. During this period the ATSR-2 and AATSR operated without
major interruptions (with the exception of some short periods on
2001 and 2002), so that the related ATSR-WFA data set can be
considered consistent in terms of data coverage. The (A)ATSR(−2)
data coverage has been analysed in order to exclude possible
misinterpretations in trend estimation. Results on overpass frequency
and number of valid orbit data analysis showed negligible trends in
Earth coverage for both instruments and for the merged data set: the
periods of reduced coverage are placed just in the middle of the
analysed time series (and in seasons characterised by low fire activity)
resulting in a negligible impact on the statistical analyses (see Fig. 1).
The consistency of fire counts time series produced by the two
instruments has been verified by analysing the hot spot retrievals
during their overlap period, i.e. July 2002 to June 2003. The ALGO1
counts from AATSR and ATSR-2 were separately binned onto a 2° × 2°
global grid to cancel possible geolocation inaccuracy, then the 2D
fields were compared cell by cell. Fig. 4 shows the histogram of ATSR2 to AATSR percentage differences (ALGO1) for the overlap period
(thin line) together with the superimposed Gaussian fit (thick line).
The bias estimated via the Gaussian fit is very small (− 0.1%), showing
that the data from the two sensors can be merged into a fully
consistent data set. The fact that AATSR counts appear to be slightly
larger than those of ATSR-2 could be due to the different sampling
time of the two sensors, as the ENVISAT ascending crossing time
precedes that of ERS-2 by 30 min. The relatively large estimated
Gaussian standard deviation (≈18%) is due to larger discrepancies at
low absolute counts (the ATSR-2 and AATSR data sets have standard
deviations as large as 153% and 161% respectively) and therefore the
effect can be considered negligible.
As to the possible impact of trends in cloud cover, the controversial
results presented in peer reviewed publications (Rossow & Schiffer,
1999; Wylie et al., 2005) do not allow a rigorous treatment and we
therefore assumed no significant cloud cover variation over the last
14 years.
3. ATSR-WFA inter-comparison with independent data sets
The ATSR-WFA validation exercises carried out during the past
years (Arino & Plummer, 2001; Arino et al., 2005) have demonstrated
the reliability of the product. In addition to these investigations we
show here a comparison between ATSR and TRMM-VIRS fire counts
for the 1998–2009 time period. We used VIRS data because the related
fire products should be less affected by the satellite overpass time, due
to its high revisit frequency, thus providing a detailed picture of the
Please cite this article as: Arino, O., et al., Global night-time fire season timing and fire count trends using the ATSR instrument series, Remote
Sensing of Environment (2011), doi:10.1016/j.rse.2011.05.025
4
O. Arino et al. / Remote Sensing of Environment xxx (2011) xxx–xxx
Fig. 2. Global maps of monthly ATSR-2 overpasses. Upper panel: March 2001; lower panel February 2002.
fire activity in the tropics, the TRMM coverage being limited to the
tropical regions.
In order to match the TRMM latitudinal coverage the ATSR fire
counts were limited to latitudes between 40°S and 40°N. The TRMM
3.7 and 11 μm brightness temperatures (TB3 and TB4 respectively)
are used in the traditional threshold TRMM fire detection algorithm: a
hot spot is identified when BT3 saturates or BT3–BT4 N 15 K (Ji &
Stocker, 2006). These TRMM-VIRS monthly files are freely available to
the public (http://pps.gsfc.nasa.gov/Fire/fireintro.html), along with
dedicated software tools for the data ingestion and manipulation.
These data are monthly collections of retrieved fire counts during
daytime and night-time conditions, and it is possible to discriminate
between the two by selecting the appropriate solar zenith angle value,
the night-time fire counts being associated to solar zenith angle larger
than 90°. The ATSR and night-time TRMM-VIRS time series of monthly
fire counts are shown in Fig. 5, where the thick line represents the
TRMM-VIRS data (ordinate axis on the left), and the thin line
represents the ATSR fire counts (ordinate axis on the right).
The vertical dashed line corresponds to the switching to AATSR
data and the grey area indicates the January–June 1996 ATSR-2 data
gap. The correlation between the two data sets is evident, except for
two very limited periods: a) during the last months of 2001 and the
first months of 2002, when ATSR underestimates fires with respect to
TRMM-VIRS; b) towards the end of 2002, when ATSR overestimates
fire with respect to TRMM. Both discrepancies can be explained by
instrumental issues: in case a) ATSR-2 was often turned off from
November 2001 to March 2002 due to instrument and platform
anomalies; in case b) TRMM experienced a hardware problem related
to one of its solar panels and the VIRS instrument was turned off from
September 6th through October 15th 2002 (Giglio & Kendall, 2003).
The ATSR vs. TRMM-VIRS night-time monthly fire counts scatter
plot is shown in Fig. 6, where the filled circles indicate months of
Please cite this article as: Arino, O., et al., Global night-time fire season timing and fire count trends using the ATSR instrument series, Remote
Sensing of Environment (2011), doi:10.1016/j.rse.2011.05.025
O. Arino et al. / Remote Sensing of Environment xxx (2011) xxx–xxx
5
Fig. 3. ATSR-2 average monthly overpass frequency maps. Upper panel: old TOA radiance product version; lower plot: latest product version (v6.0.1). Note the large data gap (dark
blue, upper panel) in the Eastern pacific area. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
reduced operations (outliers). The estimated correlation coefficient is
0.77 when using all data including outliers, and increases to 0.84
when excluding the outliers from the statistical analysis. In the latter
case the ATSR to TRMM-VIRS ratio is about 3 (2.94), and it is due to the
combination of two competing effects: the different pixel resolutions
(ATSR = 1 km 2 and TRMM-VIRS = 4 km 2) and the different overpass
frequency (much higher for TRMM-VIRS with respect to ATSR). In
addition, the lower fire detection in the Ji and Stocker VIRS data has
been noted by Generoso et al. (2003) and is explained by Giglio and
Kendall (2004) as being due to omission errors at night-time. Despite
this, the ATSR-VIRS (Ji and Stocker) comparison indicates similar
sampling of the temporal dependence of fire from the two data sets
when averaged globally. Unfortunately the most likely improved
Giglio et al. VIRS data set did not offer day/night discrimination (Giglio
& Kendall, 2004) and, thus, it was not possible to compare them with
ATSR night-time fires. The day/night discrimination the Giglio TRMM-
VIRS data has been envisaged by Giglio (private communication,
2011) and will be investigated in the future.
The MODIS fire products used for our inter-comparison exercise
are the ‘Global Monthly Fire Location Products (MCD14ML) Collection
5’ freely distributed by NASA (Giglio, 2010). These data cover a time
period spanning from 2001 to present, and the night-time fire counts
have been extracted and analysed. The adopted selection criteria for
MODIS fire counts consists in retaining the number of spots for which
the provided confidence level is ≥80%, i.e. the “high confidence level”
indicated in the MODIS Fire Product User's Guide. In addition, the
MODIS-Aqua and MODIS-Terra fire counts have been analysed
separately to account for the different overpass local time of the
two satellites.
The MODIS vs. WFA monthly night-time fire counts scatter plot is
shown in Fig. 7, where the MODIS-Aqua data are shown in blue and
the MODIS-Terra in red.
Please cite this article as: Arino, O., et al., Global night-time fire season timing and fire count trends using the ATSR instrument series, Remote
Sensing of Environment (2011), doi:10.1016/j.rse.2011.05.025
6
O. Arino et al. / Remote Sensing of Environment xxx (2011) xxx–xxx
Fig. 4. ATSR-2 to AATSR ALGO1 percentage difference histogram (July 2002–June 2003).
As can be noticed, the ATSR-WFA monthly fire counts (on global
scale, i.e. including high latitudes) are only 40% smaller than the
related MODIS-Terra fire products, while they are 50% larger than the
corresponding MODIS-Aqua. The intercept value for fire counts is
smaller than 200 for MODIS Terra and exceeding 800 for MODIS Aqua.
The uncertainty on the retrieved gain for MODIS-Terra is 3.8% and
about 5.8% for MODIS-Aqua, while the uncertainty on the intercepts is
around 200% and 50% respectively. The correlation coefficients are
very high for both MODIS data sets (0.87 for MODIS-aqua and 0.95 for
MODIS-Terra). When removing the restriction on MODIS confidence
levels (i.e. all fire products are used) the angular coefficients increase
to 1.3 for MODIS-Aqua and 2.9 for MODIS-Terra while the related
correlation coefficients remain unchanged.
This is a further confirmation of the validity of the ATSR-WFA
products, considering also the very different revisit time of ATSR and
MODIS.
The above described results show that ATSR is able to detect the
general behaviour of active fire with a reasonable degree of
confidence. If the retrievals of fire activities from VIRS and ATSR
instruments, being based on very similar principles, may be affected
by similar problems (e.g. omission errors), the comparison with
Fig. 6. ATSR vs. TRMM-VIRS monthly night-time fire counts (by Ji and Stocker). Filled
circles indicate periods of reduced instrumental operation (outliers). Dashed line shows
the fitted linear fit to the data excluding outliers. The linear fit slope value is reported
along with the correlation coefficients estimated using all data and excluding outliers.
MODIS “high confidence” products indicates that the information
content of the ATSR-WFA data set is comparable to that of MODIS
night-time products, not only for tropical regions but also at global
scale.
In addition to the active fire products comparison exercise, ATSRWFA fire counts have been compared to the corresponding estimates
of the burnt areas (BAE) extracted from the GlobCarbon dataset
(Plummer et al., 2006). The retrieval of BAE products is based on the
analysis of daytime radiance in the VIS-NIR-SWIR as measured from
different sensors (ATSR, MERIS, and VEGETATION), and the burnt
areas are individuated for cloud free scenarios for years 1998 to 2007.
The BAE values, expressed in km 2, are not affected by possible biases
introduced by changes in cloudiness patterns, thus providing
information on the fire activity which is totally independent from
ATSR hot spot retrieval.
Two examples of BAE and ATSR fire count time series are shown in
Fig. 8, in which the upper panel refers to Angola and the lower panel
to Brazil. The estimated correlation coefficients for the two considered
cases are 0.83 for Angola and 0.97 for Brazil. These results confirm the
validity of ATSR fire products for the estimates of fire timing and
trends.
Fig. 5. Monthly night-time ATSR and TRMM-VIRS fire counts (spots) vs. time. Left ordinate axis refers to TRMM-VIRS, right ordinate axis refers to ATSR. The vertical dashed line
indicates the date of passage from ATSR-2 to AATSR and the grey area indicates the ATSR-2 data gap of 1996.
Please cite this article as: Arino, O., et al., Global night-time fire season timing and fire count trends using the ATSR instrument series, Remote
Sensing of Environment (2011), doi:10.1016/j.rse.2011.05.025
O. Arino et al. / Remote Sensing of Environment xxx (2011) xxx–xxx
Fig. 7. MODIS-Aqua (blue) and MODIS-Terra (red) vs. ATSR-WFA night-time monthly
fire counts (2001 to present). (For interpretation of the references to colour in this
figure legend, the reader is referred to the web version of this article.)
7
As to high latitudes, fixed threshold algorithms are certainly not
the most appropriate for fire detection. The seasonal variation in the
background surface temperatures results in a seasonal variation in the
fire detection ability of the algorithm which is only partially
compensated by the increase in the overpass frequency with latitude.
High latitude fires occurring in winter are therefore less likely to be
detected than those occurring in summer due to the lower
background temperature. This effect is more important for the
detection of small fires, as the background signal constitutes a
significant portion of the emitted radiance. It can be assumed that at
high latitudes the ATSR hot spots refer to large sized fires, and hence,
related trends correspond to this type of fires only.
A simple simulation of the effect of different background temperatures on fire detection using the ALGO1 approach has been carried out
by assuming an effective flame temperature Tf = 1000 K, a fire fraction
‘Ff’ (i.e. the fraction of the 1 km2 ATSR pixel effectively burning) varying
from 10− 5 to 10− 3 (10 to 1000 m2), and a background temperature ‘Tb’
ranging from 273.15 to 293.15 K. The temperatures are converted to
radiances at 3.7 μm (the ATSR thermal band adopted for ALGO1) and
combined to estimate the total radiance emitted by the burning pixel
using the Planck function (emissivity = 1, atmospheric transmission = 1, nadir viewing, no smouldering fraction). The resulting
3.7 μm brightness temperatures ‘TB3.7’ seen by the ATSR instrument
Fig. 8. Time series of BAE (thick line) and ATSR spots (thin line) for years 1998–2007. Upper panel refers to Angola, lower panel to Brazil.
Please cite this article as: Arino, O., et al., Global night-time fire season timing and fire count trends using the ATSR instrument series, Remote
Sensing of Environment (2011), doi:10.1016/j.rse.2011.05.025
8
O. Arino et al. / Remote Sensing of Environment xxx (2011) xxx–xxx
Table 1
Estimated brightness temperatures at 3.7 μm for different fire size and temperature.
Tb ↓ Ff →
10− 5
10− 4
5 10− 4
273.15
278.15
283.15
288.15
293.15
278.557
282.603
286.827
291.197
295.686
303.674
305.281
307.155
309.305
311.732
340.866
341.377
342.002
342.755
343.655
under these extremely favourable conditions are listed in Table 1, where
the grey cells indicate the cases in which TB3.7 exceeds the 312 K
threshold.
As expected, active fires with effective flame temperature of
1000 K can only be detected in cases where the fire fraction is larger
than 10 − 4. On the other hand, for large fires the contribution of the
background is negligible for the 312 K saturation limit of the 3.7 μm
detection band of ATSR.
In this simulation the contribution of the “just burnt” fraction has
not been taken into account, and the above results are to be
considered only as qualitative indications of the ATSR active fire
detection limits.
(white solid line) is reported in Fig. 9, together with the associated
standard deviations (dashed lines).
It is apparent that:
a) The night-time global fire season generally has its peak between
August and September, with no statistically significant trend.
b) The maximum number of fires is found for September 1997.
c) The 2003 August–September fire counts are smaller than for the
same period of other years.
d) The global fire season duration (estimated from gaussian standard
deviation) does not vary significantly over the 1997–2008 time
period.
The two anomalous seasons 1998 and 2003 have been explained
with a strong correlation between fire activity and El Niño
(McPhaden, 2004; van der Werf et al., 2004).
The time series of the total number of fire counts per year is shown
in Fig. 10. To maximise the number of full years, fire counts have been
grouped from July to June rather than from January to December: in
this way the 1996 and 2009 data could be used and a total of 13
complete years was obtained. The largest numbers of fire counts are
found in the 1997–1998 and 2002–2003 time slots, and no significant
trend is observable for the considered period. Both El Niño events are
clearly noticeable on the graph.
4. Global fire analysis
5. Local fire analysis
As a first step, the ATSR global monthly counts were plotted
against month-of-the-year and year in order to detect possible
changes in the seasonality of the active fires during 1996–2009.
Fig. 9 shows the monthly/yearly ATSR WFA distribution: grey cells
indicate missing data, and colours indicate the number of detections
(blue to red in increasing order). For each yearly time series a
Gaussian curve is fitted to five monthly spot numbers around the
maximum, and the curve connecting the so derived expected values
Global fire counts must be aggregated following specific criteria
before any time series analysis can be carried out. This aggregation is
intended to cancel possible artefacts of acquisition (localisation/frequency) and to eliminate areas where sampling is insufficient. In this
work we adopted two aggregation types: a) displacement on a 2D
global grid, and b) grouping using state/country political borders.
For the aggregation type a), the monthly ATSR fire products have
been sampled into a 2° × 2° grid, and single cell time series have been
Fig. 9. Global ATSR-WFA hot spot monthly distribution (1995–2009). Solid line: peak fire activity; dashed lines: estimated fire season. Colour scale indicates the total number of fires
per month. Grey rectangles indicate missing data. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Please cite this article as: Arino, O., et al., Global night-time fire season timing and fire count trends using the ATSR instrument series, Remote
Sensing of Environment (2011), doi:10.1016/j.rse.2011.05.025
O. Arino et al. / Remote Sensing of Environment xxx (2011) xxx–xxx
9
Fig. 10. ATSR-WFA ALGO1 global yearly fire counts (hot spots) from July to June.
individually considered for statistical analysis. The grid size has been
chosen after several attempts (0.5°, 1°, 2°, and 5°), the 2° mesh
resulting to be the best compromise between number of fire counts
per grid cell (increasing with size) and land surface homogeneity
within the cell. The seasonal components have been removed by
applying phase averaging, i.e. removing the average number of fire
counts in each calendar month. The resulting residuals are then
analysed to detect trends. Alternative methods to remove the
seasonality effects have been attempted (e.g. spectral methods), but
results were not satisfactory due the spurious annual components
originating from stochastic events and deviations from purely
sinusoidal behaviour.
The residuals have been fitted to a straight line and the resulting
slope has been normalised to the average number of fire counts in the
series, providing the trend values in units of [%/year]. In order to fully
assess the statistical significance of trends, the Mann–Kendall test has
been adopted (hereafter indicated with M–K) prescribing a 95%
confidence level (Hirsch & Slack, 1984; Yue & Pilon, 2004). The trend
uncertainty is also estimated, so as to provide a sort of S/N ratio which,
along with the significance test, allows a robust assessment of the
trend relevance. Trends have been classified as follows:
• Statistically significant: S/N ≥ 1, M–K passed
• Potentially significant (positive or negative): S/N ≥1, M–K not
passed
• Insignificant: S/N b 1, M–K not passed
For all cases in which S/N b 1 the M–K test always gave negative
results (i.e. non significant trends). The M–K test results are given
here in the form of global maps (see Fig. 11), in which the presence of
statistically significant trends is indicated by area colour filling. The
colour code for trends is as follows: red for statistically significant and
positive; brown for potentially positive; light green for potentially
negative; green for statistically significant and negative. Light grey
areas are characterised by insignificant trends, while dark grey areas
indicate absence of useful data.
Fig. 11. 1996–2009 ATSR-WFA trends on global scale (2° × 2° cell). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this
article.)
Please cite this article as: Arino, O., et al., Global night-time fire season timing and fire count trends using the ATSR instrument series, Remote
Sensing of Environment (2011), doi:10.1016/j.rse.2011.05.025
10
O. Arino et al. / Remote Sensing of Environment xxx (2011) xxx–xxx
Fig. 12. ATSR-WFA 1996–2009 trends at district (or state) level. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
The global distribution of positive and negative trends is difficult to
interpret. The global net contribution is insignificant (see Fig. 10), and
possible changes in local fire activity can be related to very complex
local factors, from climatic to political (Krawchuk et al., 2009).
However, positive trends are found in Western US, South-western
Russia and Southern China. Similar results have been reported for the
US (Westerling et al., 2006), and by Achard et al. (2008) for Russia,
while no independent information is available for China.
Table 2
Trends at “Continent” aggregation level.
Name
N%
MK
%/y
Asia
Africa
South America
Australia
North America
Europe
Oceania
29.9
23.4
21.1
11.0
10.9
3.1
0.4
0
−1
0
0
0
0
0
− 1.58
− 1.91
1.19
− 0.815
1.62
1.19
1.74
Table 3
Trends at “Region” aggregation level.
Name
N%
MK
%/y
South America
Asiatic Russia
Australia + NZL
Northern America
Middle Africa
Eastern Africa
South Eastern Asia
Northern Africa
Western Asia
Central Asia
Western Africa
Southern Asia
Eastern Asia
Central America
Southern Africa
European Russia
Southern Europe
Eastern Europe
Polynesia
Western Europe
Caribbean
Melanesia
Northern Europe
21.1
12.0
11.0
8.4
7.1
6.4
6.0
5.2
3.4
3.0
3.0
2.7
2.5
2.2
1.7
1.3
0.8
0.6
0.3
0.3
0.3
0.2
0.1
0
0
0
0
−1
0
0
−1
−1
0
−1
1
0
0
−1
0
0
0
1
−1
0
−1
−1
1.19
0.60
− 0.82
3.80
− 2.13
− 0.64
− 8.88
− 1.42
− 1.49
1.03
− 3.63
1.99
− 0.27
− 6.59
− 4.22
2.36
1.45
3.82
3.21
− 4.18
0.36
− 18.51
− 10.13
The data aggregation scheme of type b) consists of the merging of the
ATSR fire count products based on political state and country selection
criteria. This type of aggregation is not physically relevant, but the
nature of the problem is not only physical. Regulations on land use and
on burnt forest recovery may vary significantly from state to state or
from country to country, and fire activity may vary accordingly. Four
main aggregation classes have been selected in this work: “Continent”
(Africa, Asia, Australia, Oceania, Europe, South America and North
America); “Region” (25 vast sub continental regions); “Country” (193
political countries); and “Administrative districts” (2024 areas). The
classification database adopted in this work is that produced by ESRIARCINFO (version 9.2).
For each selected area the ATSR monthly count time series has been
extracted and analysed. The way data are grouped impacts the results of
the statistical analysis. In fire count series aggregated over wide areas
(e.g. continents or continental regions), the number of fire counts is
large, but fire regimes of different amplitude, phase and trend are
merged into a single signal, which is therefore difficult to characterise.
On the other hand, when very small areas are selected the number of fire
counts per month may be extremely low, thus amplifying the impact of
possible outliers on the statistical distribution of data. In many cases fire
Table 4
Negative trends at “Country” aggregation level (10.45% of total spots).
Name
N%
MK
%/y
Zaire
Nigeria
Algeria
Niger
Mongolia
Botswana
Libya
Cameroon
Uganda
Kuwait
Malaysia
Syria
Ghana
Chile
Qatar
Papua New Guinea
Kenya
Germany
Egypt
France
2.8
1.2
1.1
0.7
0.6
0.6
0.5
0.4
0.4
0.4
0.3
0.2
0.2
0.2
0.2
0.2
0.1
0.1
0.1
0.1
−1
−1
−1
−1
−1
−1
−1
−1
−1
−1
−1
−1
−1
−1
−1
−1
−1
−1
−1
−1
− 2.47
− 7.05
− 4.56
− 7.31
− 7.69
− 9.09
− 6.30
− 5.36
− 5.31
− 3.76
− 15.24
− 11.46
− 3.66
− 6.12
− 4.19
− 21.10
− 14.50
− 4.67
− 10.17
− 6.26
Please cite this article as: Arino, O., et al., Global night-time fire season timing and fire count trends using the ATSR instrument series, Remote
Sensing of Environment (2011), doi:10.1016/j.rse.2011.05.025
O. Arino et al. / Remote Sensing of Environment xxx (2011) xxx–xxx
Table 5
Positive trends at “Country” aggregation level (10.92% of total spots).
Name
N%
MK
%/y
United States
Argentina
Iran
Paraguay
India
Uzbekistan
Congo
Saudi Arabia
Cambodia
5.0
1.6
1.6
1.1
0.8
0.3
0.2
0.2
0.1
1
1
1
1
1
1
1
1
1
7.81
2.11
1.13
4.52
4.11
5.30
2.94
4.34
3.24
regimes are driven by weather condition and agricultural practices,
which can differ in their timing depending on the terrain or cultivation
type. This implies that small area characteristics can be reduced to few
well defined land types and seasonal forcing parameters, leading to a
fire count signal which can be decomposed into a very limited number
of easily separable components. In practice, statistically significant
trends are more likely to be found for small areas which are frequently
affected by wild fires, whereas large areas show no statistically
significant trends, with the exception of Africa at continental scale,
and USA at country scale (including Alaska).
ATSR fire count trends for small scale areas, i.e. at district level, are
shown in Fig. 12, where the colour codes are the same as for Fig. 11. As
it can be easily seen in the plot, statistically significant trends are
11
found only for few districts (22 positive, 38 negative) out of more than
2000 analysed areas.
For trends at the country aggregation level the increase of the
ensemble populations leads to very different results. The United
States trend is positive and statistically significant, even if at state
level no statistically significant trends can be found in the USA (only
potentially significant positive trends). This is probably due to the
homogeneity of the fire regimes in that country, increasing the
probability of passing the M–K tests when all relative data are merged
into a single time series. Russia, extending from Urals to the Bering
Sea, shows no detectable trend, while statistically significant trends
are found at smaller scales: the signs of these trends are opposite and
their contributions are elided in the data combination. Only 9
countries show a significant positive trend, and 20 a significant
negative trend (out of 193 analysed countries).
When enlarging the areas of interest to sub-continental or “Region”
level, the fire signal becomes more “noisy”. Africa (with the exception of
Eastern Africa), Western Asia, Northern and Western Europe, and
Melanesia show significant negative trends. North America, resulting
from the combination of USA and Canada, only shows potentially
significant positive trend, while statistically significant positive trends
are found for Southern Asia and Polynesia. Finally, at continental level,
Africa shows a statistically significant negative trend, Asia and Australia
show potentially significant negative trends.
Trend values at different aggregation levels are given in Table 2
(“Continent”), Table 3 (“Region”), Table 4 (“Country”, negative trends
Fig. 13. Africa (blue) and North America (red) monthly residuals time series. Upper panel: all data; lower panel: residuals exceeding three standard deviations excluded. Dashed
lines indicate linear fits to the time series. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Please cite this article as: Arino, O., et al., Global night-time fire season timing and fire count trends using the ATSR instrument series, Remote
Sensing of Environment (2011), doi:10.1016/j.rse.2011.05.025
12
O. Arino et al. / Remote Sensing of Environment xxx (2011) xxx–xxx
only) and Table 5 (“Country”, positive trends only). In each table, the
first column reports the area name, the second column the relative
number of fire counts (i.e. “N%” = 100xNa/Ntot, where Na is the total
number of fire counts detected within the specified area, and Ntot is
the total number of fire counts globally), the related Mann–Kendall
test results are given in the third column (“MK” = −1,0,1 for negative,
absent and positive trends respectively), while trend values are
reported in the fourth column (“%/year”). In each table areas are listed
in “N%” decreasing order.
It can be seen that statistically significant positive and negative
trends at country level account for approximately the same
percentage of global fire counts. Results at “Administrative districts”
are shown in Fig. 12.
Two examples of ATSR count residuals time series are shown in the
upper panel of Fig. 13 for Africa (blue) and North America (red) at the
continental aggregation level. The coloured dashed lines are a linear
fit to the residuals time series. As mentioned above, the African fire
count trend is negative and statistically significant, while the North
American trend is positive but not statistically significant, with three
periods of enhanced fire activity during 1998, 2004 and 2005. The
African data during the short period of reduced coverage of ATSR-2
can be clearly seen as deficit of about 3000 counts in early 2002. A test
on the impact of these “outliers” on trend results was carried out by
excluding all data exceeding three standard deviations and by
repeating the trend evaluation. Results from this exercise are shown
in the lower panel of Fig. 13.
The exclusions of outliers impacted on the two time series
statistics in very different ways. The trend evaluated for Africa
remained essentially unchanged and fully statistically significant,
while the North America trend value resulted drastically reduced
(statistically not significant), as the three periods of enhanced fire
activity were excluded. This is a further indication that the reduced
ATSR-2 coverage for the early 2002 months has very little impact on
trend estimates, the “data gaps” being located in the middle of the
time series.
6. Conclusions
The analysis of new ATSR-WFA night-time fire count time series
has been performed at different data aggregation scales, from local
(2° × 2° cell) to global. No trend is identifiable at global scale, while at
smaller aggregation scales statistically significant positive and
negative fire trends have been found over all continents. The global
fire season appears not to change its length and timing during the
period under investigation. The relationships between fire distribution and trends in relation to other climatic variables have not been
investigated in this work, and will be the subject of future studies.
Although the length of the ATSR-WFA fire counts time series exceeds
14 years, results suggest that to unambiguously identify fire trends a
much longer time series is needed. To overcome this problem, the
exploitation of the ATSR-1 mission (1991–1996) is being attempted
by analysing the short-wave-infrared (SWIR) radiances. This would
add 4 years of useful data to the ATSR-WFA series analysed in this
work. Finally, the continuity of similar measurements from the “Sea
and Land Surface Temperature” radiometer (SLST) on board the
operational Sentinel-3 satellite series is now a reality in Europe.
Sentinel-3 SLST resolution, spectral and calibration characteristics are
similar to those of ATSR extending its capacity with a better sampling
due to a wider swath. In order to remedy sensor saturation during
daytime, the SLST sensor will have two channels dedicated to fire
detection (Mavrocordatos et al., 2007). Sentinel-3 launch is planned
for 2013. Future efforts will be made to ensure consistency with the
night-time only ATSR WFA and MODIS fire count time series, thus
providing a consistent global fire time series spanning perhaps
30 years.
Acknowledgments
The authors would like to acknowledge N. Houghton for promptly
providing ATSR TOA products. The comments and suggestions from
the referees have been very constructive in completing the analysis of
the data. This work has been performed in the framework of the ESA
ATSR-WFA CAT-1 project 2339.
References
Achard, F., Eva, H. D., Mollicone, D., & Beuchle, R. (2008). The effect of climate anomalies
and human ignition factor on wildfires in Russian boreal forests. Philosophical
Transactions of the Royal Society of London. Series B, 363, 2329–2337, doi:
10.1098/rstb.2007.2203
Andreae, M. O. (1991). Biomass burning: Its history, use and distribution and its impact
on the environmental quality and global climate. In J. S. Levine (Ed.), Global biomass
burning (pp. 3–21). Cambridge, Mass: MIT Press.
Arino, O., & Casadio, S. (2008). ATSR World Fire Atlas: Can we find a trend in the longest
global fire distribution series? Proceedings of the 2nd MERIS-AATSR workshop,
September 22–26, ESRIN, ESA Publication.
Arino, O., Gross, D., Ranera, F., Bourg, L., Leroy, M., Bicheron, P., et al. (2007). GlobCover:
ESA service for global land cover from MERIS. Geoscience and Remote Sensing
Symposium, IGARSS 2007, IEEE International (pp. 2412–2415).
Arino, O., & Melinotte, J. (1998). The 1993 Africa fire map. International Journal of
Remote Sensing, 19(11), 2019–2023 Cover page.
Arino, O., & Plummer, S. (2001). “Along Track Scanning Radiometer World Fire Atlas:
Validation of the 1997–98 active fire product”, IGBP-DIS working paper 23. Sweden:
IGBP.
Arino, O., Plummer, S., & Casadio, S. (2007). Fire disturbance: The twelve years time
series of the ATSR World Fire Atlas. Proceedings of ENVISAT symposium 2007, ESA
Publication, SP-636.
Arino, O., Plummer, S., & Defrenne, D. (2005). Fire Disturbance: The ten years time
series of the ATSR World Fire Atlas. Proceedings of the MERIS-AATSR Symposium, ESA
publication SP-597.
Bowman, D. M. J. S., Balch, J. K., Artaxo, P., Bond, W. J., Carlson, J. M., Cochrane, M. A.,
et al. (2009). Fire in the earth system. Science, 324, 481–484.
Calle, A., Casanova, J. L., & Romo, A. (2006). Fire detection and monitoring using MSG
Spinning Enhanced Visible and Infrared Imager (SEVIRI) data. Journal of Geophysical
Research, 111, G04S06, doi:10.1029/2005JG000116
Cisbani, E., Bartoloni, A., Marchese, M., Elisei, G., & Salvati, A. (2002). Early fire detection
system based on multi-temporal images of geostationary and polar satellites.
Proceedings of IGARSS 2002, Toronto.
Clement, R. M., & Horn, S. P. (2001). Pre-Columbian land-use history in Costa Rica: A
3000-year record of forest clearance, agriculture and fires from Laguna Zoncho. The
Holocene, 11(4), 419–426, doi:10.1191/095968301678302850
Costantini, M., Zavagli, M., Cisbani, E., & Greco, B. (2008). A technique for automatic fire
detection from geostationary optical sensors and its validation on MSG SEVIRI data.
Proceedings 2nd MERIS-AATSR Symposium, September 22–26, ESA publication.
Delcourt, P. A., Delcourt, H. R., Ison, C. R., Sharp, W. E., & Gremillion, K. J. (1998).
Prehistoric human use of fire, the Eastern agricultural complex, and Appalachian oakchestnut forests: Paleoecology of Cliff Palace Pond, Kentucky. American Antiquity, Vol.
63, No. 2. (pp. 263–278).
Dozier, J. (1981). A method for satellite identification of surface temperature fields for
subpixel resolution. Remote Sensing of Environment, 11, 221–229.
Generoso, S., Breon, F. M., Balkanski, Y., Boucher, O., & Schulz, M. (2003). Improving the
seasonal cycle and interannual variations of biomass burning aerosol sources.
Atmospheric Chemistry and Physics, 3, 1211–1222.
Giglio, L. (2010). MODIS Collection 5 Active Fire Product User's Guide. Version 2.4.
http://modis-fire.umd.edu/AF_usermanual.html
Giglio, L., Descloitres, L., Justice, C. O., & Kaufman, Y. J. (2003). An enhanced contextual
fire detection algorithm for MODIS. Remote Sensing of Environment, 87, 273–282.
Giglio, L., & Kendall, J. (2003). TRMM VIRS monthly fire product user's guide, Rev. B.
http://daac.gsfc.nasa.gov/precipitation/trmmVirsFire.shtml
Giglio, L., & Kendall, J. D. (2004). Commentary on “Improving the seasonal cycle and
interannual variations of biomass burning aerosol sources” by Generoso et al..
Atmospheric Chemistry and Physics, 4, 585–587.
Giglio, L., Kendall, J. D., & Mack, R. (2003). A multi-year fire data set for the tropics
derived from the TRMM VIRS. International Journal of Remote Sensing, 24,
4505–4525.
Goldammer, J. G. (1988). Rural land use and wildland fires in the tropics. Agroforestry
Systems, 6, 235–252.
Goloub, P., & Arino, O. (2000). Verification of the consistency of POLDER aerosol index
over land with ATSR-2/ERS-2 fire product. Geophysical Research Letters, 27(6),
899–902.
Grégoire, J. M., Cahoon, D. R., Stroppiana, D., Li, Z., Pinnock, S., Eva, H., et al. (2001).
Forest fire monitoring and mapping for GOFC: Current products and information
networks based on NOAA-AVHRR, ERS-ATSR, and SPOT-VGT. Global and regional
vegetation fire monitoring from space. In F. Ahern, J. G. Goldammer, & C. Justice
(Eds.), Planning and coordinated international effort (pp. 105–124)..
Harden, J. W., Trumbore, S. E., Stoks, B. J., Hirsch, A., Gower, S. T., O'Neil, K. P., et al.
(2000). The role of fire in the boreal carbon budget. Global Change Biology, 6
(Suppl. 1), 174–184.
Please cite this article as: Arino, O., et al., Global night-time fire season timing and fire count trends using the ATSR instrument series, Remote
Sensing of Environment (2011), doi:10.1016/j.rse.2011.05.025
O. Arino et al. / Remote Sensing of Environment xxx (2011) xxx–xxx
Hirsch, R. M., & Slack, J. R. (1984). A nonparametric trend test for seasonal data with
serial dependence. Water Resources Research, 20(6), 727–732.
Hoffmann, W. A., Schroeder, W., & Jackson, R. B. (2003). Regional feedbacks among fire,
climate, and tropical deforestation. Journal of Geophysical Research, 108, 1–11, doi:
10.1029/2003JD003494 No. D23, 4721.
Jenkins, G., Mohr, K., Morris, V., & Arino, O. (1997). The role of convective processes over
the Zaire-Congo Basin to the Southern Hemispheric ozone maximum. Journal of
Geophysical Research, 102(D15), 18963–18980.
Ji, J., & Stocker, E. (2006). TRMM fire algorithm, product and applications. Earth science
satellite remote sensing. Berlin Heidelberg: Springer, doi:10.1007/978-3-54037293-6
Kasischke, E. S., Christensen, N. L., Jr., & Stocks, B. J. (1995). Fire, global warming, and the
carbon balance of boreal forests. Ecological Applications, 5(2), 437–451.
Kasischke, E. S., Hewson, J. H., Stocks, B., van der Werf, G., & Randerson, J. (2003). The
use of ATSR active fire counts for estimating relative patterns of biomass burning —
A study from the boreal forest region. Geophysical Research Letters, 30, doi:
10.1029/2003GL017859 Nn. 18, 1969, ASC-10.
Krawchuk, M. A., Moritz, M. A., Parisien, M. -A., Van Dorn, J., & Hayhoe, K. (2009). Global
pyrogeography: The current and future distribution of wildfire. PloS One, 4(4),
1–12, doi:10.1371/journal.pone.0005102 e5102.
Le Page, Y., Pereira, J. M. C., Trigo, R., da Camara, C., Oom, D., & Mota, B. (2007).
Global fire activity patterns (1996–2006) and climatic influence: an analysis
using the World Fire Atlas. Atmospheric Chemistry and Physics Discussions, 7,
17299–17338.
Marland, G., Boden, T. A., & Andres, R. J. (2008). Global, regional, and national fossil fuel
CO2 emissions. Trends: A compendium of data on global change. U.S. Department of
Energy, Oak Ridge, Tenn., U.S.A: Carbon Dioxide Information Analysis Center, Oak
Ridge National Laboratory.
Martin, M. P., Ceccato, P., Flasse, S., & Downey, I. (1999). Fire detection and fire growth
monitoring using satellite data. In E. Chuvieco (Ed.), Remote sensing of large wildfires
in the European Mediterranean Basin (pp. 101–121). : Springer.
Matson, M., & Dozier, J. (1981). Identification of subresolution high temperature
sources using thermal IR sensor. Photogrammetric Engineering and Remote Sensing,
47(9), 1311–1318.
Mavrocordatos, C., Berruti, B., Aguirre, M., & Drinkwater, M. (2007). The Sentinel-3
mission and its topography element. Proceedings of Geoscience and Remote Sensing
Symposium, 2007, IGARSS 2007, IEEE International (pp. 3529–3532).
McPhaden, M. J. (2004). Evolution of the 2002/03 El Niño. Bulletin American
meteorological society (pp. 677–695). May 2004.
13
Mota, B. W., Pereira, J. M. C., Oom, D., Vasconcelos, M. J. P., & Schultz, M. (2005).
Screening the ESA ATSR-2 World Fire Atlas (1997–2002). Atmospheric Chemistry
and Physics Discussions, 5, 4641–4677.
Plummer, S., Arino, O., Simon, M. l, & Steffen, W. (2006). Establishing a earth observation
product service for the terrestrial carbon community: The GLOBCARBON initiative.
Mitigation and adaptation strategies for global change (MITI), Vol. 11. (pp. 97–111).
Prins, E. M., Feltz, J. M., Menzel, W. P., & Ward, D. E. (1998). An overview of GOES8 diurnal fire and smoke results for SCAR-B and 1995 fire season in South America.
Journal of Geophysical Research, 103(D24) Vol. 31, pp 821–31,835.
Prins, E. M., & Menzel, W. P. (1994). Trends in South American biomass burning
detected with the GOES VAS from 1983–1991. Journal of Geophysical Research, 99,
16719–16735.
Procter, S. (2009). 8th AATSR user bulletin, AEP.MEM.162, February 27 2009.
Roberts, G., Wooster, M. J., Perry, G. L. W., Drake, N., Rebelo, L. M., & Dipotso, F. (2005).
Retrieval of biomass combustion rates and totals from fire radiative power
observations: Application to southern Africa using geostationary SEVIRI imagery.
Journal of Geophysical Research, 110, D21111, doi:10.1029/2005JD006018
Rosenfeld, D. (1999). TRMM observed first direct evidence of smoke from forest fires
inhibiting rainfall. Geophysical Research Letters, 26, 3105–3108.
Rossow, W. B., & Schiffer, R. A. (1999). Advances in understanding clouds from the
ISCCP. Bulletin of the American Meteorological Society, 80, 2261–2287.
Spracklen, D. V., Mickley, L. J., Logan, J. A., Hudman, R. C., Yevich, R., Flannigan, M. D.,
et al. (2009). Impacts of climate change from 2000 to 2050 on wildfire activity and
carbonaceous aerosol concentrations in the western United States. Journal of
Geophysical Research, 114, D20301, doi:10.1029/2008JD010966
van der Werf, G. R., Randerson, J. T., Collatz, G. J., Giglio, L., Kasibhatla, P. S., Arellano, A.F.,
et al. (2004). Continental-scale partitioning of fire emissions during the 1997 to
2001 El Niño/La Niña period. Science, 203, 73–76.
Westerling, A. L., Hidalgo, H. G., Cayan, D. R., & Swetnam, T. W. (2006). Warming and
earlier spring increase western U.S. forest wildfire activity. Science, 313, 940–943.
Wylie, D., Jackson, D. L., Menzel, W. P., & Bates, J. J. (2005). Trends in global cloud cover
in two decades of HIRS observations. Journal of Climate, 18, 321–331.
Yue, S., & Pilon, P. (2004). A comparison of the power of the t test, Mann–Kendall and
bootstrap test for trend detection. Hydrological Sciences-Journal-des Sciences
Hydrographiques, 49(1), 21–37.
Zhukov, B., Lorenz, E., Oertel, D., Wooster, M., & Roberts, G. (2006). Spaceborne
detection and characterization of fires during the bi-spectral infrared detection
(BIRD) experimental small satellite mission (2001–2004). Remote Sensing of
Environment, 100, 29–51.
Please cite this article as: Arino, O., et al., Global night-time fire season timing and fire count trends using the ATSR instrument series, Remote
Sensing of Environment (2011), doi:10.1016/j.rse.2011.05.025