Download Testing the Sensitivity of a MODIS-Like Daytime Active Fire

Testing the Sensitivity of a MODIS-Like Daytime
Active Fire Detection Model in Alaska Using
NOAA/AVHRR Infrared Data
C.A. Seielstad, J.P. Ridderlng, S.R. Brown, L.P. Queen, and W.M. Hao
Abstract
A MODIS-like daytime active fire detection model was tested
in Alaskan biomes using NOAA-AVHRR infrared data, and its
performance was assessed across a range of channel 3 (3.8
pm) brightness temperature and contextual standard deviation thresholds. Absolute thresholding of channel 3 (T3] and
the channel 314 difference (TS4)was more effective than contextual analysis in minimizing false detections, although detection sensitivity to actual fire pixels was lower. The contextual analysis became more effective in terms of fire
detections as the T3 and standard deviation thresholds were
loosened. However, enhanced fire detection capabilities were
achieved at the expense of increased false detections associated primarily with cloud edges. False detections increased
exponentially and detections of active fires increased linearly
as thresholds were loosened. Furthermore, T3 and standard
deviation thresholds suggested for the MODIS global fire detection product appear too high for Alaska. An optimal T3
threshold between 314K and 315K and a standard deviation
threshold between 2.5 and 3.5 are proposed. These results
suggest that each biome or region may require different
thresholds to optimize algorithm performance, recognizing
that optimization of the model depends upon user goals.
Effective cloud removal is clearly the most significant issue
facing this type of fire detection method.
Introduction
Fire is both a vital disturbance mechanism and a deleterious
process that adversely affects ecological, economic, and human
resources (Agee, 1993; Cochrane et al., 1999).It is also a significant source of many atmospheric trace gases and aerosols that
influence air quality, tropospheric and stratospheric chemistry,
and global climate (Crutzen and Andreae, 1990;Penner et al.,
1992).Together, these factors promote the need for systematic
monitoring of the spatial and temporal distribution of fires using satellite-borneinstruments such as NOAA's Advanced Very
High Resolution Radiometer (AVHRR) and NASA'sModerate Resolution Imaging Spectroradiometer(MODIS).
The heritage for satellite remote sensing of fire has been
documented extensively elsewhere, most recently by Chuvieco (1999)and Kaufman et al. (1998).AVHRR-based
detection
C.A. Seielstad, J.P. Riddering, and S.R. Brown are with the
Remote Sensing Laboratory, University of Montana School of
Forestry, Science Complex 430, Missoula, MT 59812
(carlhtsg.umt.edu).
L.P. Queen is with the University of Montana School of Forestry, Science Complex 428, Missoula, MT 59812.
W.M. Hao is with the Fire Sciences Laboratory, Rocky Mountain Research Station, USDA Forest Senrice, Missoula, MT
59807.
methods dominate this heritage primarily because the AVHRR
sensors provide a moderate spatial resolution of 1.1km at nadir
with twice daily coverage (Justice et al., 1993).The MODIS instrument shares several spatialltemporal resolution characteristics with the AWRR but includes additional spectral characteristics that support detection of sub-pixel high temperature
targets. The primary advance is a fire detection channel in the
3.96-pmregion (3.931to 3.987 pm) of the electromagneticspectrum that saturates near 500K, and a high-gain band pass at
11.03 pm (10.78 to 11.28 pm) saturating at about 400K (Running et al., 1994;Kaufman et al., 1998).
Although MODIS is the first instrument of its kind designea
with fire-specificband passes, its fire detection logic is based
on 18 years of
fire remote sensing research (Dozier, 1981;
Matson and Dozier, 1981;Muirhead and Cracknell, 1984; Matson et al., 1984; Flannigan and Vonder Haar, 1986).The MoDIs
fire detection algorithm, like other detection algorithms that
utilize thermal infrared data, relies on the fact that high temperature sources elevate detector response at 3.8 pn, and that subpixel hotspots have a greater effect at 3.8 pm than at 11pm
(Dozier,1981).In general, detection algorithms take advantage
of these attributes by thresholding the 3.8-,um bandpass and/or
the differencebetween the 3.8-pm and 11-pm bandpasses. Pixels with brightness temperatures exceeding these thresholds
may be considered fires (i.e., Flannigan and Vonder Haar, 1986;
Kaufman et al., 1990; Pereira and Setzer, 1993; Arino et al.,
1993).A variation on the fixed-threshold theme is contextual
analysis, where the brightness temperatures of potential fire
pixels are compared to background temperatures (i.e.,Justice
and Dowty, 1994; Flasse and Ceccato, 1996; Kaufman et al.,
1998; Giglio et al., 1999;Nakayama et al., 1999).New thresholds are then empirically established to determine if a potential
fire pixel is significantly hotter than background to merit a positive detection.
Despite the apparent differencesbetween methods, each of
the AVHRRIMODIS fire detection routines relies on similar logic.
They are distinguished from one another primarily by the
thresholds used, the number and form of tests, and the order in
which the tests are conducted. Variations in these parameters
are typically derived empirically in response to algorithm performance issues related to biome, terrain, and season (Giglio et
al., 1999;Nakayama et al., 1999; Boles and Verbyla, 1999).
Recently, performance differences between regional algorithms have been compared in order to select a single one that
Photogrammetric Engineering & Remote Sensing
Val. 68, No. 8, August 2002, pp. 831-838.
0099-1112/02/6808-831$3.00/0
O 2002 American Society for Photogrammetry
and Remote Sensing
can be implemented globally (Kaufman et al. 1998;Giglio et al.,
1999).The selected global algorithm must mitigate the effects of
biome and seasonal differences over a range of environments in
order to produce a consistent fire occurrence dataset. However,
a potential consequence of the global approach is that the fire
detection algorithm may not be optimized for all geographic regions. For example, Giglio et al. (1999) showed that three proposed global detection algorithms performed most effectively
in tropical and temperate biomes and least effectively where
surfaces were cool and highly reflective. Although a strong argument can be made for biasing global algorithms toward tropical biomes where the majority of biomass burning takes place
(Kaufman et al., 1988),one must consider the potential shortcomings of the algorithm in other biomes, especially if model
output showing the spatial and temporal distribution of fires
will be used in local or regional applications.
In this paper, the performance of a MODIS-like
daytime fire
detection logic is tested in Alaskan biomes using AVHRR infrared data, and its detection sensitivity is assessed across a range
of channel 3 (3.8 pm) and contextual standard deviation
thresholds. This approach follows the conclusions of Nakayarna et al. (1999)that it may be more suitable to take a general
algorithm and to fine tune it regionally and temporally than to
apply a single algorithm to the entire world. The approach also
provides a means of assessing the effectiveness of different algorithm thresholds in terms of positive and false detections
and demonstrates that the algorithm can be optimized to suit
different purposes.
Approach
Assessment of a MODIS-like
active fire detection model using
AVHRR data is appropriate because these sensors share many
characteristics and they are anticipated to perform similarly as
fire detection tools (Cahoon et al., 1999; Giglio, 2001). Although MODIS is designed to demonstrate both better cloud
screening and higher saturation temperatures in the thermal
channels used for fire detection, it does not appear to gain significant advantage over AVHRRfor detection of small fires because it trades higher noise levels to achieve these temperatures
(Cahoon et al., 1999). These authors calculated a theoretical
minimum detection limit for the AVHIXRat 435 mZ(0.05 acres) of
burning area at lOOOK and postulate that MODIS will share similar sensitivity. In practice, MODIS will mitigate the noise issue
at 4 pm by using the low noise, low saturation channel 22 in
place of channel 2 1 when saturation has not occurred in the latter channel, thus rendering better overall detection sensitivity
for MODIS over AVHRR for fires of all sizes. Giglio et a]. (2001)
demonstrate that MODIS should easily outperform AVHRR for
larger fires (greater than 316 m2),and will perform at least as
well as the AVHRR for smaller fires.
The MODIS daytime fire detection algorithm relies on a
neighborhood analysis in which the brightness temperatures
of potential fire pixels in the 4-pm bandpass are compared to
mean background brightness temperatures of adjacent pixels,
and the apparent differences in brightness temperatures between the 4-pm and l l - p m bandpasses (AT,,) are compared to
median temperature differences (Kaufman et al., 1998).Clouds
are first screened from each image, and extremely energetic fire
pixels are identified using fixed thresholds in the 4-pm and
11-pmbands. This method of identifying fire pixels will hereafter be referred to as "absolute detection." A moving window
is then passed across the scene and a mean and standard deviation are calculated for background temperatures. The window
size starts at 3 by 3 pixels and increases incrementally to 2 1 by
2 1 pixels until at least three non-cloud and non-energetic fire
pixels are included in the calculations. Each pixel in the 4-pm
bandpass is then compared to the median background temperature (plus standard deviations) of its neighborhood to determine if that pixel is a fire pixel, where the brightness temperaAugust 2002
ture of a fire pixel must be larger than background plus a
number of standard deviations to merit a positive detection.
Similarly, each AT,, pixel is compared to median background
temperature differences plus standard deviations. If three
cloud-free, non-fire pixels are not identified in a neighborhood, the center pixel is labeled "indeterminant."
In this study, performance of a MODIS-like
fire detection
model was tested using AVHRR infrared data from Alaska. Initially, the algorithm was run using thresholds suggested for
MODIS (Kaufman et al., 1998)to provide a baseline for comparisons with alternative thresholds. Then, the sensitivity of the algorithm was tested at various threshold temperatures in the
3.8-pm bandpass and at different contextual standard deviation (8) thresholds during the most active portion of Alaska's
1997 fire season (22 June through 09 July).
The model incorporated two general procedures, following Giglio et al. (1999) and Kaufman et al. (1998).First, the data
were subjected to an absolute fire detection routine where energetic fire pixels were identified using fixed thresholds in the
3.8-pm bandpass and in the 3.8-pm band - 11-pmband difference (Figure 1). Secondarily, a contextual analysis was utilized, where apparent brightness temperatures were compared
to mean background temperatures plus a number of standard
deviations. The form of these tests was identical to those employed by Kaufman et al. (1998)with the following exceptions.
The brightness temperature differences between the 3.8-pm
and 11-pm bands were compared with mean differences plus
standard deviations after Giglio et al. (1999) rather than with
median differences plus standard deviations in order to provide continuity with existing comparative work that utilized
the AVHRR for fire detection purposes. For the same reasons,
the absolute threshold for the 3.8-pm - 11-pm difference was
fixed at the frequently cited midpoint of its range, 10K, rather
than 20K. Finally, Kaufman et al.'s (1998) daytime absolute
threshold of 360K at the 4-pm bandpass (which overrides the
T4, difference test) was not utilized because this band saturates
at about 320K on the A m . Instead, the primary MODIS 4-pm
threshold of 320K was utilized.
The effect of the primary threshold on model sensitivity
was evaluated by systematically reducing it from 320K to
310K. As this threshold declined, more potential fire pixels
were removed from the neighborhood analysis, generally decreasing mean background temperatures and increasing the
likelihood that a fire pixel would be warmer than background.
After selecting an "optimal" threshold in the 3.8-pm band
(based on a ratio of positive to false detections), the standard
deviation threshold in the neighborhood analysis was systematically decreased from four to two. In effect, the requirement
that a fire pixel be warmer than background was made less
stringent, increasing the likelihood that a fire pixel would be
detected.
The rationale for selection of the modified thresholds was
as follows. First, there are 429 possible combinations of Channel 3 (T,), Channel 3 - Channel 4 (T,,), and Sthresholds using
the published range of integers for each (T,: 308 to 320K, T,,: 5
to 15K, S: 2 to 4). It was unrealistic to test each of these combinations. Empirical evidence based on preliminary analysis of the
Alaska fire data suggested that algorithm performance in this
region was most sensitive to variations in T3and 8, and that the
MODIS thresholds for these tests might be too high. Several authors report similar findings (Giglio et al., 1999;Boles and Verbyla, 1999;Nakayama et al., 1999).Therefore, the T,, threshold
was fixed at the most frequently cited value of 10K, Swas set at
the mid-point of its range (3), and T, was varied across its published range of thresholds. Summary statistics were generated
for these tests, and the optimal T, threshold was selected
(314.5K)for the Sthreshold sensitivity analysis. Here, it is recognized that "optimal" is subjective and depends upon one's
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
I
I
Figure 1. Conceptual layout of a MoDlslike global daytime fire detection
processing stream. The sensitivity analysis box (center right) indicates
where the model was modified to assess optimization for Alaska biomes.
goals. For the purposes of this work, optimal is subjectively defined as the point at which the most positive detections are
gained at the least cost in terms of false detections.
A significant consideration for all detection algorithms is a
cloud-masking methodology. Clouds are generally masked
from AVHRR data by thresholding some combination of T,, T5,
T,,, channel 1, and channel 2 reflectance, and a ratio (Q of
channel 2 and channel 1(Saunders and Kriebel, 1988;Arino et
al., 1993; Flasse and Ceccato, 1996; Thornton, 1998).For this
study, empirically derived thresholds were chosen that were
less restrictive than published values in order to avoid mistakenly removing fire pixels from the analysis (Figure 1).Most significantly, channel 1and channel 2 reflectance thresholds
were set at 0.40 and 0.30, respectively. Preliminary analyses of
the Alaska data indicated that a published channel 1threshold
of 0.25 (Arino et al., 1993)and a channel 2 threshold of 0.20
(Flasse and Ceccato, 1996)removed 10 to 1 5 percent of fire pixels that were detected using lower thresholds. Giglio et al.
(1999)also note that these thresholds are too restrictive, particularly for pixels containing both smoke and active fire. Because
a primary goal of this study was to examine the sensitivity and
limits of the MODIS algorithm using all possible fires, looser
thresholds were utilized, recognizing that the less restrictive
cloud mask would result in increased false detections (and increased positive detections).
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
Finally, although Giglio et al. (1999) have demonstrated
that biome, atmospheric conditions, scan angle, and season
can affect the probability of detecting certain fires, biomes were
not stratified, atmospheric corrections were not performed, adjacent fire pixels were not consolidated, and scan-angles were
not restricted. At present, there is neither a clear rationale nor
supporting data to easily perform these functions. In terms of
scan angle, Boles and Verbyla (1999) have shown that detection accuracy is actually higher at extreme scan angles in
Alaska, and recommend not restricting analysis to near-nadir
pixels. Also, a glint-exclusion routine was not performed in
this study in order to avoid mistakenly eliminating fire pixels.
Empirical evidence from Alaska suggests that glint is only an
issue in Alaska Range snow and ice features where fires typically do not occur. It is worth noting, however, that some of the
false alarms documented below could be attributable to solar
contamination in the AVHRR'S 3.8-pm bandpass.
Data Processing and Analysis
Daily data from NOAA-14 AVHRR in level 1B format were obtained from the Alaska Geophysical Institute in Fairbanks for
the 1997 fire season (01May through 10 August). Thermal data
were converted to radiance using onboard calibration coefficients as described in the NOAA Polar Orbiter Data Users
Guide (Kidwell, 1998) and were subsequently transformed to
AL,gusr
2002
833
brightness temperatures through inversion of Planck functions.
Reflectance in the visible and near-infrared channels was calculated following Rao and Chen (1999),using updated coefficients. The MODIS active fire detection algorithms (and variants)
were run on a subset of these data from 22 June to 09 July,representing the most active portion of the fire season. Detection images were then registered to a Defense Mapping Agency
1:1,000,000-scalebase map projected to the Albers conical
equal area projection (DMA, 1992).The root-mean-squareerror
for each scene was less than one pixel. After an ocean mask was
applied, the fire detection images were compared to an Alaska
Fire Service (AFS) fire occurrence database.
The ~Fs'firebccurrence database, derived from daily fire
reports, was converted to a geographic information system
(GIS)point coverage. Coverage attributes used in this analysis
include fire location, date, time and size at three reporting times- discovery, containment, and extinguished. It is important to
note that fire sizes were estimated by AFS and that fires were
not always discovered on the date that they started. Consequently, some fires were detected by the AVHRR prior to being
identified by AFS. Also, the AFS is conservative in declaring
fires out, so a period is expected near the end of each fire when
that fire is still declared active, but is essentially not detectable
by the A m (Boles and Verbyla, 1999).Unlike the previously
cited study, the last burning days of fires were not buffered, so
the detection accuracies presented here are somewhat
conservative.
On a given day, all fires declared still active by the AFS were
included in the analysis, regardless of size. Following Boles
and Verbyla (1999),detected fire pixels had to be within three
pixels of an AFS fire location to account for positional inaccuracies in both the AFS database and the A=
imagery. For large
fires that had spread significant distances from reported points
of origin, the arrangement of pixels, the presence of smoke, and
the proximity to fire pixels from the previous day, were used to
determine if candidate pixels belonged to a particular fire.
Results and Discussion
Cloud Effects
Clouds significantly affect performance of all infrared fire detection algorithms. In this study, 24 to 75 percent of fires (by
day), with an average of 53 percent, were not detected due to
clouds (Figure 2). These results are comparable to those of
Flannigan and Vonder Haar (1986) in central Alberta (12 June
z1J*r;,~""z~ur\-fr'*~~J*~*~~O~^ru\di~O~~05-'d&o1~de-\J&
DATE
Figure 2. Daily percentage of active fires obscured by clouds
for the period 22 June through 09 July, 1997.
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August
2002
through 21 July, 1982)where 59 percent of fires were hidden
by clouds. Cloud edges and sub-pixel clouds were the primary
source of false detections. An estimated 98 percent of false detections occurred along cloud edges, particularly those over
water bodies. Most of the non-cloud false detections were persistent throughout the period of study, suggesting that they
could be removed using a regional mask. The remainder of the
land-based false detections occurred along snow and ice features in the Alaska Range where fires are relatively infrequent.
A glint exclusion routine that utilizes solar zenith angles or a
snow-ice mask may be effectivefor removing these false pixels.
Contrary to the results of Giglio et al. (1999),the contextual
algorithm generated more false alarms along cloud edges than
did the fixed-threshold algorithm. However, a channel-4 background test (T, r The,) (Justice et al., 1996)and a restrictive
channel-2 reflectance test (R22 0.20) (Flasse and Ceccato,
1996)were not employed in order to avoid missing fire detections. Giglio et al. (1999) ascribed false alarm differences between fixed-threshold and contextual algorithms to these tests.
The results presented here suggest that the use of a non-restrictive cloud mask with the contextual algorithm increases the
occurrence of false alarms along cloud edges. This problem
might be mitigated by MODIS through use of the enhanced
cloud screening algorithms that utilize band passes independent ofthose used for fire detection (Ackermanet al., 1998).Furthermore, it is difficult to assess the number of false alarms associated with solar contamination, leaving in question how
spectral response differences between the MODIS and AVHRR
mid-infrared fire bands might affect this potential problem.
Fire Detection with MODlSLlke Thresholds (T3 2 320 K, TW 2 10 K)
One-hundred fifteen fires were detected using the absolute
threshold algorithm and 128 were detected using the contextual algorithm (out of 560 cloud-free fires), representing 21 percent and 23 percent of fires, respectively, that could have been
detected. The absolute algorithm resulted in 1021 fire pixels
and 42 false pixels (4 percent false detects) for the period of
study. During the contextual detection the same 1021fire pixels
plus an additional 95 were identified, and 1774 false pixels
were detected (61 percent false detects). In terms of actual fires,
115 were detected by the absolute method and 128 by the relative method (out of 560 cloud-free fires). When compared to the
absolute detection, the relative detection added 9.3 percent
more fire pixels, 42 times more false pixels, and 11percent
more fires. Eight of the 13 fires identified contextually that
were missed by the absolute method were single-pixel detections and the remaining five were two-pixel detections. It is
worth noting here that actual fires are individual fire events as
recognized by the Alaska Fire Service and are distinct from fire
pixels. In the scheme presented, if any fire pixel is associated
with a named fire, that fire is detected. Although many fire pixels may be associated with an individual fire, only one fire detection is allowed per fire for each satellite overpass, hence the
distinction between fires and fire pixels.
These results highlight two important considerations.
First, the absolute detection, while missing some fire pixels and
fires, confers great benefit in terms of user's accuracy. For example, 91 percent of detectable fire pixels were identified by the
absolute method but 96 percent of these detections were positive fire detects. By comparison, 9 percent more fire pixels
were identified by the contextual method, but only 39 percent
of the total detects were positive. Second, the contextual algorithm is very susceptible to false detections, particularly along
cloud edges, where relatively cool sub-pixel clouds reduce
background temperatures. Although both methods fail to completely characterize fire activity, the absolute method provides
a more appropriate measure of actual fire activity than does the
relative method if one's goal is to estimate area burning at time
PHOTOGRAMMmlCENGINEERING& REMOTE SENSING
14WO
12m
1mo
J
0
4
ma
6000
a
4000
2 m
0
308
310
312
314
316
318
320
322
CHANNEL 3 BRIGHTNESS TEMPERATURETHRESHOLD (K)
Figure 3. Performance of the contextual and absolute algorithms in terms of detected fire pixels and false pixels for a
range of AVHRR channel 3 brightness temperature
thresholds.
of satellite overpass (1063of 1116 fire pixels versus 2890 of
Ill6 fire pixels).
Flre Detection wlth ModHled Thresholds
T, Thresholds
lower because more warm pixels are excluded from the neighborhood analysis. Therefore, the temperature of a potential fire
pixel is more likely to be elevated enough above background
temperature to merit a positive detection. At cloud edges where
sub-pixel clouds depress brightness temperatures, the same
principle applies, hence the contextual algorithm's propensity
for false detections in this environment.
Most of the additional fire pixels detected at lower thresholds belong to fires already detected at higher thresholds,
meaning that the primary effect of loose T3thresholds is more
complete characterization of these fires (Figure 5). Of the fires
that were detected at one threshold but not at another, all were
one- and two-pixel detections. At T3 = 310K,three fires 900m2
(0.10acres) in size were detected, representing 1 percent of fires
in this size class that could have been detected. At T3 = 313K,
none of these fires were detected. Although lower T3thresholds
clearly result in detecting more fires (distinct from fire pixels),
the number of pixels associated with these fires is small. For example, 45 more fires were detected at 310K than at 320K,represented by 83 pixels. In turn, these 83 pixels represent 4.4percent (8311862)of the fire pixels that were detected at 310K but
not at 320K.
An optimal T3threshold is difficult to identify because that
threshold depends on the goals of the detection algorithm user.
In Alaska, where fire locations are generally known, a low
threshold is appropriate because false detections can be effectively masked, Regions with less well-developed
intelligence systems probably will require higher T3 thresholds to
minimize false detections. For Alaska, a balance between detecting positive fire pixels and false pixels can be achieved at
or near T3 = 314.5K.For example, by reducing the T3threshold
As the Tgthreshold was reduced from 320K to 310K,the number of detected fire pixels increased 2.3 times from 1,433to
3,295.However, the number of false pixels increased about 3
times from 4,254to 13,062(Figure 3). A roughly exponential
increase in false pixels is observed, in comparison to a linear
increase in detected fire pixels as the T3threshold is loosened.
The number of individual fires detected increased 30 percent,
from 148 to 193,over the same range of thresholds, representing 26 and 34 percent of fires, respectively, that could have been
detected (Figure 4). Intuitively, when the T, threshold is reduced, calculated background temperatures are generally
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-A- CONTEXTUAL ALGORITHM
-& ABSOLUTE ALGORITHM
,
308
310
312
314
316
318
320
322
CHANNEL 3 THRESHOLD (K)
Figure 4. Performance of absolute and contextual algorithms in terms of detected fires. The number of fires d e
tected using proposed MODIS thresholds shown for comparison (boxes).
PHOTOGRAMM€rRICENGINEERING & REMOTE SENSING
Figure 5. Comparison of detection images for two fires in
southwestern Alaska (26 June 1997)at three AVHRR Channel
3 brightness temperature thresholds. Characterizations of
these fires using proposed MODIS thresholds are shown in
the upper two frames. The Mud Fire (on left) is relatively
inactive with no visible smoke plume. The Simels Fire (on
right) is burning actively with a welldeveloped smoke plume.
August 2002
835
from 320K to 316K, we experience a 47 percent increase in fire
detections for a 16 percent increase in false detections. When
the T, threshold is reduced to 314.5K, an additional 15 percent
fire pixels and 1 4 percent false pixels are gained. If the threshold is further reduced to 313K, 16 percent more fire pixels and
24 percent more false pixels are generated. Finally, at 310K, 51
percent additional fire pixels and 153 percent more false pixels
are detected.
Standard Deviation Thresholds
Systematicallyreducing the standard deviation (6) threshold
from four to two resulted in improved detection sensitivity, but
this improvement was marginal when compared with the associated increase in false detections (Figure6). At 6 = 2,2602 fire
pixels and 38,389 false pixels are detected; at 6 = 3,2324 fire
pixels and 5517 false pixels are identified; at 6 = 4 , there are
2235 fire pixels and 3060 false pixels. In terms of individual
fires, 200, 171, and 163 fires, respectively were detected at
thresholds of two, three, and four standard deviations (representing 36 percent, 31 percent, and 29 percent of fires that
could have been detected). Each of the fires detected at one
threshold but not at another are one- and two-pixel events.
Consequently,they do not contribute significantly to the total
number of fire pixels on any given day.
As was witnessed with the T, threshold, the number of fire
detections increases linearly as standard deviation thresholds
are loosened, while the number of false detections increases exponentially. An appropriate 6threshold lies between 2.5 and
3.5 for achieving minimum false detections and maximum positive detects. Evaluation of finer Ssteps between two and three
will be necessary to determine exactly where in this range that
the rate of false detections increases most rapidly.
Conclusions
The results presented here demonstrate the sensitivity of
~ ~ D ~ s - lfire
i k edetection logic to variations in T, thresholds
and T,, standard deviation thresholds. In Alaska, the neighborhood analysis is a more effective method in terms of actual fire
detections as the T, and Sthresholds are loosened. However,
enhanced fire detection capabilities are achieved at the cost of
increased false detections that are associated primarily with
cloud edges. An exponential increase in false detections of fire
pixels and a slight linear increase in positive detections are observed as thresholds are loosened.
220
38,389
(Absolute
Algorithm)
(Contextual
Algonthm)
MODIS
MODIS
(Absolute
(Contextual
Algonthm)
Algorithm)
S=4
6=3
6-2
Figure 6. Performance of the contextual algorithm in terms of standard deviation
(6) thresholds. Results using proposed MoDls thresholds shown for comparison.
Upper inset depicts detected fires and lower inset shows detected fire pixels and
false pixels.
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A u g u s t 2002
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
The T, and 6 thresholds suggested by the MODIS fire products team (Kaufman et al., 1998) are too high for Alaskan biomes when using AVHRR data. A T, threshold between 314K
and 315K and a Sthreshold of 2.5 to 3.5 are proposed here.
Additionally, an absolute detection algorithm may be more appropriate than a neighborhood analysis to minimize false detects. However, optimization of the MODIS active fire detection
algorithm will depend almost entirely upon the goals ofthe
user. For regions like Alaska where the locations of fires are
generally known, the detection routine could be "seeded"
with
fire locations, and
unrestrictive 3 ' 8 - ~ m
band and Sthresholds could be utilized. Useful estimates of
fire activity, growth, size, and direction-of-movement would
then be
In regions that do
have effective fire monitoring strategies in place (tropical and subtropical South America, Africa, and Asia), tighter thresholds will
be required to minimize false detections. Consequently, a large
underestimation of actual fire activity is anticipated for these
areas.
In the context of the findings of Giglio et al. (1999)and Nakayama et al. (1999),the results presented here strongly support the conclusion that each biome or region may require its
own thresholds to optimize model performance in terms of
fire detections, Although a single global fire detection algorithm is clearly desirable, it will not function ideally in all biomes and seasons. Consequently, some users of the MODIS
active fire detection product will wish to customize the algorithm to suit their own purposes,
finding argues for regional distribution of data, custom algorithms, and initial
technical support, in addition to the proposed dissemination
of singular global fire products.
One of the most critical areas for future research lies in development of more effective 'loud screeningprocedures' The
real issue in improving fire detection is not how to detect more
fires, but how not to detect more false pixels. At present, the
most
used 'loud masks
large
numbers of fire pixels from consideration. Although the
MODIS cloud products may mitigate some of the cloud contamination problems for MODIS fire detection, at present the
choice of cloud mask thresholds is an additional consideration
for users of active fire detection products, particularly for users of AVHRRdata. A buffer on cloud edges may remove many
of the false detections and allow fairly loose detection thresholds to be employed. This potential remedy should be explored further.
Finally, it has not been demonstrated that the current
MODIS fire detection model is applicable for different vegetation types and seasons, but it represents a major step toward
the systematic assessment of the extent of global biomass
burning. Upcoming validation efforts are sure to highlight its
strengths, identify its weaknesses, and lead to additional improvements and new avenues of research. Already, Giglio et al.
(1999) have suggested that utilizing a donut-shaped window,
increasing the required number of retained pixels from three to
six, and using the mean absolute deviation rather than the
standard deviation may increase model sensitivity to small
fires without sacrificing occurrence of false detections. Given
the suggestion that regional users may wish or, indeed, need
to modify healgorithm, these suggestions represent additional variables that should be factored into regional adaptations of the model.
Acknowledgments
The authors would like to acknowledge the support of this research by NASA and the USDA Forest Service Intermountain
Fire Sciences Laboratory under agreement #RMRS-98553-RwA.
In addition, the efforts of Dr. Yoram Kaufman at Goddard Space
Flight Center, Don Barry and Mary Lynch at the Alaska Fire Service, and the anonymous reviewers are appreciated.
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
References
Ackerman, S.A., K.I. Strabala, and L.E. Gumley, 1998. Discriminating
clear sky from clouds with MODIS, Journal of Geophysical Research, 103(D24):32141-32160.
Agee, J.K., 1993. Fire Ecology of Pacific North west Forests, Island Press,
Washington, D.C., 493 p.
h i n o , O., J.-M. Melinotte, and G. Calabresi, 1993. Fire, Cloud, Land,
Water: the 'lonia' AVHRR CD-Browser of ESRIN. EOQ 41 (July,
1993), ESA, ESTEC, Noordwijk, The Netherlands (available on
CD-ROM).
Boles, S.H,, and D.L, Verbyla, 1999. Effect of scan angle on AVHRR
fire detection accuracy in interior Alaska, International Journal
of Remote Sensing, 20(17): 3437-3443.
Cahoon, D.R.J., B.J. Stocks, M.E. Alexander, B.A. Baum, and J.G. Goldammer, 1999. Wildland fire detection from space: Theory and
application, Biomass Burning and It's Interrelationships with the
Climate System, Advances in Global Change Research, Vol. 3 (J.L.
Innes, M. Beniston, and M.M. Verstraete, editors), Kluwer Academic Publishers, Dordrecht, The Netherlands, pp. 98-106.
Chuvieco, E., 1999. Remote sensing of forest fires, Observing Land
from Space: Science, Customers and Technology, Advances in
Global Change Research, Vol. 4 (M.M. Verstraete, M. Menenti, and
J. Peltoniemi, editors), Kluwer Academic Publishers, Dordrecht,
The Netherlands, pp. 27-40.
Cochane, M.A., A. Alencar, and E.A. Davidson. 1999. Positive feedbacks in the fire dynamic of closed canopy tropical forests, Science, 284(5421):1832-835.
Crutzen, P.J., and M.O. Andreae, 1990. Biomass burning in the tropics:
impact on atmospheric chemistry and biogeochemical cycles, Science, 250 (4988):1669-1677.
DMA, 1992. Digital Chart of the World, North American Edition 1,
U.S. Defense Mapping Agency, Washington, D.C. (available at
www.nima.mi1).
Dozier, J., 1981. A method for satellite identification of surface temperature fields of subpixel resolution, Remote Sensing of Environment, 11(3):221-229.
Flannigan, M.D., and T.H. Vonder Haar, 1986. Forest fire monitoring
using NOAA satellite AVHRR, Canadian Journal of Forest Research, 16(3]:975-982.
Flasse, S.P., and P. Ceccato, 1996. A contextual algorithm for AVHRR
fire detection, International Journalof Remote Sensing, 17(2):
419-424.
Giglio, L., and J.D. Kendall, 2001. Application of the Dozier retrieval
to wildfire characterization: A sensitivity analysis, Remote Sensing of Environment, 77(1):34-49.
Giglio, L., J.D. Kendall, and C.O. Justice, 1999. Evaluation of global
fire detection algorithms using simulated AVHRR infrared data,
International Journal of Remote Sensing, 20(10):1947-1985.
Justice, C.O., and P. Dowty, 1994. IGBP-DIS Satellite Fire Detection
Algorithm Workshop Technical Report (February, 1993), IGBPDIS Working Paper 9, NASA-GSFC, Greenbelt, Maryland, 16 p.
Justice, C.O., J.P. Malingreau, and A.W. Setzer, 1993. Satellite remote
sensing of fires: Potential and limitations, Fire in the Environment
(P.J. Crutzen and J.G. Goldammer, editors), John Wiley and Sons,
pp. 77-88.
Justice, C.O., J.D. Kendall, P.R. Dowty, and R.J. Scholes, 1996. Satellite
remote sensing of fires during the SAFARI campaign using NOAAAVHRR data, Journal of Geophysical Research. Atmospheres,
101(D19):23851-23863.
Kaufman, Y.J., A.W. Setzer, C.J. Tucker, M.C. Pereira, and I. Fung, 1990.
Remote sensing of biomass burning in the tropics, Fire in Tropical
Biota: Ecosystem Processes and Global Challenges (J.G. Goldammer, editor), Springer-Verlag, aerlin, pp. 371-399.
Kaufman, Y.J., C.O. Justice, L.P. Flynn, J.D. Kendall, E.M. Prins, L.
Giglio, D.E. Ward, W.P. Menzel, and A.W. Setzer, 1998. Potential
global fire monitoring from EOS-MODIS, Journal of Geophysical
Research, 103(D24):32215-32238.
Kidwell, K.B., 1998. NOAA Polar Orbiter Data User's Guide (TIROSN, NOAA-6, NOAA-7, NOAA-8, NOAA-9, NOAA-10, NOAA-11,
NOAA-12, NOAA-13 and NOAA-1I),NOAA-NESDIS, Washington, D.C., 143 p.
N.Y.l
A u g ~ ~ 2002
rr
837
Matson, M., and J. Dozier, 1981. Identification of subresolution high
temperature sources using a thermal IR sensor, Photogrammetric
Engineering 6- Remote Sensing, 47(9]:1311-1318.
Matson, M., S.R. Schneider, B. Aldridge, and B. Satchwell, 1984. Fire
Detection Using the NOAA Series Satellites, NOAA Technical
Report NESDIS-7, NOAA, Washington, D.C., 34 p.
Muirhead, K., and A.P. Cracknell, 1984. Identification of gas flares in
the North Sea using satellite data, International Journal of Remote
Sensing, 5(1):199-212.
Nakayama, M., M. Maki, C.D. Elvidge, and S.C. Liew, 1999. Contextual
algorithm adapted for NOAA-AVHRR fire detection in Indonesia,
International Journal of Remote Sensing, 20(17):3415-3421.
Penner, J.E., R.E. Dickenson, and C.A. O'Neill, 1992. Effects of aerosol
from biomass burning on the global radiation budget, Science,
256(5062):1432-1434.
Pereira, M.C., and A.W. Setzer, 1993, Spectral characteristics of deforestation fires in NOAAIAVHRR images, International Journal of
Remote Sensing, 14(3):583-597.
Rao, C.R., and J. Chen, 1999. Revised post-launch calibration of the
visible and near infrared channels of the Advanced Very High
Resolution Radiometer (AVHRR) on the NOAA-14 spacecraft, International Journal of Remote Sensing, 20(18):3485-3490.
Roy, D.P., L. Giglio, J.D. Kendall, and C.O. Justice, 1999. Multi-temporal
active-fire based burn scar detection algorithm, International Journal of Remote Sensing, 20(5):1031-1038.
Running, S.W., C.O. Justice, V. Salomonson, D. Hall, J. Barker, Y.J.
Daufman, A.H. Strahler, A.R. Huete, J.-P. Muller, V. Vanderbilt,
Z.M. Wan, P. Teillet, and D. Carneggie, 1994. Terrestrial remote
sensing science and algorithms planned for EOSIMODIS, International Journal of Remote Sensing, 15(17): 3587-3620.
Saunders, R.W., and K.T. Kriebel, 1988. An improved method for detecting clear sky and cloudy radiances from AVHRR data, International Journal of Remote Sensing, 9(1):123-150.
Thornton, P.E., 1998. Regional Ecosystem Simulation: Combining Surface and Satellite Based Observations to Study Linkages between
Terrestrial Energy and Mass Budgets, Ph.D. Thesis, University of
Montana, Missoula, Montana, 280 p.
(Received 26 January 2001; accepted 29 January 2002; revised 27 February 2002)
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