A data-mining approach to associating MISR smoke plume heights withMODIS fire measurements
Dominic Mazzoni a,c,⁎, Jennifer A. Logan b,c, David Diner a,c, Ralph Kahn a,c,Lingling Tong b,c, Qinbin Li a,c
a Jet Propulsion Laboratory, California Institute of Technology, United Statesb Division of Engineering and Applied Sciences, Harvard University, United States
c Department of Computer Sciences, University of Texas, Austin, United States
Received 4 March 2006; received in revised form 11 August 2006; accepted 15 August 2006
Keywords: MISR; MODIS; Smoke plumes; Boreal fires; Injection height; Aerosol transport; Data mining; Support vector machines; Neural networks
1. Introduction
The injection height of smoke plumes from forest fires is alarge source of uncertainty in transport models used to predictthe effect of emissions from fires on air quality and climate. It iswell known that crown fires generate sufficient energy to loftsmoke plumes above the boundary layer (Cofer et al., 1996;Lavoue et al., 2000), facilitating long-range transport of gasesand particulate matter (e.g., Bertschi et al., 2004; Colarco et al.,2004; Kahn et al., in press). A large fraction of smoke aerosolsremain in the near-surface boundary layer, and do not formdiscrete “plumes” that are the focus of this paper. Emissions that
rapidly escape the boundary layer are more likely to contributeto long distance transport.
Case studies have shown that smoke from large boreal firescan be injected to the lower stratosphere by supercell convection(Fromm & Servranckx, 2003). The frequency of high-altitude(and thus long-lifetime) smoke injection has not been quantifiedsystematically (Fromm et al., 2004). It is possible that borealand mid-latitude fires may become more common in the futureas a result of global warming (e.g., Brown et al., 2004;Flannigan et al., 2000). Understanding the impacts of fires onair quality and climate requires, in part, the use of transportmodels to relate particle and gas emissions to their downstreamdispersal. Observations of aerosol injection are necessary toinitialize and validate the models and to develop relationshipsbetween injection height and local surface and meteorologicalconditions. To facilitate progress in this area, we are using data
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from the Terra satellite to obtain statistics on the geographicdistribution, extent, orientation, and injection height of plumes(Averill et al., 2005). Terra data acquisition began in February2000, and NASA recently approved an extension of the missionthrough 2009. This paper describes the specific process we usedto automatically find and extract measurements from smokeplumes, using machine learning techniques and custom imageanalysis algorithms. We are in the process of refining thesealgorithms, but our initial results demonstrate the utility of theautomated approach.
The development work discussed here uses data collectedfrom June to September 2004 over North America. We selectedthis period for initial study because of record setting fires inAlaska and the adjacent Yukon Territory of Canada. More than2.6×106 ha burned in Alaska and 1.7×106 ha in the YukonTerritory (CFS, 2004; NIFC, 2004). Records were also set forthe number of days with reduced visibility caused by wildfiresmoke, 42 days in Fairbanks, compared to the previous recordof 19 days in 1977, as noted by Averill et al. (2005).
Smoke plumes from these fires were intercepted by aircrafton the INTEX-NA field campaign over the United States whichtook place from July 1 to August 15 (Singh et al., 2002, inpress), and enhanced CO was observed by MOPITT (Measure-ments Of Pollution In The Troposphere) as a continental scaleplume over North America (e.g., Pfister et al., 2005). A case ofpyroconvection was documented in June over Alaska, withaerosol enhancements observed near the tropopause (Damoahet al., 2006). Knowledge of the injection heights of the emis-sions from severe fires such as those in 2004 is required for aquantitative assessment of their effects on atmospheric com-position. The work described below is a first step towardsproviding this information, for plumes immediately downwindof fires.
The Terra Multi-angle Imaging SpectroRadiometer (MISR)instrument observes the Earth in reflected sunlight with a 10:30AM local time equator crossing, and its typical data collectionmode is to observe the Earth globally at nine different viewzenith angles in four spectral bands (446, 558, 672, and 866 nm)(Diner et al., 1998). The fore–aft cameras are paired in asymmetrical arrangement and acquire images with nominalview angles, relative to the Earth's surface, at 0°, 26.1°, 45.6°,and 70.5°. In its global observing mode, the nadir camera data inall bands, and the red band data of all of the off-nadir camerasare downlinked at the full spatial resolution of the instrument,275 m. All other channels are averaged on-board to 1.1-kmresolution. The swath width observed in common among allnine cameras is about 380 km. Complete coverage between±82° latitude is obtained every 9 days. Absolute geolocationuncertainty for the nadir camera is about ±45 m, and relative co-registration errors among the nine cameras are typically lessthan 275 m.
MISR data make possible unique smoke plume identificationand characterization approaches. The use of oblique-angleimagery from MISR enhances plume sensitivity because of thelonger optical path through the atmosphere, and the combina-tion of multiangle and multispectral information assists indistinguishing smoke from clouds or other types of aerosols
(Mazzoni et al., 2006-this issue). Furthermore, automated patternmatching algorithms (Moroney et al., 2002; Muller et al., 2002;Zong et al., 2002) take advantage of the stereoscopic nature ofMISR data, and as part of MISR operational data processingdetermine the geometric parallax (horizontal displacement) thatoccurs for a given plume due to its altitude above the surface.Pattern matching is aided by the moderately high spatialresolution of MISR imagery and the 14-bit radiometric depth.Photogrammetric algorithms using accurate camera geometricmodels transform the derived parallaxes into cloud-top or plume-top heights. Using the nadir and near-nadir cameras, as is donefor the standard MISR product, the quantized precision of theresulting height field is ±560 m. Height accuracies for low cloudshave been validated to a few hundred meters (Naud et al., 2004);since the technique is purely geometric, comparable accuracy isexpected for smoke plumes. Altitudes for clouds as well assmoke, dust, and volcanic plumes are routinely retrieved, andreported on a 1.1-km resolution geolocated grid. However, theMISR standard stereo product does not provide a scene classifieralong with the heights which identifies whether the observedtarget is cloud or aerosol. In this paper, we describe the use of aSupport Vector Machine approach for providing this classifica-tion in conjunction with height extraction from the MISR stereoproduct.
Data captured by the MODerate-resolution Imaging Spectro-radiometer (MODIS) instrument (Barnes et al., 1998), co-located with MISR on the Terra spacecraft, provides invaluableinformation about fires, including fire occurrence maps andmean radiative power. Burned area products are beingdeveloped with the algorithms of Roy et al. (2005) for theentire MODIS data set. MODIS observes the Earth in 36spectral bands from 0.4 to 14.4 μm. Its scan pattern sees a 2330-km swath, providing near global daily coverage. MODIS'sthermal-infrared sensing capabilities give it the ability to detectactive fires with high temporal resolution. By combining MISRand MODIS data over hundreds of fires, and supplementing theTerra data with meteorological information, we are developing astatistical database that will make it possible to empiricallyrelate a fire's power and local atmospheric conditions to theresulting smoke plume injection height. While case studies haveshown that this technique is feasible (Kahn et al., in press),performing this computation for hundreds of smoke plumes isdaunting, not only due to the calculations required, but also dueto the relative rarity of smoke plumes in terabytes of satelliteimages and the challenge of finding them.
2. Method
Our method was developed based on the assumption that it isunreasonably time-consuming to identify and extract data fromevery smoke plume manually. Using machine learning and datamining techniques, we have developed an approach forextracting smoke plume and fire data automatically fromMISR and MODIS imagery and higher-level data products andretrieving several properties including the plume direction andinjection height. Since this automated system cannot identifyplumes with perfect accuracy, all individual plumes found are
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spot-checked before being included in our database. Thishuman validation does not take very much time and provides anappropriate balance between mostly automated processing andscientific integrity.
Our method is designed to find as many plumes as possible,while minimizing the number of false detections. So as not tocreate an unreasonable amount of manual labor, we sought toachieve an automatic false detection rate of no more than 3 or4 times the number of true detections. Thus in our automatedalgorithm we describe below, we sometimes made designdecisions to eliminate certain potential plumes, knowing that itwould reject a small number of true plumes, because it wouldalso reject a far greater number of false detections. Unfortu-nately, this does mean that some plumes are not detected by thecurrent system. We discuss the implications of missed plumes inSection 4.
2.1. Scene pruning
To facilitate the fusion of MISR and MODIS data, it isnecessary to define a common coordinate system for the dataproducts from the two instruments. The MISR swath isnarrower and a strict subset of the MODIS swath. We thereforefound it advantageous to conduct the plume search in the SpaceOblique Mercator coordinate system used for MISR's level 1and 2 data products. In addition, we define a “scene” to consistof one MISR block, comprised of 512×128 pixels at 1.1-kmresolution. Our data mining strategy starts with two testsdesigned to quickly focus on scenes that likely contain smokeplumes. This way the more expensive algorithms to trace theshape of plumes and extract their properties are run on only afraction of the data. As discussed below, our initial tests rejectthe vast majority of scenes.
2.2. Identifying fire locations using MODIS
We start with the MODIS Thermal Anomalies product tolook for evidence of fires (see Fig. 1). This is a daily Level 3product at 1-km resolution, and it classifies each pixel as fire,cloud, water, or land, with three levels of fire confidence (high,nominal, or low). It uses the 4 μm brightness temperature andthe difference between the 4 mm and 11 μm brightnesstemperatures in comparison to nearby pixels to detect fires andother thermal anomalies (Kaufman & Justice, 1998). We mapthe MODIS product onto the MISR grid using a fast nearest-neighbor mapping. More accurate registration is not necessarybecause we allow several kilometers of tolerance between ourestimate of the fire location and the observed source of thesmoke plume.
After the MODIS thermal anomalies have been projectedonto the MISR grid, the initial pruning occurs at the scene level.If one scene has no high or nominal confidence fire detections atall, it is immediately rejected from further analysis. We foundthat this eliminates 82.5% of all scenes. (The performancestatistics cited in this paper are derived from application of ourmethodology to the fire season over North America, and maynot be appropriate for other regions, such as the tropics where
fire occurrence is more frequent.) Any scene that has one ormore fire pixels on that day is retained for further analysis.
2.3. Identifying smoke in MISR data using a support vectormachine
The next step in the data mining is to identify which pixelscontain smoke. To do this, we leverage earlier work (Garay et al.,2005; Mazzoni et al., 2006-this issue) in which we developed ascene classifier able to distinguish between cloud, smoke, dust,land, water, and ice/snow using a combination of spectral,angular, and texture features fromMISR (see Fig. 2). EachMISRpixel at 1.1-km resolution is classified independently — this isthe same resolution as the stereo-derived height product. Theclassifier was trained using Support Vector Machines (SVMs)(Cortes & Vapnik, 1995), a modern and powerful machinelearning technique. The SVM takes only radiance data fromMISR and geometric information as input. We trained the SVMusing hundreds of hand-labeled scenes containing those sixclasses, and tested it by applying the resulting classifier to newscenes. On our independent validation scenes, we determined thatthe SVMwas able to distinguish successfully between all of theseclasses, though with an accuracy of only 81% at the 1.1-km pixellevel. This is sufficient to make it usable since most plumes areover 40 km long and are several kilometers wide; therefore,hundreds of pixels are part of the plume. As long as most of themare classified correctly, we will detect the presence of smoke.Furthermore, the classifier is biased in favor of smoke: it isapproximately 2.7 times more likely to misclassify a non-smokepixel as smoke than it is to misclassify a smoke pixel as anotherclass. This biasworks to our advantage, as wewould prefer to finda few extra false positives than to miss an important smoke plumethat we could have analyzed. Using different terminology, recallis more important to us than precision.
As a second stage in pruning, we eliminate scenes that do nothave a significant amount of smoke. In our current implemen-tation, we eliminate scenes that have less than 2% smoke, becausesuch scenes generally did not contain smoke plumes sufficientlylarge enough to provide usable data. Eliminating this pruning stepwould possibly lead to more small smoke plumes being found, ata cost of 15% more scenes to be analyzed.
Although themajority of remaining scenes contain evidence ofsmoke and fires, only a small fraction of them contain distinctsmoke plumes. In some instances, an entire scene can beblanketed with smoke, and one or more large plumes are clearlyembedded within it. Our next step is to identify plumes by shape.Our initial attempts involved a template-matching approach,where we compared known smoke plume images to objects seenin the scenes. This successfully found only about 25% of theplumes. We abandoned this approach, realizing that there is toomuch variability in smoke plume shape. Our preferred approachinvolves several steps, as described in the next sections.
2.4. Creating a smoke mask image
In order to accurately identify the shape of each object thatmight be a plume, we need to first generate a mask image for
Fig. 1. Left, the MODIS thermal anomalies product. Fires are indicated in red (high confidence) or yellow (low confidence). Right, an overlay of the MODIS firedetections on the MISR image of a smoke plume. MISR orbit 24313, blocks 38–40, July 13, 2004. (For interpretation of the references to color in this figure legend,the reader is referred to the web version of this article.)
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each scene identifying pixels that might belong to a smokeplume. The straightforward approach is to use the SVM toclassify every pixel in the scene as smoke or not smoke. This issuccessful for scenes that contain a smoke plume surrounded bynon-smoke pixels, but it fails when smoke pervades the entirescene, though one or more plumes are still visible. For the lattertype of scene, a simple brightness threshold often does anexcellent job of masking the plumes.
We apply the brightness threshold as follows: let pSVM be thepercentage of pixels classified as smoke by the SVM. ChoosepBr, the percentage of smoke pixels that will be masked outusing a brightness threshold, to be:
pBr ¼ maxðpSVM−25%; 0Þ
Thus if 100% of the pixels in the scene are classified assmoke by the SVM, we choose the brightness threshold so that75% of the pixels are masked out. If 25% or fewer of the pixels
Fig. 2. Left, a true-color image fromMISR's AN (Nadir) camera. On the right, the resuclearly detected, as is additional smoke in the lower-right corner of the image, even th40, July 13, 2004.
are classified as smoke by the SVM, we do not mask outanything due to brightness, and in-between, some fraction of thepixels are masked out. This is illustrated in Fig. 3: there ispervasive smoke in the MISR image on the left, and in fact over90% of the pixels are classified as smoke by the SVM, so theSVM mask reveals no interesting structure. However, whenabout 70% of those pixels are masked out using a brightnessthreshold, the result is the image seen on the right, where severalsmoke plumes are clearly visible. We found that 22% of sceneshad more than 25% smoke and thus required dynamicthresholding.
2.5. Finding distinct smoke plumes
Given a smoke mask image such as the one in Fig. 3, ourgoal is now to identify specific plumes. We found template-matching approaches to be inadequate because of the widevariety of possible shapes for smoke plumes. Instead, we sought
lt of the support vector machine (SVM) classification of the image. The plume isough it is practically invisible in the nadir image. MISR orbit 24313, blocks 38–
Fig. 3. The SVM classifies this scene as entirely smoke, so the SVM cannot be used to determine the shape of the plume. The image on the right shows the smoke maskcomputed using dynamic radiance thresholding, and two distinct plumes are visible. MISR orbit 24109, blocks 35–37, June 29, 2004.
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to find shapes in the image consisting of a connected set ofpixels, with a long thin shape, and a tip near a detected firesource. We think this definition is broad enough to catch mostplumes, while specific enough to find a manageable number offalse positives.
Here we describe the specific algorithm we implemented tofind smoke plumes. This algorithm is somewhat arbitrary andother approaches could conceivably improve upon it, but itworks in practice. Our approach in a nutshell is to start withpixels near a fire source that could possibly be the tip of aplume, and explore its connected region to determine if it is longand thin.
We defined a simple metric to identify tips in the mask imageby looking at an 11×11 square of pixels at a time. We requirethat the center pixel of the square is in the mask (i.e., it isclassified as smoke), that there is a connected line of smokepixels all the way to the edge of the square, and that thepercentage of smoke pixels in the square is strictly between 20%and 40%. This successfully finds the tip of most plumes, butalso finds several times as many additional false tips. We rejecttips that are not within a 15-pixel radius (about 16.5 km) of aMODIS fire detection. This large radius is used to account for
Fig. 4. Illustration of the algorithm used to identify smoke plumes, given an image thpoint p0, the algorithm finds the set of all connected smoke pixels within some maximpoint from p1 within the set, then we have found the start of a valid smoke plume ifexample on the right, the point we found is not the tip of a smoke plume (though it
the fact that some plumes are very narrow near the source, or thefire is not hot enough to be detected by MODIS near the tip ofthe plume. While there are occasional examples of real plumesthat did not have MODIS fire detections nearby, they arerelatively rare; the alternative of not rejecting tips too far from afire results in a large number of false positives.
Next we explore the region of pixels connected to that tip todetermine if it is long and thin. The specific algorithm isdescribed below and illustrated in Fig. 4. The threshold valuescited in the following algorithm were chosen by trying manypossible values systematically and choosing the values that gavethe best accuracy on a test set of plume images.
1. Let p0 be the coordinates of the pixel at the tip of the plume.Compute the set S of all pixels that are classified as smokeand are connected to p0 by a continuous chain of adjacentsmoke pixels. If this set is large, we only consider pixelswithin a certain radius around p0 (we chose a radius of about60 pixels, or 66 km).
2. If |S| (the number of pixels in the set S) is too small, we rejectthe shape as not being large enough. Shapes that are toosmall are not only more likely to be false positives, but even
at has already been separated into smoke and non-smoke pixels. Given a startingum radius. If p1 is the farthest point from p0 within the set and p2 is the farthestp0 is close to p2, as in the example on the left. When p0 is far from p2, as in themay be a different point on a smoke plume).
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if they are real plumes they are less likely to be large enoughfor us to determine the smoke plume height with theautomated stereo height algorithm. We require that |S|≥250.
3. Let p1 be the point in S farthest from p0. We require that ||p1−p0|| ≥22, to ensure that the shape is long enough to be apossible plume.
4. Let p2 be the point in S farthest from p1. If p0 really was thetip of the plume, then p2 and p0 should be the same, but if p0was a false tip and was in the middle of the set of pixels,then p2 will be very different than p0. We require that ||p2−p0|| ≤12; if so, we replace our previous tip with p2 —experiments showed that p2 is actually a more accurate guessas to the actual source.
5. Let pctr be the center of mass of the pixels in S, which iseasily computed as the average of the x coordinates andaverage of the y coordinates of all pixels in S. The vector v=(pctr−p0) / ||pctr−p0|| is our approximation of the direction ofthe plume.
6. Finally, we eliminate from S all points which are not within a50-pixel (about 55 km) radius of p2, as these are less likely tobe useful for measuring properties or computing the injectionheight.
Fig. 5. Above, an image fromMISR's AN (Nadir) camera with the smoke plumemask outlined. Below, a plot of the extracted stereo height (above sea level) ofeach pixel in the plume as a function of its distance from the source. The rise ofthe plume over the first 3–4 km can be seen. MISR orbit 24166, blocks 38–40,July 3, 2004.
Fig. 6. Above, the geographic distribution of smoke plumes that were foundusing our automated approach. Below, the distribution of smoke plume heights(above sea level) we computed. There were 77 plumes found, during the summerof 2004.
Out of all plumes found, it is not uncommon to end up withseveral detections that are actually of the same plume, but withdifferent tips that are off by only a couple of pixels. Thereforewe perform an arbitration step, where we randomly choose oneplume out of all overlapping detections, and eliminate theothers. After this step is done, for each plume we save the tip p2and the set of points S for further analysis.
2.6. Limitations of this approach
There are several examples of smoke plumes that we wouldlike to be able to detect but our algorithm is currently unable toidentify. It was not a design goal to find plumes that originateoutside of the MISR swath, because we would have lessinformation about the fire, so our approach deliberately rejectssuch examples. Smoke plumes that are very near the edge of theMISR swath can confound the algorithm. A more commonexample of a failure was when two independent smoke plumesthat originate very near each other merge to form a single shape.Most of the time our algorithm rejects such examples. Weexpected cases of mixed clouds and smoke to cause problems,but we found several examples of smoke plumes with
Table 1Table of data collected from 77 smoke plumes observed over North America in the summer of 2004
Date (2004) UTC time Terra orbit no. Lat. Lon. Orient. Plume height (km) Fire area (km2) Mean radiative power (MW)
An electronic version can be found at this URL: http://www-misr.jpl.nasa.gov/mission/data/plume_data/PlumeDB-2006-02-06.csv.
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pyrocumulus clouds that were successfully detected by oursystem.
2.7. Computing the injection height and other properties
For each plume found, and for which associated stereoheights have been retrieved, we determine several propertiesassociated with the plume from the MISR andMODIS data. Themost important of the properties we derive from the MISR datais the maximum observed height of the plume, as one of ourprimary goals is to empirically relate the plume's injectionheight to the power of the fire and local weather conditions.
MISR has two different stereo-derived height products: withand without wind correction. Because there is approximately a1-min delay between the time when each of MISR's camerasimages each ground pixel, disparities in the location of featuresthat appear in multiple cameras could be due to either to parallaxresulting from the feature's height or to true displacementresulting from advection, or as is most likely, a combination ofboth. Because the density of stereo height retrievals that do nothave the wind correction applied is larger, our initial derivationof plume height makes use of the non-wind-corrected values.Correction for wind is then applied as a final step.
Fig. 5 shows a plot of non-wind-corrected heights above sealevel of the pixels in a particular plume, as a function of theirdistance from the tip of the plume. The plot clearly shows therise of the plume and its stabilization at around 3 km. Note thatthe vertical resolution of the height calculation has a precisionof approximately 560 m, which is why the heights appear to bein discrete bins. In theory, the injection height we are computingwould be the maximum height obtained by the plume given aplot such as the one in Fig. 5. However, frequently the heightretrieval gives erroneous results for as many as 10% of thepixels in a plume, sometimes due to stereo matching errors, ordue to detection of higher clouds that were not screened out. Asa result, we found it necessary to add some heuristics toeliminate outliers and estimate the true injection height.Specifically, given the set of height retrievals H in km, wefirst define a threshold max(median(H)⁎1.5, median(H)+1.5),and eliminate all heights above this threshold. This eliminateshigher clouds. Then, instead of taking the maximum of H, we
take the average value of the top 10% of H — this appears togive a better estimate of the true maximum height in thepresence of a small amount of noise in the retrieval. Note thatthese heuristics were developed while examining a few dozeninitial plumes. Analysis of more data could lead to more formalways of eliminating outliers.
As a final step in the determination of injection height, awind correction equation is applied to the results. The MISRstereo algorithm is designed to separate the apparent motion dueto height from the true motion due to wind using camera triplets,but this calculation does not work on all pixels and sometimesresults in isolated blunders. To deal with potentially problematicMISR wind retrievals, we use the following approach: first, oursystem retrieves all heights within the plume without any windcorrection. Then, all stereo height retrievals within a 3-blockarea that have both a “non-wind-corrected” and “wind-corrected” value are regressed and a linear fit is obtained.This provides a height-dependent correction equation for thislocal region, essentially allowing the estimation of a mean windcorrection to the heights while ignoring small errors in the windretrieval or blunders in individual pixels.
3. Results and analysis
We have analyzed four months of data from the summer of2004 (June through September) over North America. Our initialstudy coincides with the time period studied by the Intercon-tinental Chemical Transport Experiment North America(INTEX-NA) field campaign (Singh et al., 2002, in press),motivated by the relatively large number of fires in Alaska andthe Yukon territory that summer. As noted in Averill et al.(2005), more than 2.6×106 ha burned in Alaska, and poorvisibility was recorded in Fairbanks on 42 of the 92 days ofsummer.
We processed a total of 39,105 individual scenes (MISR datablocks) for June to September of 2004 over North America. Aspreviously noted, 82.5% of these were immediately pruned dueto lack of MODIS fire detections, and another 15% of theremaining were pruned due to lack of smoke detections by theSVM classifier. Of the remaining scenes, our analysis softwarefound distinct plumes in only 196 of them. A total of 325
Fig. 7. A page from our website showing detailed information and images about all of the plumes we have analyzed. Available from the following URL:http://www-misr.jpl.nasa.gov/mission/data/plume.html.
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candidate plumes were identified (some scenes contained morethan one plume).
We manually examined each of the 325 potential plumes todetermine if each was a proper detection. It turned out that 138 ofthem were true detections and 187 were false positives. The falsedetections included long, thin branches of a real smoke plume,smoke from a distant source that was trapped in a long, thinvalley (giving the appearance of a plume), and even thin smokeover top of a long, thin river (because the smoke is easier to detectover the water than the surrounding land). More than half of thefalse positives clearly contained smoke, but not a plumeoriginating from a source on the ground. Despite the largenumber of false positives from the automated algorithm, thisresult is considered quite acceptable because the alternativewould be human examination of a data volume two orders ofmagnitude larger. Of the properly detected plumes, 61 hadinconclusive stereo height data fromMISR's operational product,either because the plume was too small or thin, or because theplume was surrounded by other smoke and clouds that made ittoo difficult to separate the plume from its surroundings.
Fig. 6 shows a histogram of the 77 plume heights (above sealevel) obtained using the automated algorithm. The range isfrom 0.72 km to 5.18 km, with a mean of 2.42 km and a medianof 2.16 km. None of our plumes came close to reaching thelower stratosphere, possibly indicating that plumes reaching
those heights are rare. MISR has observed such plumes before,such as the plume from the Chisholm forest fire near Edmonton,Alberta, Canada, which reached heights of 12–13 km above sealevel, as observed by MISR on May 29, 2001 (Diner et al.,2004).
A summary of all of the data we collected on all 77 plumes isfound in Table 1. For each plume, we indicate the date and UTCtime, the orbit number of the Terra spacecraft, the latitude andlongitude of the plume source, the orientation (clockwise with0° for North), injection height in km (as measured by MISR'soperational stereo height product with the corrections describedin the text), the fire area as retrieved by MODIS, and the meanradiative power in MWas retrieved by MODIS. We have postedan electronic version of this table at the following URL: http://www-misr.jpl.nasa.gov/mission/data/plume_data/PlumeDB-2006-02-06.csv and subsequent updates will be available here:http://www-misr.jpl.nasa.gov/mission/data/plume.html.
On this site you will also find more detailed informationabout each plume, including a plot of the geographic coor-dinates on a globe for context, a plot of the pixel heights (like inFig. 5), and an animation of images from the different MISRcamera angles, making the three-dimensional structure of theplume more apparent. An example page from this site is seen inFig. 7. We intend to update this site from time to time as weimprove our algorithms and collect more data.
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Not counting the time to develop and debug our system oracquire the raw data, processing the four months of data for thisstudy required approximately three days of computation time onan ordinary Linux workstation. The raw data consumed 720 GBon disk. Examining the resulting plumes and filtering out falsepositives required about two hours of human time. Extrapolat-ing these numbers, in order to achieve our goal of analyzing fiveyears of data, we estimate that we will need approximately11 TB of disk space, a month and a half of CPU time (thoughthis is trivially parallelizable across multiple machines), and aweek of manual labor to eliminate false positives.
4. Discussion and future work
We have shown how data mining methods applied toimagery and higher level data products from MISR andMODIS on Terra are capable of generating partially automatedretrievals of smoke plume injection heights over large spatialareas. The methodology enables pinpointing smoke sourcesand injection heights, which are needed for accurate modelingof 3-D transport. The ability to deal with large volumes of datamakes it possible to assemble a statistical database ofinformation that can be used in realistic simulations of theeffects of fires on air quality. The database we are developingwill make possible tests for fire models that predict plumeheights for specific fires, based on the energy release. We planto investigate the associated meteorology and likely fuelconsumption for the fire/plume events we have identified inAlaska and the Yukon.
More detailed information on plume profiles is potentiallyavailable from the CALIPSO lidar (Cloud-Aerosol Lidar andInfrared Pathfinder Satellite Observation), which launched inApril 2006. Lidar measurements are also needed to validate theplume heights retrieved by MISR. However, the locations atwhich the CALIPSO ground track crosses a given plume will bean even greater matter of serendipity than is the case withMISR. The spatial and temporal sampling characteristics ofMISR enable observation of any given mid-latitude locationonce every 4–5 days on average. Even so, many plumes that arevisible within the broad MODIS swath are missed, and failureof any stage of our algorithms could also reduce the potentialsize of the database. Nonetheless, the methodology describedhere makes possible an empirical investigation of the relation-ship between source conditions and plume heights that couldnot be done prior to the launch of Terra. We will partially makeup for plume undersampling through sheer volume, and arecurrently in the process of scaling this approach to many yearsof MISR and MODIS data over North America.
Acknowledgements
Portions of the research described in this paper wasperformed at the Jet Propulsion Laboratory, California Instituteof Technology, under a contract with the National Aeronauticsand Space Administration. Jennifer Logan was funded by EPAgrant RD-83227501-0 to Harvard University. MISR data wereobtained from the Atmospheric Sciences Data Center located at
the NASA Langley Data Active Archive Center (DAAC). TheMODIS fire products were obtained from Earth ResourcesObservation and Science (EROS) Data Center.
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