NOTICE -- The LBA-ECO Project website is no longer being supported.  This archive is a snapshot, as it existed in 2013, of the LBA-ECO website, maintained by NASA Goddard Space Flight Center, and now archived at the ORNL DAAC.  Links to external websites may be inactive. Final data products from the LBA project can be found at the ORNL DAAC.
banner banner banner banner banner banner
banner banner banner banner banner banner banner
home aboutlibrarynews archivecontacts banner

Abstracts & Profiles
Research Sites
Synthesis Groups
Field Support
Find LBA Data
Investigator Checklist
Process & Policy
Documentation & Archive
Training & Education
Activities Summary
T&E Goals
Student Opportunities
  Folha Amazônica


LC-23 (Morisette / Schroeder / Pereira)

LBA Dataset ID:



      5. SCHROEDER, W.

Point(s) of Contact:

ORNL DAAC User Services Office Oak Ridge National Laboratory Oak Ridge, Tennessee 37 (

Dataset Abstract:

This data set contains data associated with MODIS fire maps generated using two different algorithms and compared against fire maps produced by ASTER. These data relate to a paper (Morisette et al., 2005) that describes the use of high spatial resolution ASTER data to evaluate the characteristics of two fire detection algorithms, both applied to MODIS-Terra data and both operationally producing publicly available fire locations. The two algorithms are NASA\'s operational Earth Observing System MODIS fire detection product and Brazil\'s National Institute for Space Research (INPE) algorithm. These data are the ASCII files used in the logistic regression and error matrices presented in the paper.

Beginning Date:


Ending Date:


Metadata Last Updated on:


Data Status:


Access Constraints:


Data Center URL:

Distribution Contact(s):

ORNL DAAC User Services Office Oak Ridge National Laboratory Oak Ridge, Tennessee 37 (

Access Instructions:


Data Access:

IMPORTANT: The LBA-ECO Project website is no longer being supported. Links to external websites may be inactive. Final data products from the LBA project can be found at the ORNL DAAC. Please follow the fair use guidelines found in the dataset documentation when using or citing LBA data.

LBA-ECO LC-23 ASTER and MODIS Fire Data Comparison for Brazil: 2003-2004:

Documentation/Other Supporting Documents:

LBA-ECO LC-23 ASTER and MODIS Fire Data Comparison for Brazil: 2003-2004:

Citation Information - Other Details:

Morisette, J. T., L. Giglio, I. Csiszar, A. Setzer, W. Schroeder, D. Morton and C. O. Justice. 2006. LBA-ECO LC-23 ASTER and MODIS Fire Data Comparison for Brazil: 2003-2004. Data set. Available on-line [] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A. doi:10.3334/ORNLDAAC/839

Keywords - Theme:

Parameter Topic Term Source Sensor


Keywords - Place (with associated coordinates):

(click to view profile)
(click to view profile)
North South East West
  BRAZIL 5.00000 -11.00000 -54.00000 -68.00000

Related Publication(s):

Morisette, J.T., L. Giglio, I. Csiszar, A. Setzer, W. Schroeder, D. Morton, and C.O. Justice. 2005. Validation of MODIS Active Fire Detection Products Derived from Two Algorithms. Earth Interactions 9(9):1-25.

Data Characteristics (Entity and Attribute Overview):

Data Characteristics:

Data Set Contents: The output data are ASCII files where each row represents a MODIS fire pixel and the summarized information from the ASTER fire mask pixels within that MODIS pixel.

The data files used for and resulting from this analysis are contained in 3 subdirectories.

The directory input_inpe contains the input MODIS fire product derived from the INPE algorithm HDF files.

The directory output_mod14 compares ASTER to the MODIS fire detection from the NASA/UMD algorithm.

The directory output_INPE compares ASTER to the MODIS fire detection from the INPE algorithm.

Input Files: The MODIS fire product derived from the INPE algorithm is provided in the files contained in the input_inpe directory as HDF files.

Example input file name:

INPE.A2003028.1435.001.hdf derived from the MODIS at-sensor scaled radiance swath data collected in year 2003, day 028, at 1435 GMT.

Output Files: The output files reside in two directories; the first for the comparison of the ASTER fire mask with the standard NASA/Univeristy of Maryland fire product (output_mod14) and the second for the comparison of the ASTER fire mask with the INPE MODIS algorithm (output_inpe).

Example output file names:

Output files of ASTER vs MODIS/EOS algorithm (within the output_mod14 directory):


Output files of ASTER vs MODIS/INPE algorithm (within the output_inpe directory):


Output file name syntax:

Up to the first . in the file name distinguishes between the MODIS/EOS and MODIS/INPE files.

Between the first and second . represents the day of the year for the MODIS data.

Between the second . and the - represents the time of the MODIS data collection

Between the - and the third . represents the unique component of the ASTER local granule ID from the ASTER scene used.

<p class=i2>

A Note about the Standard Input Files:</b></p>

<p class=i2>

The standard MODIS fire product and original ASTER files are available through the Land Processes Distributed Active Archive Center (LP DAAC,

and searchable via the filenames listed in the companion filelist.csv file, therefore, no attempt was made to create a redundant archive at the ORNL DAAC.


<p class=i2>


<p class=i2>Contents of the Output Files: </b>The output data are ASCII files

with header information (lines 1-4) and where each subsequent data row represents an

individual 1km MODIS fire pixel and the summarized information from the ASTER fire mask pixels within that MODIS pixel.<blockquote>

<p class=i2> <table border=2 cellpadding=2 cellspacing=1 bordercolor=#111111 width=90% id=table1>


<td width=204 bgcolor=#99CCFF>

<p align=center><font size=4>Start of File</font></b></td>

<td width=496 bgcolor=#99CCFF>

<p align=center> <font size=4>Description</font></b></td>



<td width=204 bgcolor=#D2E9FF>

<p class=i2> line 1</td>

<td width=496 bgcolor=#D2E9FF>

<p class=i2> ASTER file name</td>



<td width=204 bgcolor=#D2E9FF>

<p class=i2> line 2</td>

<td width=496 bgcolor=#D2E9FF>

<p class=i2> MODIS geolocation file name</td>



<td width=204 bgcolor=#D2E9FF>

<p class=i2> line 3</td>

<td width=496 bgcolor=#D2E9FF>

<p class=i2> File giving the location of either the MODIS or INPE

algorithm fire locations</td>



<td width=204 bgcolor=#D2E9FF>

<p class=i2> line 4</td>

<td width=496 bgcolor=#D2E9FF>

<p class=i2> File for the binary ASTER fire mask</td>



<td width=204 bgcolor=#D2E9FF>

<p class=i2> lines 5 through N+5 </td>

<td width=496 bgcolor=#D2E9FF>

<p class=i2>Where N = the number of MODIS pixels that have corresponding

ASTER data.<p class=i2> There are 15 columns of data,

these are:</b></td>



<td width=204 bgcolor=#D2E9FF> relX</td>

<td width=496 rowspan=2 bgcolor=#D2E9FF nowrap>

<p align=left>relX and relY are the pixel coordinates of

the MODIS swath data, relative to the subset of MODIS data

overlapping the ASTER image such that (0,0) is the upper left MODIS

pixel for the overlapping area.</td>



<td width=204 bgcolor=#D2E9FF> relY</td>



<td width=204 bgcolor=#D2E9FF> X</td>

<td width=496 rowspan=2 bgcolor=#D2E9FF>X and Y are the

pixel coordinates in MODIS swath image (listed on line 3), with

upper left corner pixel (0, 0)

X = along scan direction

Y = along track direction</td>



<td width=204 bgcolor=#D2E9FF> Y</td>



<td width=204 bgcolor=#D2E9FF> lat</td>

<td width=496 rowspan=2 bgcolor=#D2E9FF>lat and lon are latitude and longitude in

decimal degrees
(Southern latitude is negative, Eastern longitude is positive)</td>



<td width=204 bgcolor=#D2E9FF> lon</td>



<td width=204 bgcolor=#D2E9FF> MODIS</td>

<td width=496 bgcolor=#D2E9FF>MODIS is the MODIS fire classification

(more information is at

<a href=></a>

)<p>0: not processed (missing input data)
2: not processed (other reason)
3: water mask (no fire algorithm applied)
4: cloudy (significantly obscured by clouds so that no attempt is made to

extract fire information)
5: no fire
6: unknown (information from adjacent pixel is unknown and contextual

classifier can not be applied and single pixel information is not

7: low-confidence fire
8: nominal-confidence fire
9: high-confidence fire</td>



<td width=204 bgcolor=#D2E9FF> count </td>

<td width=496 bgcolor=#D2E9FF>count is the number of ASTER

fire pixels within corresponding MODIS pixel</td>



<td width=204 bgcolor=#D2E9FF> Moran</td>

<td width=496 bgcolor=#D2E9FF>Moran is the Moran's I calculation for the ASTER fire data

within the corresponding MODIS pixel</td>



<td width=204 bgcolor=#D2E9FF> variance</td>

<td width=496 bgcolor=#D2E9FF>variance is the variance of ASTER fire data within

corresponding MODIS pixel</td>



<td width=204 bgcolor=#D2E9FF> mean ASTER fire

cluster size</td>

<td width=496 bgcolor=#D2E9FF>mean ASTER fire cluster size, is

the number of individual ASTER fire pixels divided by the number of

contiguous ASTER fire clusters, within a given MODIS pixel</td>



<td width=204 bgcolor=#D2E9FF> MODIS band

21/22 brightness temperature</td>

<td width=496 bgcolor=#D2E9FF>MODIS band 21/22 brightness

temperature is the 3.96 micron channel brightness temperature of

fire pixel. Comes from either band 21 or 22; 22 saturates first, at

which point we switch to 21.</td>



<td width=204 bgcolor=#D2E9FF> MODIS band 31

brightness temperature</td>

<td width=496 bgcolor=#D2E9FF>MODIS band 31 brightness

temperature is the Band 31 brightness temperature of fire pixel.</td>



<td width=204 bgcolor=#D2E9FF> FRP</td>

<td width=496 bgcolor=#D2E9FF>FRP is currently unused -- set

to 0</td>



<td width=204 bgcolor=#D2E9FF> MODIS fire

pixel confidence</td>

<td width=496 bgcolor=#D2E9FF>MODIS fire pixel confidence is a

Heuristic confidence estimate of detection confidence. Range 0 -

100, with 0 lowest and 100 highest. ***</td>



<p class=i2>*** For details, please see: Giglio, L., J. Descloitres, C. O.

Justice, and Y. Kaufman, 2003: An enhanced contextual fire detection

algorithm for MODIS. Remote Sens. Environ., 87, 273-282. [In this paper the

confidence numbers range from 0 to 1.0, these have been linearly scaled to

integers between 0 and 100 in the output files.]<p class=i2> </blockquote>

<p class=i2>Companion Files: </b></p>

<p class=i2>The companion file comp/filelist.csv is an ASCII comma separated value file that lists the

file names and the temporal and spatial details of the MODIS and ASTER files used in the analysis and

as listed in Table 1 of Morisette et al. (2005).</p>

<p class=i2>filelist.csv sample record:</b></p>


<table border=1 width=90% bgcolor=#D2E9FF id=table2>


<td> <p>Day (dd), Month (mmm), Year (yyyy), MOD03 file name (geolocation), MOD14 File name (UMD MODIS input), INPE File name (INPE MODIS fire input), ASTER Local Granule ID, ASTER Granule, Acquisition Time,

ASTER Center Latitude, ASTER Center Longitude, ASTER % Cloud Cover, Related figure


<p>19, Jan,2003, MOD03.A2003019.1440.004.2003020002948.hdf, MOD14.A2003019.1440.004.2003020203252.hdf, NPE.A2003019.1440.001.hdf, AST_L1B#003_01192003144147_06052003101611.hdf, SC:AST_L1B.003:2013696081, 14:41:47, 4.14,

-61.17, 59 ,</p>

<p>19, Jan, 2003, MOD03.A2003019.1440.004.2003020002948.hdf, MOD14.A2003019.1440.004.2003020203252.hdf, INPE.A2003019.1440.001.hdf, AST_L1B#003_01192003144156_06052003101600.hdf, SC:AST_L1B.003:2013696076, 14:41:56, 3.61, -61.28, 27 ,</p>

<p>19, Jan, 2003, MOD03.A2003019.1440.004.2003020002948.hdf, MOD14.A2003019.1440.004.2003020203252.hdf, INPE.A2003019.1440.001.hdf, AST_L1B#003_01192003144204_03152003155855.hdf, SC:AST_L1B.003:2011896728, 14:42:04, 3.07, -61.4, 13 ,






Data Application and Derivation:

Data are used to relate the MODIS fire detections (1km) with a 30m spatial resolution fire map derived from ASTER.

Quality Assessment (Data Quality Attribute Accuracy Report):

Quality Assessment:

Assessing the MODIS data quality is actual the purpose of these data. The ASTER fire map is assumed to be correct. While the accuracy of the ASTER fire map is not quantified, due to its spatial resolution is it assumed to be accurate enough to assess the 1km MODIS product. One should be careful, as we are in Morisette et al., 2005, to note that the data compare MODIS to ASTER fire counts (and do not compare MODIS to actual ground truth fire counts or fire size).

Process Description:

Data Acquisition Materials and Methods:

Please see also section 3 in: Morisette, J.T., L. Giglio. I. Csiszar, A. Setzer, W. Schroeder, D. Morton, C O. Justice, 2005, Validation of MODIS active fire detection products derived from two algorithms, Earth Interaction, vol. 9, paper 9, where you will find the following information:

Data: Satellite fire detection algorithms</b>

MODIS (Kaufman et al. 1998) is a 36-band instrument with substantially improved capabilities for fire mapping as compared to the AVHRR. The first MODIS sensor is on board the Terra satellite, which was launched in December 1999 and has a daytime local overpass of about 10:30 A.M. The second MODIS sensor is on board the Aqua satellite, launched in May 2002, with a 1:30 P.M. daytime local overpass. One of the land products derived from the MODIS sensor is a pixel resolution fire mask, separated into files representing 5 min of image acquisition along a given swath (Justice et al. 2002). The increased saturation temperatures of the 1-km-resolution 3.9- and 11- m sensors decrease the ambiguities leading to false alarms or omission errors typical of the AVHRR-based fire products (Giglio et al. 2003).


Starting mid-2002, daily processing of MODIS direct broadcast data began at INPE. INPE\'s satellite receiving station located in Cuiaba, Mato Grosso, in central Brazil receives Terra and Aqua imagery and disseminates that information to the Centro de Previsao de Tempo e Estudos Climaticos (CPTEC: Center for Weather Forecast and Climate Studies) in Cachoeira Paulista, Sao Paulo, where fire products are designed and implemented. The MODIS INPE algorithm relies on the well-consolidated methodology of fixed threshold algorithms (Setzer and Pereira 1991; Setzer et al. 1994; Setzer and Malingreau 1996; Li et al. 2001). INPE has successfully used this method with the NOAA AVHRR series of satellite data for nearly two decades. The daytime algorithm uses empirically derived thresholds. Pixels are classified as fire if two conditions are satisfied: band 20 > 3000 digital numbers (DNs) and band 9 < 3300 DNs. The band 20 test is used to determine pixels that are potentially associated with vegetation fires at the surface while the band 9 test is used to eliminate eventual sources of contamination that affect the fire product (e.g., bright targets). The nighttime algorithm requires one condition, band 20 > 3000. Text files with fire coordinates are disseminated to regional fire monitoring centers (e.g., PROARCO) and made available to the user community under a Web-based GIS system within approximately 2 h after the satellite overpass time (information online at


Fire detection within the EOS MODIS fire products is performed using a contextual algorithm that exploits the strong emission of midinfrared radiation from fires (Dozier 1981; Matson and Dozier 1981). Briefly, multiple tests are applied to each pixel of the MODIS swath that look for the characteristic signature of an active fire in which the 4- m brightness temperature, as well as the 4- and 11- m brightness temperature difference, departs substantially from that of the nonfire background. Relative thresholds are adjusted based on the natural variability of the scene. Additional specialized tests are used to eliminate false detections caused by sun glint, desert boundaries, and errors in the water mask. The algorithm ultimately assigns to each pixel one of the following classes: missing data, cloud, water, nonfire, fire, or unknown. A detailed description of the detection algorithm is provided by Giglio et al. (Giglio et al. 2003). In this study we used the Collection 4 level 2 (swath based) fire product, available from the Land Processes Distributed Active Archive Center (DAAC) via the EOS Data Gateway (


ASTER (Yamaguchi et al. 1998), also on board the Terra satellite, provides near-nadir view measurements in four visible and near-infrared bands between 0.52 and 0.86 m, six shortwave infrared (SWIR) bands between 1.6 and 2.43 m, and five thermal infrared (TIR) bands between 8.125 and 11.65 m at 15-, 30-, and 90-m resolutions, respectively. The coincident high-resolution, multispectral measurements within a 60 km swath near the center of the MODIS swath provide a unique opportunity to analyze the finescale features within the MODIS pixels, such as active fires. In this study we utilized 22 ASTER Level 1B calibrated radiance scenes obtained through the NASA Earth Observing System Data Gateway (EDG) ( ).

The companion file (filelist.csv) provides a table containing the file names that provide the unique identifier for each image data set for the Terra MODIS Thermal Anomalies/Fire 5-min Level 2 1-km swath (MOD14), the Terra MODIS Level 1A Geolocation data (MOD03; required input for proper geolocation of MOD14 swath data), and the ASTER Level 1B data. All of these data can be found in the EOS data gateway by searching for this file name as the local granule ID. Figure 1 shows the distribution of these scenes in space and the companion file provides details for the acquisition date, time, center latitude and longitude, cloud cover, and file name for each ASTER scene and the associated MODIS file names.


Dozier, J. (1981), A method for satellite identification of surface temperature fields of subpixel resolution, Remote Sensing of Environment, 11:221-229.

Giglio, L., J.Descloitres,., C.O.Justice, and Y.Kaufman (2003), An enhanced contextual fire detection algorithm for MODIS. Remote Sensing of Environment, 87,273-282.

Justice, C. O., L.Giglio, S. Korontzi, J. Owens, J. Morisette,D. Roy, D., J. Descloitres, S. Alleaume, F. Petitcolin, and Y. Kaufman,( 2002), The MODIS fire products, Remote Sensing of Environment, 83, 244-262.

Kaufman, Y. J., C.O.Justice, L.Flynn, J.D. Kendall, E.M. Prins, L. Giglio, D. Ward, W. Menzel, and A. Setzer (1998) Potential global fire monitoring from EOS-MODIS. Journal of Geophysical Research, 103, 32215-32238.

Li, Z., Y.Kaufman, C. Ichoku, R. Fraser, A.Trishchenko, L. Giglio, J-Z. Jin, and X. Yu (2001), A review of AVHRR-based fire detection algorithms: Principles, Limitations and Recommendations. In Global and Regional Wildfire Monitoring from Space: Planning a Coordinated International Effort. Edited by F. Ahern, J. Goldammer and C.O. Juctice. SPB Academic Publishing, The Hague, The Netherlands, 199-225.

Matson, M., and J. Dozier (1981), Identification of subresolution high temperature sources using a thermal IR sensor. Photogrammetric Engineering and Remote Sensing, 47,1311-1318.

Setzer, A. W., and J.P. Malingreau (1996), AVHRR Monitoring of Vegetation Fires in the Tropics: Toward the Development of a Global Product. In Biomass Burning and Global Change, Vol. 1: Remote Sensing, Modeling and Inventory Development, and Biomass Burning in Africa, edited by J. S. Levine, pp. 25-39, MIT Press, Cambridge MA, USA.

Setzer, A.W., A.C. Pereira Jr, and M.C. Pereira (1994), Satellite studies of biomass burning in Amazonia: some practical aspects, Remote Sensing Reviews, 10, 91-103.

Setzer, A. W., and M. Pereira (1991a), Operational detection of fires in Brazil with NOAA-AVHRR. Proceed. 24th. Int. Symp. Remote Sensing of Environment, R.Janeiro, 27-31 maio, 1991, vol I, pp. 469-482, ERIM, Ann Arbor, Michigan, USA.

Yamaguchi, Y., A.B. Kahle, H. Tsu, T. Kawakami and M. Pniel (1998), Overview of Advanced Spaceborned Thermal Emission and Reflection Radiometer (ASTER). IEEE Transactions on Geoscience and Remote Sensing, 46, 1062-1071.


NASA logo
Get Acrobat Reader