MODIS Collection 4 Active Fire Product User's Guide Version 2.2
Contents
1. MJ AAA Figure 1 MODIS tiling scheme 2 1 3 Climate Modeling Grid CMG MODIS Level 3 and Level 4 products can also be defined on a coarser resolution Climate Modelling Grid CMG The objective is to provide the MODIS land products at consistent low resolution spa tial and temporal scales suitable for global modeling In practice there is a fair amount of variation in the spatial and temporal gridding conventions used among the MODIS land CMG products 2 1 4 Collections Reprocessing of the entire MODIS data archive is periodically performed so as to incorporate better calibration algorithm refinements and improved upstream products into all MODIS products The updated MODIS data archive resulting from each reprocessing is referred to as a collection Later collections supersede all earlier collections For the Terra MODIS Collection 1 consists of the first products generated following launch Terra MODIS data were first reprocessed for the first time in June 2001 to produce Collection 3 Note that this first reprocessing was numbered Collection 3 rather than Collection 2 as one would expect Collection 3 was also the first produced for the Aqua MODIS products Collection 4 reprocessing was initiated in December 2002 for the Terra MODIS and somewhat later for the Aqua MODIS and it forms the current archive of the MODIS products Currently Collection 5 reprocessing is2. ALY Metadata A O BAS 4 1 6 Example Code o ars a bal a a RAE da Ea 42 MODI4Al and MYDIBAL e ASDA Pira Mask clica a td dE A dt LD OA A A A A ce E A at weit Manb pS A nN 4 3 4 4 423 Example Code 00 A AA MOD14A2 and MYD I4A2 a AS Fr Mask a et A ee Ba AA ee et Bes AZ SOAR A Sd es A MA en de 4 3 3 Example Code ome uo ee ra OL a de ee CMG Fire Products MODI4CMH MYDI4CMH etC 4 4 1 CMG Naming Convention e 442 Data Layers idas a as hae BA a ar ps a aes 4 4 3 Global Metadata cocos ora dd es 4 4 4 Climate Modeling Grid Navigation e AAS Example Code o coi lab Caveats and known Problems 5 1 5 2 Cave is as a e is ht od ao A A de 5 1 1 Fire Pixel Locations vs Gridded Fire Products Collection 4 Known Problems e 5 2 1 Pre November 2000 Data Quality 5 2 2 Regional False Alarms o o e e 5 2 3 Detection Confidence oc e a s a evs add a a a a a e A A Frequently Asked Questions 6 1 6 2 6 3 6 4 Terra and Aqua Satellites ee A 6 1 1 Where can I find general information about the Terra and Aqua satellites 6 3 1 How are the fires and other thermal anomalies identified in the MODIS fire products detected oo o e 6 3 2 What is the smallest fire size that can be detected with MODIS What about the
3. Table 1 MODIS fire product availability Product Source Level 2 and most Level 3 fire products MOD14 MYD14 MOD14A1 MYDI4A1 MOD14A2 MYD14A2 EOS Data Gateway see Section 3 1 Level 2G fire products MOD14GD MOD14GN MYD14GD MYD14GN Unavailable not archived CMG fire products MOD14CMH MYD14CMH MOD14C8H MYD14C8H University of Maryland see Section 3 2 Active fire locations in ASCII and ESRI shape file MODIS Web Fire Mapper format Fire and corrected reflectance JPEG imagery and MODIS Land Rapid Response System ASCII fire locations 3 1 The EOS Data Gateway EDG Most of the MODIS land products may be obtained from the Land Processes Distributed Active Archive Center LP DAAC using a Web based interface known as the EOS Data Gateway EDG Below is a brief tutorial for using the EDG to order the fire products 1 Goto the LP DAAC EOS Data Gateway web site shown below http edcimswww cr usgs gov pub imswelcome The EOS Data Gateway is also available from other DAACs You may order the MODIS fire products from any of these alternate sites since they talk to one another but the fire products are in fact archived only at the LP DAAC Regardless of which particular EDG you use therefore the Level 2 and Level 3 fire products will end up coming from the LP DAAC n EOS Data Gateway Welcome H Land Processes e
4. A pixel class of 1 was defined for historical reasons but will never actually occur in the fire mask 4 1 2 Detection Confidence A detection confidence intended to help users gauge the quality of individual fire pixels is included in the Level 2 fire product This confidence estimate which ranges between 0 and 100 is used to assign one of the three fire classes low confidence fire nominal confidence fire or high confidence fire to all fire pixels within the fire mask In some applications errors of comission or false alarms are particularly undesirable and for these applications one might be willing to trade a lower detection rate to gain a lower false alarm rate Conversely for other applications missing any fire might be especially undesirable and one might then be willing to tolerate a higher false alarm rate to ensure that fewer true fires are missed Users requiring fewer false alarms may wish to retain only nominal and high confidence fire pixels and treat low confidence fire pixels as clear non fire land pixels Users requiring maximum fire detectability who are able to tolerate a higher incidence of false alarms should consider all three classes of fire pixels 4 1 3 Algorithm Quality Assessment Bits Pixel level QA is stored in a 32 bit unsigned integer SDS named algorithm QA with individual fields stored in specific bits The exact meaning of these bit fields is defined in the Level 2 Fire 17 Product file spe
5. M Li R R and Jus tice C O 2003 Fires and smoke observed from the Earth Observing System MODIS instrument products validation and operational use International Journal of Remote Sensing 24 1765 1781 Kaufman Y J Justice C O Flynn L P Kendall J D Prins E M Giglio L Ward D E Menzel W P and Setzer A W 1998 Potential global fire monitoring from EOS MODIS Journal of Geophysical Research 103 32215 32238 Morisette J Giglio L Csiszar I and Justice C O 2002 Validation of the MODIS Active fire product over Southern Africa with ASTER data International Journal of Remote Sensing in press Morisette J T Giglio L Csiszar I Setzer A Schroeder W Morton D and Justice C O 2005 Validation of MODIS active fire detection products derived from two algorithms Earth In teractions 9 9 1 25 Roy D P Borak J S Devadiga S Wolfe R E Zheng M and Descloitres J 2002 The MODIS Land product quality assessment approach Remote Sensing of Environment 83 62 76 7 2 Relevant Web and FTP Sites e MODIS Fire and Thermal Anomalies General information about the MODIS Fire Ther mal Anomalies and Burned Area products http modis fire umd edu e MODIS File Specifications Detailed file descriptions of all MODIS land products ftp modular gsfc nasa gov pub LatestFilespecs e MODIS LDOPE Tools A collection of programs written by members of the Land Data
6. sds_index SDnametoindex sd_id most confident detected fire FAIL exit 2 if sds_id SDselect sd_id sds_index FAIL exit 3 if SDgetinfo sds_id char NULL amp rank dim_sizes amp data_type amp nattr FAIL exit 4 check rank and data type if rank 2 exit 5 if data_type DFNT_UINT8 exit 6 if SDreaddata sds_id start NULL edges void fire mask FAIL exit 7 if SDendaccess sds_id FAIL exit 8 if SDend sd_id FAIL exit 9 simple example count grid cells containing fires nfire 0 for i 0 i lt ROWS i for j 0 j lt COLS j if fire_mask i j gt 7 nfire printf Sd grid cells containing fires n nfire exit 0 24 4 4 CMG Fire Products MOD14CMH MYD14CMAH etc The CMG fire products are gridded statistical summaries of fire pixel information intended for use in regional and global modeling and are currently generated at 0 5 spatial resolution for time periods of one calendar month MOD14CMH MYD14CMH and eight days MOD14C8H MYD14C8H Higher resolution 0 25 CMG fire products will eventually be produced as well At present the CMG products are distributed from the University of Maryland via anonymous ftp see Section 3 2 For convenience the products are distributed in multiple standard data formats Currently HDF and Flexible Image Transport System FITS files are available additional formats
7. If the num ber of fire pixels is zero all of the FP SDSs will have length zero and there s nothing left to process so close the file and go on to whatever else you d like to do 3 Find the number of lines in the granule Call this number nlines In the product this quantity corresponds to the dimension number_of_scan lines Since it is difficult or impossible to determine the value of a named dimension directly with the HDF library you must instead determine the dimensions of an SDS for which the named dimension applies You can use either the fire mask or algorithm QA SDSs for this as they both have dimen sions number_of_scan_lines by pixels per scan line The HDF library function SDgetinfo returns this information in IDL use HDF _SD_GETINFO You can determine the number of samples too pixels_per_scan _line if you like but it will always be 1354 4 Read the the FP_line and FP_sample SDSs in their entirety These arrays contain pixel co ordinates within the granule for all of the quantities in the other FP SDSs Hereafter we ll assume these have been read and stored in internal arrays named FP_line and FP_sample respectively 5 Create a 2 D array to hold whatever FP_ quantity it is that you d like to use Assuming you want the band 21 22 brightness temperature FP_T21 then in IDL you could do this T21 fltarr nlines 1354 32 6 Read the entire FP SDS that you d
8. Op erational Product Evaluation LDOPE group to assist in the analysis and quality assessment of MODIS Land MODLAND products http LPDAAC usgs gov landdaac tools ldope info about asp e MODIS Reprojection Tool MRT Release 3 2a Software for reprojecting tiled MODIS Level 3 products into man different projections http LPDAAC usgs gov landdaac tools modis index asp 41 MODIS Web Fire Mapper Generates custom maps of active fires detected by the Terra and Aqua MODIS instruments Users can also active fire locations in ESRI shape file and ARC INFO formats http maps geog umd edu MODIS Rapid Response System Access to near real time Terra and Aqua MODIS re flectance fire vegetation index and land surface temperature imagery Includes a multi year archive http rapidfire sci gsfc nasa gov EOS Data Gateway at the LP DAAC The primary distribution site for most of the MODIS land products http edcimswww cr usgs gov pub imswelcome MODIS Land Product Quality Assessment Product quality assessment QA related in formation including a very complete archive of known land product issues with descriptions and examples http landweb nascom nasa gov cgi bin QA WWWhewPage cgi 42
9. S Distributed Active Archive Center Notice If you are new to the EOS Data Gateway or are having difficulties placing an order please refer to the quick start tutorial for assistance For questions and answers please visit the LP DAAC s customer support center Earth Observing System Data Gateway Search for and order earth science data products from NASA and affiliated centers Data Center Status D Internet zone Enter the Data Gateway Enter as guest Enter as a registered user Other Data Gateway Sites My Account Become a registered user Forgot my password What s New Landsat 7 ETM Dataset Transition New Data Sets Data Gateway News EOS Program News How to User Support FAQ Tutorial Browser check out Useful links Earth science data sources and archives NASA Earth Observing System Sample data Outreach and education Information for data providers Calendar updates data providers GSFC Due to the number of large data requests ECS orders may be delayed As an alternative you may use the Data Pool option located in the On Line Access column in EDG or use http daac gsfc nasa qov data datapoo and download your data via FTP If you have any questions regarding your order please contact GDAAC User ces LARC Users obtaining SAGE 111 data via the EOS Data Gateway Langley DAAC Web Access or Data Pool should note that the version number on those systems represents the Earth Scie
10. fire land pixels in grey and fire pixels in white 2 6 Climate Modeling Grid Fire Products MOD14CMH MYD4CMH etc The CMG fire products are gridded statistical summaries of fire pixel information intended for use in regional and global modeling The products are currently generated at 0 5 spatial resolution for time periods of one calendar month MOD14CMH MYD14CMH and eight days MOD14C8H MYD14C8H Higher resolution 0 25 CMG fire products will eventually be produced as well An example of the corrected fire pixel count layer of the product is shown in Figure 5 Figure 5 Example of the corrected fire pixel count data layer from the January 2001 CMG fire product 2 7 The Rapid Response Fire Products The MODIS Rapid Response System produces near real time global imagery including true and false color corrected reflectance superimposed with fire locations Figure X Normalized Differ ence Vegetation Index NDVI and land surface temperature Burned area maps will be added in the near future The locations of Terra and Aqua MODIS fire pixels are also available in ASCII files For further information see the MODIS Rapid Response web site Inttp rapidfire sci gsfc nasa gov 3 Obtaining the MODIS Active Fire Products All MODIS products are available to users free of charge There are currently three different ways to obtain the MODIS fire products Not all products are available through each of these methods See Table 1
11. largest is a ta a ia 6 3 3 Why didn t MODIS detect a particular fire o 6 3 4 How do I obtain the MODIS fire products o 6 3 5 What validation of the MODIS active fire products has been performed 6 3 6 Idon t want to bother with strange file formats and or an unfamiliar ordering interface and or very large data files Can t you just give me the locations of fire pixels in plain ASCII files and Pll bin them myself 6 3 7 How well can MODIS detect understory burns 6 4 1 Why do the Level 2 product files vary in size 6 4 2 How should the different fire detection confidence classes be used 6 4 3 How can I take data from the fire pixel table SDSs i e the one dimensional SDSs with the prefix FP _ and place the values in the proper locations of a two dimensional array that matches the swath based fire mask and al gorithm QA SDSs 2 2 o 30 30 30 30 30 30 30 30 31 31 31 31 31 31 32 32 32 6 4 4 Why are the values of the fire radiative power FRP I see in the Level 2 product inconsistent with those I find in the CMG products and published in various journal papers o ee ee 33 6 4 5 What is the area of a MODIS pixel at the Earth s surface 33 6 5 Level 3 Tiled Fire Products socere ma ee ee 35 6 5 1 Why do coastlines in the tile based Level 3 products looked so warped 35 6 6 Level
12. like to use In the above example this is FP_T21 Following our earlier convention we ll assume this SDS is read into an internal array named FP_T21 7 Populate pixels in the T21 array by indexing it with FP_line and FP_sample In IDL you would do this in one shot T21 FP_T21 FP_line FP_sample In non vector based languages you d have to write an explicit loop In C for example do this for i 0 i lt num_fire_pixels i T21 FP_line i FP_sample i FP_T21 il Note that the coordinates in FP line and FP_sample are zero based In a language like Fortran with the first array element numbered 1 you d have to add 1 to all values in FP_line and FP_sample 8 Do whatever you want with the 2 D T21 array it can now be indexed just like the fire mask and QA SDSs would be if you had read them from the file Note though that the newly created T21 array will only contain data in those pixels where fires were detected This is true for 2 D arrays created from any of the other FP SDSs as well 9 Go back to step 4 for the remaining FP quantities you want to use 10 Close the HDF file 6 4 4 Why are the values of the fire radiative power FRP I see in the Level 2 product in consistent with those I find in the CMG products and published in various journal papers The FP_power SDS in the Collection 4 Level 2 product actually contains radiative power per unit area despite the f
13. points in the satellite s along track direction Figure 2 Example MOD14 granule with water shown in blue clouds in purple clear land in grey and active fires in white The along track direction points toward the top of the page 2 3 Level 2G Daytime and Nighttime Fire Products MOD14GD MOD14GN Terra and MYD14GD MYD14GN Aqua The Level 2 active fire products sensed over daytime and nighttime periods are binned without resampling into an intermediate data format referred to as Level 2G The Level 2G format provides a convenient geocoded data structure for storing granules and enables the flexibility for subsequent temporal compositing and reprojection The Level 2G fire products are a temporary intermediate data source used solely for producing the Level 3 fire products and are consequently not available from the permanent MODIS data archive 2 4 Level 3 8 Day Daily Composite Fire Products MOD14A1 Terra and MYD14A1 Aqua The MODIS daily Level 3 fire product is tile based with each product file spanning one of the 460 MODIS tiles 326 of which contain land pixels The product is a 1 km gridded composite of fire pixels detected in each grid cell over each daily 24 hour compositing period For convenience eight days of data are packaged into a single file Figure 3 shows the Terra fire mask for 19 September 2001 from the 14 21 September 2001 daily Level 3 fire product Collection 4 The tile is located in Northern Australia H3
14. the MODIS Active Fire Products Here we provide a general overview of the MODIS active fire products More detailed descriptions of these products and example ingest code can be found in Section 4 2 1 Terminology Before proceeding with a description of the MODIS Fire Products we must briefly describe some of the terminology that you will encounter when ordering and working with MODIS products Specifically we ll define the terms granule tile and collection and the acronym CMG as each applies to the MODIS products 2 1 1 Granules A granule is simply an unprojected segment of the MODIS orbital swath containing about 5 minutes of data MODIS Level 0 Level 1 and Level 2 products are granule based 2 1 2 Tiles MODIS Level 2G Level 3 and Level 4 products are defined on a global 250 m 500 m or 1 km sinusoidal grid the particular spatial resolution is product dependent Because these grids are unmanageably large in their entirety 43200 x 21600 pixels at 1 km and 172800 x 86400 pixels at 250 m they are divided into fixed tiles approximately 10 x 10 in size Each tile is assigned a horizontal H and vertical V coordinate ranging from 0 to 35 and 0 to 17 respectively Figure 1 The tile in the upper left i e northernmost and westernmost corner is numbered 0 0 0 0 ral N7 pr E NL T aS
15. the MODIS tile during that day Product files containing less than eight days of data will occasionally be encountered during time periods of missing data and should be handled in ingest software 4 2 2 QA Each of the daily fire masks has a corresponding QA layer This layer is simply a copy of the pixel level QA from the particular swath pixel of the Level 2 product that populates the Level 3 grid cell 4 2 3 Example Code Example 2 MATLAB code to read the Level 3 MODIS daily fire mask using the MATLAB routine hdfread This is probably the easiest way to read individual HDF SDSs in MATLAB mod14al_file MOD14A1 A2001289 h23v02 004 2003164035111 hdf read entire most confident detected fire SDS in one shot fire mask hdfread mod14al_file most confident detected fire display fire mask for the first day in MOD14A1 MYD14A1 note how image is transposed so that North appears on top imagesc fire_mask 1 5 21 4 3 MOD14A2 and MYD14A2 The MOD14A2 Terra and MYD14A2 Aqua daily Level 3 8 day summary fire products are tile based with each product file spanning one of the 460 MODIS tiles 326 of which contain land pixels The product is a 1 km gridded composite of fire pixels detected in each grid cell over each 8 day compositing period NOTE The Collection 5 MOD14A2 and MYD14A2 fire products will differ significantly from the current Collection 4 versions of these products Im prove
16. to Step 5 otherwise skip to Step 6 not go on to Step 6 Click on whichever MODIS button you did not select in Step 3 11 You should only be here if you want to order both Terra and Aqua MODIS fire products If EOS Data Gateway Primary Data Search http edcimswww cr usgs gov 80 ims bin pub secured nph ims cgi endform 1 amp u 596043 amp sid E gt EOS Data Gateway NASA Watch HDF Viewing Tools Primary Data Search O Data Granule ID Search O Local Granule ID Search Ask a Question Report a problem Choose Search Area Find location using Gazetteer Comment form Enter a range of latitudes and longitudes to specify your search 180 120 60 o 60 120 188 Tegion Formats degree or degree minute or degree minute second 96 38 Northern latitude 5 0000 Western longitude Eastern longitude 35 1000 35 2000 Southern latitude 4 9100 180 120 60 6 68 Display Lat Lon Range on Map O Orthographic Java O Stereographic S pole Type in Lat Lon Range O Equatorial O Stereographic N pole O Type in Path Row Range O Global Search O Global granules only O Type in Lat Lon Point Choose a Date Time Range not required Date format YY YY DDD 1967 025 Time format HH MM 14 30 or HH MM SS 14 30 01 You may also enter a date without a time a start date only or an end date only Use the help link for information on default values Start Date 2003 001 Time UTC 0
17. 0 are too often assigned much higher confidence estimates of 50 or higher This will be corrected for Collection 5 29 6 Frequently Asked Questions 6 1 Terra and Aqua Satellites 6 1 1 Where can I find general information about the Terra and Aqua satellites See NASA s Terra and Aqua web sites for a start http terra nasa gov http aqua nasa gov 6 1 2 When were the Terra and Aqua satellites launched 18 December 1999 and 4 May 2002 respectively 6 2 General MODIS Questions 6 2 1 Where can I find Algorithm Technical Basis Documents ATBDs for the MODIS land products ATBDs for all of the MODIS land products are available from MODARCH Note that many are not up to date In particular the fire product ATBD pre dates the launch of both the Terra and Aqua satellites and describes the very obsolete pre launch detection algorithm 6 3 General Fire Product Questions 6 3 1 How are the fires and other thermal anomalies identified in the MODIS fire products detected Fire detection is performed using a contextual algorithm Giglio et al 2003 that exploits the strong emission of mid infrared radiation from fires Dozier 1981 Matson and Dozier 1981 The algo rithm examines each pixel of the MODIS swath and ultimately assigns to each one of the following classes missing data cloud water non fire fire or unknown Pixels lacking valid data are immediately classified as missing data and excluded from further conside
18. 0 00 00 End Date 2003 365 Time UTC 23 59 59 Clear Time Fields E O Standard Date Range O Julian Date Range O Annually Repeating 6 In the Choose Search Area section of the page enter latitude and longitude boundaries for the geographic region of interest To search the entire globe you can simply select the button Global Search below the map and save yourself some typing 7 Choose a time period of interest in the Choose a Data Time Range section of the page 8 Click on the large pale yellow Start Search icon at the bottom of the page 12 O Y our order has been running for 00m 02s of real time END ORDER NOW Data Data Center Center Status Comments O Connecting Also please read the NASA Privacy Security Notices and the EOS Data Gateway accessibility policy Comments Questions or Problems Email us Created by EOS Data Gateway version 3 6 3 Webmaster Chao Hsi Chang chao hsi_chang sesda com Responsible NASA Official Medora Macie Mail Code 423 NASA GSFC Greenbelt MD 20771 9 Wait while the system searches for products meeting your search criteria This usually takes a few minutes 13 EOS Data Gateway Listing Skip navigator text browsers You are automatically being shown the Granule list since there was only one data set returned Starred links open new windows Search and Order User Preferences Search Creation Search Status R
19. 1 The number of files one must deal with balloons since most users request that individual data layers be written to separate files 2 it is difficult to include useful metadata without writing separate header files increasing the total number of files to handle even further 3 it is possible for data and its accompanying metadata to become separated and 4 production ingest and analysis software is much more likely to break when changes are made to the product 6 6 4 Where can I find information about the FITS file format Information about FITS is available from NASA s FITS Support Office You may also find the TRMM VIRS Monthly Fire Product User s Guide Giglio and Kendall 2003 helpful particularly Appendix 2 6 6 5 How can I display images in FITS files Two good choices are SAOimage Figure 8 and DS9 Figure 9 Use the u1 switch with SAOimage and the orient y switch with DS9 to orient North upwards otherwise the grid will appear upside down nttp tdc www harvard edu software saoimage html Shttp hea www harvard edu RD ds9 37 OOO IXI SAOimage A file MOD14CMH 200302 004 03 fits dire 412 0 169 0 100 3 Figure 8 SAOimage displaying the CorrFirePix data layer in the February 2003 MOD14CMH product e 06 X SAOImage ds9 gt File Edit Frame Zoom Scale Color Region Analysis Help Value LINEAR d Physical Image iFramel Zoo Re
20. 1V10 In this image water is shown in blue clouds in violet non fire land pixels in grey unknown pixels in yellow and fire pixels in white Figure 3 Example of MODI14A1 fire mask for a tile in Northern Australia Water is shown in blue clouds in violet non fire land pixels in grey and fire pixels in white 2 5 Level 3 8 Day Summary Fire Products MOD14A2 Terra and MYD14A2 Aqua The MODIS daily Level 3 8 day summary fire product is tile based with each product file spanning one of the 460 MODIS tiles of which 326 contain land pixels The product is a 1 km gridded composite of fire pixels detected in each grid cell over each 8 day compositing period Figure 4 shows the 8 day summary fire mask from the 26 June 3 July 2002 8 day Level 3 Terra fire product The tile is located in the eastern United States HO8V05 The 8 day composite is the maximum value of the individual Level 2 pixel classes that fell into each 1 km grid cell over the entire 8 day compositing period Due to the way the three different fire pixel confidence levels are defined Section 4 1 the Level 3 8 day fire product is sometimes said to contain the most confident detected fires This description can sometimes be misleading in that pixel values are defined even for those grid cells in which no fire pixels were detected Figure 4 Example of 8 day MOD14A2 fire mask for a tile in eastern United States Wa ter is shown in blue clouds in violet non
21. 3 CMG Fire Products 2 o o e 35 6 6 1 I need to reduce the resolution of the 0 5 CMG fire product to grid cells that are a multiple of 0 5 in size How do I go about doing this 35 6 6 2 Why don t you distribute a daily CMG fire product 37 6 6 3 Why don t you distribute the CMG fire products as plain binary or ASCII TUES ho ah Sate oy ra he ae Ba a ob By Bead a a pija ak nn ate aT 37 6 6 4 Where can I find information about the FITS file format 37 6 6 5 How can I display images in FITS files 2 ee 37 6 7 Hierarchical Data Format HDF o ee es 40 6 7 1 What are HDF files o o e ee 40 6 7 2 How do I read HDF4 files o o e 40 6 7 3 Can t I just skip over the HDF header and read the data directly 40 6 7 4 How can I list the contents of HDF4 files 40 References 41 7 1 Journal Papers and Technical Reports o o o 41 7 2 Relevant Web and FTP Sites 2 20 0 00000000002 ee 41 1 Introduction This document contains the most current information about the Terra and Aqua Moderate Resolution Imaging Spectrometer MODIS fire products It is intended to provide the end user with practical information regarding their use and misuse and to explain some of the more obscure and potentially confusing aspects of the fire products and MODIS products in general 2 Overview of
22. CorrFirePix Rebinning corrected fire pixel counts from 0 5 grid cells left to a 1 grid cell right when missing data values of 1 are present In this case we flag the entire 1 grid cell as lacking valid data which is appropriate when for example we are going to compare independent gridded fire products that won t generally have missing data values in exactly the same grid cells For other applications it would be sufficient to simply exclude the missing values from the sum yielding a result of 300 400 700 fire pixels in the 1 grid cell 0 5 MeanPower MW 1 MeanPower MW E 30 EN Rebinning the mean FRP from 0 5 grid cells left to a 1 grid cell right The result is the average of the FRP in the four 0 5 grid cells nested within the 1 grid cell weighted by their individual corrected fire pixel counts Using the corrected fire pixel counts from the first example above this yields 10 MW x100 20 MW x200 30 MW x300 40 MWx400 __ 30 MW 100 200 300 400 S 36 6 6 2 Why don t you distribute a daily CMG fire product Because a MODIS product at daily temporal resolution will be plagued by extremely large sampling bias errors At most latitudes a single MODIS instrument simply does not sample the Earth s surface adequately in time periods shorter than about 8 days to average out most of the sampling bias 6 6 3 Why don t you distribute the CMG fire products as plain binary or ASCID files
23. MODIS Collection 4 Active Fire Product User s Guide Version 2 2 Louis Giglio Science Systems and Applications Inc 16 November 2005 Contents 1 Introduction 2 Overview of the MODIS Active Fire Products 2 1 TEMO LOL YA A ARA te A AA BOE RE Ak ZA Li Granules ge ea e Bk ee a a de ees Za MACS E Bate dee kay toe nag AR AR Man tes 2 1 3 Climate Modeling Grid CMG oo o e 21 4 Collections eas aa ts a rn el pad a o 2 2 Level 2 Fire Products MOD14 Terra and MYD14 Aqua 2 3 Level 2G Daytime and Nighttime Fire Products MOD14GD MOD14GN Terra and MYD14GD MYD14GN AQUA o o e 2 4 Level 3 8 Day Daily Composite Fire Products MOD14A1 Terra and MYD14A1 AQUA ars a a had rs de 2 5 Level 3 8 Day Summary Fire Products MOD14A2 Terra and MYD14A2 Aqua 2 6 Climate Modeling Grid Fire Products MOD14CMH MYD4CMH etc 2 7 The Rapid Response Fire Products o e e 3 Obtaining the MODIS Active Fire Products 3 1 The EOS Data Gateway EDG 0 000002 eee ee ees 3 2 MODIS CMG Active Fire Product Distribution 0 4 4 Detailed Product Descriptions 4 1 MODI4G nd MYD A ce ek ee Bk ee a SO he ad a oes EEU OP iresMask codos ace a wie ee ware a ancora aaa Bod legen anes 4 1 2 DetectionConfidence 2 2 0 0 00 a a ee 4 1 3 Algorithm Quality Assessment Bits o 4 1 4 Fire Pixel Table
24. S TERRA THERMAL ANOMALIES FIRE 8 DAY L3 GLOBAL E ISIN GRID V003 MODIS TERRA THERMAL ANOMALIES FIRE 8 DAY L3 GLOBAL 1KM SIN GRID V004 MODIS TERRA THERMAL ANOMALIES FIRE DAILY L3 GLOBAL 1KM ISIN GRID V003 MODIS TERRA THERMAL ANOMALIES FIRE DAILY L3 GLOBAL 1KM SIN GRID V004 MODIS TERRA VEGETATION CONTINUOUS FIELDS YEARLY L3 GLOBAL SOOM ISIN GRID V003 Atmosphere Cryosphere Land Oceans Solar Other O AIRS AMSU A HSB O MODIS Aque O AMSR AMSR E O AMSR AMSR E O ADEOS O ACRIM O AMSR AMSRE OMODIS Terra O AVHRR O ASTER AMSR E O Elevation O AVHRR OMOPITT OGLAS MCESt O AVHRR O Field In Situ O CERES Aqua OSAGE O MODIS Aqua OGLAS CESat O GLAS ICESAt O CERES Terra Ossma O MODIS Tesra O Landsat 1 5 O Meteor 3M SAGE III O CERES TRMM O TOMS QSAR Q Landsat 7 O SORCE O GLAS CESat O TRMM Q SSMA Q QUARS O MISR QUARS O MODIS Tere Q SSMA Q SSMA By Discipline not responding Use the non javascript version By Discipline O By Categories Attributes Choose a Data Search Type O Primary Data Search O Data Granule ID Search O Local Granule ID Search BE 3 Select either MODIS Terra or MODIS Aqua depending on the MODIS instrument for which you would like data If you d like to order both Terra and Aqua MODIS fire data during a single EDG session choose either button Scroll through the list box until you find the entry for the particular fire product you d like to order Click on it If you want products from both MODIS instruments go on
25. S fire product has primarily been performed using coincident obser vations from the Advanced Spaceborne Thermal Emission and Reflection Radiometer ASTER see Morisette et al 2005 and Morisette et al in press for details A very brief discussion of the general validation procedure with some preliminary results can be found in Justice et al 2002 6 3 6 Idon t want to bother with strange file formats and or an unfamiliar ordering interface and or very large data files Can t you just give me the locations of fire pixels in plain ASCII files and Pl bin them myself You can obtain MODIS fire pixel locations via the Web Fire Mapper but this doesn t necessarily mean that fire pixel locations are the most appropriate source of fire related information The fire pixel location files include no information about cloud cover or missing data and depending on the sort of analysis you are performing itis sometimes possible to derive misleading or even incorrect results by effectively ignoring these other types of pixels In many cases it is more appropriate to use one of the 1 km Level 3 or CMG fire products See Section 5 1 1 for more information about this issue 6 3 7 How well can MODIS detect understory burns The likelihood of detection beneath a tree canopy is unknown but probably very low Understory fires are usually small which already makes MODIS less likely to detect them but with the addition 31 of a tree canopy to obstruct
26. SFC Greenbelt MD 20771 15 3 2 MODIS CMG Active Fire Product Distribution At present the CMG products are distributed from the University of Maryland via the ftp server fuoco geog umd edu login name is fire and password is burnt in the directory modis cmg For convenience the products are distributed in multiple standard data formats Currently HDF and Flexible Image Transport System FITS files are available additional formats may be produced in the future 16 4 Detailed Product Descriptions 4 1 MOD14 and MYD14 MOD14 MYD14 is the most basic fire product in which active fires and other thermal anomalies such as volcanoes are identified The Level 2 product is defined in the MODIS orbit geometry covering an area of approximately 2340 by 2030 km in the across and along track directions re spectively It is used to generate all of the higher level fire products 4 1 1 Fire Mask The fire mask is the principle component of the Level 2 MODIS fire product and is stored as an 8 bit unsigned integer Scientific Data Set SDS named fire mask In it individual 1 km pixels are assigned one of nine classes The meaning of each class is listed in Table 2 Table 2 MOD14 MYD14 fire mask pixel classes Class Meaning 0 not processed missing input data 2 not processed other reason 3 water 4 cloud 5 non fire clear land 6 unknown 7 low confidence fire 8 nominal confidence fire 9 high confidence fire
27. act that the units attribute of this SDS is assigned a value of megawatts this is an error These values must be multiplied by the appropriate pixel area at the surface of the Earth to obtain the FRP To obtain the FRP in megawatts MW use the following formula FRP MW power values stored in the Level 2 product x pixel area km Note that the area of a MODIS pixel varies with its position in the MODIS scan see the next question for details Note also that starting with Collection 5 the Level 2 products will have this multiplication performed during processing and will therefore contain the correct FRP 6 4 5 What is the area of a MODIS pixel at the Earth s surface The area of a MODIS pixel is nominally 1 km but grows away from nadir A handy polynomial approximation for the area of a 1 km MODIS pixel in km as a function of sample number is 8 Ata Y qa 1 k 0 33 where the sample number x 0 1 2 1353 The coefficients cz are given in Table 6 This approximation is accurate to within 21 which is less than the error entailed by treating the pixel as having sharp edges Use double precision floating point arithmetic when evaluating this polynomial Table 6 Polynomial coefficients Ck km OADM HPWNK OD 9 742 1684 0 091159223 0 00051138175 1 7683231 x 107 3 8048273 x 107 5 0660609 x 1071 4 0471196 x 10715 1 7739490 x 10718 3 2795410 x 10722 34 6 5 Level 3 Tiled Fire Prod
28. and_atbd html 30 See Giglio et al 2003 for a detailed description of the version 4 detection algorithm 6 3 2 What is the smallest fire size that can be detected with MODIS What about the largest MODIS can routinely detect both flaming and smoldering fires 1000 m in size Under very good observing conditions e g near nadir little or no smoke relatively homogeneous land surface etc flaming fires one tenth this size can be detected Under pristine and extremely rare observing conditions even smaller flaming fires 50 m can be detected Unlike most contextual fire detection algorithms designed for satellite sensors that were never intended for fire monitoring e g AVHRR VIRS ATSR there is no upper limit to the largest and or hottest fire that can be detected with MODIS 6 3 3 Why didn t MODIS detect a particular fire This can happen for any number of reasons The fire may have started and ended in between satellite overpasses The fire may be too small or too cool to be detected in the 1 km MODIS footprint Cloud cover heavy smoke or tree canopy may completely obscure a fire Occasionally the MODIS instruments are inoperable for extended periods of time e g the Terra MODIS in September 2000 and can of course observe nothing during these times 6 3 4 How do I obtain the MODIS fire products See Section 3 6 3 5 What validation of the MODIS active fire products has been performed Validation of the Terra MODI
29. cification 4 1 4 Fire Pixel Table The fire pixel table is simply a collection of SDSs containing relevant information about individual fire pixels detected within a granule Due to HDF file format and library limitations the Fire Pixel Table is stored as 19 separate SDSs A brief summary of these SDSs is provided in Table 3 Table 3 Collection 4 Level 2 fire product SDSs comprising the fire pixel table SDS Name Data Type Unit Description FP_line int16 Granule line of fire pixel FP_sample int16 Granule sample of fire pixel FP latitude float32 degrees Latitude of fire pixel FP longitude float32 degrees Longitude of fire pixel FP_R2 float32 Near IR band 2 reflectance of fire pixel daytime only FP_T21 float32 K Channel 21 22 brightness temperature of fire pixel FP_T31 float32 K Channel 31 brightness temperature of fire pixel FP _MeanT21 float32 K Background channel 21 22 brightness temperature FP_MeanT31 float32 K Background channel 31 brightness temperature FP_MeanDT float32 K FP_MAD_T21 float32 K FP MAD T31 float32 K FP_MAD_DT float32 K FP_power float32 Wm Fire radiative power per unit area FP_AdjCloud uint8 Number of adjacent cloud pixels FP_AdjWater uint8 Number of adjacent water pixels FP_WinSize uint8 Background window size FP_NumValid int16 Number of valid background pixels FP confidence uint8 Detection confidence estimate 4 1 5 Metadata Every MODIS pr
30. d longitude in degrees of the center of the grid cell may be computed as follows latitude 89 75 0 5 x y longitude 179 75 0 5 x x Here x and y are again zero based image coordinates for one based image coordinates first subtract 1 from both x and y 26 4 4 5 Example Code Example 6 IDL code for reading the cloud corrected fire pixel layer within the MODIS Collection 4 CMG monthly and 8 day fire products HDF4 format read CloudCorrFirePix array in CMG product HDF4 format cmg_file MYD14CMH 200412 004 01 hdf sd_id HDF_SD_START cmg_file READ index HDF_SD_NAMETOINDEX sd_id CloudCorrFirePix sds_id HDF_SD_SELECT sd_id index HDF_SD_GETDATA sds_id CloudCorrFirePix HDF_SD_ENDACCESS sds_id HDF_SD_END sd_id Example 7 IDL code for reading the cloud corrected fire pixel layer within the MODIS Collection 4 CMG monthly and 8 day fire products FITS format read CloudCorrFirePix array in CMG product FITS format cmg_file MYD14CMH 200412 004 01 fits FITS_OPEN cmg_file fcb ihdu FIND_HDU fcb CloudCorrFirePix READ_ARRAY fcb ihdu CloudCorrFirePix ndims dims FITS_CLOSE fcb 27 5 Caveats and known Problems 5 1 Caveats Some caveats to bear in mind when using the MODIS fire products 5 1 1 Fire Pixel Locations vs Gridded Fire Products We urge caution in using fire
31. e cloud pixels Number of pixels assigned a class of unknown in granule Number of land pixels obscured by cloud in granule Number of water pixels obscured by cloud in granule always 0 since the internal cloud mask is not applied over water pixels Number of pixels in granule contaminated with Sun glint Number of tentative fire pixels that were rejected due to apparent Sun glint contamination Number of tentative fire pixels that were rejected due to apparent water contamination of the contextual neighborhood Number of tentative fire pixels that were rejected as apparent hot desert surfaces Number of daytime pixels in granule Number of nighttime pixels in granule Name of satellite Terra or Aqua Program version string e g 4 3 3 File name of MODO21KM Terra or MYDO21KM Aqua Level 1B radiance input granule File name of MODO3 Terra or MYDO3 Aqua geolocation input granule Operating system identification string 19 4 1 6 Example Code Example 1 IDL code for reading the fire mask SDS in the MODIS Level 2 fire product r E se se open sd_id find index selec sds_id DE _SD_ finis mod14_file MOD14 A2002 the HDF file for the SDS index to 177 1830 004 2002192223417 hdf reading HDF_SD_START mod14_file READ the MOD14 fire mask HDF_SD_NAMETOIND EX sd_id fire mask t and read
32. esults Data Set gt Results Granule My Folder Shopping Cart Exit to Home gt Data Search Detailed Document Summary Document AIRS Browse Tutorial 6 http edcimswww cr usgs gov 80 ims bin pub secured nph ims cgi u469261 Results Granule Listing Have a question a problem or a comment Help for this page Add selections to cart Customize this table add additional information change columns number of rows sort order et Show Show Add map time selections coverage coverage to folder items selected on all pages Text only version for printing or import into a spread sheet Select Data Granule ID Local Granule ID SC MOD14A1 004 2016626884 MOD14A1 A2003256 h24v03 004 2003265235719 hdf SC MOD14A1 004 2016655144 MOD14A1 A2003257 h24v03 004 2003266190739 hdf SC MOD14A1 004 2016923214 MOD14A1 A2003258 h24v03 004 2003273231022 hdf SC MOD14A1 004 2016950494 MOD14A1 A2003259 h24v03 004 2003274135529 hdf SC MOD14A1 004 2016755593 MOD 14A1 A2003260 h24v03 004 2003269 100727 hdf SC MOD14A1 004 2016771915 MOD14A1 A2003261 h24v03 004 2003269203838 hdf SC MOD14A1 004 2016796328 MOD 14A1 A2003262 h24v03 004 2003270124958 hdf SC MOD14A1 004 2016830812 MOD 14A1 A2003263 h24v03 004 200327 1113959 hdf SC MOD14A1 004 2016869168 Granule On line Attributes Pricing Attributes Pricing Attributes Pricing Attributes Pricing Attrib
33. etween many of the 500 m and 1 km bands The dead detectors are known to introduce at least three specific artifacts in the pre November 2000 fire products striping undetected small fires and undetected large fires In some very rare instances severe miscalibration of band 21 in the first weeks of the MODIS data archive February and March 2000 will cause entire scan lines to be identified as fire 5 2 2 Regional False Alarms Recurring false alarms attributed to industrial sources and urban land cover boundaries have have been observed in eastern China near Shanghai and Shaoxing A particularly bad example is shown in Figure 7 28 Figure 7 Example of false alarms near Shanghai from the Terra MODIS 18 July 2004 02 45 UTC Pixels identified as containing fires are outlined in red superimposed on a true color corrected reflectance image courtesy of Jacques Descloitres 5 2 3 Detection Confidence A detection confidence intended to help users gauge the quality of individual fire pixels is included in the Level 2 fire product This confidence estimate which ranges between 0 and 100 is used to assign one of the three fire classes low confidence fire nominal confidence fire or high confidence fire to all fire pixels within the fire mask In the Collection 4 fire product this confidence estimate does not adequately identify highly questionable low confidence fire pixels Such pixels which by design should have a confidence close to
34. gion exit Figure 9 DS9 displaying the CorrFirePix data layer in the February 2003 MOD14CMH prod uct The longitude and latitude respectively at the center of the pixel beneath the cursor is shown in the upper left hand corner of the window in the two numeric fields to the right of the word LINEAR 39 6 7 Hierarchical Data Format HDF 6 7 1 What are HDF files The Hierarchical Data Format HDF developed at the National Center for Supercomputing is one of various file formats used to portably archive and distribute scientific data HDF files are more or less self describing in that they can include extensive metadata about the data stored within the file Note that there are two incompatible flavors of HDF in use HDF4 the format in which all MODIS products are stored and HDF5 which is actually a completely different file format that is not backwards compatible with HDF4 See the NCSA HDF web site for more information 6 7 2 How do I read HDF4 files If you are writing your own software in a traditional programming language such as C or Fortran you will need obtain the HDF4 library from NCSA Some commercial software packages however including MATLAB IDL and ENVI have the HDF library built in in which case you will not need to install the library 6 7 3 Can t I just skip over the HDF header and read the data directly Put any thought of reading or writing HDF files without the HDF library
35. ipping receipt has been emailed to the account krankheit nasa gov NOTE If you are ordering HDF and or HDF EOS formatted data e g ASTER MODIS MISR MOPITT CERES you may need HDF and HDF EOS tools to read view and manipulate the data Please see NASA s Viewing HDF and HDF EOS files page for more information Data Center LPDAAC Status Comments Order Received Thank you for your order Please contact the LP DAAC to arrange payment if necessary You will receive an order confirmation notice by email with payment details Your order will not be processed until payment is received if applicable Once your order is processed you will receive a completion notification by email Please allow one to three weeks for the media to arrive Shipping times will vary depending upon your location The customer must pay any additional shipping charges such as customs duties for international orders Order ID Number 0300865367 Contact name Land Processes DAAC LP DAAC User Services U S Geological Survey 605 594 6116 605 594 6963 edc eos nasa gov If your order status shows failed please contact the data center immediately Also please read the NASA Privacy Security Notices and the EOS Data Gateway accessibility policy Comments Questions or Problems Email us Created by EOS Data Gateway version 36 3 Webmaster Chao Hsi Chang chao hsi_chang sesda com Responsible NASA Official Medora Macie Mail Code 423 NASA G
36. may be produced in the future 4 4 1 CMG Naming Convention Monthly CMG fire products The file names of the monthly CMG product files have the structure M D14CM YYYYMM CCC VV XXX where M D14CM is a prefix encoding the satellite and product spatial resolution see Figure 6 YYYY is the four digit product year MM is the two digit calendar month CCC denotes the Collection see Section 2 1 4 VV denotes the product version within a Collection and XXX is a suffix indicating the file format Eight day CMG fire products The file names of the 8 day CMG product files have the structure M D14C8 YYYYDDD CCC VV XXX where M D14C8 is a prefix encoding the satellite and product spatial resolution see Figure 6 YYYY is the four digit product year DDD is the two digit calendar month CCC denotes the Collection see Section 2 1 4 VV denotes the product version within a Collection and XXX is a suffix indicating the file format Satellite O Terra Y Aqua C combined Terra Aqua es Temporal Resolution Spatial Resolution M monthly H 0 59 8 8 days Q 0 25 Figure 6 MODIS CMG fire product naming prefix ESDT convention In MODIS speak this prefix is usually referred to as an Earth Science Data Type ESDT 25 4 4 2 Data Layers The CMG fire products contain seven separate data layers summarized in Table 5 For the 0 5 prod ucts each layer is a 720 x 360 numeric arra
37. ments are being implemented in preparation for a February 2006 Col lection 5 reprocessing and will require changes to the both the structure and meaning of the product data layers These changes will most likely break exist ing code written to ingest the Collection 4 MOD14A2 and MYD14A2 products 4 3 1 Fire Mask The fire mask is stored as a 1200 x 1200 8 bit unsigned integer SDS awkwardly named most confident detected fire for historical reasons 4 3 2 QA The QA layer contains pixel level quality assessment information stored in a 1200 x 1200 8 bit unsigned integer image Only the two least significant bits are used and the values stored in this array correspond to the original Land Data Operational Product Evaluation LDOPE QA summary See Roy et al 2002 for more information about these values 4 3 3 Example Code Example 3 MATLAB code to read the Level 3 MODIS 8 day fire mask using the MATLAB routine hdfread This is probably the easiest way to read individual HDF SDSs in MATLAB mod14a2_file MYD14A2 A2004193 h08v08 004 2004207151726 hdf 9 read entire most confident detected fire SDS in one shot fire mask hdfread mod14a2_file most confident detected fire o display fire mask transposed so that North appears on top imagesc fire_mask 22 Example 4 Longer version of MATLAB code to read the Level 3 MODIS 8 day fire mask This is probably the better approach to u
38. nce Data Type version rather than the SAGE III Data version Accordingly the newly released Y3 data is stored as version 002 LP DAAC Landsat 7 ETM Dataset Transition MODIS Terra V003 Returns The Golden Month of MODIS Terra V003 is again available All other MODIS Terra V003 have been superseded by reprocessing to Version 004 2 Register select Become a registered user or enter as guest Registering will prevent you from having to reenter contact information each time you place an order When done regis tering enter the Data Gateway Enter as registered user 10 EOS Data Gateway Primary Data Search Search and Order User Preferences gt Search Creation Search Status Results Data Set Results Granule My Folder Shopping Cart Exit to Home gt Data Search Detailed Document Summary Document AIRS Browse Tutonal FAQ User Manual User Support Contacts Check Order Status Other EDG Sites HDF F Viewing Tools Search Creation Primary Data Search Have a question a problem or a comment Help for this page Save Restore search Clear search Choose Data Sets Text Search Pick a discipline topic for example Armosphere MISR then choose from the list of data sets For ee topics choose one topic de data sets then the next ropie amp data sets To se more than one data set use Ctrl click for PCs Apple click for Macintosh MODI
39. oduct carries with 1t ECS mandated metadata stored in the HDF global attributes CoreMetadata 0 and ArchiveMetadata 0 Each attribute is an enormous string of ASCII characters encoding many separate metadata fields in Parameter Value Language PVL Among other infor mation the ArchiveMetadata 0 attribute usually contains product specific metadata included at the discretion of the PI However since the PVL is awkward to read and tedious to parse we have stored many of the product specific metadata fields as standard HDF global attributes These are summa rized in Table 4 Descriptions of the product specific metadata stored in the ECS ArchiveMetadata 0 attribute may be found in the MOD14 MYD14 file specification see Section 7 2 18 Table 4 MODIS Level 2 fire product metadata stored as standard gobal HDF attributes Attribute Name Description FirePix MissingPix LandPix WaterPix WaterAdjacentFirePix CloudAdjacentFirePix UnknownPix LandCloudPix WaterCloudPix GlintPix GlintRejectedPix CoastRejectedPix HotSurfRejectedPix DayPix NightPix Satellite Process VersionNumber MOD021KM input file MOD03 input file SystemID Number of fire pixels detected in granule Number of pixels in granule lacking valid data for processing Number of land pixels in granule Number of water pixels in granule Number of fire pixels that are adjacent to one or more water pixels Number of fire pixels that are adjacent to one or mor
40. out of your head HDF was intended to be not so much a physical file format but instead an application interface As such the format is fairly complicated and has in fact changed over time and it would be very time consuming and risky to roll your own HDF ingest code The physical file format is nothing like the typical header followed by data common to many other formats and it is not easy to simply skip over the metadata fragments in an HDF file 6 7 4 How can I list the contents of HDF4 files The NCSA HDF4 Library includes a utility named ncdump which will do this Be sure to use the switch h otherwise you will be inundated with ASCII dumps of all numeric arrays in the file Shttp hd ncsa uiuc edu Thttp hdf ncsa uiuc edu hdf4 html 40 7 References 7 1 Journal Papers and Technical Reports Giglio L Descloitres J Justice C O and Kaufman Y 2003 An enhanced contextual fire de tection algorithm for MODIS Remote Sensing of Environment 87 273 282 Giglio L and Kendall J 2003 TRMM VIRS Monthly Fire Product User s Guide Revision B ftp lake nascom nasa gov data TRMM VIRS Fire docs VIRS Fire_Users_Guide B pdf Justice C O Giglio L Korontzi S Owens J Morisette J Roy D Descloitres J Alleaume S Petitcolin F and Kaufman Y 2002 The MODIS fire products Remote Sensing of Environ ment 83 244 262 Kaufman Y Ichoku C Giglio L Korontzi S Chu D A Hao W
41. pixel locations in lieu of the 1 km gridded MODIS fire products The former includes no information about cloud cover or missing data and depending on the sort of analysis that is being performed it is sometimes possible to derive misleading or even incorrect results by not accounting for these other types of pixels It is also possible to grossly misuse fire pixel locations even for regions and time periods in which cloud cover and missing observations are negligible Some caveats to keep in mind when using MODIS fire pixel locations e The fire pixel location files allow users to temporally and spatially bin fire counts arbitrarily However severe temporal and spatial biases may arise in any MODIS fire time series analysis employing time intervals shorter than about eight days e Known fires for which no entries occur in the fire pixel location files are not necessarily missed by the detection algorithm Cloud obscuration a lack of coverage or a misclassifica tion in the land sea mask may instead be responsible but with only the information provided in the fire location files this will be impossible to determine 5 2 Collection 4 Known Problems 5 2 1 Pre November 2000 Data Quality Prior to November 2000 the Terra MODIS instrument suffered from several hardware problems that adversely affected all of the MODIS fire products In particular some detectors were rendered dead or otherwise unusable in an effort to reduce unexpected crosstalk b
42. ration Cloud and water pixels are identified using cloud and water masks and are assigned the classes cloud and water respectively Processing continues on the remaining clear land pixels A preliminary classification is used to eliminate obvious non fire pixels For those potential fire pix els that remain an attempt is made to use the neighboring pixels to estimate the radiometric signal of the potential fire pixel in the absence of fire Valid neighboring pixels in a window centered on the potential fire pixel are identified and are used to estimate a background value If the background characterization was successful a series of contextual threshold tests are used to perform a relative fire detection These look for the characteristic signature of an active fire in which both 4 um bright ness temperature and the 4 and 11 um brightness temperature difference depart substantially from that of the non fire background Relative thresholds are adjusted based on the natural variability of the background Additional specialized tests are used to eliminate false detections caused by sun glint desert boundaries and errors in the water mask Candidate fire pixels that are not rejected in the course of applying these tests are assigned a class of fire Pixels for which the background characterization could not be performed i e those having an insufficient number of valid pixels are assigned a class of unknown Shttp modarch gsfc nasa gov data atbd l
43. scheduled to begin in early 2006 2 2 Level 2 Fire Products MOD14 Terra and MYD14 Aqua This is the most basic fire product in which active fires and other thermal anomalies such as volca noes are identified The Level 2 product is defined in the MODIS orbit geometry covering an area of approximately 2340 by 2030 km in the across and along track directions respectively It is used to generate all of the higher level fire products and contains the following components e An active fire mask that flags fires and other relevant pixels e g cloud e apixel level quality assurance QA image that includes 19 bits of QA information about each pixel e a fire pixel table which provides 19 separate pieces of radiometric and internal algorithm information about each fire pixel detected within a granule e extensive mandatory and product specific metadata e a grid related data layer to simplify production of the Climate Modeling Grid CMG fire product Section 2 6 Product specific metadata within the Level 2 fire product includes the number of cloud water non fire fire unknown and other pixels occurring within a granule to simplify identification of granules containing fire activity Figure 2 shows an example of the active fire mask for the Terra granule acquired on 19 August 2002 at 03 00 In this image water is shown in blue clouds in violet non fire land pixels in grey and fire pixels in white The bottom edge of the image
44. se 1f multiple subsets of an SDS will be read in sequence since the HDF file will be opened and closed only once The shorter approach using hdfread requires that the file be opened and closed for each read mod14a2_file MYD14A2 A2004193 h08v08 004 2004207151726 hdf sd_id hdfsd start mod14a2_ file DFACC_RDONLY sds_index hdfsd nametoindex sd_id most confident detected fire sds_id hdfsd select sd_id sds_index o prepare to read entire SDS always 1200 x 1200 pixels in size start 0 0 edges 1200 1200 fire_mask status hdfsd readdata sds_id start edges status hdfsd endaccess sds_id status hdfsd end sd_id o display fire mask transposed so that North appears on top imagesc fire_mask 23 Example 5 C code for reading Level 3 MODIS 8 day fire mask using HDF library functions include lt stdio h gt include lt stdlib h gt include mfhdf h define ROWS 1200 define COLS 1200 main int argc char argv int32 sd_id sds_index sds_id int32 rank data_type nattr dim _sizes MAX_VAR_DIMS int32 start 2 int32 edges 2 char infile bt Lo long nfire uint8 fire mask ROWS COLS infile MOD14A2 A2002169 h09v05 004 2003240104900 hdf if sd_id SDstart infile DFACC_READ FAIL exit 1 start 0 start 1 0 edges 0 ROWS edges 1 COLS if
45. th n HDF_SD_SELECT s GETDATA sds_id hed with SDS DF_SD_ finis DF_SD_ ENDACCESS sds_id hed with HDF file END sd_id tire fire mask SDS d_id index fire mask 20 4 2 MOD14A1 and MYD14A1 The MODI14A1 and MTDI14A1 daily Level 3 fire products are tile based with each product file spanning one of the 460 MODIS tiles of which 326 contain land pixels The product is a 1 km gridded composite of fire pixels detected in each grid cell over each daily 24 hour compositing period For convenience eight days of data are packaged into a single file NOTE The Collection 5 MOD14A1 and MYD14A1 fire products will differ significantly from the current Collection 4 versions of these products Im provements are being implemented in preparation for an February 2006 Col lection 5 reprocessing and will require changes to the both the structure and meaning of the product data layers These changes will most likely break exist ing code written to ingest the Collection 4 MOD14A1 and MYD14A1 products 4 2 1 Fire Mask The fire mask is stored as an 8 or less x 1200 x 1200 8 bit unsigned integer SDS awkwardly named most confident detected fire for historical reasons The SDS contains eight successive daily fire masks for a specific MODIS tile Each of these daily masks is a maximum value composite of the Level 2 fire product pixel classes Table 2 for those swaths overlapping
46. the view of a fire detection becomes very unlikely 6 4 Level 2 Fire Products 6 4 1 Why do the Level 2 product files vary in size Level 2 granules can contain slightly different numbers of scans More importantly internal HDF compression is used to reduce the size of the files 6 4 2 How should the different fire detection confidence classes be used Three classes of fire pixels low confidence nominal confidence high confidence are provided in the fire masks of the MODIS Level 2 and Level 3 fire products Users requiring fewer false alarms may wish to consider only nominal and high confidence fire pixels and treat low confidence fire pixels as clear non fire land pixels Users requiring maximum fire detectability who are able to tolerate a higher incidence of false alarms should consider all three classes of fire pixels 6 4 3 How can I take data from the fire pixel table SDSs i e the one dimensional SDSs with the prefix FP 2 and place the values in the proper locations of a two dimensional array that matches the swath based fire mask and algorithm QA SDSs 1 Open a MOD14 MYD14 Level 2 granule for reading using your favorite programming lan guage 2 Determine the number of fire pixels in the granule The easiest way to do this is to read the global HDF attribute FirePix If you are a masochist you can read and parse the ECS CoreMetadata 0 string for the product specific attribute FIREPIXELS instead
47. ucts 6 5 1 Why do coastlines in the tile based Level 3 products looked so warped The tile based Level 3 products are defined on a global sinusoidal grid which preserves areas but greatly distorts the shape of land masses at longitudes far from the prime meridian 6 6 Level 3 CMG Fire Products 6 6 1 I need to reduce the resolution of the 0 5 CMG fire product to grid cells that are a multiple of 0 5 in size How do I go about doing this For all pixel count data layers simply sum the values of the 0 5 grid cells that lie within the larger grid cells Be sure to handle grid cells flagged with the missing data value of 1 At the very least this entails excluding the negative missing data values from the resulting sum but depending upon the application it may be more appropriate to flag the coarser grid cell as lacking valid data entirely When coarsening the mean fire radiative power layer MeanPower you should weight the individual 0 5 mean FRP values by the corrected fire pixel counts CorrFirePix handling by at least excluding missing FRP values of 0 in the process A few examples are shown in the following figures 0 5 CorrFirePix 1 CorrFirePix k Rebinning corrected fire pixel counts from 0 5 grid cells left to a 1 grid cell right The result is simply the sum of the pixel counts of the four 0 5 grid cells 100 200 300 400 1000 fire pixels nested within the 1 grid cell 35 0 5 CorrFirePix 1
48. utes Prici Attributes Pricing Attributes Pricing Attributes Pricing Attributes Image Information Access Quicklook Access Image Unavailable Unavailable Access Image Unavailable Unavailable Access Image S Unavailable Unavailable 1 Access Image Unavailable Unavailable 1 Access Image S Unavailable Unavailable 1 Access Image 5 Unavailable Unavailable 1 Access Image Unavailable Unavailable 1 Access Image S Unavailable Unavailable 1 Access Image Ye gt 10 You will presented with a listing of all files matching your search criteria Select those that you would like to order and click on the yellow Add selections to cart button 11 The display will change slightly and you may then select a delivery method for the files you have selected Choose FtpPull if you intend to download the data using ftp 12 13 Click on the Go to Step 2 Order Form button Submit Order Now button 14 Step 9 while this is happening 14 Update your contact information or enter it if you are logged in as a guest and click on the Wait while the system process your request You will receive updates resembling those in 15 When your order has been completed you should see something like this Ask a Question A 6 ee Order Submitted Have a question a problem or a comment Help for this page Your order has been submitted to the appropriate data centers and a copy of the sh
49. y Table 5 Summary of data layers in the CMG fire products Layer Name Data Type Unit Description CorrFirePix int16 Corrected number of fire pixels CloudCorrFirePix int16 Corrected number of fire pixels with an addi tional correction for cloud cover MeanCloudFraction int8 Mean cloud fraction RawFirePix int16 Uncorrected count of fire pixels CloudPix int32 Number of cloud pixels TotalPix int32 Total number of pixels MeanPower float32 MW Mean fire radiative power 4 4 3 Global Metadata Global metadata are stored as global attributes in the HDF product files and primary HDU key words in the FITS product files 4 4 4 Climate Modeling Grid Navigation Forward navigation Given the latitude and longitude in degrees of a point on the Earth s surface the image coordinates x y of the 0 5 CMG grid cell containing this point are computed as follows y trunc 90 0 latitude 0 5 x trunc longitude 180 0 0 5 Here trunc denotes truncation of the fractional part of a real number i e trunc 2 2 2 These equations yield image coordinates satisfying the inequalities 0 lt x lt 719 0 lt y lt 319 which are appropriate for programming languages using zero based array indexing such as C and IDL for languages using one based array indexing e g Fortran MATLAB add 1 Inverse navigation Given coordinates x y of a particular grid cell in the 0 5 CMG fire prod ucts the latitude an
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