MODIS Aggregate for Amazon Basin/Large Scale Biosphere-Atmosphere Experiment; MODIS/Terra Thermal Anomalies/Fire Daily L3 Global 1km ISIN Grid 1/1/03-1/1/05

This dataset represents spatial aggregates of the MODIS/Terra Thermal Anomalies/Fire Daily L3 Global 1km ISIN Grid (MOD14A1), as provided by the EROS Data Center (EDC). This product is available for muultiple days.

The University of New Hampshire's (UNH) Earth Science Information Partner (ESIP), EOS-WEBSTER (http://eos-earthdata.sr.unh.edu/), has created this MODIS Fire aggregate product from MOD14A1 Daily Fire tiles. MOD14A1 data are provided every 8 days as a gridded level-3 product in the Integerized Sinusoidal projection. UNH has aggregated multiple tiles to create a large-region fire detection product, which can be spatially subset within the EOS-WEBSTER Search & Retrieve data ordering tool. Our system has also reprojected these data from the original Integerized Sinusoidal projection to Geographic, with a pixel resolution of 30 arc seconds.

The aggregate products contain all the layers from the original input products plus an additional layer created by EOS-WEBSTER, which provides a look-up code to map each pixel in the aggregate back to its original input tile.

These aggregate products allow the users of EOS-WEBSTER to subset MODIS Thermal Anomalies/Fire data across tile boundaries and to customize the spatial region of interest using an on-line GUI interface. Data are also provided in a generic binary format (BSQ) with detailed header information, which can be read into most image or data processing applications. EOS-WEBSTER has broken the original 2 layers in the input tiles into 2 logical holdings so that a user may order 1 or both of the band sets (holdings). The layers for this product are: 1) daily 8-day time series of fire detection and 2) algorithm QA. For this product, the original 8-day fire detection time series came as an HDF 3-dimensional field. When these data are ordered each daily fire detection layer will be a separate 2D band in a BSQ file. When ordering these data through EOS- WEBSTER, the user may order the 8-layer fire holding (8 days of fire prediction) and the 1-layer QA holding either separately, or together.

Version-4 input products were used to create these output files. Please see the Global Change Master Directory (GCMD) to learn more about the MOD14A1 input data (http://gcmd.nasa.gov/getdif.htm?MOD14A13).

ADDITIONAL INFORMATION ABOUT MOD14A1 from the Global Change Master Directory Listing for MOD14A1:

MOD14A1 is one of MODIS' 3-D data products. In addition to X- and Y-dimensions, Level 3 Daily fire products include 8 separate days of data detailing pixels according to their level of confidence as fires. The Fire Mask contains eight, band (day) sequential images of fire data representing consecutive days of data collection. The images above have been pseudo-colored to display one of the eight days of fire data in a granule of MOD14A1.

The Terra MODIS instrument acquires data twice daily (10:30 am and 10:30 pm), as will the Aqua MODIS (2:30 pm and 2:30 am). These four daily MODIS observations will advance global fire monitoring with 1 km resolution fire channels centered at the 4 and 11 micrometer wavelengths. Fire detection in the MODIS Thermal Anomalies/Fire products is performed using channel brightness temperatures from those wavelengths. The fire detection strategy is based on absolute detection of the fire (if the fire is strong enough) and on detection relative to the background to account for variability of the surface temperature and reflection by sunlight. MODIS data will also be used to monitor burn scars, vegetation type and condition, smoke aerosols, water vapor and clouds for overall monitoring of the fire process and its effects on ecosystems, the atmosphere, and climate.

A data base of active fire products will be generated and archived at 1 km resolution and summarized on a grid of 10 km and .5 degree latitude/longitude at daily, 8-day, and monthly intervals. It includes the fire occurrence and location, the rate of emission of thermal energy from the fire and a rough estimate of the smoldering/flaming ratio. This information will be used in monitoring the spatial and temporal distribution of fires in different ecosystems, detecting changes in fire distribution and identifying new fire frontiers, wild fires, and changes in the frequency of the fires or their relative strength.

Data Set Characteristics:
Output format: BSQ with header,GEOtiff, netCDF, HDF-ESO, ASCII grid
Logical Data Holdings:
8-day fire detection time series: 8 fire detection bands
Algorithm QA: 1 bit field band
Input Tile Code: 1 band