Burned area over Venezuela from the VNP64A1.001 product from November 1st, 2018.
View full-size imageThe daily NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Burned Area (VNP64A1) Version 1 data product is a monthly, global gridded 500 meter (m) product containing per-pixel burned area and quality information. The VNP64 burned area mapping approach employs 750 m VIIRS imagery coupled with 750 m VIIRS active fire observations. The hybrid algorithm applies dynamic thresholds to composite imagery generated from a burn-sensitive Vegetation Index (VI) derived from VIIRS shortwave infrared channels M8 and M11, and a measure of temporal texture. VIIRS bands that are both sensitive and insensitive to biomass burning are used to detect changes caused by fire and to differentiate them from other types of change. The mapping algorithm ultimately identifies the date of burn, to the nearest day, for 500 m grid cells within the individual sinusoidal tile being processed. The date is encoded in a single data layer of the output product as the ordinal day of the calendar year on which the burn occurred (range 1–366), with a value of 0 for unburned land pixels and additional values reserved for missing data and water grid cells. The VNP64A1 data product is designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined burned area product to promote the continuity of the Earth Observation System (EOS) mission.
VNP64A1 has been released on a limited basis due to concerns over the quality of the data along the edges of inland water bodies and at high latitudes. These regions contain grid cells falsely identified as burned as a result of coarse resolution inputs to the cloud mask used in the generation of the 750 m VIIRS active fire observations. Users are urged to exercise caution when using this provisional data in research. The Version 2 burned area product generated with an improved cloud mask is scheduled for release in the summer of 2020.
The data layers provided in the VNP64A1 product include Burn Date, Burn Date Uncertainty, Quality Assurance (QA), along with First Day and Last Day of reliable change detection of the year. A low resolution browse is also provided showing the burned date layer with a color map applied in JPEG format.
Due to the provisional maturity status of the VNP64A1 data product, users are encouraged to exercise caution when using the data.
| Characteristic | Description |
|---|---|
| Collection | Suomi NPP VIIRS |
| DOI | 10.5067/VIIRS/VNP64A1.001 |
| File Size | ~25 MB |
| Temporal Resolution | Monthly |
| Temporal Extent | 2014-01-01 to 2019-01-01 |
| Spatial Extent | Global |
| Coordinate System | Sinusoidal |
| Datum | N/A |
| File Format | HDF-EOS |
| Geographic Dimensions | 1200 km x 1200 km |
| Characteristic | Description |
|---|---|
| Number of Science Dataset (SDS) Layers | 5 |
| Columns/Rows | 2400 x 2400 |
| Pixel Size | 500 m |
| SDS Name | Description | Units | Data Type | Fill Value | No Data Value | Valid Range | Scale Factor |
|---|---|---|---|---|---|---|---|
| Burn Date | Burn day of year | Day | 16-bit signed integer | -2 to 0 | N/A | 1 to 366 | N/A |
| Burn Date Uncertainty | Estimated uncertainty in day of burn | Percent | 8-bit unsigned integer | 0 | N/A | 1 to 100 | N/A |
| QA | Quality Assurance Indicators | Bit Field | 8-bit unsigned integer | N/A | N/A | 0 to 255 | N/A |
| First Day | First day of the year of reliable change detection | Day | 16-bit signed integer | -2, -1 | N/A | 1 to 366 | N/A |
| Last Day | Last day of the year of reliable change detection | Day | 16-bit signed integer | -2, -1 | N/A | 1 to 366 | N/A |
| Value | Label |
|---|---|
| 0 | Unburned |
| -1 | Fill |
| -2 | Water |
| Value | Label |
|---|---|
| -1 | Fill |
| -2 | Water |
The Quality Assessment (QA) layer is stored in an efficient bit-encoded manner. The unpack_sds_bits executable from the LDOPE Tools is available to the user community to help parse and interpret the quality layer.
Quality assurance information should be considered when determining the usability of data for a particular science application. The ArcGIS MODIS-VIIRS Python Toolbox contains tools capable of decoding quality data layers while producing thematic quality raster files for each quality attribute.
For complete information about product quality, refer to the VIIRS Land Product Quality Assessment website.
For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website. Additional information on falsely identified burn areas related to cloud mask artifacts and cropland burning is provided in section 6 of the User Guide.