Methods applied in the production process of MTBS are sequentially conducted as follows:
Fire History Database Compilation
Existing fire history and location databases were compiled into a single, standardized project database that formed the basis for image scene selection. Fire history sources were from two general origins, federal agency databases and state databases. In some cases state and federal agencies have collaborated in developing and maintaining a single database for state and federal incidents. Federal agency data are essentially aggregated into the Incident Command System database known as the ICS 209 (named after the form number used to report incident status), maintained by the National Interagency Fire Center (NIFC) in Boise, ID (http://www.nifc.gov/). ICS 209 data make up the bulk of the records in the MTBS project database. States were solicited for state maintained data where there was uncertainty of their inclusion in the ICS 209.
While there is some level of standardization that occurs within ICS 209, federal land management agencies have varying or no standards for content, geospatial accuracy, and nomenclature. Duplicate records are common due to reporting of a given incident by multiple agencies. Cases of gross geospatial inaccuracies have also been noted. Similar inconsistencies and errors have been observed within and across state databases. Further standardization and correction, where possible, was performed as part of the compilation of an MTBS project database. For the purposes of this project, standardization was accomplished by selecting data elements common to the source databases and not through record editing or manipulation of the source data. The exception being geospatial coordinates. In cases where a record was grossly and obviously incorrect, and a correction could be made confidently, coordinates were adjusted.
Image Scene Selection and Data Pre-processing
Scene selection is driven by the MTBS fire history database. Scenes are selected using the Global Visualization Image Selection (GLOVIS) browser developed by USGS-EROS (http://glovis.usgs.gov/). Enhancements were made to GLOVIS specifically to facilitate the magnitude of scene selection effort required for this project. These enhancements, available to all GLOVIS users, include the ability to incorporate ARCGIS shape files in the viewer to aid scene selection and scene specific AVHRR greenness graphs for determining peak periods of photosynthetic activity, or ‘peak of green’ periods. The fire history shapefile, specific to a MTBS mapping zone, is loaded into the viewer and analysts use fire locations to guide scene selection for each fire. Prefire and postfire images are selected for each incident. Scenes selected for fires that will be processed as an extended assessment (next growing season) are based on ‘peak of green’ condition or as close as cloud-free data are obtainable. Limitations in data availability due to atmospheric conditions will naturally compromise selections for fires in areas prone to summer and fall cloud and smoke obscurity. Northern latitudes will also be subject to a shorter period of optimal scene selection due to undesirable sun angles in the fall.
Selected scenes are ordered and processed according to existing USGS-EROS protocols. Image data are geometrically (including terrain correction) and radiometrically corrected through the NLAPS process. Image data are delivered to EROS and RSAC analysts to be processed into fire severity information. Landsat image data acquired for this project will become part of the national image archive and will be available at NLCD archive costs (currently 80 US dollars/scene). Current estimates expect an increase in available archive data of more than 7000 scenes. USGS and USDA Forest Service image archives will be among the available portals to access these data.
Fire Severity and Perimeter Mapping
The NBR index is calculated for prefire and postfire images. Prefire and postfire images are inspected for co-registration accuracy and corrected if spatial differences are excessive and extensive (> 30 meters). NBR images are differenced for each fire-scene pair to generate the dNBR. A relativized dNBR (RdNBR) is also calculated to evaluate potential limitations of dNBR to characterize fire severity on low biomass sites and potentially enhance inter-fire comparability of the results at larger scales. The RdNBR data have been shown to have stronger correlations to Composite Burn Index plot data in some western ecosystems.
Ecological Severity Thresholding
Processing the Landsat image data to dNBR is a straightforward series of objective calculations requiring limited analyst interaction and relying principally on automated production sequences. Subsequent to dNBR derivation, the process of developing fire severity and perimeter maps becomes much more dependent on analyst interpretation. The dNBR data are calculated as signed 16-bit with a maximum digital number (DN) range of negative 32767 to positive 32767. However the practical range of DN values representing fire related change and no change is typically within negative 600 to positive 1300. Values further away from zero represent greater change as a result of both first and second order fire effects (within the fire perimeter). Negative values indicate a positive vegetation response (growth) and positive values indicate a negative vegetation response (mortality). The analyst evaluates the dNBR data range and determines where significant thresholds exist in the data to discriminate between severity classes. Interpretations are conducted on the dNBR data aided by raw prefire and postfire imagery, plot data, and analyst experience with fire behavior and effects in a given ecological setting. Composite Burn Index (CBI) data have been the most commonly collected ground-based data to estimate post-fire effects. Correlations between CBI and dNBR have been used to demonstrate the sensitivity of dNBR to post-fire effects and to establish numerical thresholds in dNBR data that discriminate severity categories. Where CBI and similar plot data have been collected, and plot-dNBR relationships published, analysts will guide their interpretations based on these relationships. Limited interpolation of plot-based thresholds beyond their geographic bounds but within ecologically similar conditions will be examined.
Thresholding dNBR data into thematic class values results in an intuitive map depicting a manageable number of ecologically significant classes (typically 4 to 7 class values). There are uncertainties in this approach stemming from analyst subjectivity and limited or no plot data to guide threshold selection. Ecological significance is issue dependent and one set of thresholds cannot be expected to apply equally well to all analysis objectives and management issues.
In addition to ecological thresholds as a means of discriminating severity classes, dNBR will be arithmetically partitioned into discreet classes to facilitate objective and flexible pattern and trend analysis. The relative ease and quickness of arithmetically partitioning dNBR data will allow for rapid evaluation of meaningful spatial and temporal scales in the context of fire severity trends. Moreover, dNBR data can be efficiently analyzed and classified to suit the fire severity information needs of a specific management issue.
Methods for partitioning dNBR have yet to be determined and the algorithm(s) and subsequent grain of partitioning will depend on a given technique's ability to reveal meaningful patterns in fire severity over time. Arithmetic partitioning is not intended to provide information on the ecological severity of fires at large spatial scales or limited temporal extents.
Fire perimeters will be generated by on-screen interpretation and delineation of dNBR images. Analysts will digitize perimeters around dNBR values reflecting fire induced change. To ensure consistency and high spatial precision, digitization will be performed at on-screen display scales between 1:24000 and 1:50000. Incident perimeters, where available will be used in an ancillary fashion to inform the analyst. This can be particularly useful in identifying unburned islands within a perimeter or isolated, disjunct spots outside the main perimeter. Due to limited and variable availability as well as inconsistent spatial precision incident perimeters were not considered appropriate as a source for MTBS project perimeters.
Tabular data will be generated from statistical summaries of the fire severity class layers. Reporting units will vary in extent depending on the needs of WFLC but at a minimum summary data will be produced for each project mapping zone as well as at a national extent. Three sets of tabular data are currently specified in the MTBS product suite.
Summarizing acres burned by severity class and vegetation cover types requires consistent geospatial vegetation data of similar resolution to provide the most meaningful stratifications. Existing vegetation types currently being mapped by the LANDFIRE program will offer the most spatially extensive and nationally consistent data by which severity classes can be summarized. Since Landsat imagery is the spatial basis for both MTBS and LANDFIRE data, uncertainties that may result from summarizing severity classes within vegetation cover types mapped at a significantly different spatial scale should be minimized. In conjunction with the information needs of WFLC, the accuracy of LANDFIRE data will need to be evaluated to determine the most appropriate thematic scale for reporting.
It is recognized that these tabular data may have limited utility at finer spatial scales and for addressing research and management information needs not considered within the scope of this project. The production and distribution of both continuous and thematic spatial data sets are considered the primary data legacy available to scientific and operational interests outside this project.
All spatial and tabular data will be distributed through web based interfaces. The MTBS project website
http://mtbs.gov/index.html is the primary access point for the data and associated reports as they are completed and become available. Additional distribution nodes may be developed in partnership with other federal and academic institutions.