Documentation Revision Date: 2017-08-30
Data Set Version: 1
The algorithm is based on Normalized Burned Ratio differencing (dNBR) and is designed specifically to capture late season fires. The annual MODIS Vegetation Continuous Fields (VCF) 250 m Collection 5.1 (MOD44B) product allowed for additional vegetation-dependent dNBR thresholds within the algorithm’s processing steps.
There are 30 data files in shapefile format (.shp files compressed into .zip files) with this data set. This includes 15 files for annual burned area, one file for each year 2001-2015, for the circumpolar high northern latitudes and 15 files for the North American subset. The data are also provided in .kmz format for viewing in Google Earth.
Loboda, T.V., J.V. Hall, A.H. Hall, and V.S. Shevade. 2017. ABoVE: Cumulative Annual Burned Area, Circumpolar High Northern Latitudes, 2001-2015. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1526
Table of Contents
- Data Set Overview
- Data Characteristics
- Application and Derivation
- Quality Assessment
- Data Acquisition, Materials, and Methods
- Data Access
Data Set Overview
The Arctic Boreal Burned Area (ABBA) dataset provides annual cumulative end-of-season burned area in circumpolar high northern latitudes (HNL) above 60 degrees north for the years 2001-2015. The ABBA product (a MODIS-based algorithm) is an updated version of the algorithm developed in a previous study (Loboda et al., 2011). The algorithm is based on Normalized Burned Ratio differencing (dNBR) and is designed specifically to capture late season fires. The product is delivered in two spatial domains: circumpolar and a North American subset for areas above 60 degree north with a focus on the NASA Arctic - Boreal Vulnerability Experiment (ABoVE) study area.
Project: Arctic-Boreal Vulnerability Experiment
The Arctic-Boreal Vulnerability Experiment (ABoVE) is a NASA Terrestrial Ecology Program field campaign taking place in Alaska and western Canada between 2016 and 2021. Climate change in the Arctic and Boreal region is unfolding faster than anywhere else on Earth. ABoVE seeks a better understanding of the vulnerability and resilience of ecosystems and society to this changing environment.
Spatial Coverage: High northern latitudes (circumpolar above 60 degrees N)
ABoVE Site Designation:
Domain: Core ABoVE
Spatial Resolution: The input data were from several MODIS products at resolutions ranging from 250m to 500m
Temporal Coverage: 20010101 to 20151231
Temporal Resolution: Annual
Study Area (All latitude and longitude given in decimal degrees)
|Site||Westernmost Longitude||Easternmost Longitude||Northernmost Latitude||Southernmost Latitude|
|Circumpolar above 60°N||-179.814||179.1537||72.59167||60|
|North America above 60°N||-179.814||-96.4412||69.675||60|
Data file information
There are 30 compressed shapefiles (.shp contained in .zip files) in this data set. This includes 15 files for annual burned area, one file for each year 2001-2015, for the circumpolar higher latitudes, and 15 files for the North American subset. These data are also provided as companion files in .kmz format for viewing in Google Earth.
Table 1. File names and descriptions
|ABBA_HNL_YYYY.zip||Fifteen shapefiles (.shp) provided in compressed .zip files of burned area data in the high northern latitudes above 60 degrees N for the years 2001-2015. In the filenames, "HNL" refers to high northern latitudes and "YYYY" to the year of data.|
|ABBA_NA_YYYY.zip||Fifteen shapefiles (.shp) provided in compressed .zip files of burned area data in North America above 60 degrees N for the years 2001-2015. In the filenames, "NA" refers to North America and "YYYY" to the year of data.|
Properties of the Shapefiles
Table 2. The attributes in the shapefiles are the cumulative end-of-season burned area.
|Area_ha||Area of burn in hectares|
|Area_ac||Area of burn in acres|
Table 3. Extents of the shapefiles
Application and Derivation
These data could be of use to fire management, fire mapping, and land use/land cover change studies.
An intercomparison of burned area (km2) between the ABBA product, MODIS MCD64A1 Collection 6 Burned Area product (Giglio, 2015), and fire perimeter data obtained from the Alaskan Interagency Coordination Center (AICC) and the Natural Resources Canada (NRC) was carried out to assess the performance of the ABBA algorithm. Yearly comparisons between the mapped burned area from ABBA and MCD64A1 and the fire perimeters were undertaken between 2001 and 2014. Results (refer to Table 4; Fig 2 has an example for 2014) indicate that while area burned estimates from both MODIS-based products show strong statistical relationships with the estimates from the auxiliary burned area perimeters, ABBA estimates match the AICC and NRC estimates much more closely and with a greater consistency over the entire time period. Only estimates from the 2001 fire season diverge from this general pattern with very poor estimates delivered by the MODIS-based products. The poor performance during the 2001 season was mostly due to the fact that the MODIS instrument was turned off for substantial periods of time during the growing season. While the instrument was off, no active fire detections were acquired and since those represent an integral part for both ABBA and MCD64A1 products, even larger scars detectable on the surface later during the growing season were not reliably mapped.
Table 4. Intercomparison between ABBA and MCD64A1 burned area products (2001 – 2014) and the AICC and NRC fire perimeter datasets. Small fire years (annual cumulative area < 10,000 km2 are denoted with *) and large fire years (annual cumulative area ≥ 10,000 km2 are denoted with ** ).
|Year||Cumulative Fire Database Area (km2)||ABBA R2 (slope)||MCD64A1 R2 (slope)|
|2001*||2009||-0.175 (-0.01)||0.747 (0.17)|
|2002*||9074||0.975 (0.97)||0.842 (0.66)|
|2003*||4044||0.998 (1.04)||0.975 (0.62)|
|2004 **||49362||0.997 (0.96)||0.981 (0.71)|
|2005 **||22915||0.993 (1.04)||0.979 (0.73)|
|2006*||2578||0.977 (0.87)||0.738 (0.40)|
|2007*||7448||0.984 (0.91)||0.947 (0.60)|
|2008*||4669||0.982 (0.92)||0.982 (0.79)|
|2009 **||14248||0.997 (0.99)||0.970 (0.57)|
|2010*||9618||0.980 (0.85)||0.689 (0.25)|
|2011*||4631||0.985 (0.95)||0.907 (0.49)|
|2012*||4721||0.962 (0.87)||0.872 (0.61)|
|2013 **||11760||0.986 (0.94)||0.813 (0.45)|
|2014 **||36423||0.999 (0.94)||0.991 (0.66)|
Figure 2. 2014 burned area (km2) comparison between the fire perimeters from AICC and NRC and the ABBA (left) and MCD64A1 (right) burned area products. ABBA mapped an additional 298 km2 of cumulative burned area (non-overlapping the AICC and NRC fire perimeters), while MCD64A1 mapped an additional 523 km2. These values were excluded from the analysis.
Data Acquisition, Materials, and Methods
Annual maps of the cumulative end-of-season burned area in the circumpolar high northern latitudes (HNL) above 60 degrees N were developed in a previous study using the ABBA algorithm with MODIS data from collection 5 (Loboda et al., 2011). The data in this dataset were derived with MODIS collection 6 products and are for the years 2001-2015, generated using ABBA.
The ABBA algorithm is based on Normalized Burned Ratio differencing (dNBR) and is designed specifically to capture late season fires. The algorithm inputs the MODIS Surface Reflectance 8-Day Composite product (MOD09A1; Vermote, 2015), the MODIS Active Fire product (MOD14ML; Giglio, 2015), the MODIS Vegetation Continuous Fields (VCF) 250 m Collection 5.1 (MOD44B; Hansen et al., 2003) product and the MODIS 250 m land-water mask (MOD44W; Carroll et al., 2009). The algorithm development follows the methodology published in Loboda et al. (2007) and Loboda et al. (2011).
Table 5. Summary of data products used in the study.
|Satellite-based fire monitoring programs and products||Purpose|
|MODIS active fire detections (MCD14ML) Collection 6 (Giglio, 2015)||Assessment of observed fire activity (fire location). This is a change introduced to the inputs for the burned area production since the publication of the algorithm (Loboda et al., 2007 and 2011)|
|MODIS 8-day Level 3 500m MOD09A1 Surface Reflectance Collection 6 (Vermote, 2015)||Used to create surface reflectance composites for dNBR. This is a change introduced to the inputs for the burned area production since the publication of the algorithm (Loboda et al., 2007 and 2011)|
|MODIS 250 m land-water mask (MOD44W; Carroll et al., 2009)||Used for the identification of water bodies. Only larger water bodies with a minimum area of ~ 21ha (~ 53 ac) – corresponding to four 231.65 m grid cells – were mapped as water. Due to the preponderance of very small waterbodies in the HNL, a relaxed aggregation rule was used to reduce the omission of large amounts of land area and thus reduce the patchy appearance of burn scars. This is a change introduced to the inputs for the burned area production since the publication of the algorithm (Loboda et al., 2007 and 2011)|
|MODIS Vegetation Continuous Fields (VCF) 250 m Collection 5.1 (MOD44B; Hansen et al., 2003)||Allowed for additional vegetation-dependent dNBR thresholds within the algorithm’s processing steps|
|Alaskan Interagency Coordination Center (AICC) and the Natural Resources Canada (NRC) fire perimeters||Used to assess reported burned area estimates per individual fire scar (2001 – 2015)|
ABBA algorithm modifications and methodology
In order to optimize the mapping accuracy of the ABBA algorithm, the original Loboda et al. (2007) methodology was modified to include pre-season and post-season spring composites:
- First, burned areas were mapped following the original methodology of Loboda et al. (2007). This methodology is based on semi-automated processing of standard MODIS land products into burned area maps using information about changes in surface reflectance due to fire and the record of fire activity. The algorithm ingests the MODIS 500m 8-day surface reflectance composites (MOD09A1) collected during the year of interest and the year before, masks out poor-quality data using MOD09A1 quality bits, and produces 8-day dNBR grids.
- A set of vegetation-dependent dNBR thresholds was then developed by an analyst to reflect ecosystem-specific changes in surface reflectance due to fire. These masks of potential burning, created by separating the dNBR grids into ‘potentially burned’ and ‘unburned’ categories, were further compared with the observed fire activity recorded by the MODIS active fire detections (Giglio et al., 2003) to eliminate instances of surface reflectance change due to reasons other than fire. At this stage, two thresholds are set for active fire detections included in the analysis: (1) the area threshold that defines a minimum of active fires detections per unit burned area, and (2) the temporal threshold, which is defined by the longevity of a burn scar, the possible length of a fire-event occurrence, and the possible length of a period of persistent cloud cover impeding surface observations.
- The resultant 8-day burn masks were merged into an end-of season burned area product.
Regional dNBR thresholds and updates to define burned area pixels
With the changes introduced in the MODIS Collection 6, new updates to acceptability of pixels for burned area mapping were defined. Regional dNBR thresholds were established at 0.25 and 0.2 for areas with tree cover >10% and ≤10% respectively. MODIS pixels with values above those thresholds were considered potentially burned. The potentially burned pixels were further compared with the active fire detection to ensure that the observed change in surface reflectance occurred owing to burning. The active fire detection thresholds, designed to limit the selection of appropriate samples of active fires from the MODIS active fire product, were set at three times the area mapped by active fires for the area threshold and 64 days before the date of the given composite for the temporal threshold. For a more complete description of threshold identification, see Loboda et al. (2007).
The spring composite was automatically created from the MODIS 8-day composite files acquired during late April–May (composite Julian Dates (JD) 121–153) to develop a pre-fire season clear surface view. Analysis of active fire detections showed that few fires occurred during this time and they were easily separated from the fire scars of the previous year. The JD 153 MODIS 8-day composite was then modified using the information contained in the quality layer to mask out pixels of low quality. The masked-out pixels from the base composite were filled with acceptable-quality pixels from the composite of the previous date (JD 8). The process was repeated until the JD reached 121, the cut-off date based on snow-melt trajectories, after which the remaining poor-quality pixels were permanently masked out.
Visual analysis of multiyear burned area mapping using the original regional burned area algorithm showed that new burn scars were frequently found to be adjacent to the burns from the previous year. To eliminate the potential confusion in the year of burning, a pre-fire season spring composite was created to exclude previously burned areas. MOD09A1 composites between JD 121 and 153 were combined in a single clear surface pre-burn composite for year i and year i-1 with preferential selection of later dates in the compositing time-window. The subsequent processing included analysis of three spring composites: the spring pre-fire season composite (year i), the spring post-fire season composite of the following year (year i+1), and the spring pre-fire-season composite of the previous year (year i-1). dNBR images were calculated for composites from (1) year i-1 and year i, and (2) year i and year i+1. The dNBR images were further processed using the regional thresholds and using cumulative fire detections from the previous year for the pre-fire season dNBR composite and from the current fire season for the post-fire dNBR composite. The resultant burned area masks from the post-fire season were merged with the burns mapped using the original algorithm. Finally, the pre-fire season masks were erased from the resultant product.
For a more complete description, see Loboda et al. (2011).
These data are available through the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).
ABoVE: Cumulative Annual Burned Area, Circumpolar High Northern Latitudes, 2001-2015
Contact for Data Center Access Information:
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Carroll, M.L., J.R. Townshend, C.M. DiMiceli, P. Noojipady and R.A. Sohlberg. A new global raster water mask at 250 m resolution. International Journal of Digital Earth Vol. 2 , Iss. 4,2009 https://doi.org/10.1080/17538940902951401
Giglio, L. 2015. MODIS Collection 6 Active Fire Product User’s Guide Revision A. Unpublished manuscript, Department of Geographical Sciences, University of Maryland. https://cdn.earthdata.nasa.gov/conduit/upload/3865/MODIS_C6_Fire_User_Guide_A.pdf.
Giglio L., J. Descloitres, C.O. Justice, and Y.J. Kaufman. 2003. An enhanced contextual fire detection algorithm for MODIS. Remote Sensing of Environment 87(2–3), 273–282. https://doi.org/10.1016/S0034-4257(03)00184-6.
Hansen, M.C., R.S. DeFries, J.R.G. Townshend, M. Carroll, C. Dimiceli, and R.A. Sohlberg. 2003. Global percent tree cover at a spatial resolution of 500 meters: First results of the MODIS vegetation continuous fields algorithm. Earth Interactions, 7(10), 1-15.
Loboda, T.V., E.E. Hoy, L. Giglio, and E.S. Kasischke. 2011. Mapping burned area in Alaska using MODIS data: a data limitations-driven modification to the regional burned area algorithm. International Journal of Wildland Fire, 20(4), 487-496. https://doi.org/10.1071/WF10017
Loboda, T.V., K.J. O'neal, and I. Csiszar. 2007. Regionally adaptable dNBR-based algorithm for burned area mapping from MODIS data. Remote Sensing of Environment, 109(4), 429-442. https://doi.org/10.1016/j.rse.2007.01.017
Vermote, E. (2015). MOD09A1 MODIS/Terra Surface Reflectance 8-Day L3 Global 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/modis/mod09a1.006