{ "snippet": "The first goal of our study was to identify the most influential factors driving high-severity fire for each ecoregion in the western US using observed fire data and a statistical modelling framework. The second goal of our study was to use the aforementioned statistical models to produce \u201cwall-to-wall\u201d maps depicting the probability of high-severity fire, were a fire to occur, for each ecoregion under a range of potential weather scenarios. In achieving the second goal, we used satellite imagery from 2016 and therefore provide a fairly up-to-date assessment of the potential for high severity fire.", "summary": "The first goal of our study was to identify the most influential factors driving high-severity fire for each ecoregion in the western US using observed fire data and a statistical modelling framework. The second goal of our study was to use the aforementioned statistical models to produce \u201cwall-to-wall\u201d maps depicting the probability of high-severity fire, were a fire to occur, for each ecoregion under a range of potential weather scenarios. In achieving the second goal, we used satellite imagery from 2016 and therefore provide a fairly up-to-date assessment of the potential for high severity fire.", "accessInformation": "We acknowledge funding from the Joint Fire Science Program (Project #: 15-1-3\u201320) and from the National Fire Plan through the Rocky Mountain Research Station.", "thumbnail": "thumbnail/thumbnail.png", "maxScale": 577790.55428899999, "typeKeywords": [ "ArcGIS Server", "Data", "Image Service", "Service" ], "description": "
The geospatial products described and distributed here depict the probability of high-severity fire, if a fire were to occur, for several ecoregions in the contiguous western US. <\/span><\/p> The ecological effects of wildland fire \u2013 also termed the fire severity \u2013 are often highly heterogeneous in space and time. This heterogeneity is a result of spatial variability in factors such as fuel, topography, and climate (e.g. mean annual temperature). However, temporally variable factors such as daily weather and climatic extremes (e.g. an unusually warm year) also may play a key role. <\/span><\/p> Scientists from the US Forest Service Rocky Mountain Research Station and the University of Montana conducted a study in which observed data were used to produce statistical models describing the probability of high severity fire as a function of fuel, topography, climate, and fire weather. Observed data from over 2000 fires (from 2002-2015) were used to build individual models for each of 19 ecoregions in the contiguous US (see Parks et al. 2018, Figure 1). High severity fire was measured using a fire severity metric termed the relativized burn ratio, which uses pre- and post-fire Landsat imagery to measure fire-induced ecological change. Fuel included pre-fire metrics of live fuel amount such as NDVI. Topography included factors such as slope and potential solar radiation. Climate summarized 30-year averages of factors such as mean summer temperature that spatially vary across the study area. Lastly, fire weather incorporated temporally variable factors such as daily and annual temperature. <\/span><\/p> In turn, these statistical models were used to generate \"wall-to-wall\" maps depicting the probability of high severity fire, if a fire were to occur, for 13 of the 19 ecoregions. Maps were not produced for ecoregions in which model quality was deemed inadequate. All maps use fuel data representing the year 2016 and therefore provide a fairly up-to-date assessment of the potential for high severity fire. For those ecoregions in which the relative influence of fire weather was fairly strong (n=6), two additional maps were produced, one depicting the probability of high severity fire under moderate weather and the other under extreme weather. An important consideration is that only pixels defined as forest were used to build the models; consequently maps exclude pixels considered non-forest.<\/span><\/p><\/div><\/div><\/div>",
"licenseInfo": " These data were collected using funding from the U.S. Government and can be used without additional permissions or fees. If you use these data in a publication, presentation, or other research product please use the following citation: Parks, Sean A.; Holsinger, Lisa M.; Panunto, Matthew H.; Jolly, W. Matt; Dobrowski, Solomon Z.; Dillon, Gregory K. 2018. High-severity fire: evaluating its key drivers and mapping its probability across western US forests. Environmental Research Letters. 13: 044037. https://doi.org/10.1088/1748-9326/aab791. <\/span><\/p> Data obtained from https://www.frames.gov/NextGen-FireSeverity.<\/span><\/p> The USDA Forest Service makes no warranty, expressed or implied, including the warranties of merchantability and fitness for a particular purpose, nor assumes any legal liability or responsibility for the accuracy, reliability, completeness or utility of these geospatial data, or for the improper or incorrect use of these geospatial data. These geospatial data and related maps or graphics are not legal documents and are not intended to be used as such. The data and maps may not be used to determine title, ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that may be in place on either public or private land. Natural hazards may or may not be depicted on the data and maps, and land users should exercise due caution. The data are dynamic and may change over time. The user is responsible to verify the limitations of the geospatial data and to use the data accordingly. <\/span><\/p>