Whitehorse, Yukon modified ABoVE: Landsat-derived Annual Dominant Land Cover 1984-2054

We developed a compound modeling approach that enabled us to refine the available evergreen forest category in the original Arctic Boreal Vulnerability Experiment(ABoVE) dataset (https://daac.ornl.gov/ABOVE/guides/Annual_Landcover_ABoVE.html) to include lodgepole pine, subalpine fir, and white spruce and hemlock. The data is a geotiff (30 meter resolution) with 18 land cover classes. We identified subalpine fir locations on a scale of 1:5000 (5 k) vegetation inventory dataset (Vegetation Inventory-5K. Forest Management Branch. Available online: http://yukon.maps.arcgis.com/home) from 2012 which covered approximately 2/3 of our study area. Outside the area of overlap, a minimum elevation of 1200 meters was used based on a statistical analysis of mean elevation of subalpine fir pixels, which is also confirmed by species observations. All evergreen pixels in the 2014 ABoVE landcover which identified as subalpine fir were reclassified accordingly. Then, we trained a new gradient boosted model on lodgepole pine locations in the same 5 k inventory dataset, which allowed us to extrapolate to the rest of the study area. All remaining other evergreen forest pixels were classified as white spruce. The published paper with the methods can be found at: https://doi.org/10.3390/f14081577. This archive includes 1984, 1994, 2004, 2014 and predicted 2024, 2034, 2044, and 2054. Because medium resolution landcover data that include species detail are lacking, we developed a compound modeling approach that enabled us to refine the available evergreen forest category into highly flammable species and less flammable species. We then expanded our refined landcover at decadal time steps from 1984 to 2014. With the aid of an existing burn model, FlamMap, and simple succession rules, we were able to predict future landcover at decadal steps until 2054. Our resulting land covers provide important information to communities in our study area on current and future wildfire risk and vegetation changes and could be developed in a similar fashion for other areas. These data will then be used to assess wildfire hazards and risk.