Description: This dataset is derived from the USGS Burned Area Products (Hawbaker et al. 2017). We used Burned Area (BA) version 2 products (USGS 2019). We evaluated the annualBAECV Burn Probability (BP) datasets –which are raster datasets – for evidence of burns. The annual datasets span an entire calendar year (e.g.,Jan 1 through Dec 31) and indicate the maximum BP within the year (0-100%). For each year between 1994 and 2020, we combined the annual datasets of interest within individual ARD Tiles into a single annual raster dataset (i.e., we mosaicked the tiles) for further processing. We performed all additional processing steps on the annual mosaicked datasets as this provided statewide consistency. We identified pixels as burned or unburned according to their probability value; initially, we retained all pixels with an annual BP between 85-100% based on Hawbaker et al. (2017). Values between 90-100% were then converted to presence/absence rasters and we used image processing methods to remove ‘speckling’ (e.g.,fill in small holes within a burned area and remove groups of pixels less than a specified size/amount). This process resulted in annual rasters and vectors indicating burn presence (with 90-100% probability) for groups of pixels greater than ~2.24 acres (e.g.,10 30m pixels, in any arrangement). We also assigned dates from the Burn Date (BD) dataset to these same pixels as a surrogate for seasonality. We evaluated these products against fire records for three pilot areas. For each area, we held a meeting with fire managers, either in person or via web conferencing methods. We invited managers to inspect the data with us to evaluate their thoughts on the products. Through this process, managers provided many explanations for why no burn was detected and where/why fire detection was performing very well, as well as some ideas and suggestions for moving forward (all of which we relayed to USGS). Many of these comments reflect known limitations previously documented (see Hawbaker et al. 2017, Vanderhoof et al. 2017). Based on these meetings, we have applied the processing “logic” across the entire state at 90-100%BP. Fire regime metrics such as number of times burned, year last burned, and time since previous fire (as measured from 2020) are included in the dataset upload, and were derived using these annual presence/absence rasters and vectors.
Service Item Id: af382d456108499894a94ea83bcf5017
Copyright Text: Florida Fish and Wildlife Conservation Commission, Fish and Wildlife Research Institute, Center for Spatial Analysis