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Filters: partyWithName: U.S. Geological Survey (X) > Types: Downloadable (X) > partyWithName: Stephen Boyte (X)

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Exotic annual grasses are one of the most damaging biological stressors in western North America and increase the susceptibility of landscapes to wildfire occurrence. Here we couple estimates of long-term rangeland component fractions (e.g. exotic annual grasses) with remote sensing, climate data, and machine learning techniques to estimate the long-term (1985 to 2019) probability of wildfire occurrence (30-m spatial resolution) in sagebrush-dominated landscapes of the western United States.
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Defining site potential for an area establishes its possible long-term vegetation growth productivity in a relatively undisturbed state, providing a realistic reference point for ecosystem performance. Modeling and mapping site potential helps to measure and identify naturally occurring variations on the landscape as opposed to variations caused by land management activities or disturbances (Rigge et al. 2020). We integrated remotely sensed data (250-m enhanced Moderate Resolution Imaging Spectroradiometer (eMODIS) Normalized Difference Vegetation Index (NDVI) (https://earthexplorer.usgs.gov/)) with land cover, biogeophysical (i.e., soils, topography) and climate data into regression-tree software (Cubist®). We...
    map background search result map search result map Using Targeted Training Data to Develop Site Potential for the Upper Colorado River Basin from 2000 - 2018 Modelled long-term wildfire occurrence probabilities in sagebrush-dominated ecosystems in the western US (1985 to 2019) Using Targeted Training Data to Develop Site Potential for the Upper Colorado River Basin from 2000 - 2018 Modelled long-term wildfire occurrence probabilities in sagebrush-dominated ecosystems in the western US (1985 to 2019)