Satellite-derived spectral indices such as the relativized burn ratio (RBR) allow fire severity maps to be produced in a relatively straightforward manner across multiple fires and broad spatial extents. These indices often have strong relationships with field-based measurements of fire severity, thereby justifying their widespread use in management and science. However, satellite-derived spectral indices have been criticized because their non-standardized units render them difficult to interpret relative to on-the-ground fire effects. In this study, we built a Random Forest model describing a field-based measure of fire severity, the composite burn index (CBI), as a function of multiple spectral indices, a variable representing spatial variability in climate, and latitude. CBI data primarily representing forested vegetation from 263 fires (8075 plots) across the United States and Canada were used to build the model. Overall, the model performed well, with a cross-validated R2 of 0.72, though there was spatial variability in model performance. The model we produced allows for the direct mapping of CBI, which is more interpretable compared to spectral indices. Moreover, because the model and all spectral explanatory variables were produced in Google Earth Engine, predicting and mapping of CBI can realistically be undertaken on hundreds to thousands of fires. We provide all necessary code to execute the model and produce maps of CBI in Earth Engine. This study and its products will be extremely useful to managers and scientists in North America who wish to map fire effects over large landscapes or regions.

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Vegetation in the Arctic is often sparse, spatially heterogeneous, and difficult to model. Synthetic Aperture Radar (SAR) has shown some promise in above-ground phytomass estimation at sub-arctic latitudes, but the utility of this type of data is not known in the context of the unique environments of the Canadian High Arctic. In this paper, Artificial Neural Networks (ANNs) were created to model the relationship between variables derived from high resolution multi-incidence angle RADARSAT-2 SAR data and optically-derived (GeoEye-1) Soil Adjusted Vegetation Index (SAVI) values. The modeled SAVI values (i.e., from SAR variables) were then used to create maps of above-ground phytomass across the study area. SAVI model results for individual ecological classes of polar semi-desert, mesic heath, wet sedge, and felsenmeer were reasonable, with r 2 values of 0.43, 0.43, 0.30, and 0.59, respectively. When the outputs of these models were combined to analyze the relationship between the model output and SAVI as a group, the r 2 value was 0.60, with an 8% normalized root mean square error (% of the total range of phytomass values), a positive indicator of a relationship. The above-ground phytomass model also resulted in a very strong relationship (r 2 = 0.87) between SAR-modeled and field-measured phytomass. A positive relationship was also found between optically derived SAVI values and field measured phytomass (r 2 = 0.79). These relationships demonstrate the utility of SAR data, compared to using optical data alone, for modeling OPEN ACCESSRemote Sens. 2014, 6 2135 above-ground phytomass in a high arctic environment possessing relatively low levels of vegetation.

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Until Euro-American colonization, Indigenous people used fire to modify eco-cultural systems, developing robust Traditional Ecological Knowledge (TEK). Since 1980, wildfire activity has increased due to fire suppression and climate change. In 2017, in Waterton Lakes National Park, AB, the Kenow wildfire burned 19,303 ha, exhibiting extreme fire behavior. It affected forests and the Eskerine Complex, a native-grass prairie treated with prescribed burns since 2006 to reduce aspen (Populus tremuloides) encroachment linked to fire suppression and bison (Bison bison bison) extirpation. One year post-fire, the Kenow wildfire caused vigorous aspen sprouting, altered stand structure to an early-seral state and changed dominant land cover from grass to mineral soil. It did not change aspen-cover extent or cause non-native grass eruption, but it reduced native-grass diversity and produced more pronounced shifts in ecosystem structure and biodiversity than the prescribed burn. The 2017 Kenow wildfire and prescribed burns differed in phenological timing, scale, and severity. Prescribed burns occurred in late spring, with little fuel available, while the Kenow wildfire occurred in late summer, with abundant fuel-amplifying the difference in severity. As in other climate-limited fire regimes, prescribed burns treatments did not mitigate the severity of the Kenow wildfire. To more effectively reduce the extent of aspen cover, future prescribed burns in this system could be applied in the late season. Incorporating TEK in adaptive comanagement can help create ecosystems more resilient to fire and pervasive stressors such as invasive plants, provided one contextualizes current conditions and how they differ from historical conditions.