Symposia 13
Wednesday, April 11, 2018 11:15 am–11:30 am
Water Tower Parlor, Palmer House Hilton
The Arctic landscape is changing at unprecedented rates due to climate change, natural disturbances, and anthropogenic development. Moreover, the Arctic is also projected to warm at a rate twice that of the global average in the coming century, accelerating climate feedbacks, including those from wildfire disturbances. Wildfires are the dominant disturbance impacting boreal forests, and climate-driven shifts in fire frequency, intensity, and extent produce large changes in fire regimes. To understand changing fire regimes, we applied a random forest machine learning algorithm to estimate wildfire locations based on 500-m resoluton MODIS and 1-km resolution Daymet variables in the State of Alaska. We employed the Google Earth Engine (GEE) platform and its inventory of satellite imagery, meteorology, elevation, and atmospheric data to detect and map the 70 large boreal fires of the 2004 Alaska wildfire season. Using the national Monitoring Trends in Burn Severity (MTBS) data for validation, our random forest method achieved scores of 0.87, 0.95, and 0.91 for the precision, recall, and F1-score metrics when multiple vegetation indices, elevation, and individual spectral bands were incoporated into the analysis. Inclusion of Daymet data with no time lags did not significantly improve the performance of the detection method. The EVI and SAVI indices, plus the NIR spectral band, were the most significant inputs to the random forest algorithm. Results suggest that large burned areas may not be reported and included in the MTBS database. In future work, we plan to extend this methodology to higher resolution observations (15 m to 30 m) from Landsat.