Abstract
Wildfires are a dominant disturbance to boreal forests, and in North America, they typically cause widespread tree mortality. Forest fire burn severity is often measured at a plot scale using the Composite Burn Index (CBI), which was originally developed as a means of assigning severity levels to the Normalized Burn Ratio (NBR) computed from Landsat satellite imagery. Our study investigated the potential to map biophysical indicators of burn severity (residual green vegetation and charred organic surface) at very high (3 cm) resolution, using color orthomosaics and vegetation height models derived from UAV-based photographic surveys and Structure from Motion methods. These indicators were scaled to 30 m resolution Landsat pixel footprints and compared to the post-burn NBR (post-NBR) and differenced NBR (dNBR) ratios computed from pre- and post-fire Landsat imagery. The post-NBR showed the strongest relationship to both the fraction of charred surface (exponential R2 = 0.79) and the fraction of green crown vegetation above 5 m (exponential R2 = 0.81), while the dNBR was more closely related to the total green vegetation fraction (exponential R2 = 0.69). Additionally, the UAV green fraction and Landsat indices could individually explain more than 50% of the variance in the overall CBI measured in 39 plots. These results provide a proof-of-concept for using low-cost UAV photogrammetric mapping to quantify key measures of boreal burn severity at landscape scales, which could be used to calibrate and assign a biophysical meaning to Landsat spectral indices for mapping severity at regional scales.
Highlights
Wildfires are a major cause of disturbance in North American boreal forests, burning an annual average of approximately 10,000 km2 in the 1960’s and more than 31,000 km2 in the 1990’s [1]
The post-Normalized Burn Ratio (NBR) and differenced NBR (dNBR) indices could each explain a similar amount of the variation in the unmanned aerial vehicles (UAVs)-derived green vegetation fraction, while the post-NBR was a better predictor of the UAV char fraction
While we found that such imagery is suitable for separating broad and distinct classes, such as green vegetation and charred surface, more detailed vegetation characterization could benefit from using miniaturized multi- or hyper-spectral instruments that provide additional spectral information, and where radiance is normalized based on incident light sensors
Summary
Wildfires are a major cause of disturbance in North American boreal forests, burning an annual average of approximately 10,000 km in the 1960’s and more than 31,000 km in the 1990’s [1]. Fires can cause large fluxes of carbon within an ecosystem [4] and release a tremendous quantity of carbon to the atmosphere [5]. This can lead to a boreal forest becoming a significant net carbon source, when combined with severe insect disturbances [2]. The active layer depth often shows a Remote Sens. 2017, 9, 279; doi:10.3390/rs9030279 www.mdpi.com/journal/remotesensing
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