Carbon (C) emissions from wildfires are a key terrestrial-atmosphere interaction that influences global atmospheric composition and climate. Positive feedbacks between climate warming and boreal wildfires are predicted based on top-down controls of fire weather and climate, but C emissions from boreal fires may also depend on bottom-up controls of fuel availability related to edaphic controls and overstory tree composition. Here we synthesized data from 417 field sites spanning six ecoregions in the northwestern North American boreal forest and assessed the network of interactions among potential bottom-up and top-down drivers of C emissions. Our results indicate that C emissions are more strongly driven by fuel availability than fire weather, highlighting the importance of fine-scale drainage conditions, overstory tree species composition, and fuel accumulation rates for predicting total C emissions. By implication, climate change-induced modification of fuels needs to be considered for accurately predicting future C emissions from boreal wildfires.
Main TextClimate warming and drying in parts of the boreal forest have led to heightened wildfire activity 1,2 , with large increases in the annual area burned over recent decades 3,4 (Figure 1).Climate influences the amount and type of fuel available to burn over long timescales. At shorter timescales, weather patterns dictate the flammability of fuels and weather parameters are expressed as percentiles relative to longer-term climate patterns. Consequently, carbon (C) emissions from boreal wildfires have been considered to be dominated by top-down controls of fire-conducive weather [5][6][7] . The Canadian Forest Fire Weather Index (FWI) System 8 is broadly used to predict fire activity and C emissions throughout the boreal forest and even globally [9][10][11]
Large fire years in which >1% of the landscape burns are becoming more frequent in the Alaskan (USA) interior, with four large fire years in the past 10 years, and 79 000 km2 (17% of the region) burned since 2000. We modeled fire severity conditions for the entire area burned in large fires during a large fire year (2004) to determine the factors that are most important in estimating severity and to identify areas affected by deep-burning fires. In addition to standard methods of assessing severity using spectral information, we incorporated information regarding topography, spatial pattern of burning, and instantaneous characteristics such as fire weather and fire radiative power. Ensemble techniques using regression trees as a base learner were able to determine fire severity successfully using spectral data in concert with other relevant geospatial data. This method was successful in estimating average conditions, but it underestimated the range of severity. This new approach was used to identify black spruce stands that experienced intermediate- to high-severity fires in 2004 and are therefore susceptible to a shift in regrowth toward deciduous dominance or mixed dominance. Based on the output of the severity model, we estimate that 39% (approximately 4000 km2) of all burned black spruce stands in 2004 had <10 cm of residual organic layer and may be susceptible a postfire shift in plant functional type dominance, as well as permafrost loss. If the fraction of area susceptible to deciduous regeneration is constant for large fire years, the effect of such years in the most recent decade has been to reduce black spruce stands by 4.2% and to increase areas dominated or co-dominated by deciduous forest stands by 20%. Such disturbance-driven modifications have the potential to affect the carbon cycle and climate system at regional to global scales.
Airborne laser scanning (ALS) can be utilised to derive canopy height models (CHMs) for individual tree crown (ITC) delineation. In the case of forest areas subject to defoliation and dieback as a result of disease, increased irregularities across the canopy can add complications to the segmentation of ITCs. Research has yet to address this issue in order to suggest appropriate techniques to apply under conditions of forest stands that are infected by phytopathogens. This study aimed to find the best method of ITC delineation for larch canopies affected by defoliation as a result of a Phytophthora ramorum infection. Sample plots from two study sites in Wales, United Kingdom, were selected for ITC segmentation assessment across a range of infection levels and stand characteristics. The performance of two segmentation algorithms (marker-controlled watershed and region growing) were tested for a series of CHMs generated by a standard normalised digital surface model and a pit-free algorithm, across a range of spatial resolutions (0.15 m, 0.25 m and 0.5 m). The results show that the application of a pit-free CHM generation method produced improved segmentation accuracies in moderately and heavily infected larch forest, compared to the standard CHM. The success of ITC delineations was also influenced by CHM resolution. Across all plots the CHMs with a 0.25 m pixel size performed consistently well. However, lower and higher CHM resolutions also provided improved delineation accuracies in plots dominated by larger and smaller canopies respectively. The selected segmentation method also influenced the success of ITC delineations, with the marker-controlled watershed algorithm generating significantly more accurate results than the region growing algorithm (p < 0.10). The results demonstrate that ITCs in forest stands infected with Phytophthora ramorum can be successfully delineated from ALS when a pit-free algorithm is applied to CHM generation.
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