Severity of wildfires is a critical component of the fire regime and plays an important role in determining forest ecosystem response to fire disturbance. Predicting spatial distribution of potential fire severity can be valuable in guiding fire and fuel management planning. Spatial controls on fire severity patterns have attracted growing interest, but few studies have attempted to predict potential fire severity in fire-prone Eurasian boreal forests. Furthermore, the influences of fire weather variation on spatial heterogeneity of fire severity remain poorly understood at fine scales. We assessed the relative importance and influence of pre-fire vegetation, topography, and surface moisture availability (SMA) on fire severity in 21 lightning-ignited fires occurring in two different fire years (3 fires in 2000, 18 fires in 2010) of the Great Xing'an Mountains with an ensemble modeling approach of boosted regression tree (BRT). SMA was derived from 8-day moderate resolution imaging spectroradiometer (MODIS) evapotranspiration products. We predicted the potential distribution of fire severity in two fire years and evaluated the prediction accuracies. BRT modeling revealed that vegetation, topography, and SMA explained more than 70% of variations in fire severity (mean 83.0% for 2000, mean 73.8% for 2010). Our analysis showed that evergreen coniferous forests were more likely to experience higher severity fires than the dominant deciduous larch forests of this region, and deciduous broadleaf forests and shrublands usually burned at a significantly lower fire severity. High-severity fires tended to occur in gentle and well-drained slopes at high altitudes, especially those with north-facing aspects. SMA exhibited notable and consistent negative association with severity. Predicted fire severity from our model exhibited strong agreement with the observed fire severity (mean r2 = 0.795 for 2000, 0.618 for 2010). Our results verified that spatial variation of fire severity within a burned patch is predictable at the landscape scale, and the prediction of potential fire severity could be improved by incorporating remotely sensed biophysical variables related to weather conditions.
|State||Published - Mar 8 2018|
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Although the parallel comparison of the two models did not show strictly consistent modeling 5. Conclusions relationships, the models generally demonstrated that fire severity was strongly controlled by the coverage of certain vegetation types that have high flammability or fire resistance. The topographic conditions can help determine the distribution of flammable plant types and communities. Topography can also directly influence fuel moisture and create firebreaks through the drainage systems. Remotely sensed fuel moisture proxies (such as MODIS ET products) were also proven to play important roles in modeling fire severity. These findings reveal that fire severity is predictable at the landscape scale in our study area, and its prediction can be improved by incorporating spatial variables related to fire behavior. Our study provides an overview of the hotspot areas within the landscape where severe fires are most likely distributed. Such mapping capabilities can allow managers to optimize fuel treatment strategies by considering the vegetation, topography, and spatial patterns of land surface moisture. The modeling framework employed in our study can readily incorporate new observations and simulated spatial datasets, promoting the more reliable predimoisturctie.on ofThefimodelingre severity frameworkin the futuemployedre. in our study can readily incorporate new observations and simulated spatial datasets, promoting the more reliable prediction of fire severity in the future. Acknowledgments: This work is funded by the National Key R&D Program of China (2017YFA0604403 and 2016YFA0600804), the National Natural Science Foundation of China (Project No. 31500387, 31270511, and and 2016YFA0600804), the National Natural Science Foundation of China (Project No. 31500387, 31270511, 31470517) and the CAS Pioneer Hundred Talents Program. We thank three anonymous reviewers and academic and 31470517) and the CAS Pioneer Hundred Talents Program. We thank three anonymous reviewers and academic editor for comments that improved this manuscript.
© 2018 by the authors.
- Boreal forest
- Fire severity
- Great Xing'an Mountains
- Remote sensing
- Spatial controls
- Surface moisture
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