Fuels Fragmentation and Fire Severity

Started on July 6, 2017, the Elephant Hill forest fires burned 191,865 hectares in BC's south-central Interior region,  including parts of both the Kamloops Fire Center and Cariboo Fire Center  (BC Gov News, 2020). At the height of the fires, nearly  50,000 people  were forced to flee their homes as flames scorched huge swaths of timber, bush and grassland (Ghoussoub, 2017). Economically speaking, the 2017 wildfires resulted in an estimated $31 million in direct economic losses for the region,  including almost 100,000 hours of lost employment hours that worth approximately $1 million  (Brady, 2018). The 2017 Elephant Hill forest fire has posed a long-term and significant challenge on forest management and fire prediction due to its severe effects on ecosystem functioning.

Quantifying and examining the fragmentation of fuel types help future fire control and aid decision making regarding sustainable forest management (Stratton, 2004). All fuel types are dynamic in that they change to some degree over time (Johnston, 2012), and fire severity varies with forest composition and structure (Pinto and Femandes, 2014). Fire behavior is the result of complex interactions between various factors, and fuel types play an important role in determining the fire severity. The identification of the relationship between fuel types fragmentation and fire severity will explore a new way to obtain knowledge about the role of fuel types in contributing to fire severity.

Two landscapes have been used for analysis -- one is FuelSat (which indicates the current landscape), the other is Random (which is produced through  Burn P-3 Model , under control of environmental parameters). Also, different phenomena of burn probability and fire intensity regarding two landscapes have been visualized as maps (also produced by  Burn P-3 Model ). To analyze the relationship between forest fragmentation and fire severity, it is done by calculating  landscape metrics , and followed by extracting four target classes regarding their burn probability and fire intensity.

There are four target classes that the study focused: C-2 (Boreal Spruce), C-3 (Mature Pine), Slash, and Standing Grass.

In terms of both visualization and scientific indices, it is evident that the FuelSat Landscape is less fragmentated than the Random Landscape, on both landscape level and class level. Looking at the burn probability and fire intensity of four classes (C-2, C-3, slash, and standing grass), high fragmentated landscape has lowered the overall fire intensity, but has contributed to a more extreme event -- there are more extreme fire intensity events occurred. Regarding the burn probability, the fragmentation level of landscape does not affect much on this -- there are no obvious changes of burn probability from FuelSat Landscape to Random Landscape. Moran's I computation has been used to test the relationship between fuel fragmentation and fire severity, and it does show there's autocorrelation between them.

In both old and new human-colonization frontiers, forest fragmentation is always associated with short- or long-term human establishment, and the ever-increasing commercial and subsistence needs for forest resources (e.g. timber, charcoal, firewood, game animals, fibers, nuts) (Coimbra-Filho and Caˆmara 1996; Cullen et al. 2000; Carvalho et al. 2001; Laurance et al. 2001a; Peres 2001; Tabarelli et al., 2004). Placing this study content under a global scale, there are expansion of logging activities and needs of using agricultural fire for clearing forest, ancient forests have experienced dramatics decline in terms of both area and ecological quality. 

Global forest is becoming higher and higher fragmented because of logging activities, and human-infrastructures development. Based on the result from this study, it is evident that with higher fragmented landscape, specifically the slash area, where there are logging events and human activities occurred, have more extreme fire intensity (maximum) events occurred. In other words, though there are fewer fuel loads for each fire ignition event, it is likely leading to a more extreme fire intensity event compared to a less fragmented landscape, when combining various effects such as dry/wet seasons and fuel moisture. 

In order to have a better understanding of how forest contributing to fire events, it is suggested that combining various characteristics of forest, such as wood moisture, tree height, and fragmentation, instead of one single factors. Also, it is suggested resulting from the initial stage of the fuel type simulation that, the spatial variability of lightning ignited fires has been restricted within the fire zone at several fixed points (Pickell et al., 2020). such contrasting effects on the different aspects of flammability relate to variation in fuel structural traits and emphasize the need to consider flammability in terms of its constituent measures rather than treating it as a composite measure (Engber & Varner, 2012; Pausas et al., 2012; Santana & Marrs, 2014; Tabarelli et al., 2004).

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Pinto, A., & Fernandes, P. M. (2014). Microclimate and modeled fire behavior differ between adjacent forest types in northern Portugal. Forests, 5(10), 2490–2504.  https://doi.org/10.3390/f5102490 

Pickell, P. D., Chavardès, R. D., Daniels, L. D.& Li, S. J (2020 unpublished). A heuristic approach on how the spatial distribution of fuels influences fire behavior in Interior British Columbia. Transactions on Geoscience & Remote Sensing.

Santana VM, Marrs RH. 2014. Flammability properties of British heathland and moorland vegetation: models for predicting fire ignition. Journal of Environmental Management 139:88–96 DOI 10.1016/j.jenvman.2014.02.027.

Stratton, R. D. (2004). Assessing the effectiveness of landscape fuel treatments on fire growth and behavior. Journal of Forestry, 102(7), 32–40.  https://doi.org/10.1093/jof/102.7.32 

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Tabarelli, M., Cardoso Da Silva, J. M., & Gascon, C. (2004). Forest fragmentation, synergisms and the impoverishment of neotropical forests. Biodiversity and Conservation, 13(7), 1419–1425. https://doi.org/10.1023/B:BIOC.0000019398.36045.1b