Assessing Lightning and Wildfire Hazard by Land Properties and Cloud to Ground Lightning Data with Association Rule Mining in Alberta, Canada

Abstract

Hotspot analysis was implemented to find regions in the province of Alberta (Canada) with high frequency Cloud to Ground (CG) lightning strikes clustered together. Generally, hotspot regions are located in the central, central east, and south central regions of the study region. About 94% of annual lightning occurred during warm months (June to August) and the daily lightning frequency was influenced by the diurnal heating cycle. The association rule mining technique was used to investigate frequent CG lightning patterns, which were verified by similarity measurement to check the patterns’ consistency. The similarity coefficient values indicated that there were high correlations throughout the entire study period. Most wildfires (about 93%) in Alberta occurred in forests, wetland forests, and wetland shrub areas. It was also found that lightning and wildfires occur in two distinct areas: frequent wildfire regions with a high frequency of lightning, and frequent wild-fire regions with a low frequency of lightning. Further, the preference index (PI) revealed locations where the wildfires occurred more frequently than in other class regions. The wildfire hazard area was estimated with the CG lightning hazard map and specific land use types.

Applicability of Remote Sensing-Based Vegetation Water Content in Modeling Lightning-Caused Forest Fire Occurrences

“…These variations might be related to one or more of the following reasons. (i) The existing differences in subregion-specific landscape properties, in particular those related to climate, geology, topography, and vegetation [29], could affect the spatial and temporal patterns of lightning strikes [9]. (ii) It is known that the randomness of lightning strike locations highly depends on atmospheric conditions [11,12].…”

“…Overlaying the observed lightning-caused fires during the same time period on the derived fire risk map revealed that~69% of the fires fell under the top three classes, i.e., relatively high, high, and extremely high. In addition, over Alberta, Canada, Cha et al [9] analyzed the spatial and temporal patterns of lightning strikes based on landscape properties, i.e., elevation, slope, soil type, and land use to generate a lightning hazard map during the 2010-2014 period. Upon analyzing the lightning strikes' temporal distribution, i.e., occurring hours and months, they identified two thematic maps.…”

Joint Analysis of Lightning-Induced Forest Fire and Surface Influence Factors in the Great Xing’an Range

“…The dominant tree species are Xing'an larch, white birch, and Mongolian pine (Pinus sylvestris var. Mongolia) [50]. This region has an intact community structure with relatively less artificial disturbance.…”

Understand Daily Fire Suppression Resource Ordering and Assignment Patterns by Unsupervised Learning

“…ARA methods have been implemented in some wildland fire studies. For example, [30] use ARA to identify hotspots of lightning and wildfires occurrence in Alberta, Canada. Ref.…”