Analysis and exploitation of landforms for improved optimisation of camera-based wildfire detection systems

Andries Heyns*, Warren du Plessis, Kevin M. Curtin, Michael Kosch, Gavin Hough

*Motsvarande författare för detta arbete

Forskningsoutput: TidskriftsbidragArtikelVetenskapligPeer review

1 Citeringar (Scopus)

Sammanfattning

Tower-mounted camera-based wildfire detection systems provide an effective means of early forest fire detection. Historically, tower sites have been identified by foresters and locals with intimate knowledge of the terrain and without the aid of computational optimisation tools. When moving into vast new territories and without the aid of local knowledge, this process becomes cumbersome and daunting. In such instances, the optimisation of final site layouts may be streamlined if a suitable strategy is employed to limit the candidate sites to landforms which offer superior system visibility. A framework for the exploitation of landforms for these purposes is proposed. The landform classifications at 165 existing tower sites from wildfire detection systems in South Africa, Canada and the USA are analysed using the geomorphon technique, and it is noted that towers are located at or near certain landform types. A metaheuristic and integer linear programming approach is then employed to search for optimal tower sites in a large area currently monitored by the ForestWatch wildfire detection system, and these sites are then classified according to landforms. The results support the observations made for the existing towers in terms of noteworthy landforms, and the optimisation process is repeated by limiting the candidate sites to selected landforms. This leads to solutions with improved system coverage, achieved within reduced computation times. The presented framework may be replicated for use in similar applications, such as site-selection for military equipment, cellular transmitters, and weather radar.

OriginalspråkEngelska
Referentgranskad vetenskaplig tidskriftFire Technology
Volym57
Utgåva5
Sidor (från-till)2269-2303
Antal sidor35
ISSN0015-2684
DOI
StatusPublicerad - 10.04.2021
MoE-publikationstypA1 Originalartikel i en vetenskaplig tidskrift

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