Remote Sensing Applications and Decision Support

Strata-based forest fuel classification for wild fire hazard assessment using terrestrial LiDAR

[+] Author Affiliations
Yang Chen

Monash University, School of Earth, Atmosphere and Environment, Faulty of Science, 9 Rainforest Walk, Wellington Road, Clayton, Victoria 3800, Australia

Bushfire and Natural Hazards CRC, 340 Albert Street, East Melbourne, Victoria 3002, Australia

Xuan Zhu, Sarah Harris, Nigel Tapper

Monash University, School of Earth, Atmosphere and Environment, Faulty of Science, 9 Rainforest Walk, Wellington Road, Clayton, Victoria 3800, Australia

Marta Yebra

Bushfire and Natural Hazards CRC, 340 Albert Street, East Melbourne, Victoria 3002, Australia

Australian National University, Fenner School of Environment and Society, College of Medicine, Biology and Environment, Canberra, ACT 2601 Australia

J. Appl. Remote Sens. 10(4), 046025 (Dec 07, 2016). doi:10.1117/1.JRS.10.046025
History: Received June 22, 2016; Accepted November 11, 2016
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Abstract.  Fuel structural characteristics affect fire behavior including fire intensity, spread rate, flame structure, and duration, therefore, quantifying forest fuel structure has significance in understanding fire behavior as well as providing information for fire management activities (e.g., planned burns, suppression, fuel hazard assessment, and fuel treatment). This paper presents a method of forest fuel strata classification with an integration between terrestrial light detection and ranging (LiDAR) data and geographic information system for automatically assessing forest fuel structural characteristics (e.g., fuel horizontal continuity and vertical arrangement). The accuracy of fuel description derived from terrestrial LiDAR scanning (TLS) data was assessed by field measured surface fuel depth and fuel percentage covers at distinct vertical layers. The comparison of TLS-derived depth and percentage cover at surface fuel layer with the field measurements produced root mean square error values of 1.1 cm and 5.4%, respectively. TLS-derived percentage cover explained 92% of the variation in percentage cover at all fuel layers of the entire dataset. The outcome indicated TLS-derived fuel characteristics are strongly consistent with field measured values. TLS can be used to efficiently and consistently classify forest vertical layers to provide more precise information for forest fuel hazard assessment and surface fuel load estimation in order to assist forest fuels management and fire-related operational activities. It can also be beneficial for mapping forest habitat, wildlife conservation, and ecosystem management.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Yang Chen ; Xuan Zhu ; Marta Yebra ; Sarah Harris and Nigel Tapper
"Strata-based forest fuel classification for wild fire hazard assessment using terrestrial LiDAR", J. Appl. Remote Sens. 10(4), 046025 (Dec 07, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.046025


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