7 December 2016 Strata-based forest fuel classification for wild fire hazard assessment using terrestrial LiDAR
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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.
© 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)
Yang Chen, Yang Chen, Xuan Zhu, Xuan Zhu, Marta Yebra, Marta Yebra, Sarah Harris, Sarah Harris, Nigel Tapper, Nigel Tapper, } "Strata-based forest fuel classification for wild fire hazard assessment using terrestrial LiDAR," Journal of Applied Remote Sensing 10(4), 046025 (7 December 2016). https://doi.org/10.1117/1.JRS.10.046025 . Submission:
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