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Remote Sens. 2018, 10(1), 10; doi:10.3390/rs10010010

Impact of Error in Lidar-Derived Canopy Height and Canopy Base Height on Modeled Wildfire Behavior in the Sierra Nevada, California, USA

1
Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA 94720-3114, USA
2
Division of Agriculture and Natural Resources (UCANR), University of California, Berkeley, CA 94720-3114, USA
3
State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
4
Sierra Nevada Research Institute, School of Engineering, University of California at Merced, Merced, CA 95343, USA
5
Center for Fire Research and Outreach, University of California, Berkeley, CA 94720-3114, USA
*
Author to whom correspondence should be addressed.
Received: 17 November 2017 / Revised: 19 December 2017 / Accepted: 19 December 2017 / Published: 22 December 2017
(This article belongs to the Section Forest Remote Sensing)
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Abstract

Light detection and ranging (Lidar) data can be used to create wall-to-wall forest structure and fuel products that are required for wildfire behavior simulation models. We know that Lidar-derived forest parameters have a non-negligible error associated with them, yet we do not know how this error influences the results of fire behavior modeling that use these layers as inputs. Here, we evaluated the influence of error associated with two Lidar data products—canopy height (CH) and canopy base height (CBH)—on simulated fire behavior in a case study in the Sierra Nevada, California, USA. We used a Monte Carlo simulation approach with expected randomized error added to each model input. Model 1 used the original, unmodified data, Model 2 incorporated error in the CH layer, and Model 3 incorporated error in the CBH layer. This sensitivity analysis showed that error in CH and CBH did not greatly influence the modeled conditional burn probability, fire size, or fire size distribution. We found that the expected error associated with CH and CBH did not greatly influence modeled results: conditional burn probability, fire size, and fire size distributions were very similar between Model 1 (original data), Model 2 (error added to CH), and Model 3 (error added to CBH). However, the impact of introduced error was more pronounced with CBH than with CH, and at lower canopy heights, the addition of error increased modeled canopy burn probability. Our work suggests that the use of Lidar data, even with its inherent error, can contribute to reliable and robust estimates of modeled forest fire behavior, and forest managers should be confident in using Lidar data products in their fire behavior modeling workflow. View Full-Text
Keywords: wildfire burn probability; crown fire; forest fuels; Sierra Nevada; Lidar; error wildfire burn probability; crown fire; forest fuels; Sierra Nevada; Lidar; error
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Kelly, M.; Su, Y.; Di Tommaso, S.; Fry, D.L.; Collins, B.M.; Stephens, S.L.; Guo, Q. Impact of Error in Lidar-Derived Canopy Height and Canopy Base Height on Modeled Wildfire Behavior in the Sierra Nevada, California, USA. Remote Sens. 2018, 10, 10.

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