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

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.

Improving silvicultural practices for Mediterranean forests through fire behaviour modelling using LiDAR-derived canopy fuel characteristics

“…Crown fires are serious and dangerous events, as fire suppression efforts are more complex than in the case of surface fires, owing to higher spread rate, fireline intensity, smoke production, spotting and turbulence (Cruz and Alexander 2013). The use of LiDAR-derived forest variables may thus strengthen fire behaviour modelling (Kelly et al 2017).…”

A Ground Elevation and Vegetation Height Retrieval Algorithm Using Micro-Pulse Photon-Counting Lidar Data

“…Ground elevation is a key input of earth surface models and plays a vital role in understanding earth surface processes. Vegetation height is one of the most basic structural parameters of forests, and it is of great significance for estimating forest biomass and predicting biodiversity [1][2][3][4]. Therefore, it is necessary to rapidly and accurately detect both the canopy surfaces and the underlying topography [5][6][7][8].…”

Assessment of Above-Ground Carbon Storage by Urban Trees Using LiDAR Data: The Case of a University Campus

“…Moreover, it should be noted that there was a gap of approximately 1 year 4 months between the LiDAR and field surveys, which may have resulted in estimation errors. With respect to mismatched trees, the literature has discussed that canopy conditions can lead to "false" trees [77]. When looking at the third study limitation, species substitution was needed, which may not give accurate AGB estimates.…”