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Canopy base height (CBH) is a key parameter used in forest-fire modeling, particularly crown fires. However, estimating CBH is a challenging task, because normally, it is difficult to measure it in the field. This has led to the use of simple estimators (e.g., the average of individual trees in a plot) for modeling CBH. In this paper, we propose a method for estimating CBH from airborne light detection and ranging (LiDAR) data. We also compare the performance of several estimators (Lorey's mean, the arithmetic mean and the 40th and 50th percentiles) used to estimate CBH at the plot level. The method we propose uses a moving voxel to estimate the height of the gaps (in the LiDAR point cloud) below tree crowns and uses this information for modeling CBH. The advantage of this approach is that it is more tolerant to variations in LiDAR data (e.g., due to season) and tree species, because it works directly with the height information in the data. Our approach gave better results when compared to standard percentile-based LiDAR metrics commonly used in modeling CBH. Using Lorey's mean, the arithmetic mean and the 40th and 50th percentiles as CBH estimators at the plot level, the highest and lowest values for root mean square error (RMSE) and root mean square error for cross-validation (RMSEcv) and R2 for our method were 1.74/2.40, 2.69/3.90 and 0.46/0.71, respectively, while with traditional LiDAR-based metrics, the results were 1.92/2.48, 3.34/5.51 and 0.44/0.65. Moreover, the use of Lorey's mean as a CBH estimator at the plot level resulted in models with better predictive value based on the leave-one-out cross-validation (LOOCV) results used to compute the RMSEcv values. [ABSTRACT FROM AUTHOR]

Extracting digital elevation models (DTMs) from LiDAR data under forest canopy is a challenging task. This is because the forest canopy tends to block a portion of the LiDAR pulses from reaching the ground, hence introducing gaps in the data. This paper presents an algorithm for DTM extraction from LiDAR data under forest canopy. The algorithm copes with the challenge of low data density by generating a series of coarse DTMs by using the few ground points available and using trend surfaces to interpolate missing elevation values in the vicinity of the available points. This process generates a cloud of ground points from which the final DTM is generated. The algorithm has been compared to two other algorithms proposed in the literature in three different test sites with varying degrees of difficulty. Results show that the algorithm presented in this paper is more tolerant to low data density compared to the other two algorithms. The results further show that with decreasing point density, the differences between the three algorithms dramatically increased from about 0.5m to over 10m. [ABSTRACT FROM AUTHOR]