Estimating canopy bulk density and canopy base height using UAV LiDAR and multispectral images

Wildfire behavior modeling programs require spatial layers of Canopy Bulk Density (CBD) and Canopy Base Height (CBH) to predict fire spread. However, the two canopy fuel metrics have been investigated by only a few studies in China. Inaccurate spatial estimates may result from the utilization of traditional field-based estimates, which assume averages across spatial extents. Recently, unmanned aerial vehicles (UAVs) have emerged as valuable tools that provide LiDAR point clouds and multispectral images for estimating CBD and CBH at fine resolution. The main objective of this study is to develop an area-based approach to estimate CBD and CBH and evaluate the accuracy of various UAV datasets at 10 m resolution at the local scale in China. A case study area is set up in Jiaohe City, Jilin Province, which is predominantly covered by coniferous forests in low mountains and hills. Field data, species, crown base height, total tree height, and diameter at breast height are obtained from 106 circular plots and served as modeling and validation datasets. The Fire and Fuels Extension of Forest Vegetation Simulator is used to calculate CBD and CBH for each plot. Best subset regression and random forest models are employed to establish relationships between the 106 field data points collected and the predictive variables derived from UAV LiDAR and multispectral imagery. Given the nonlinearity of the data, the Box–Cox procedure is utilized and shows that 0.5 power transformation is appropriate for best subset regression. The R2 value of CBD is always lower than that of CBH when the same models and input dataset are used. The fusion of LiDAR with multispectral imagery produces the best accurate estimation of CBD when random forest is employed (R2 = 0.5142, root mean squared error [RMSE] = 0.0773 kg/m3, relative RMSE [rRMSE] = 40.73%). LiDAR achieves the most accurate estimation for CBH (R2 = 0.6477, RMSE = 1.6245 m, rRMSE = 31.17%). For the best subset regression and random forest models, the use of LiDAR point clouds alone has higher accuracy in estimating CBD and CBH compared with the use of multispectral imagery. The best subset regression models have R2 values that are greater than those of the random forest models for multispectral imagery alone. This finding indicates that the CBD and CBH values estimated using multispectral imagery are higher than those estimated using LiDAR at a margin of the study area because of crop land. For the various models, fusing LiDAR with multispectral imagery does not necessarily improve estimation accuracy compared with using LiDAR and multispectral imagery alone. Therefore, we recommend using the random forest model that fuses LiDAR and multispectral imagery and LiDAR alone to map CBD and CBH in the study area, respectively, because they have the lowest RMSE. The best subset regression model involves 3 to 6 variables, and the random forest models have 10 to 52 predictive variables. Among the original LiDAR predictor variables, height features are the most important, and structure features have considerable importance. The selected multispectral imagery features of both models exhibit diversity in various canopy flue metrics. This study provides clear evidence that UAV LiDAR and multispectral imagery can be used to derive fine-resolution CBD and CBH, which are crucial for fire behavior modeling at the landscape scale and for forest management activities and decision-making. © 2024 Science Press. All rights reserved.