Construction of a wood volume model based on branch attribute factors of ALS and TLS fusion data

As an important unit of forest stock estimation, standing wood volume has important significance in forest resource investigation. This paper studied the method of obtaining the attribute factors of standing wood branches based on multi-source Lidar, and explored the ability of tree point cloud to build a better predicting model of standing wood volume.

In this paper, based on the fusion point cloud data of ground-based Lidar (TLS) and airborne Lidar (ALS), a three-dimensional single tree model was established by using the geometric characteristics of tree skeleton and the extraction algorithm of incomplete simulation of water and nutrient transport (ISTTWN), and the branch attribute factors of individual poplar trees were obtained. By constructing a prediction model of stand volume with branch attribute factor as independent variable, the optimal estimation model of stand stock was explored.

The accuracy of branch attribute factors after fusion was much improved compared with that before fusion, and the extraction accuracy was in the order of branch height > branch length > chord length > branch growth angle > branch angle > bow height. Among them, the fit degree of branch length was the highest with R² of 0.989. Compared with the linear and nonlinear product volume models established by the feature parameters, the model constructed based on the feature parameters increased by 0.088 and 0.110 respectively, while the RMSE decreased by 0.012 and 0.009 m³ respectively. The linear and nonlinear models fit 0.688 and 0.709 respectively, which was the best among the six groups of volume prediction models.

After the fusion of point cloud data between TLS and ALS, the high point cloud density can be effectively improved due to the mutual compensation between the data, and the extraction accuracy of branch attribute factors can be significantly improved in the research and development of 3D tree models. At the same time, adding the independent variable of branch attribute factor into the volume prediction model can effectively improve the accuracy of the model prediction.