Accurate diameter at breast height (DBH) and tree height (H) information can be acquired through terrestrial laser scanning (TLS) and airborne LiDAR scanner (ALS) point cloud, respectively. To utilize these two features simultaneously but avoid the difficulties of point cloud fusion, such as technical complexity and time-consuming and laborious efforts, a feature-level point cloud fusion method (FFATTe) is proposed in this paper. Firstly, the TLS and ALS point cloud data in a plot are georeferenced by differential global navigation and positioning system (DGNSS) technology. Secondly, point cloud processing and feature extraction are performed for the georeferenced TLS and ALS to form feature datasets, respectively. Thirdly, the feature-level fusion of LiDAR data from different data sources is realized through spatial join according to the tree trunk location obtained from TLS and ALS, that is, the tally can be implemented at a plot. Finally, the individual tree parameters are optimized based on the tally results and fed into the binary volume model to estimate the total volume (TVS) in a large area (whole study area). The results show that the georeferenced ALS and TLS point cloud data using DGNSS RTK/PPK technology can achieve coarse registration (mean distance ≈ 40 cm), which meets the accuracy requirements for feature-level point cloud fusion. By feature-level fusion of the two point cloud data, the tally can be achieved quickly and accurately in the plot. The proposed FFATTe method achieves high accuracy (with error of 3.09%) due to its advantages of combining different LiDAR data from different sources in a simple way, and it has strong operability when acquiring TVS over large areas.

Accurate estimation of forest volume is crucial for forest management, carbon budget balance and ecosystem monitoring. However, rapid, large-scale and high-accuracy acquisition of forest volume is still challenging. We proposed a method of coupling allometric growth model and multi-source data for forest volume estimation (CAMFVe). Firstly, the DBH estimation model is constructed by Terrestrial Laser Scanning (TLS) and Airborne Laser Scanning (ALS) to obtain more accurate measured volume. Secondly, the spectral attributes of Landsat and structural attributes of ALS are extracted and upscaled onto the 30 m plot scale, and the optimal attributes for volume estimation are selected. Thirdly, the model of CAMFVe is constructed and applied to obtain the volume of study area. Finally, the applicability of CAMFVe is evaluated under four forest growth environments (different canopy closure and slope categories), and the accuracy is compared to Multiple Linear Regression (MLR), Random Forest (RF), and Support Vector Machine (SVM). The results show that: (1) The DBH estimation model by TLS and ALS improves the DBH calculation accuracy of ALS with a 2.058 cm reduction in RMSE. (2) The mean of canopy height (Hmean) and Enhanced Vegetation Index (EVI) are identified as the optimal structural and spectral attributes, respectively. (3) The model constructed by Hmean and EVI consistently achieves higher accuracy for most forest growth environments, and the addition of spectral attribute improves volume estimation accuracy with a 10.152% reduction in RMSE compared to the Hmean-based model. (4) Compared with MLR, RF, and SVM, CAMFVe offers higher accuracy, requires fewer parameters, and is simpler and more efficient. Our proposed method, based on allometric growth model and utilizing vegetation index instead of DBH, provides a solution for large-scale and high-accuracy volume estimation by combining spaceborne LiDAR and optical satellite images.