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 forest parameters are crucial for ecological protection, forest resource management and sustainable development. The rapid development of remote sensing can retrieve parameters such as the leaf area index, cluster index, diameter at breast height (DBH) and tree height at different scales (e.g., plots and stands). Although some LiDAR satellites such as GEDI and ICESAT-2 can measure the average tree height in a certain area, there is still a lack of effective means for obtaining individual tree parameters using high-resolution satellite data, especially DBH. The objective of this study is to explore the capability of 2D image-based features (texture and spectrum) in estimating the DBH of individual tree. Firstly, we acquired unmanned aerial vehicle (UAV) LiDAR point cloud data and UAV RGB imagery, from which digital aerial photography (DAP) point cloud data were generated using the structure-from-motion (SfM) method. Next, we performed individual tree segmentation and extracted the individual tree crown boundaries using the DAP and LiDAR point cloud data, respectively. Subsequently, the eight 2D image-based textural and spectral metrics and 3D point-cloud-based metrics (tree height and crown diameters) were extracted from the tree crown boundaries of each tree. Then, the correlation coefficients between each metric and the reference DBH were calculated. Finally, the capabilities of these metrics and different models, including multiple linear regression (MLR), random forest (RF) and support vector machine (SVM), in the DBH estimation were quantitatively evaluated and compared. The results showed that: (1) The 2D image-based textural metrics had the strongest correlation with the DBH. Among them, the highest correlation coefficient of −0.582 was observed between dissimilarity, variance and DBH. When using textural metrics alone, the estimated DBH accuracy was the highest, with a RMSE of only 0.032 and RMSE% of 16.879% using the MLR model; (2) Simply feeding multi-features, such as textural, spectral and structural metrics, into the machine learning models could not have led to optimal results in individual tree DBH estimations; on the contrary, it could even reduce the accuracy. In general, this study indicated that the 2D image-based textural metrics have great potential in individual tree DBH estimations, which could help improve the capability to efficiently and meticulously monitor and manage forests on a large scale.

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.