Forest inventory parameters such as crown projection area and crown volume are critical to the study of single-tree biomass and 3D green biomass. The objective of this study is to develop highly precise measurement and calculation methods for crown projection area and volume of single trees based on a terrestrial 3D laser scanning system. A total of 20 sample trees with different species (Pinus tabuliformis, Larix gmelinii, Betula platyphylla, Picea asperata, Fraxinus mandschurica) were selected, and 3D laser scanning system was used to capture the initial point-cloud data of all of the sample trees. A series of processing including point-cloud registration, denoising, and extraction was carried out to obtain the final point-cloud data of single trees. Finally, the proposed improved convex hull algorithms were used to calculate the crown projection area and crown volume of the single trees and compared with the results obtained by the traditional method and the geometrical simulation method. The results indicated that traditional measurement had significant fluctuation compared with the other two methods due to human-induced factors during measurement. The crown projection area (or crown volume) obtained by different methods was strongly correlated, especially for the geometric simulation and improved convex hull algorithms. With regard to the improved convex hull algorithms, the mean of absolute difference in the percentage of geometrical simulation-based results was 4.31% for crown projection area and 4.59% for crown volume, respectively. Meanwhile, the time consumed by the improved algorithms was significantly reduced, which took only 21% of the time required by the traditional measurement method, and 38% of the geometrical simulation method. Therefore, the proposed method can acquire the crown projection area and crown volume of a single tree effectively and quickly, which is of great significance for the application of 3D laser scanning technology in forestry practice.

Automatic and accurate individual tree species identification is essential for the realization of smart forestry. Although existing studies have used unmanned aerial vehicle (UAV) remote sensing data for individual tree species identification, the effects of different spatial resolutions and combining multi-source remote sensing data for automatic individual tree species identification using deep learning methods still require further exploration, especially in complex forest conditions. Therefore, this study proposed an improved YOLOv8 model for individual tree species identification using multisource remote sensing data under complex forest stand conditions. Firstly, the RGB and LiDAR data of natural coniferous and broad-leaved mixed forests under complex conditions in Northeast China were acquired via a UAV. Then, different spatial resolutions, scales, and band combinations of multisource remote sensing data were explored, based on the YOLOv8 model for tree species identification. Subsequently, the Attention Multi-level Fusion (AMF) Gather-and-Distribute (GD) YOLOv8 model was proposed, according to the characteristics of the multisource remote sensing forest data, in which the two branches of the AMF Net backbone were able to extract and fuse features from multisource remote sensing data sources separately. Meanwhile, the GD mechanism was introduced into the neck of the model, in order to fully utilize the extracted features of the main trunk and complete the identification of eight individual tree species in the study area. The results showed that the YOLOv8x model based on RGB images combined with current mainstream object detection algorithms achieved the highest mAP of 75.3%. When the spatial resolution was within 8 cm, the accuracy of individual tree species identification exhibited only a slight variation. However, the accuracy decreased significantly with the decrease of spatial resolution when the resolution was greater than 15 cm. The identification results of different YOLOv8 scales showed that x, l, and m scales could exhibit higher accuracy compared with other scales. The DGB and PCA-D band combinations were superior to other band combinations for individual tree identification, with mAP of 75.5% and 76.2%, respectively. The proposed AMF GD YOLOv8 model had a more significant improvement in tree species identification accuracy than a single remote sensing sources and band combinations data, with a mAP of 81.0%. The study results clarified the impact of spatial resolution on individual tree species identification and demonstrated the excellent performance of the proposed AMF GD YOLOv8 model in individual tree species identification, which provides a new solution and technical reference for forestry resource investigation combined multisource remote sensing data.

Handheld mobile laser scanning (HMLS) can quickly acquire point cloud data, and has the potential to conduct forest inventory at the plot scale. Considering the problems associated with HMLS data such as large discreteness and difficulty in classification, different classification models were compared in order to realize efficient separation of stem, branch and leaf points from HMLS data. First, the HMLS point cloud was normalized and ground points were removed, then the neighboring points were identified according to three KNN algorithms and eight geometric features were constructed. On this basis, the random forest classifier was used to calculate feature importance and perform dataset training. Finally, the classification accuracy of different KNN algorithms-based models was evaluated. Results showed that the training sample classification accuracy based on the adaptive radius KNN algorithm was the highest (0.9659) among the three KNN algorithms, but its feature calculation time was also longer; The validation accuracy of two test sets was 0.9596 and 0.9201, respectively, which is acceptable, and the misclassification mainly occurred in the branch junction of the canopy. Therefore, the optimal classification model can effectively achieve the classification of stem, branch and leaf points from HMLS point cloud under the premise of comprehensive training.

A mixed integer programming model was developed to estimate the delivered cost of woody biomass, including the costs associated with stumpage, bundling (if any), extraction, storage, loading and hauling, and chipping–grinding under different woody biomass handling systems. The model was designed to optimize a woody biomass based biofuel facility’s location with the objective of minimizing the total annual delivered cost of woody biomass under resource and operational constraints. The model was applied in the central Appalachian region within the state of West Virginia. Results showed that the optimal plant location would be at Addison or Grantsville in West Virginia, depending on the system used when daily demand is 900 tonnes of dry woody biomass. For that base-case scenario, the average delivered cost ranged from $2.30·GJ^–1to $3.02·GJ^–1across the systems. Extensive sensitivity analysis was performed under different scenarios, including biomass availability and purchase–stumpage price, demand, extraction distance, and fuel pricing. The delivered cost was mostly affected by woody biomass demand. Skidding distance had the least impact on the delivered cost. The results would be useful in facilitating the research and economic development of woody biomass utilization for bioenergy in the region.

China is a country rich in diverse forest ecosystems due to the large span of the country, complex topography, and multiple climate regimes. In this paper, the basic information of forest resources in China was briefly introduced and the current state in the measurements of forest biodiversity and the establishment of forest biodiversity index systems in related studies were reviewed. The results showed that a lot of studies on forest biodiversity have been conducted mostly at landscape or stand level in China and the commonly used biodiversity indicators were identified and compared. Several comprehensive forest biodiversity index systems were proposed. However, there are still some problems during the construction of forest biodiversity assessment system. Due to the late establishment of biodiversity monitoring system in China, the availability of data that could be included in a forest biodiversity index system is limited, which hurdles the precise assessment of forest biodiversity. It is suggested to develop long-term monitoring stations and keep data recording consistently. Concerns should also be given to the construction of the framework of the forest biodiversity index system and the determination of the indicators’ weight. The results will provide reference for the establishment of national or regional forest biodiversity evaluation indicator systems in China.