Rapid and accurate identification of tree species via remote sensing technology has become one of the important means for forest inventory. This paper is to develop an accurate tree species identification framework that integrates unmanned airborne vehicle (UAV)-based hyperspectral image and light detection and ranging (LiDAR) data under the complex condition of natural coniferous and broad-leaved mixed forests. First, the UAV-based hyperspectral image and LiDAR data were obtained from a natural coniferous and broad-leaved mixed forest in the Maoer Mountain area of Northeast China. The preprocessed LiDAR data was segmented using a distance-based point cloud clustering algorithm to obtain the point cloud of individual trees; the hyperspectral image was segmented using the projection outlines of individual tree point clouds to obtain the hyperspectral data of individual trees. Then, different hyperspectral and LiDAR features were extracted, respectively, and the importance of the features was analyzed by a random forest (RF) algorithm in order to select appropriate features for the single-source and multi-source data. Finally, tree species identification in the study area were conducted by using a support vector machine (SVM) algorithm together with hyperspectral features, LiDAR features and fused features, respectively. Results showed that the total accuracy for individual tree segmentation was 84.62%, and the fused features achieved the best accuracy for identification of the tree species (total accuracy = 89.20%), followed by the hyperspectral features (total accuracy = 86.08%) and LiDAR features (total accuracy = 76.42%). The optimal features for tree species identification based on fusion of the hyperspectral and LiDAR data included the vegetation indices that were sensitive to the chlorophyll, anthocyanin and carotene contents in the leaves, the partial components of the transformed independent component analysis (ICA), minimum noise fraction (MNF) and principal component analysis (PCA), and the intensity features of the LiDAR echo, respectively. It was concluded that the framework developed in this study was effective in tree species identification under the complex conditions of natural coniferous and broad-leaved mixed forest and the fusion of UAV-based hyperspectral image and LiDAR data can achieve enhanced accuracy compared the single-source UAV-based remote sensing data.

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.

Plasticizer Combinations and Performance of Wood Flour–Poly(Lactic Acid) 3D Printing Filaments

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.

Hand-held mobile laser scanning (HMLS) can quickly and effectively obtain tree point cloud data, which has great potential to conduct forest resource survey at the plot or stand scale. To improve the estimation precision of Diameter at Breast Height (DBH) for individual trees based on HMLS data, four algorithms were compared and the ideal conditions (slice thickness, inclination, point cloud integrity) of use for each algorithm were evaluated. First, the original point cloud data were denoised, sliced, layered, and clustered based on the normalized point cloud data. Then, the tree DBH was estimated by the four algorithms under different conditions. Results showed that: (1) The optimal point cloud thickness for estimating tree DBH was 5 cm for the two-dimensional algorithms and 15 cm for the three-dimensional algorithm. (2) The root mean square error (RMSE) varied by approximately 0.5 and 0.18 cm with the change of inclination for two-dimensional algorithms and three-dimensional algorithm, respectively. (3) A reduction in point cloud integrity can affect the accuracy of all four algorithms, while the fast convex hull algorithm was the most impacted. The two-dimensional algorithms were computationally more efficient than the three-dimensional one. However, the former was less robust than the latter.

This paper constructed a carbon footprint calculation model and analyzed the carbon footprint characteristics and impact mechanism of forestry enterprises in the Greater Khinggan Range, northeast China, based on the survey and statistical data during 2017–2021. The process-based life cycle assessment (LCA) was used to calculate the total carbon footprint and carbon footprint intensity; then, a panel data model combined with ridge regression was used to explore the impacts of different factors on the carbon footprint of the forestry enterprises. Results showed that the forestry enterprises’ total carbon footprint and carbon footprint intensity showed a general trend of increasing first and then decreasing from 2017 to 2021. The average annual carbon footprint of the forestry enterprises ranged from 2354 t CO₂-eq to 24,354 t CO₂-eq, and the average annual carbon footprint intensity ranged from 3.48 kg CO₂-eq hm⁻² to 31.76 kg CO₂-eq hm⁻². Fire area, the number of hired labor, and vehicle usage intensity are significant driving factors of the carbon footprint in forestry enterprises. The study results can provide references for policy formulation in relation to carbon footprint control in forest regions.