Pine wood nematode disease has harmed forests in several countries, and can be reduced by locating and clearing infested pine trees from forests. The target detection model of deep learning was utilized to monitor a pine nematode-infested wood. The detecting effect was good, but limited by low-resolution photos with poor accuracy and speed. Our work presents a staged detection and classification approach for a dead nematode-infested pine wood based using You Only Look Once version 4 (YOLO v4) and Google Inception version 1 Net (GoogLeNet), employing high-resolution images acquired by helicopter. Experiments showed that the detection accuracy of the staged detection and classification method and the method using only the YOLO v4 model were comparable for a dead nematode-infested pine wood when the amount of data was sufficient, but when the amount of data was limited the detection accuracy of the former was higher than that of the latter. The staged detection and classification method retained the fast training and detection speed of the one-stage target detection model, further improving the detection accuracy with limited data volume, and was more flexible in achieving accurate classification, meeting the needs of forest areas for pine nematode disease epidemic prevention and control.

The dynamic monitoring of forest resources is an integral component of forest resource management and forest eco-system stability maintenance. In recent years, LiDAR (Light Detection and Ranging) has been increasingly utilized in precision forest surveys due to its great penetrating ability and capacity to detect forest vertical structure information. However, the present airborne LiDAR data individual tree segmentation algorithms are not highly adaptable to forest types, particularly in mixed coniferous and broad-leaved forest zones, where the accuracy of individual tree extraction is low, and trees are incorrectly recognized and missed. In order to address these issues, in this study, spectral images and LiDAR data of a red pine conifer–broadleaf mixed forest in the Changbai Mountain Nature Reserve in Jilin Province were chosen, and the normalized point cloud was segmented iteratively using the distance-threshold-based individual tree segmentation method to obtain the initial segmented individual tree vertices. For individual trees with deviations in the initial vertex identification position, and unidentified individual trees, identification anchor points of real tree vertices are added within the canopy of the trees. These identification anchor points have strong position directivity in LiDAR data, which can mark the individual trees whose vertices were misidentified or missed during the initial individual tree segmentation process and identify these two tuples. The tree vertices may be inserted precisely based on the 3D shape of the individual tree point cloud, and the seed-point-based individual tree segmentation method is used to segment the normalized point cloud and finish the extraction of individual trees in red pine mixed conifer forests. The results indicate that, compared to the previous individual tree segmentation approach based on the relative spacing between individual trees, this study enhances the accuracy of individual tree segmentation from 83% to 96%. The extremely high segmentation accuracy indicates that the proposed method can accurately identify individual trees based on remote sensing techniques to segment forest individual trees, can provide a data basis for subsequent individual tree information extraction, and has great potential in practical applications.

Forest biomass is a key index to comprehend the changes of ecosystem productivity and forest growth and development. Accurate acquisition of single tree scale biomass information is of great significance to the protection, management and monitoring of forest resources. LiDAR technology can penetrate the forest canopy and obtain information on the vertical structure of the forest. Aerial photography technology has the advantages of low cost and high speed, and can obtain information on the horizontal structure of the forest. Therefore, in this study, multispectral imagery and LiDAR data were integrated, and a part of the Zengcheng Forest Farm in Guangdong Province was selected as the study area. Large-scale and high-precision Eucalyptus biomass estimation research was gradually carried out by screening influencing factors and establishing models. This study compared and analysed the performance of multiple stepwise regression methods, random forest algorithms, support vector machine algorithms and decision tree algorithms for Eucalyptus biomass estimation to determine the best method for Eucalyptus biomass estimation. The results demonstrated that the accuracy of the model established by the machine learning method was higher than that of the linear regression model, and in the machine learning model, the random forest model had the best performance on both the training set (R² = 0.9346, RMSE = 8.8399) and the test set (R² = 0.8670, RMSE = 15.0377). RF was more suitable for the biomass estimation of Eucalyptus in this study. The spatial resolution of Eucalyptus biomass distribution was 0.05 m in this study, which had higher accuracy and was more accurate. It can provide data reference for the details about biomass distribution of Eucalyptus in the majority of provinces, and has certain practical reference significance.