Tree height is an essential indicator in forestry research. This indicator is difficult to measure directly, as well as wind disturbance adds to the measurement difficulty. Therefore, tree height measurement has always been an issue that experts and scholars strive to improve. We propose a tree height measurement method based on tree fisheye images to improve the accuracy of tree height measurements. Our aim is to extract tree height extreme points in fisheye images by proposing an improved lightweight target detection network YOLOX-tiny. We added CBAM attention mechanism, transfer learning, and data enhancement methods to improve the recall rate, F₁ score, AP, and other indicators of YOLOX-tiny. This study improves the detection performance of YOLOX-tiny. The use of deep learning can improve measurement efficiency while ensuring measurement accuracy and stability. The results showed that the highest relative error of tree measurements was 4.06% and the average relative error was 1.62%. The analysis showed that the method performed better at all stages than in previous studies.

Trunk pests have always been one of the most important species of tree pests. Trees eroded by trunk pests will be blocked in the transport of nutrients and water and will wither and die or be broken by strong winds. Most pests are social and distributed in the form of communities inside trees. However, it is difficult to know from the outside if a tree is infected inside. A new method for the non-invasive detecting of tree interiors is proposed to identify trees eroded by trunk pests. The method is based on electromagnetic inverse scattering. The scattered field data are obtained by an electromagnetic wave receiver. A Joint-Driven algorithm is proposed to realize the electromagnetic scattered data imaging to determine the extent and location of pest erosion of the trunk. This imaging method can effectively solve the problem of unclear imaging in the xylem of living trees due to the small area of the pest community. The Joint-Driven algorithm proposed by our group can achieve accurate imaging with a ratio of pest community radius to live tree radius equal to 1:60 under the condition of noise doping. The Joint-Driven algorithm proposed in this paper reduces the time cost and computational complexity of tree internal defect detection and improves the clarity and accuracy of tree internal defect inversion images.

This paper focuses on the current urgent demand for the accurate measurement of forest inventory variables in the fields of forestry carbon sink measurement, ecosystem research, and forest resource conservation, and proposes the use of images to construct a three-dimensional measurement model of forest inventory variables, which is a new method to realize the automatic extraction of forest inventory variables. This method obtains sample site information by using high-definition images taken in the forest by a smartphone, which significantly improves the field operation efficiency and simple operation, and effectively alleviates the problems of long field operation times, complicated operations, and expensive equipment used by current methods for obtaining forest inventory variables. We propose to optimize the Eps parameters of the DBSCAN algorithm based on the MVO algorithm for point cloud clustering to obtain single wood point clouds, which improves the accuracy of the model and can effectively solve the problem of large interference from human factors. The scale coefficients of the image and the actual model are obtained by the actual measurement of tree height and diameter at breast height to complete the construction of the three-dimensional measurement model of the stand and are then combined with the AdQSM algorithm to realize the automatic extraction of forest inventory variables, which provides a new interdisciplinary method for the comprehensive extraction of forest inventory variables. The accuracy of the model measured in the experimental sample site of Fraxinus mandshurica Rupr was as follows: the absolute error of tree height measurement ranged from 0.05 to 0.37 m, the highest relative error of measurement was 2.03%, and the average relative error was 1.53%; for the absolute error of diameter at breast height, measurement ranged from 0.007 to 0.057 m, the highest relative error of measurement was 7.358%, and the average relative error was 3.616%. The method proposed in this study can be directly applied to the process of acquiring and visualizing the variables of forest inventory in the field of ecological research, which has good flexibility and can meet individual research needs.

Reactive power is the core problem of voltage stability and economical operation in power systems. Aiming at the problem that multi-objective normalization reactive power optimization function is dependent on weight, an integrated synthesis of adaptive multi-objective particle swarm optimization (ISAMOPSO) is proposed to achieve weight adaptive. Through seven test functions and three algorithm comparison experiments, it is proved that the ISAMOPSO algorithm has stronger global search capability and better convergence. Considering the optimal access position and capacity of distributed generation (DG), the ISAMOPSO algorithm is used for three-objective reactive power optimization. Finally, the results indicate that the ISAMOPSO algorithm can not only provide a variety of optimization schemes to meet different needs, but also realize dynamic reactive power optimization, which further proves that the algorithm can provide effective technical support for solving reactive power optimization problems in practical engineering.

Pest and disease damage to forests cannot be underestimated, so it is essential to detect diseased trees in time and take measures to stop their spread. The detection of discoloration standing trees is one of the important means to effectively control the spread of pests and diseases. In the visible wavelength range, early infected trees do not show significant color changes, which poses a challenge for early detection and is only suitable for monitoring middle and late discolor trees. The spectral resolution of hyperspectral restricts the improvement of its spatial resolution, and there are phenomena of different spectral of the same and foreign objects in the same spectrum, which affect the detection results. In this paper, the method of hyperspectral and CCD image fusion is used to achieve high-precision detection of discoloration standing trees. This paper proposes an improved algorithm MSGF-GLP, which uses multi-scale detail boosting and MTF filter to refine high-resolution data. By combining guided filtering with hyperspectral images, the spatial detail difference is enhanced, and the injection gain is interpolated into the difference of each band, so as to obtain high-resolution and high-quality hyperspectral images. This research is based on hyperspectral and CCD data obtained from LiCHy, Chinese Academy of Forestry, Maoershan Experimental Forest Farm, Shangzhi City, Heilongjiang Province. The evaluation framework is used to compare with the other five fusion algorithms to verify the good effect of the proposed method, which can effectively preserve the canopy spectrum and improve the spatial details. The fusion results of forestry remote sensing data were analyzed using the vegetation Normalized Difference Water Index and Plant Senescence Reflectance Index. The fused results can be used to distinguish the difference between discoloration trees and healthy trees by the multispectral vegetation index. The research results can provide good technical support for the practical application of forest remote sensing data fusion, and lay the foundation for promoting the scientific, automatic and intelligent forestry control.