This study presents auxiliary support techniques for tree selection strategies based on the spatial structure indices and three competition indices in secondary forests, and discusses the importance of tree competition in forest management. The spatial structure parameter in the structured management is used as a quantitative index—the uniform angle index and three competition indices are used in the design of the algorithm for selective thinning for secondary forest. Based on the target tree-based management principles, simulation of selective thinning was carried out using GIS and C# programming languages. Data for this study were collected from experimental sample plots at Jilin Wangqing Forestry Bureau in China. The simulation results strongly support the use of auxiliary technology for scientifically selecting trees for thinning, avoiding the subjectivity of the traditional manual selection. Selection is largely based on the uniform angle index and competition index. Hegyi’s competition index and its improved version used in the algorithm provided almost identical simulation results, i.e., thinning intensities suggested by these indices for the first sample plot are 21.8% and 21.5%, respectively, and for the second plot are 21.3% and 21.1%, respectively. Thus, one of these competition indices can be used to select trees for thinning. The comprehensive competition index (CCI, a combination of an improved version of Hegyi’s competition index with tree species mingling) can avoid the selection of individual trees with high mingling and help maintain the tree species diversity. CCI suggests thinning intensities of 18.3% and 18.4% for the first and second sample plots, respectively. Presented methods and results may provide auxiliary supports for scientific thinning and help promote the application of information technology in forest management.

Allometric models of internodes are an important component of Functional-Structural Plant Models (FSPMs), which represent the shape of internodes in tree architecture and help our understanding of resource allocation in organisms. Constant allometry is always assumed in these models. In this paper, multilevel nonlinear mixed-effect models were used to characterize the variability of internode allometry, describing the relationship between the last internode length and biomass of Pinus tabulaeformis Carr. trees within the GreenLab framework. We demonstrated that there is significant variability in allometric relationships at the tree and different-order branch levels, and the variability decreases among levels from trees to first-order branches and, subsequently, to second-order branches. The variability was partially explained by the random effects of site characteristics, stand age, density, and topological position of the internode. Tree- and branch-level-specific allometric models are recommended because they produce unbiased and accurate internode length estimates. The model and method developed in this study are useful for understanding and describing the structure and functioning of trees.

The abundant satellite data have enabled the study of the dynamics of forest logging and its corresponding carbon balance with remote sensing. Change detection techniques with moderate-resolution imagery have been widely developed. Yet the signal processing or machine learning methods are sample-dependent, lacking an understanding of spectral signals of forest growth and logging cycles, which is necessary to distinguish logging from other types of disturbance, and mechanism models addressing post-logging tree changes are too complex for parameter inversion. We therefore proposed an efficient physical-based model for spectral simulation of annual forest logging by coupling forest dynamic model ZELIG and the stochastic radiative transfer (SRT) model. The forest logging simulation was conducted and validated by Abies forest field data before and after logging in Wangqing County, Northeastern China (R² = 0.85, RMSE = 10.82 t/ha). The spectral changes in Abies forest stands with annual growth and varying logging intensities were simulated by the novel model. The annual Landsat-8 and Gaofen-1 fusion multispectral imagery of the study area from 2013 to 2016 was furtherly used to extract annual sequence spectral data of 350 forest plots and perform inversion of the annual difference in above-ground biomass (dAGB). With the inversion method combining the look-up table of the ZELIG-SRT model and the random forest regression, the retrieved dAGB of the 350 plots indicated consistency with the measured data on the whole (R² = 0.71, RMSE = 13.32 t/ha). The novel physical-based approach for AGB monitoring is more efficient than previous 3D computer models and less dependent on field samples than data-driven models. This study provides a theoretical basis for understanding the remote sensing response mechanism of forest logging and a methodological basis for improving forest logging monitoring algorithms.