A New Tree-Level Multi-Objective Forest Harvest Model (MO-PSO): Integrating Neighborhood Indices and PSO Algorithm to Improve the Optimization Effect of Spatial Structure

Abstract

Accurate, efficient, impersonal harvesting models play a very important role in optimizing stand spatial structural and guiding forest harvest practices. However, existing studies mainly focus on the single-objective optimization and evaluation of forest at the stand- or landscape-level, lacking considerations of tree-level neighborhood interactions. Therefore, the study explored the combination of the PSO algorithm and neighborhood indices to construct a tree-level multi-objective forest harvest model (MO-PSO) covering multi-dimensional spatial characteristics of stands. Taking five natural secondary forest plots and thirty simulated plots as the study area, the MO-PSO was used to simulate and evaluate the process of thinning operations. The results showed that the MO-PSO model was superior to the basic PSO model (PSO) and random thinning model Monte Carlo-based (RD-TH), DBH dominance (DOMI), uniform angle (ANGL), and species mingling (MING) were better than those before thinning. The multi-dimensional stand spatial structure index (L-index) increased by 1.0%~11.3%, indicating that the forest planning model (MO-PSO) could significantly improve the spatial distribution pattern, increase the tree species mixing, and reduce the degree of stand competition. In addition, under the four thinning intensities of 0% (T1), 15% (T2), 30% (T3), and 45% (T4), L-index increased and T2 was the optimal thinning intensity from the perspective of stand spatial structure overall optimization. The study explored the effect of thinning on forest spatial structure by constructing a multi-objective harvesting model, which can help to make reasonable and scientific forest management decisions under the concept of multi-objective forest management.

Optimizing the Spatial Structure of Metasequoia Plantation Forest Based on UAV-LiDAR and Backpack-LiDAR

“…The results of this study showed that all the spatial structure parameters could be improved by reasonable thinning, and among the four selected spatial structure parameters, the competition index CI and the openness K had the most obvious effect. Qiu et al [18] showed that selective harvesting could reduce competition for limited resources and increase the growing space of individual trees by reducing the number of standing trees in a stand, which could explain the above results. Secondly, the results of this study also showed that the aggregation index R of the stand increased further after optimization, indicating that the stand tended to be more uniformly distributed, whereas Dong et al [1] showed in their study that the spatial distribution pattern of the stand tended to be randomly distributed after optimization.…”

“…The openness K is within five intervals of (0, 0.2]; (0. In the case of planted forests, a more ideal stand spatial structure means less competition within the stand, more space for growth, and a more even horizontal distribution pattern [18,51]. Therefore, this study used the multiplication and division method to multiply the parameters that were expected to become larger and divide the parameters that were expected to become smaller, and finally, the individual objective functions were combined to form a composite function Q for evaluating and optimizing the stand spatial structure.…”

“…Based on the theory of multi-objective planning, by setting the conflicting evaluation indexes as different objective functions with corresponding constraints, the combined optimization of all objective functions could be achieved by solving the function. Subsequently, Dong et al [17], Qiu et al [18], Sun et al [19], and Bettinger et al [20] started based on different objectives and used heuristic algorithms to solve the problem, and all of them finally succeeded in solving the forestry planning problem, which also provides an effective method for forest management control based on the optimization of forest stand spatial structure.…”

Time-Optimal Trajectory Planning for Woodworking Manipulators Using an Improved PSO Algorithm

“…In this process, the GoldS-PSO algorithm proposed in this paper can be used for the training of neural networks and optimization of control model structure in the intelligent control system [48,49], as well as for the optimization of target recognition, positioning, and tracking technology in visual detection systems [50,51]. Meanwhile, some relevant studies indicate that it has great potential in optimizing forestry management structure [52].…”

Effects of Anthropogenic Disturbance on the Structure, Competition, and Succession of Abies ziyuanensis Communities

“…Competition is a critical factor affecting the growth of individual trees [37,40,41]. In this study, we compared the WCI of Abies ziyuanensis within undisturbed and disturbed forest communities.…”