【目的】构建林分空间结构优化栅格间伐模型,以期获得高效、便捷的间伐方案。【方法】在随机间伐模型的基础上,提出将林地栅格化处理的栅格间伐法,并使用随机间伐法对栅格间伐法进行检验,最后以金盆山次生林为例验证模型的适用性。【结果】间伐模拟前,次生林空间结构整体表现不够理想,在角尺度、开敞度和竞争指数方面有待提高;恢复年限33、40、45 a次生林的最佳间伐强度分别为34.69%、21.14%、29.90%,评价指数分别提高了47.94%、53.94%和79.73%,其中33和40 a次生林适用于栅格间伐法,采伐方式分别为栅格边长7 m栅格内伐去3株胸径最小的林木和栅格边长6 m栅格内伐去3株胸径最小的林木。【结论】栅格间伐模型能有效地改善林分空间分布格局,但仅适用于林木分布较为均匀的次生林林分,对于分布不均匀的林分,模型只能确定样地内的间伐木及间伐强度。

【Objective】 A grid thinning model based on forest spatial structure optimization was constructed to obtain an efficient and convenient thinning method. 【Method】 Based on the random thinning model, the grid thinning method was proposed, and the random thinning method was used to test the grid thinning method. The grid thinning method indicates that the rasterized forest land is divided into several continuous square grids with the length of integer A, the cutting amount K, and square grid edge length A. Using these variables, all possible thinning methods were enumerated to simulate thinning. Finally, the applicability of the model was verified using the secondary forest of the Jinpenshan Mountain. 【Result】The spatial structure of the secondary forest is not ideal and needs to be improved in terms of the uniform angle index, opening degree and competition index. The optimal thinning intensities of the 33, 40 and 45 years old secondary forests were 34.69%, 21.14% and 29.90%, respectively, and the evaluation index increased by 47.94%, 53.94% and 79.73%, respectively. The 33 and 40 years old forests were suitable for using the grid thinning method, with cutting methods as follows: three trees with the smallest DBH were removed from the grid having a length of 7 m; three trees with the smallest DBH were removed from the grid having a length of 6 m. 【Conclusion】The grid thinning model can effectively improve the spatial distribution pattern of stands, but it is only applicable to the secondary stands with a more uniform forest distribution. For unevenly distributed stands, the model can only determine the interlogging and thinning intensities for the sample plots.