Optimization of replanting space of natural secondary forest in Daxing’anling Mountains of northeastern China

ObjectiveThis paper aims to optimize the spatial structure of stand, improve the quality of existing stands, accelerate the restoration of forest ecosystems, accelerate the succession of forest communities to top communities in the Daxing’anling Mountains of northeastern China, and determine the replanting species and replanting sites of natural secondary forests.

MethodTaking three typical forest types of birch, birch-larch mixed forest and larch coniferous mixed forest in the Daxing’anling area as an example, based on the natural regeneration of the secondary forest, the Voronoi diagram was used to determine the spatial unit and correct the edge by Voronoi nodal. The inverse distance weighted method was used to visualize the spatial structure parameters of the forest, and the spatial structure information of the forest area was not included in the forest stand and it was used as the basis for replanting. The entropy method was used to determine the weight of each spatial structure parameter, and the image interpolated by spatial structure parameters was weighted superimposed. With the non-spatial structure as the constraint and the optimization of the stand structure, we discussed the species and location of replanting trees under canopy of secondary forest, and provide theoretical support and methods for forest management in the Daxing'anling Mountains.

Result(1) Larix gmelinii was selected as replanting tree species. The number of replanting trees of each forest type was 660, 1 970, 315, respectively. (2) After the replanting, the mingling degree of birch seedings and saplings in the pure birch forest increased to 0.52, and that of other tree species seedings and saplings increased to 0.51. The mingling degree of birch seedings and saplings in larch and birch mixed forest increased to 0.84, and that of other tree species seedings and saplings increased to 0.70; the mingling degree of birch species seedings and saplings in larch mixed coniferous forest increased to 0.78, and the mingling degree of other tree species seedings and saplings increased to 0.50. (3) After replanting, the coefficients of variation of the Voronoi diagram polygons of forest seedings and saplings under canopy of each forest type were 1.31, 1.41, 1.36, and they were all in random distribution state. The variation coefficient of the Voronoi diagram polygon of larch tree seedings and saplings in the pure birch forest, larch and birch mixed forest and larch mixed coniferous forest was 1.27, 1.40 and 1.37, respectively, they were also all in random distribution state.

ConclusionHarvesting and replanting are two opposite spatial optimization methods. The inverse distance weight of the spatial structure parameters of trees can predict the spatial structure parameters of non-forest areas in forests. The interpolated image pixel size can be set to the forest area for replanting trees. According to the optimization goal of stand spatial structure and the regulation goal of replanting quantity, extracting the spatial structure parameter value of forest area that does not contain tree, we can determine the species and location of the replanted forest under the canopy.