Stand structure optimization and adjustment of natural forest in Changbai Mountains based on AHP-CRITIC combination weight method
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摘要:
目的 合理采伐能促进林木生长，提高林分生产力。探索林分空间结构优化，以期为该地区天然林林分采伐木的选择提供新的指导工具。 方法 本文采用4块不同优势树种的天然林固定样地监测数据，基于混交度（M）、角尺度（W）、大小比数（U）和Hegyi竞争指数（CI）4种空间结构指数，根据AHP-CRITIC 组合赋权法构建单木采伐指数（Fi），分析不同强度的采伐模拟前后林分空间结构变化。 结果 4个样地采伐后的空间结构均得到一定程度的优化：混交度提高，林木大小分化程度减少，林分水平分布格局趋向于随机分布，林分竞争压力也得到很大的缓解。其中竞争指数随着采伐强度的增加呈现持续下降的趋势，在30%采伐强度下最多减少了24.80% ~ 34.88%，大小比数最多减少了24.97%，混交度最高提升了12.76%。采伐木主要集中于林分密度较大的区域，采伐木的胸径以中小型居多。 结论 对于天然纯林，适度加大采伐强度可以更好地调节林分空间结构，而对于天然混交林，进行相对较低强度的采伐可以在其整体结构获得优化的同时保持较高程度的树种混交。研究同时证明了AHP-CRITIC 组合赋权法用于构建采伐指数的合理性，基本实现了对长白山地区天然林林分结构的优化调整，可为合理选取采伐木、科学经营森林提供技术支持。 -
关键词:
- 采伐模拟 /
- 空间结构 /
- AHP-CRITIC组合赋权法 /
- 采伐木
Abstract:Objective Rational cutting can promote the growth of trees and increase the productivity of stands. It is of no important practical significance to determine cutting with the aim of optimizing the spatial structure of stands. In this study, the natural forest composed of different tree species in Changbai Mountain of northeastern China was taken as the object, and the combination weight method of AHP-CRITIC was adopted to construct single tree cutting index, in order to provide a new guiding tool for the selection of natural forest cutting wood in this area. Method In this paper, the monitoring data of 4 natural forest fixed samples with different dominant tree species were adopted, four spatial structure indexes, including mingling degrees (M), uniform angle index (W), dominance degrees (U) and Hegyi competition index (CI) were used, and the individual tree felling index (Fi) was constructed according to AHP-CRITIC combination weight method. The spatial structure changes of stand before and after cutting simulation with different intensities were analyzed. Result After cutting, the spatial structure of the four sample plots was optimized to a certain extent: the mingling degrees increased, the degree of tree size differentiation decreased, the horizontal distribution pattern of stand tended to random distribution, and the pressure of stand competition was greatly relieved. The competition index showed a trend of continuous decline with the increase of cutting intensity. Under 30% cutting intensity, the competition index decreased by 24.80%−34.88% at most, the size ratio decreased by 24.97% at most, and the mixing degree increased by 12.76% at the highest. The felled trees were mainly concentrated in the area with high density, and most of the felled trees were small and medium-sized in DBH. Conclusion Through comparative analysis of data before and after simulated cutting, it is proved that for natural pure forest, moderately increasing cutting intensity can better regulate the spatial structure of the stand, while for natural mixed forest, relatively low intensity cutting can optimize its overall structure and maintain a higher degree of tree species mixing. At the same time, the research proves that it is reasonable for AHP-CRITIC combination weight method to construct cutting index, which basically realizes the optimization and adjustment of natural forest stand structure in Changbai Mountain, and can provide technical support for reasonable selection of cutting wood and reasonable management of forest. -
图 1 4个样地各空间结构指数随着采伐强度增加的变化趋势
Figure 1. Trends of spatial structure indices of four stands with increasing cutting intensity
图 2 不同树种的采伐木相对于整个核心区树木的频率分布
Figure 2. Frequency distribution of harvested wood of different tree species relative to trees in the entire core area
表 1 样地信息统计表
Table 1. Statistical table of sample plot information
样地号
Sample plot No．树种组成
Tree species composition调查年份
Year of observation面积/hm2
Area/ha平均胸径
Mean DBH/cm平均高
Average height/m林分密度/（株∙hm−2）
Stand density/(tree∙ha−1)1 7A1P1PK1B 2003 0.20 20.8 19.5 740 2 4PK3A1P1T1AP 2009 0.30 27.0 20.4 337 3 4L2P2A1BC1T 2007 0.30 21.8 20.7 800 4 7P2A1PK 2009 0.25 26.5 19.9 432 注：A. 冷杉；P. 云杉；PK. 红松；B. 白桦；T. 椴树；AP.色木槭；L. 落叶松；BC. 枫桦。Notes: A, Abies nephrolepis; P, Picea koraiensis; PK, Pinus koraiensis; B, Betula platyphylla; T, Tilia amurensis; AP, Acer pictum; L, Larix gmelinii ; BC, Betula costata. 
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表 2 基于AHP-CRITIC法的各指标综合权重值
Table 2. Comprehensive weight value of each indicator based on the AHP-CRITIC method
样地号
Sample plot No．赋权方法
Weighting method大小比数
Neighborhood
comparison (U)角尺度
Uniform angle
index (W)混交度
Mingling
degree (M)竞争指数
Competition
index (CI)1 AHP 0.286 0.142 0.286 0.286 CRITIC 0.339 0.230 0.249 0.182 组合赋权 Combination weighting 0.318 0.197 0.263 0.222 2 AHP 0.286 0.142 0.286 0.286 CRITIC 0.290 0.300 0.266 0.144 组合赋权 Combination weighting 0.288 0.246 0.273 0.193 3 AHP 0.286 0.142 0.286 0.286 CRITIC 0.355 0.281 0.246 0.118 组合赋权 Combination weighting 0.335 0.241 0.258 0.166 4 AHP 0.286 0.142 0.286 0.286 CRITIC 0.317 0.253 0.243 0.187 组合赋权 Combination weighting 0.304 0.208 0.261 0.227
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表 3 样地在不同采伐强度下的林分结构参数统计
Table 3. Stand structure parameter statistics at different harvesting intensities in the sample plots
样地号
Sample
plot No.采伐强度
Cuttting
intensity/%U W M CI 采伐指数
Felling
index (F)径阶数量
Number of
diameter
class (D)树种数量
Number of
tree species
(nSP)相邻径阶
株数之比
Ratio of
neighboring
diameter class
number (Q)1 0 0.482 0.500 0.662 1.689 0.673 17 7 1.347 5 0.481 （−0.25%） 0.487 （−2.60%） 0.705 （6.49%） 1.558 （−7.80%） 0.695 （3.33%） 17 7 1.322 10 0.462（−4.02%） 0.497（−0.68%） 0.729（10.26%） 1.525（−9.72%） 0.711（5.72%） 17 7 1.327 15 0.449（−6.73%） 0.486（−2.90%） 0.743（12.27%） 1.411（−16.47%） 0.725（7.73%） 17 7 1.319 20 0.442（−8.18%） 0.469（−6.15%） 0.731（10.46%） 1.361（−19.44%） 0.737（9.63%） 17 7 1.297 25 0.459（−4.71%） 0.459（−8.20%） 0.730（10.27%） 1.311（−22.41%） 0.750（11.47%） 17 7 1.326 30 0.461（−4.40%） 0.465（−7.02%） 0.706（6.73%） 1.262（−25.29%） 0.762（13.32%） 17 7 1.324 2 0 0.485 0.592 0.783 2.068 0.678 21 10 1.338 5 0.461（−5.02%） 0.566（−4.31%） 0.820（4.75%） 1.836（−11.18%） 0.699（3.17%） 21 10 1.326 10 0.471（−2.88%） 0.578（−2.37%） 0.832（6.24%） 1.660（−19.71%） 0.712（5.14%） 21 10 1.311 15 0.469（−3.30%） 0.575（−2.93%） 0.846（8.10%） 1.652（−20.12%） 0.729（7.59%） 21 10 1.263 20 0.472（−2.69%） 0.583（−1.45%） 0.856（9.37%） 1.643（−20.55%） 0.741（9.40%） 21 10 1.291 25 0.475（−2.02%） 0.529（−10.56%） 0.868（10.80%） 1.522（−26.37%） 0.753（11.19%） 21 10 1.292 30 0.463（−4.64%） 0.537（−9.24%） 0.883（12.76%） 1.346（−34.88%） 0.769（13.47%） 21 10 1.334 3 0 0.474 0.501 0.779 2.244 0.711 20 11 1.322 5 0.462（−2.44%） 0.507（1.17%） 0.797（2.25%） 2.192（−2.32%） 0.727（2.26%） 20 11 1.313 10 0.468（−1.26%） 0.483（−3.64%） 0.817（4.94%） 2.026（−9.69%） 0.741（4.18%） 20 11 1.300 15 0.468（−1.31%） 0.482（−3.84%） 0.812（4.20%） 1.967（−12.32%） 0.753（5.88%） 20 11 1.301 20 0.467（−1.38%） 0.471（−6.12%） 0.797（2.25%） 1.881（−16.15%） 0.764（7.36%） 20 11 1.290 25 0.465（−1.83%） 0.483（−3.58%） 0.803（3.12%） 1.788（−20.33%） 0.774（8.80%） 20 11 1.287 30 0.463（−2.36%） 0.472（−5.77%） 0.803（3.10%） 1.687（−24.80%） 0.785（10.31%） 20 11 1.344 4 0 0.496 0.546 0.657 1.814 0.644 20 5 1.665 5 0.477（−3.86%） 0.534（−2.26%） 0.674（2.60%） 1.647（−9.18%） 0.666（3.36%） 20 5 1.599 10 0.452（−8.87%） 0.532（−2.69%） 0.690（5.07%） 1.643（−9.45%） 0.678（5.23%） 20 5 1.570 15 0.432（−12.94%） 0.521（−4.62%） 0.699（6.39%） 1.496（−17.51%） 0.694（7.67%） 20 5 1.521 20 0.411（−17.27%） 0.527（−3.59%） 0.701（6.66%） 1.459（−19.58%） 0.705（9.41%） 20 5 1.507 25 0.389（−21.56%） 0.524（−4.10%） 0.712（8.28%） 1.374（−24.25%） 0.720（11.70%） 20 5 1.546 30 0.372（−24.97%） 0.520（−4.76%） 0.735（11.80%） 1.361（−24.99%） 0.731（13.41%） 20 5 1.538 注：括号里数据表示采伐后相对于采伐前的变化率。Note: the data in parentheses indicate the rate of change after cutting relative to before cutting.
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