金沟岭林场三种林型最优林分结构的研究
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摘要
本文以金沟岭林场云冷杉针阔混交林、杨桦次生林和人工落叶松三种林型为研究对象,基于14块皆伐标准地、检查法样地274个样点、40块速生丰产林样地、12块杨桦次生林样地和22株大径级解析木数据,运用森林经理学和统计学的方法,构建了金沟岭林场3种林型最优直径结构、最优树高结构、最优树种空间隔离度、最优空间格局和最优竞争结构。得到的主要结论有：
(1)针叶树种：红皮云杉数量成熟龄112.5年、鱼鳞云杉数量成熟龄135年、冷杉数量成熟龄150年和落叶松数量成熟龄47年。阔叶树种：白桦数量成熟龄46年、枫桦数量成熟龄144年、山杨数量成熟龄43年、椴树数量成熟龄146.5年、水曲柳数量成熟龄160年和色木数量成熟龄186.5年。
(2)提出并且验证了两种估算林分蓄积的方法——平均实验形数法和地理权重回归分析法。本研究第一次以数学推理角度,理论证明实验形数法的合理性,从树种胸高形率和树干曲线方程理论推导出实验形数方程。本文推算11个树种的实验形数与经验实验形数法得到的差别不大。第一次证明影响实验形数取值的只有树种,常数项K趋于3(变异系数为0.02)。利用平均实验形数法估算林分蓄积精度也很高,其中di为0.46,di为1.34,Srf为1%,saf为3%, R2为0.97,AIC为24.1,预估精度为0.98。同时利用地理权重回归分析模型(GWR)构建二元材积公式。从空间分析角度,研究了模型的胸径、树高与材积三者的关系。地理权重回归分析法(R2=0.94；p=0.92)优选模型的拟合度、预测能力都要高于最小二乘法拟合的优选模型(R2=0.93；p=0.91)。地理权重回归分析模型的变量参数可以反映出在样地内的空间分布规律及其稳定性,地理权重回归分析法反映局部空间信息的能力为最小二乘法所不及。基于地理权重回归分析法拟合得到精度较高的单木材积模型,进而推算得到林分蓄积。
(3)在估算单木成熟和林分蓄积之后,利用经验和理论模型拟合得到三种林型能够生长达到的每公顷最大胸高断面积,并分成<10、10～20、20～30、30～40、40～50m/hm25个胸高断面积区间,分别比较求算各胸高断面积区间的蓄积生长量。最后利用负指数模型,结合林木径阶能够持续生长的情况,拟合出云冷杉针阔混交林和杨桦次生林最优直径结构；利用weibull分布模型,拟合出人工落叶松林最优直径结构。云冷杉针阔混交林最大胸高断面积约为60m2/hm2,当林分胸高断面积为40m2/hm2,平均Q值为1.4时,林分蓄积生长量最大,为10.5m3/hm2.a。杨桦次生林最大胸高断面积约为45m2/hm2,当林分胸高断面积为20m2/hm2,平均Q值为1.5时,林分蓄积生长量最大,为5.7m3/hm2.a。人工落叶松林最大胸高断面积约为45m2/hm2,当林分胸高断面积为30m2/hm2,平均Q值为1.45时,林分蓄积生长量最大,为8.99m3/hm2.a。
(4)由于人工落叶松林通过确定林分胸高断面积即可确定空间最优,所以本研究仅讨论云冷杉针阔混交林和杨桦次生林树种的空间隔离度,空间格局和竞争关系。通过系统聚类方法和树种多样性混交度研究两种林型物种空间隔离度和林分畜积生长量的关系。在立地条件、株数密度、混交比、树种种类和树种数量相同时,云冷杉针阔混交林固定样地内林分蓄积生长量与树种空间隔离度呈正比关系：天然杨桦次生林固定样地内林分蓄积生长量与树种空间隔离度呈反比关系。两种林型的大径级林木呈均匀分布,中小径级林木呈聚集分布的格局为最优；林木竞争能力由四周向中心依次增强的排列,即呈锥形的竞争结构为最优。
(5)基于天然混交林分结构特点,构建适用于当地情况的竞争因子--树种耐阴性竞争指数：并且建立天然混交林竞争指数计算系统。
Spruce-fir mixed forest, birch-aspen secondary forest and larch plantations were studied in this research. Based on14clear-cut sample plots,274sample points of control method plots,40fast-growing forest plots,12birch-aspen secondary forest plots and22big diameter grade analytic trees, applying forest management method and statistics method, the optimal diameter structure, optimal tree height structure, optimal spatial isolation of trees, optimal spatial pattern and optimal competition structure were established for the three forest type. Main conclusions were as follows:
(1) Coniferous species:maturity age of Koyama Spruce was112.5; maturity age of Yezo Spruce was135; maturity age of fir was150; maturity age of larch was47; Broad-leaf species:maturity age of white birch was135; maturity age of white birch was46; maturity age of ribbed birch was144; maturity age of aspen was43; maturity age of linden was146.5; maturity age of ash was160; maturity age of maple was186.5;
(2) Experimental form factor and geographically weighted regression were proposed in the research. The Experimental form factor volume formula was first deduced from the theoretical stem curve formula with the breast height form quotient. In a comparison of results obtained by the classical approach, EFF values for11species applying the theoretical derivation marginally differed. The EFF was verified to be affected only by species. Item K was certified to be constant term with a coefficient of variance of0.02. Meanwhile, absolute deviation, relative deviation, absolute mean deviation, mean relative deviation, determination coefficient, AIC and Predictive accuracy are separately0.46,1.34,1%,3%,0.97,24.1and0.98. The standard volume model for sample trees in the north of Changbai Mountains was established by applying geographical weighted regression method. The relationships between diameter at breast height, tree height and individual volume were analyzed from the perspective of spatial analysis. Results show that the geographical weighted regression model (R2=0.94; p=0.92) is superior to the ordinary least square model (R2=0.93; p=0.91) in goodness of fit and prediction ability. The geographical weighted regression model parameters could reflect the spatial distribution rule of trees in the sample plot and their stability, which further reveal the competitive relationship between trees. With the spatial analysis of data, the geographical weighted regression method has the potential to reveal the local patterns in the spatial distribution of a parameter, which would be ignored by the ordinary least square approach. Finally, the whole stand volume estimation model was obtained.
(3) After the relationship between basal areas at breast height per hectare and stand volume per hectare was analyzed by using different kinds of models, the mixed basal area at breast height per hectare was obtained. Simultaneously, the basal areas at breast height per hectare of the forest were divided for five intervals, such as<10m2/ha,10-20m2/ha,20-30m2/ha,30-40m2/ha and40-50m2/ha. The stand volume growths were separately investigated by the intervals. Applying negative exponential distribution and weibull distribution, the optimal stand structures of three forest types were obtained separately. The largest basal area at breast height of Spruce-fir mixed forest was60m2/hm2. When the basal area at breast height of Spruce-fir mixed forest was40m2/hm2and Q value was1.4, the stand volume growth was the biggest (10.5m3/hm2). The largest basal area at breast height of birch-aspen secondary forest was45m2/hm2. When the basal area at breast height of Spruce-fir mixed forest was20m2/hm2and Q value was1.5, the stand volume growth was the biggest (5.7m3/hm2). The largest basal area at breast height of larch plantations was45m2/hm2. When the basal area at breast height of Spruce-fir mixed forest was30m2/hm2and Q value was1.45, the stand volume growth was the biggest (8.99m3/hm2).
(4) Due the optimal structure of larch plantations was determined by largest basal area at breast height, spruce-fir mixed forest and birch-aspen secondary forest were only considered in the part of Spatial isolation of trees and Spatial pattern. Applying hierarchical cluster method, the plots were divided by the same diversity of trees and species evenness. Then the relationship between stand volume increment and spatial isolation of trees was analyzed by tree species diversity mingling method. With the same of sit quality, diversity of trees, diversity of tree and mixed proportion, results manifested that stand volume increment in plots increased with the incenseinent of spatial isolation of trees in spruce-fir mixed forest. Nevertheless, stand volume increment in plots declined with the incensement of spatial isolation of trees in birch-aspen secondary forest. The optimal spatial pattern of big diameter grade trees is uniform distribution. Meanwhile, the optimal spatial pattern of small diameter grade trees is clumped distribution. The optimal competition structure is that competition abilities are declined from the center to outside. The outlook is like cone-shape.(5) Based on the characters of mixed forest structure, the fitted competition indices were established, which were called Shadow tolerance competition indices. The equations were as follow:.the calculation system of natural forest competition indices was established.
(1)针叶树种：红皮云杉数量成熟龄112.5年、鱼鳞云杉数量成熟龄135年、冷杉数量成熟龄150年和落叶松数量成熟龄47年。阔叶树种：白桦数量成熟龄46年、枫桦数量成熟龄144年、山杨数量成熟龄43年、椴树数量成熟龄146.5年、水曲柳数量成熟龄160年和色木数量成熟龄186.5年。
(2)提出并且验证了两种估算林分蓄积的方法——平均实验形数法和地理权重回归分析法。本研究第一次以数学推理角度,理论证明实验形数法的合理性,从树种胸高形率和树干曲线方程理论推导出实验形数方程。本文推算11个树种的实验形数与经验实验形数法得到的差别不大。第一次证明影响实验形数取值的只有树种,常数项K趋于3(变异系数为0.02)。利用平均实验形数法估算林分蓄积精度也很高,其中di为0.46,di为1.34,Srf为1%,saf为3%, R2为0.97,AIC为24.1,预估精度为0.98。同时利用地理权重回归分析模型(GWR)构建二元材积公式。从空间分析角度,研究了模型的胸径、树高与材积三者的关系。地理权重回归分析法(R2=0.94；p=0.92)优选模型的拟合度、预测能力都要高于最小二乘法拟合的优选模型(R2=0.93；p=0.91)。地理权重回归分析模型的变量参数可以反映出在样地内的空间分布规律及其稳定性,地理权重回归分析法反映局部空间信息的能力为最小二乘法所不及。基于地理权重回归分析法拟合得到精度较高的单木材积模型,进而推算得到林分蓄积。
(3)在估算单木成熟和林分蓄积之后,利用经验和理论模型拟合得到三种林型能够生长达到的每公顷最大胸高断面积,并分成<10、10～20、20～30、30～40、40～50m/hm25个胸高断面积区间,分别比较求算各胸高断面积区间的蓄积生长量。最后利用负指数模型,结合林木径阶能够持续生长的情况,拟合出云冷杉针阔混交林和杨桦次生林最优直径结构；利用weibull分布模型,拟合出人工落叶松林最优直径结构。云冷杉针阔混交林最大胸高断面积约为60m2/hm2,当林分胸高断面积为40m2/hm2,平均Q值为1.4时,林分蓄积生长量最大,为10.5m3/hm2.a。杨桦次生林最大胸高断面积约为45m2/hm2,当林分胸高断面积为20m2/hm2,平均Q值为1.5时,林分蓄积生长量最大,为5.7m3/hm2.a。人工落叶松林最大胸高断面积约为45m2/hm2,当林分胸高断面积为30m2/hm2,平均Q值为1.45时,林分蓄积生长量最大,为8.99m3/hm2.a。
(4)由于人工落叶松林通过确定林分胸高断面积即可确定空间最优,所以本研究仅讨论云冷杉针阔混交林和杨桦次生林树种的空间隔离度,空间格局和竞争关系。通过系统聚类方法和树种多样性混交度研究两种林型物种空间隔离度和林分畜积生长量的关系。在立地条件、株数密度、混交比、树种种类和树种数量相同时,云冷杉针阔混交林固定样地内林分蓄积生长量与树种空间隔离度呈正比关系：天然杨桦次生林固定样地内林分蓄积生长量与树种空间隔离度呈反比关系。两种林型的大径级林木呈均匀分布,中小径级林木呈聚集分布的格局为最优；林木竞争能力由四周向中心依次增强的排列,即呈锥形的竞争结构为最优。
(5)基于天然混交林分结构特点,构建适用于当地情况的竞争因子--树种耐阴性竞争指数：并且建立天然混交林竞争指数计算系统。
Spruce-fir mixed forest, birch-aspen secondary forest and larch plantations were studied in this research. Based on14clear-cut sample plots,274sample points of control method plots,40fast-growing forest plots,12birch-aspen secondary forest plots and22big diameter grade analytic trees, applying forest management method and statistics method, the optimal diameter structure, optimal tree height structure, optimal spatial isolation of trees, optimal spatial pattern and optimal competition structure were established for the three forest type. Main conclusions were as follows:
(1) Coniferous species:maturity age of Koyama Spruce was112.5; maturity age of Yezo Spruce was135; maturity age of fir was150; maturity age of larch was47; Broad-leaf species:maturity age of white birch was135; maturity age of white birch was46; maturity age of ribbed birch was144; maturity age of aspen was43; maturity age of linden was146.5; maturity age of ash was160; maturity age of maple was186.5;
(2) Experimental form factor and geographically weighted regression were proposed in the research. The Experimental form factor volume formula was first deduced from the theoretical stem curve formula with the breast height form quotient. In a comparison of results obtained by the classical approach, EFF values for11species applying the theoretical derivation marginally differed. The EFF was verified to be affected only by species. Item K was certified to be constant term with a coefficient of variance of0.02. Meanwhile, absolute deviation, relative deviation, absolute mean deviation, mean relative deviation, determination coefficient, AIC and Predictive accuracy are separately0.46,1.34,1%,3%,0.97,24.1and0.98. The standard volume model for sample trees in the north of Changbai Mountains was established by applying geographical weighted regression method. The relationships between diameter at breast height, tree height and individual volume were analyzed from the perspective of spatial analysis. Results show that the geographical weighted regression model (R2=0.94; p=0.92) is superior to the ordinary least square model (R2=0.93; p=0.91) in goodness of fit and prediction ability. The geographical weighted regression model parameters could reflect the spatial distribution rule of trees in the sample plot and their stability, which further reveal the competitive relationship between trees. With the spatial analysis of data, the geographical weighted regression method has the potential to reveal the local patterns in the spatial distribution of a parameter, which would be ignored by the ordinary least square approach. Finally, the whole stand volume estimation model was obtained.
(3) After the relationship between basal areas at breast height per hectare and stand volume per hectare was analyzed by using different kinds of models, the mixed basal area at breast height per hectare was obtained. Simultaneously, the basal areas at breast height per hectare of the forest were divided for five intervals, such as<10m2/ha,10-20m2/ha,20-30m2/ha,30-40m2/ha and40-50m2/ha. The stand volume growths were separately investigated by the intervals. Applying negative exponential distribution and weibull distribution, the optimal stand structures of three forest types were obtained separately. The largest basal area at breast height of Spruce-fir mixed forest was60m2/hm2. When the basal area at breast height of Spruce-fir mixed forest was40m2/hm2and Q value was1.4, the stand volume growth was the biggest (10.5m3/hm2). The largest basal area at breast height of birch-aspen secondary forest was45m2/hm2. When the basal area at breast height of Spruce-fir mixed forest was20m2/hm2and Q value was1.5, the stand volume growth was the biggest (5.7m3/hm2). The largest basal area at breast height of larch plantations was45m2/hm2. When the basal area at breast height of Spruce-fir mixed forest was30m2/hm2and Q value was1.45, the stand volume growth was the biggest (8.99m3/hm2).
(4) Due the optimal structure of larch plantations was determined by largest basal area at breast height, spruce-fir mixed forest and birch-aspen secondary forest were only considered in the part of Spatial isolation of trees and Spatial pattern. Applying hierarchical cluster method, the plots were divided by the same diversity of trees and species evenness. Then the relationship between stand volume increment and spatial isolation of trees was analyzed by tree species diversity mingling method. With the same of sit quality, diversity of trees, diversity of tree and mixed proportion, results manifested that stand volume increment in plots increased with the incenseinent of spatial isolation of trees in spruce-fir mixed forest. Nevertheless, stand volume increment in plots declined with the incensement of spatial isolation of trees in birch-aspen secondary forest. The optimal spatial pattern of big diameter grade trees is uniform distribution. Meanwhile, the optimal spatial pattern of small diameter grade trees is clumped distribution. The optimal competition structure is that competition abilities are declined from the center to outside. The outlook is like cone-shape.(5) Based on the characters of mixed forest structure, the fitted competition indices were established, which were called Shadow tolerance competition indices. The equations were as follow:.the calculation system of natural forest competition indices was established.
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