本文選題：杉木 + 削度方程　； 參考：《中國(guó)林業(yè)科學(xué)研究院》2017年碩士論文

【摘要】：杉木(Cunninghamia lanceolata)是我國(guó)重要鄉(xiāng)土針葉用材樹種,第八次全國(guó)森林資源清查顯示杉木人工林面積和蓄積現(xiàn)已達(dá)到全國(guó)人工林優(yōu)勢(shì)樹種的首位。因此,在森林資源調(diào)查中需要準(zhǔn)確的評(píng)估和預(yù)測(cè)杉木人工林的蓄積量。削度方程在精確預(yù)測(cè)樹干直徑和材積有著非常重要的作用。目前國(guó)內(nèi)缺乏對(duì)基礎(chǔ)模型的系統(tǒng)性比較分析,尤其少見針對(duì)杉木的可變指數(shù)削度方程的構(gòu)建,可變密度可變指數(shù)削度方程的構(gòu)建。故本文以江西年珠林場(chǎng)密度試驗(yàn)林為研究對(duì)象,擬利用混合效應(yīng)模型建立針對(duì)杉木的可變密度可變指數(shù)削度方程和材積預(yù)測(cè)模型。主要研究結(jié)論如下:(1)可變指數(shù)削度方程擬合優(yōu)度最好,其次是分段削度方程,最后是簡(jiǎn)單削度方程。多個(gè)可變指數(shù)削度方程彼此間的擬合優(yōu)度沒有較為明顯區(qū)別。從30個(gè)削度方程中選擇出Zeng(1997)、Bi(2000)、Kozak(2004)、Sharma(2004)4個(gè)擬合優(yōu)度最高的可變指數(shù)削度方程作為基礎(chǔ)模型。(2)針對(duì)選擇的基礎(chǔ)模型建立基于樣地效應(yīng)、基于樣木效應(yīng)和基于嵌套兩水平效應(yīng)的混合效應(yīng)模型�；旌闲�(yīng)模型可以提高模型擬合精度,考慮樣地效應(yīng)時(shí),混合效應(yīng)模型的調(diào)整決定系數(shù)提高0.0016~0.0020,但不能消除觀測(cè)值之間的自相關(guān)性�？紤]樣木效應(yīng)和嵌套兩水平的混合模型的調(diào)整決定系數(shù)均比基于樣地效應(yīng)提高0.0104~0.0117,且可以消除大部分觀測(cè)值之間的自相關(guān)性。Kozak(2004)可變指數(shù)削度方程在基于樣木效應(yīng)和嵌套兩水平混合模型中表現(xiàn)了最高的預(yù)估精度,但由于基于樣地效應(yīng)的混合模型結(jié)構(gòu)比嵌套兩水平混合模型簡(jiǎn)單,故選擇基于樣木效應(yīng)的Kozak(2004)混合模型作為最優(yōu)削度方程。(3)在結(jié)論(2)的基礎(chǔ)上建立了包含密度因子的可變指數(shù)削度方程。在削度方程中加入密度因子后,提高了削度方程的擬合精度,調(diào)整決定系數(shù)達(dá)到0.9945。不同密度下的樹干表現(xiàn)出不同的削度,樹干的削度隨著密度的增大而逐漸減少,減少的程度也隨密度的增大而降低。(4)利用建立的包含密度的可變指數(shù)削度方程對(duì)杉木進(jìn)行材積預(yù)測(cè),可變指數(shù)削度方程預(yù)測(cè)法的平均偏差、均方根誤差、平均絕對(duì)偏差和相對(duì)誤差均小于二元材積表法和簡(jiǎn)單削度方程積分求積法。三種方法在高徑比和胸高形樹較低的范圍內(nèi)預(yù)測(cè)材積精度較差,但是在高徑比和胸高形樹較高范圍內(nèi),三種預(yù)測(cè)材積方法精度較高。三種方法中,包含密度的可變指數(shù)削度方程預(yù)測(cè)材積精度最高。
[Abstract]:Chinese fir (Cunninghamia lanceolata) is an important native coniferous timber species in China. The eighth national forest resource inventory shows that the area and accumulation of Chinese fir plantation have now reached the top of the national dominant tree species. Therefore, it is necessary to accurately evaluate and predict the volume of Chinese fir plantation in forest resource survey. The truncation equation plays an important role in accurately predicting the diameter and volume of trunk. At present, there is a lack of systematic comparative analysis of the basic model, especially the construction of variable index taper equation and variable density variable index taper equation for Cunninghamia lanceolata (Cunninghamia lanceolata). Therefore, this paper takes the density test forest of Nianzhu Forest Farm in Jiangxi Province as the research object, and uses the mixed effect model to establish the variable density variable index taper equation and volume prediction model for Chinese fir. The main conclusions are as follows: (1) the variable exponential taper equation is the best, followed by the piecewise taper equation, and finally the simple taper equation. There is no obvious difference in the goodness of fit between several variable exponential taper equations. Four variable exponential taper equations of Zeng (1997) Bi (2000) Kozak (2004) Kozak (2004) and Sharma (2004) were selected from 30 taper equations as the basic model. (2) the mixed effect models based on sample effect, sample wood effect and nested two-level effect were established for the selected basic model. The mixed effect model can improve the fitting accuracy of the model. When the sample effect is considered, the adjustment decision coefficient of the mixed effect model is increased by 0.0016 ~ 0.0020, but the autocorrelation between the observed values can not be eliminated. The adjusted decision coefficients of the mixed model considering both the sample effect and the nested level are 0.0104 / 0.0117 higher than those based on the sample effect, and can eliminate the autocorrelation between most of the observed values. Kozak (2004) variable exponential taper equation is based on the sample wood effect. In the nested two-level mixing model, the prediction accuracy is the highest. However, the structure of hybrid model based on sample effect is simpler than that of nested two-level hybrid model. Therefore, the Kozak (2004) mixed model based on sample wood effect is chosen as the optimal taper equation. (3) based on conclusion (2), the variable exponential taper equation with density factor is established. With the addition of density factor into the taper equation, the fitting accuracy of the taper equation is improved, and the adjusting decision coefficient reaches 0.9945. The trunks with different densities showed different taper, and the taper of the trunks decreased with the increase of the density. The degree of reduction also decreases with the increase of density. (4) the volume of Chinese fir is predicted by using the variable exponent taper equation including density, the mean deviation and root mean square error of the prediction method of variable exponential taper equation are obtained. The mean absolute deviation and relative error are smaller than the binary volume table method and the integral quadrature method of the simple taper equation. The accuracy of the three methods in predicting volume is poor in the range of height to diameter ratio and chest height tree, but the accuracy of the three methods is higher in the range of height to diameter ratio and chest height tree. Among the three methods, the variable exponential taper equation including density has the highest prediction accuracy.
【學(xué)位授予單位】：中國(guó)林業(yè)科學(xué)研究院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：S791.27

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