本文關(guān)鍵詞：江西省主要樹種不同立地等級(jí)的地上生物量與不確定性估計(jì)　出處：《中國(guó)林業(yè)科學(xué)研究院》2017年博士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 立地分級(jí) 地上生物量估計(jì) 不確定性估計(jì) 生物量增量模型

【摘要】：森林立地生產(chǎn)力是森林植被的潛在生產(chǎn)能力,是指導(dǎo)森林經(jīng)營(yíng)管理、制定經(jīng)營(yíng)決策方案的重要指標(biāo),對(duì)森林可持續(xù)經(jīng)營(yíng)有著重要的意義。森林生物量是森林群落在其生命過(guò)程中所產(chǎn)生的干物質(zhì)的積累量,可以作為反映森林立地生產(chǎn)力的指標(biāo),并與立地類型和質(zhì)量息息相關(guān)。由于立地條件的多樣性或差異性,同一樹種在不同區(qū)域的生物量估計(jì)結(jié)果以及生物量估計(jì)誤差也會(huì)隨立地質(zhì)量的不同而變化,忽略立地質(zhì)量差異引起生物量估計(jì)結(jié)果以及生物量估計(jì)誤差不同的結(jié)果必然是粗略而不精準(zhǔn)的。為了精準(zhǔn)獲得不同立地條件下的森林生物量及立地生產(chǎn)力的估計(jì),本文將我國(guó)江西省第六次和第七次國(guó)家森林資源連續(xù)清查數(shù)據(jù)主要樹種分為杉木、馬尾松、硬闊和軟闊四類,對(duì)主要樹種立地條件進(jìn)行了樣地水平的立地分級(jí);采用異速生物量模型和生物量擴(kuò)展轉(zhuǎn)換因子(BCEF)模型,估計(jì)了區(qū)域不同立地條件下森林生物量及其不確定性;探究了林分起源、齡組、密度等因素對(duì)森林地上生物量及其各組分估計(jì)誤差的影響;建立了基于氣候和立地因子的江西省主要樹種的生物量增量模型,為降低森林生物量估計(jì)的不確定性、改進(jìn)生物量估計(jì)模型精度提供技術(shù)和方法基礎(chǔ)。得到的主要結(jié)論如下:(1)采用樹高分級(jí)法,建立了江西省杉木林、馬尾松林、硬闊林、軟闊林的優(yōu)勢(shì)木樹高—胸徑模型,用優(yōu)勢(shì)木樹高等級(jí)代表立地等級(jí),將江西省杉木林分立地質(zhì)量為7個(gè)等級(jí),馬尾松林分立地質(zhì)量分為5個(gè)等級(jí),硬闊林分立地質(zhì)量分為5個(gè)等級(jí),軟闊林分立地質(zhì)量分為3個(gè)等級(jí)。通過(guò)計(jì)算杉木林和馬尾松林分在不同立地等級(jí)下的生物量均值和誤差估計(jì),發(fā)現(xiàn)不同立地等級(jí)的區(qū)域馬尾松優(yōu)勢(shì)林分地上生物量均值隨著立地水平提高而增大,即立地質(zhì)量越高,森林生物量密度越大。證明了優(yōu)勢(shì)木樹高分級(jí)用作劃分立地等級(jí)依據(jù)的可行性。(2)采用三種異速生物量模型、兩種生物量擴(kuò)展轉(zhuǎn)換因子(BCEF)模型,結(jié)合蒙特卡洛模擬法估計(jì)了江西省杉木和馬尾松在不同立地等級(jí)下的地上生物量均值及誤差。以地上生物量均值的相對(duì)均方根誤差的大小作為評(píng)價(jià)指標(biāo),比較并確定了三種異速生物量模型、兩種BCEF模型在估計(jì)杉木和馬尾松不同立地等級(jí)下地上生物量均值的最優(yōu)模型形式。A.三種異速地上生物量模型形式下的杉木和馬尾松地上生物量估計(jì)結(jié)果相比,加入樹高變量的單木生物量模型比單獨(dú)用胸徑做變量的模型能夠提高估計(jì)精度。在同時(shí)擁有胸徑和樹高兩種變量存在的時(shí)候,馬尾松擁有兩個(gè)參數(shù)的模型較有三個(gè)參數(shù)的模型獲得了更好的估計(jì)效果。而杉木則是三個(gè)參數(shù)的模型好于兩參數(shù)的模型,具體應(yīng)用時(shí)應(yīng)根據(jù)樹種選模型形式。異速模型的生物量估計(jì)誤差在立地平均水平——也就是中間立地等級(jí)的誤差最小;立地質(zhì)量越接近平均水平,生物量估計(jì)的相對(duì)誤差越小。B.兩種BCEF模型相比較,經(jīng)驗(yàn)(回歸)模型法估計(jì)的各立地等級(jí)的生物量均值誤差相差不大,而用連續(xù)函數(shù)法估計(jì)的各立地等級(jí)的生物量均值誤差相差較大。異速生物量模型與BCEF模型相比較,在估計(jì)區(qū)域尺度的生物量均值時(shí),BCEF模型相對(duì)誤差較異速模型估計(jì)誤差要小。在估計(jì)不同立地等級(jí)的生物量時(shí)經(jīng)驗(yàn)(回歸)模型要優(yōu)于異速生物量模型,異速生物量模型優(yōu)于BCEF連續(xù)函數(shù)模型,對(duì)于包含多種不同立地條件的大尺度區(qū)域,BCEF經(jīng)驗(yàn)(回歸)模型法的估計(jì)結(jié)果更可靠。對(duì)于江西省杉木和馬尾松而言,BCEF均值在不同立地等級(jí)下的差異不大。(3)選擇合適的預(yù)測(cè)變量是建立生物量增長(zhǎng)模型的關(guān)鍵,比較各生物地理氣候因子的重要性后發(fā)現(xiàn),林木競(jìng)爭(zhēng)是江西省森林生物量增長(zhǎng)的主要影響因素。對(duì)于針葉林而言,次要影響因素是氣候因子(無(wú)霜期天數(shù)、年平均溫度)、齡組,最后是地形因子(海拔)。對(duì)于闊葉林來(lái)說(shuō),立地等級(jí)是次要的影響因子,最后是氣候因子。立地等級(jí)對(duì)于針葉林生物量增量估計(jì)影響不大,影響江西省森林(主要樹種)生物量增量的氣候因子是溫度。地形因子對(duì)闊葉林影響無(wú)顯著性,對(duì)針葉林影響較小。因此,在用生物量增長(zhǎng)估計(jì)江西省針葉林立地生產(chǎn)力時(shí),氣候因子和齡組可以作為立地指數(shù)替代變量;而在估計(jì)闊葉林立地生產(chǎn)力時(shí),立地等級(jí)似乎是更好的選擇。(4)不同起源分類對(duì)于杉木林異速生物量模型估計(jì)地上及各組分生物量均值影響不大,對(duì)于馬尾松林人工林生物量均值模型誤差要高于天然林分,對(duì)馬尾松各組分中樹枝、樹葉影響較大。起源對(duì)于異速生物量模型估計(jì)誤差的影響因樹種而異。隨著齡組增大,杉木地上生物量及各組分生物量的模型誤差估計(jì)值是隨著齡組增大而降低的。齡組對(duì)馬尾松異速生物量模型估計(jì)誤差的影響為幼齡林模型誤差最大,其次是中齡林、成熟林、近熟林和過(guò)熟林。密度對(duì)杉木異速生物量模型地上生物量估計(jì)誤差影響不大,對(duì)于樹枝和樹葉而言,低密度林分中的生物量均值估計(jì)模型誤差相較高密度林分及區(qū)域水平更大。密度對(duì)馬尾松異速生物量模型地上生物量估計(jì)誤差影響不大,對(duì)于各組分而言,除樹葉在區(qū)分密度后生物量均值模型誤差較不分類時(shí)升高而外,在樹干、樹皮及樹枝中,低密度和高密度林分的生物量均值模型誤差較不分類時(shí)均降低。在分組分計(jì)算馬尾松生物量均值時(shí),使用林分密度分類是降低模型誤差的好方法。(5)杉木和馬尾松在調(diào)查數(shù)據(jù)原始抽樣設(shè)計(jì)和三個(gè)抽樣間距、三種多起點(diǎn)系統(tǒng)抽樣設(shè)計(jì)下的地上生物量均值及誤差值估計(jì)的差異不大,每一種抽樣方式均能很好的反映江西省杉木和馬尾松總體的地上生物量均值。隨著抽樣間距的增大,抽樣單元增多,抽取樣地?cái)?shù)的減少,相對(duì)均方根總誤差的絕對(duì)值和相對(duì)值在上升。這是由于抽樣誤差的絕對(duì)值和相對(duì)值均在上升,而模型誤差差異不大。相應(yīng)的,抽樣誤差在總誤差中的占有率也在上升。但是,綜合考慮抽樣難度和成本,三種不同抽樣間距設(shè)計(jì)及三種多起點(diǎn)系統(tǒng)抽樣設(shè)計(jì)均能很好的反映調(diào)查總體的地上生物量均值水平,可以為其他的大尺度區(qū)域調(diào)查時(shí)的系統(tǒng)抽樣設(shè)計(jì)提供參考。
[Abstract]:Forest site productivity is the potential productivity of forest vegetation, is the guidance of forest management, an important indicator of making management decisions, is of great significance to the sustainable management of forest. Forest biomass is the dry matter accumulation of forest community generated in the process of life, can reflect the productivity index of forest site. It is closely related to the site type and quality. Because the site conditions of diversity or differences, the same species and biomass estimation with site quality varied in the estimation of biomass in different regions, ignoring the site quality differences caused biomass estimation results and biomass estimation results of different error is inevitable rough and not precise. In order to obtain accurate forest biomass under different site conditions and estimation of site productivity, this will be China's Jiangxi Province, Sixth And the seventh national forest resources inventory data for Chinese fir trees, pine, hardwood and softwood four kinds of main tree species in the site conditions of site classification sample level; the allometric biomass model and biomass expansion factor conversion (BCEF) model to estimate the regional forest biomass in different site under the condition of uncertainty; explores the stand origin, age group, density factors estimation error influence on biomass and forest biomass increment were established; the model of main tree species in Jiangxi Province Based on the climate and site factors, in order to reduce the uncertainty in the estimation of forest biomass estimation, and provide basic technology methods the accuracy of the model improved biomass. The main conclusions are as follows: (1) the tree classification method, Jiangxi Province set up Chinese fir forest, masson pine forest, hardwood forest, softwood forest the dominant height and DBH. Type, with dominant height level representative site grade in Jiangxi Province, the site quality of Chinese fir forests into 7 grades, masson pine forest site quality is divided into 5 grades, hardwood forest site quality is divided into 5 grades, softwood forest site quality is divided into 3 grades. The mean biomass estimation and error calculation of Chinese fir and Masson Pine Forest in different site level, different site grade regional advantage Forest Aboveground Biomass of Pinus massoniana with mean site level increases, the site quality is high, the forest biomass density is higher. It is proved that the advantages of high grade trees as feasibility of dividing site grade basis. (2) using three kinds of allometric biomass models, two kinds of biomass expansion factor conversion (BCEF) model, Monte Carlo simulation method to estimate the Jiangxi Province, Chinese fir and Masson Pine in different site grade under ground biomass and mean Error. With biomass average relative root mean square error of the size as the evaluation index, and identified three allometric biomass models, two kinds of BCEF model in the estimation of Chinese fir and Masson pine of different site grade under ground biomass model.A. optimal biomass models of mean three different speed on the ground under Chinese fir and Masson pine aboveground biomass estimation results compared to single tree biomass model with tree height variables compared with DBH variable model can improve the estimation accuracy. When there have both tree height and DBH of two kinds of variables, Ma omatsu has two parameter model with three parameters the model get better estimation effect. Chinese fir is the model parameters of the three models are better than the two parameters, the application should choose according to the tree model. The biomass allometric model estimation error in average site Which is in the middle level of site grade error; site quality is close to the average level, the relative error estimation of biomass is smaller.B. two BCEF model comparison, experience (regression) biomass average estimation error model of each site grade difference, the biomass and mean error in continuous function estimation the site grade is larger. The allometric biomass model and the BCEF model are compared in the estimation of biomass average regional scale, BCEF model of relative error is allometric model estimation error is smaller. Experience in the estimation of biomass in different site grade when (regression) model of allometric biomass is better than the model, different the speed of biomass model is better than the BCEF continuous function model for large scale region contains a variety of different site conditions, BCEF (regression) estimation model more reliable results. For Chinese fir and Masson Pine and Jiangxi Province BCEF, mean difference in site grade under little. (3) choose the suitable forecasting variable is key to establish biomass growth model, the importance of bio geographical climate factors found that forest competition is the main influence factors of Jiangxi forest biomass growth. For coniferous forest, the secondary factor is climatic factors (frost free days, the annual average temperature), age group, and finally terrain factors (elevation). For the broad-leaved forest, site grade is a minor factor, finally is climate factor. Site grade for coniferous forest biomass increment estimation has little effect on the impact of Jiangxi province forest (main species) bio climatic factors the temperature increment is. The influence of terrain factors on the broad-leaved forest had no significant effect on small coniferous forest. Therefore, with the growth in biomass estimation in Jiangxi province coniferous forest productivity, climatic factors and age groups Can be used as substitute variables and site index; in the estimation of broad-leaved forest productivity, site level appears to be a better choice. (4) different origin classification for Chinese fir forest biomass allometric models to estimate ground biomass and mean little effect for Pinus massoniana forest plantation biomass average model error is higher than that of natural stand on the branches of Pinus massoniana components in leaves of influence. As for the origin of allometric biomass models, the effect of the estimation error varies among species. With the increasing age of Chinese fir, aboveground biomass and biomass of the model error estimate is reduced with the increase of age. The age group of Masson Pine allometry biomass model estimation error for maximum error model of young forest, followed by forest, mature forest, near mature forest and over mature forest. The density of Chinese fir model of allometric biomass biomass estimation error influence No, the branches and leaves, biomass average low stand density estimation in high density and phase error model of regional level greater density on the model. The allometric biomass of Pinus massoniana biomass estimation error has little effect on the components, in addition to the leaves increased. The distinction between density biomass average model the error is not classified in the trunk, bark and branches, the mean biomass model error of low density and high density stand is not decreased. The classification calculation of Pinus massoniana biomass in the mean grouping, using the stand density is a good way to reduce the error of classification model. (5) in the original survey data of Chinese fir and masson pine the design of sampling and three sampling interval, sample design three different starting point system under ground biomass and mean value estimation error has little difference, each sampling method can well reflect the Jiangxi The average biomass of Chinese fir and Masson pine, overall land. With the increase of the sampling interval, the sampling unit increased, reduce the number of sampling plots, the absolute value of relative root mean square error of the total and relative value is on the rise. This is due to the absolute value of sampling error and relative values are on the rise, while the model error is insignificant. Accordingly, the sampling error share in total error is also on the rise. However, considering the difficulty and cost of sampling, the sampling design of three different sampling space design and three kinds of starting point system can well reflect the overall investigation on the biomass level, can provide a reference for the design of sampling system the investigation of other regions.

【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】：博士
【學(xué)位授予年份】：2017
【分類號(hào)】：S718.5

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