湖南省主要树种单木和林分生长率模型研建

以湖南省1999年、2004年、2009年和2014年4期的森林资源连续清查为基础，利用大量、连续、系统的固定样地和样木数据，根据胸径和生长率的一般分布规律，选取常用的生长率回归方程作为基础模型式，采用非线性回归估计方法，构建了11个树种组的单木胸径生长率和材积生长率模型，以及9个树种组的林分蓄积量生长率模型。结果表明：各模型确定系数R²均在0.88以上，单木生长率模型的总体相对误差和平均预估误差均在4%以内，胸径生长率模型的平均预估误差大部分在10%以内；林分蓄积量生长率模型的平均预估误差和总体相对误差基本在4%以内，蓄积量生长率模型的平均预估误差在20%以内。各项指标表明，拟合模型能满足精度要求，具有较高的实用性，可为湖南省森林资源年度更新和森林经营管理提供技术支撑。     

北京市二类调查小班蓄积量预估模型研究

为解决当前北京市二类调查通过角规绕测技术预估林分蓄积量存在的问题,基于北京市二类调查数据,根据优势树种(组)的不同,将北京市森林划分成10个不同的树种组。在此基础上,利用一类清查数据,以林分蓄积量为因变量,林分参数及立地参数为自变量构建非线性蓄积量预估模型,计算确定系数(R~2)、总相对误差(TRE)、估计值的标准差(SEE)、平均系统误差(MSE)、平均预估误差(MPE)和平均百分标准误差(MPSE),并对模型拟合效果进行评价。结果表明:构建的蓄积量预估模型拟合效果较好,各树种组蓄积量预估模型的确定系数(R~2)均大于0.94,MPE均小于5%,MPSE基本在10%以下,可以应用于北京市二类调查中蓄积量的预估。     

青海省不同生境下青海云杉胸径生长模型研究

[目的 ]研究不同生境压力下青海云杉的林木胸径生长规律及生长模型,为有效保护、合理经营青海云杉林提供经验模型。[方法 ]利用青海地区青海云杉的树轮数据,计算单木胸径生长量,分析不同起源、不同坡位条件下胸径生长规律,构建单木胸径生长模型,对比与评价不同模型的拟合优度结果。随后选取基础模型,建立考虑起源和坡位的青海云杉单木胸径混合效应模型,采用全部数据对模型进行检验。[结果 ]总体来看,青海云杉生长到胸高位置后,单木胸径生长量随着年龄的增加呈现下降后平缓变化趋势;青海云杉天然林、人工林单木生长的速生期分别为29—44 a、29—39 a,连年生长量(CAI)和平均生长量(MAI)均在0.40 cm以上,随后天然林单木CAI和MAI的变化平缓,人工林的变化幅度较大。不同坡位的单木胸径生长趋势具有差异。生长模型结果显示,不同起源、坡位条件下各树种最优胸径生长模型的决定系数(R2)均在0.913以上,总体相对误差(TRE)和平均系统误差(MSE)均在±2%以内,平均预估误差(MPE)大多在5%以内,平均百分标准误差(MPSE)在35%以内。以Gompertz模型为基础模型构建的混合效应模型的R2...     

广西桉树地径与胸径多元混合效应模型研建

为解决森林资源管理工作中利用伐桩测算立木蓄积量的实际问题，以广西桉树(Eucalyptus spp.)人工林为研究对象，组织建模样本和验证样本；采用模型优选法比较11种常见一元地径-胸径模型的精度，选出最优模型；为进一步提高模型精度，以林龄、林分密度、海拔和坡位4个样地因子为随机因子，建立以胸径为因变量、地径和样地因子为自变量的多元混合效应模型；利用验证样本进行模型适用性检验。结果表明，多元混合效应模型精度最高，确定系数(R²)为0.966，赤池信息量准则(AIC)为434.7，估计值的标准差(SEE)为0.946，总相对误差(TRE)为0.004%，平均系统误差(MSE)为0.000 05%，平均预估误差(MPE)为1.067%，平均百分标准误差(MPSE)为5.156%，适合作为拟合广西桉树胸径与地径相关关系的模型。     

基于机载激光雷达数据估计林分蓄积量及平均高和断面积

基于东北林区191个红松林(Pinus koraiensis)样地的机载激光雷达数据和地面实测数据,首先,通过多元线性回归和非线性回归估计方法,确定林分蓄积量及平均高、断面积的基础回归模型;然后,利用误差变量联立方程组方法,建立基于激光雷达变量的林分蓄积量与平均高、断面积的模型系统。结果显示:建立的多元线性、多元和二元非线性林分蓄积量回归模型,其确定系数R~2分别为0.858,0.846和0.821,平均预估误差MPE分别为2.57%,2.66%和2.85%,平均百分标准误差MPSE分别为26.35%,16.35%和17.88%;利用模型系统对林分平均高、断面积和蓄积量进行估计,其R~2分别为0.597,0.750和0.822,MPE分别为1.90%,2.52%和2.84%,MPSE分别为10.85%,15.28%和17.73%。结果表明:基于机载激光雷达数据估计林分蓄积量、平均高等主要森林参数,非线性模型优于线性模型,而且基于点云高度变量(中位数)和强度变量(75%分位数)的二元非线性模型就能达到比较理想的预估效果;误差变量联立方程组方法,是建立林分蓄积量与平均高、断面积回归模型系统的一种可行方法;所建立的东北红松林平均高、断面积和蓄积量联立模型,其预估精度达到森林资源调查相关技术规定要求,可以在实践中推广应用。     

北京杨树相容性立木材积系列模型研建

以北京市最重要的阔叶树种杨树(Populus)为研究对象,利用1 678株样木的材积测量数据,通过采用哑变量模型和误差变量联立方程组方法,建立了毛白杨(P.tomentosa)、速生杨(P.×euramericana)和加拿大杨(P.×canadensis)3个树种(组)的相容性二元立木材积方程、胸径和地径一元立木材积方程、树高胸径回归模型及地径胸径回归模型,并分析了不同树种之间的差异。结果表明:二元立木材积方程的平均预估误差均在2%以内,胸径一元材积方程和地径一元材积方程的平均预估误差也大都在3%以内,达到了立木材积表的编制精度要求。所建模型可为北京市杨树林的蓄积量估计提供准确的计量依据。     

广西桂北马尾松不同起源单木和林分材积生长率模型研究

基于广西第七次、第八次森林资源清查数据中马尾松样地样木资料,对不同起源下的单木和林分材积生长率模型进行对比分析。结果表明:对数函数模型Y=a+bln(X)和幂函数模型Y=a×X~b,分别作为单木和林分不同起源下的最优材积生长率模型。马尾松幼龄林生长期,人工林材积生长率要高于天然林,且变化幅度大;生长后期,天然林材积生长率要高于人工林,变化趋向平稳。单木不同起源间的材积生长率差异显著,林分不同起源间差异不显著。     

北京市主要森林类型蓄积量生物量碳储量模型研建

林分水平的蓄积量、生物量和碳储量模型,是开展森林资源规划设计调查的计量基础。基于北京市2016年森林资源连续清查的1 425个乔木林样地数据,分别利用非线性独立回归估计、误差变量联立方程组和含哑变量的误差变量联立方程组方法,建立了油松林、侧柏林、栎树林、桦木林、榆树林、刺槐林、杨树林、其他硬阔林、其他软阔林、乔木经济林等10种主要森林类型的林分蓄积量、生物量和碳储量模型。结果显示:10种主要森林类型的蓄积量、生物量和碳储量模型的确定系数(R~2)都在0.93以上,总体相对误差(TRE)和平均系统误差(ASE)都在±3%以内且多数趋近于0,平均预估误差(MPE)都在5%以内,平均百分标准误差(MPSE)都在15%以内。结果表明:不同森林类型的蓄积量主要取决于林分断面积和平均高,生物量主要取决于蓄积量和林分平均高;含哑变量的非线性误差变量联立方程组方法,是建立林分水平三储量(森林蓄积量、生物量和碳储量)模型系统的可行方法;所建北京市10种主要森林类型的蓄积量、生物量和碳储量模型,其预估精度达到相关技术规定要求,可以在实践中推广试用;为进一步提高模型的准确度,可采用基于二元模型计算的蓄积量和生物量样地数据对所建模型进行修正。     

海南省桉树生长率模型研建

利用海南省1998年、2003年和2008年森林资源连续清查中,复查的3 642株桉树保留木、135个桉树实测样地资料,研究建立了桉树胸径生长率、材积生长率和林分材积生长率模型。所建模型拟合效果良好,无明显系统偏差,建立的桉树胸径生长率模型预估精度达96%以上,一元、二元林分材积生长率模型预估精度均在99%以上,可为以后海南省开展各类森林资源调查估算桉树生长量提供参考依据。     

引入林分优势高和气候因子的杉木和桉树树高-胸径模型研建

基于广西森林资源年度监测评价项目中25块杉木样地和25块桉树样地的每木胸径和树高实测数据，从7个传统树高-胸径曲线中筛选出拟合精度最优模型作为基础模型，引入林分优势高和气候因子构建广义非线性模型，通过10折交叉验证法进行检验，并对一元材积公式的估计误差进行评价。结果显示：1)各传统树高-胸径曲线模型中，Richards模型为杉木和桉树最优的基础模型；2)引入了林分优势高和气候因子的杉木和桉树树高-胸径广义非线性模型，拟合精度相对基础模型更高，杉木的R²,MPE和MPSE的值分别为0.797 6,0.58%和13.91%,桉树的R²,MPE和MPSE值分别为0.720 7,0.62%和11.58%;3)采用一元材积公式得到的杉木和桉树总体蓄积与实测蓄积相差较大，其中桉树相对误差为-13.51%,超过了林业行业标准要求范围，运用树高-胸径广义非线性模型和二元材积公式计算总体蓄积，与实测值相对误差不超过0.5%,构建的杉木和桉树树高-胸径广义非线性模型能运用于实践生产。     

Comparison of treewise and standwise forest simulators by means of quantile regression

Predicting forest development under varying treatment schedules forms the basis of forest management planning. The actual growth predictions are made with a forest simulator which includes growth equations and additional models for predicting a number of varying tree, forest and site properties. Forest growth simulators typically include either tree-level or stand-level growth models, but these two approaches have not been thoroughly compared. We set out here to compare these two approaches with the SIMO simulator framework in a small data set from southern Finland based on 60 sample plots in 30 stands, the development of which was known for 20 years. The stands chosen were very dense, so that the simulators could be tested under extreme conditions. The results show that the stand-level model is more accurate in almost all cases and its computational burden is much lower. It could therefore be advisable to use tree-level models for short-term predictions, which would ensure detailed information on forest structure for planning the near-future operations. Stand-level models would be more advisable in longer term predictions, especially when accurate volume estimates are considered more important than the forest structure. The errors observed in these simulators were analysed further by quantile regression, which allows empirical estimates of confidence intervals to be obtained for the simulator.     

基于激光雷达数据的东北林区航空林分材积表编制

利用东北林区云冷杉林、落叶松林、樟子松林、红松林、栎树林、桦树林、杨树林、榆树林、椴树林和水胡黄林10种森林类型的1947个样地的激光雷达数据和地面实测蓄积量数据,首先通过多元线性回归和非线性回归方法,分别建立基于机载激光雷达数据的森林蓄积量回归估计模型,并通过对比分析,确定统一形式的基础回归模型;然后利用哑变量建模方法,建立基于不同森林类型参数和相同激光雷达变量的蓄积量模型。结果表明,研究建立的10种森林类型的线性蓄积量回归模型的解释变量个数在2~7之间,确定系数在0.460~0.858之间;非线性蓄积量回归模型的解释变量个数在2~4之间,确定系数在0.461~0.846之间。基于点云平均高度和平均强度建立的10种森林类型的二元蓄积量模型(研究称之为标准模型),其确定系数在0.440~0.815之间,平均预估误差在2.88%~4.42%之间,平均百分标准误差在16.76%~25.52%之间,预估精度基本达到森林资源规划设计调查技术规定要求。依据研究建立的10种森林类型的蓄积量模型,可以编制基于激光雷达数据的航空林分材积表,在森林资源调查实践中推广应用。     

Total tree,merchantable stem and branch volume models for miombo woodlands of Malawi

The objective of this study was to develop general (multispecies) models for prediction of total tree, merchantable stem and branch volume including options with diameter at breast height (dbh) only, and with both dbh and total tree height (ht), as independent variables. The modelling data set was based on destructively sampled trees and comprised 74 trees from 33 tree species, collected from four forest reserves located in different ecological zones of Malawi. The dbh and ht ranges for the data set were 5.3–111.2?cm and 3.0–25.0?m, respectively. A number of alternative model forms were tested and the final model selection was based on root mean square error (RMSE) values calculated using a leave-one-out cross-validation procedure. The model performances and the evaluations of the finally selected models (R? 2 range 0.72 to 0.92; RMSE range 38% to 71%; mean prediction errors range ?1.4% to 1.3%) suggest that all models can be used over a wide range of geographical and ecological conditions in Malawi with an appropriate accuracy in predictions. The appropriateness of the developed models was also supported by the fact that the mean prediction errors of these models were much lower than the mean prediction errors (range ?23.6% to 48.9%) of some previously developed models tested on our data.     

Biomass,stem basic density and expansion factor functions for five exotic conifers grown in Denmark

Adequate allometric equations are needed for estimating carbon pools of fast growing tree species in relation to international reporting of CO₂ emissions and for assessing their possible contribution to increasing forest biomass resources. We developed models for predicting biomass, stem basic density and expansion factors of stem to above-ground biomass for five fast growing conifers. Data included destructive measurements of 236 trees from 14 sites, covering a wide range of growth conditions. To ensure model efficiency, models for predicting stem, crown and total above-ground biomass for the five species were estimated simultaneously using a linear, mixed effects model that allowed contemporaneous correlations between the different tree components. Models differed among species and included dbh and tree height. The models explained more than 98% of the variation in above-ground biomass and reflected differences in the allometry between tree species. Stem density differed among species but generally declined with increasing site index and dbh. The overall model for predicting stem basic density included dbh, H₁₀₀ and site index and explained 66% of the total variation. Expansion factors decreased from 1.8–2.0 in small trees (dbh < 10 cm) to 1.1–1.2 for large trees (dbh > 25 cm), but differed among species. The overall model explained 86% of the variation and included quadratic mean diameter and dbh.     

Relationships between diameter and height of trees in natural tropical forest in Tanzania

The relationship between tree height (h) and tree diameter at breast height (dbh) is an important element describing forest stands. In addition, h often is a required variable in volume and biomass models. Measurements of h are, however, more time consuming compared to those of dbh, and visual obstructions, rounded crown forms, leaning trees and terrain slopes represent additional error sources for h measurements. The aim of this study was therefore to develop h–dbh relationship models for natural tropical forest in Tanzania. Both general forest type specific models and models for tree species groups were developed. A comprehensive data set with 2 623 trees from 410 different tree species collected from a total of 1 191 plots and 38 sites covering the four main forest types of miombo woodland, acacia savanna, montane forest and lowland forests was applied. Tree species groups were constructed by using a k-means clustering procedure based on the h–dbh allometry, and a number of different non-linear model forms were tested. When considering the complexity of natural tropical forests in general and in particular variations of h–dbh relationships due to high species diversity in such forests, the model fit and performance were considered to be appropriate. Results also indicate that tree species group models perform better than forest type models. Despite the fact that the residual errors level associated with the models were relatively high, the models are still considered to be applicable for large parts of Tanzanian forests with an appropriate level of reliability.     

Propagating the errors of initial forest variables through stand- and tree-level growth simulators

Developments in the field of remote sensing have led to various cost-efficient forest inventory methods at different levels of detail. Remote-sensing techniques such as airborne laser scanning (ALS) and digital photogrammetry are becoming feasible alternatives for providing data for forest planning. Forest-planning systems are used to determine the future harvests and silvicultural operations. Input data errors affect the forest growth projections and these effects are dependent on the magnitude of the error. Our objective in this study was to determine how the errors typical to different inventory methods affect forest growth projections at individual stand level during a planning period of 30 years. Another objective was to examine how the errors in input data behave when different types of growth simulators are used. The inventory methods we compared in this study were stand-wise field inventory and single-tree ALS. To study the differences between growth models, we compared two forest simulators consisting of either distance-independent tree-level models or stand-level models. The data in this study covered a 2,000-ha forest area in southern Finland, including 240 sample plots with individually measured trees. The analysis was conducted with Monte Carlo simulations. The results show that the tree-level simulator is less sensitive to errors in the input data and that by using single-tree ALS data, more precise growth projections can be obtained than using stand-wise field inventory data.     

Spatiotemporally interactive growth dynamics in selected South African forests: Edaphoclimatic environment, crowding and climate effects

Stand-level tree diameter growth patterns were explored for evergreen moist forests in the southern Cape, South Africa. Results of standard multiple regression analyses, involving 934 permanent sample plots with data spanning a 10-year interval, revealed that stand-level increment of canopy species in the canopy layer (>30 cm dbh) was significantly determined by inherent species-specific growth capacities (species composition of the stand), water availability, forest matrix crowding and tree condition impairment (age-related manifestations of reduced vitality indicated by signs of crown die-back, damage and stem rot). In contrast, stand-level increment of trees of canopy species in the subcanopy layer (10-20 cm dbh) was prominently shaped by light availability, as mainly determined by the degree of canopy-level disturbance (mortality rate of trees >30 cm dbh), crowding (canopy-level overhead and forest matrix crowding) and proximity to conspecific adults (within 6-8 m). In addition to species-inherent and resource factors, considerable variation in stand-level growth resulted from site-climate interactions. For 507 of the permanent sample plots, increment data was available for two consecutive 10-year intervals; permitting the analysis of spatiotemporal interactions of growth patterns (repeated measures ANOVA). In the Knysna forests higher canopy-level increment rates were associated with the moister southerly facing slope sites in comparison with the drier northerly facing and ridge sites during the first increment period. During the second increment period, increment rates on the drier, but better illuminated sites had increased disproportionately. In contrast, in the Tsitsikamma forests, higher increment rates during the second increment period were encountered on moister flat bottomland sites (with extended periods of subsoil wetness) than on the comparatively drier southerly facing slope sites (increment period × site-based water availability × forests interaction). In both forests relatively higher growth performance of subcanopy-level trees during the second increment period was associated with stands experiencing conditions of enhanced light availability. Atmospheric temperatures were higher during the second increment period (mean periodic T_max: + 0.64 °C). The detected spatiotemporal interactions were interpreted as site × climate interactions where site-related conditions of favourable light or water availability resulted in enhanced temperature-linked growth responses during the second increment period. A metabolic performance trade-off model provided a framework for the interpretation of these complex site-climate interactions by placing the patterns of forest growth into an ecophysiological explanatory context.     

东北落叶松相容性立木材积和地上生物量方程研建

以东北落叶松立木材积和地上生物量数据为例,通过改进模型的结构形式,采用误差变量联立方程组的方法,研究建立了相容的立木材积方程、地上生物量方程及生物量转换函数。结果表明:与常用的非线性模型相比,在材积方程和生物量方程中增加截距常数,能显著改进模型的拟合效果;建立的一元相容性方程,地上生物量和立木材积的预估误差均不超过5%;二元相容性方程,地上生物量的预估误差约为4%,立木材积的预估误差则小于3%。     

Individual tree biomass equations and growth models sensitive to climate variables for <Emphasis Type="Italic">Larix</Emphasis> spp. in China

Climate change has increased the need of information on amount of forest biomass. The biomass and carbon storage for larch (Larix spp.) in large geographic regions in China were failed to be accurately estimated from current biomass equations, because they were usually based on a few sample trees on local sites, generally incompatible to volume estimation, and not additive between components and total biomass. China needs reliable biomass estimation of the important species in the whole country. This study was based on the mensuration data of above- and belowground biomass from 600 and 198 destructive sample trees of larch from four regions in China, respectively. The main purpose was to develop compatible individual tree equations on both national and regional levels for above- and belowground biomass, biomass conversion factor and root-to-shoot ratio, using the nonlinear error-in-variable simultaneous equation approach. In addition, diameter at breast height (D) and tree height (H) growth models were also developed, and effects of key climate variables on biomass variation and growth process were analyzed. The results showed that mean prediction errors (MPEs) of regional aboveground biomass models were from 3.86 to 7.52%, and total relative errors (TREs) are within ±3%; and for regional belowground biomass equations, the MPEs are from 9.91 to 28.85%, and the TREs are within ±4%. The above- and belowground biomass and D- and H-growth were significantly related to mean annual temperature and mean annual precipitation. The biomass equations and growth models developed in this paper will provide good basis for estimating and predicting biomass of larch forests in China.     

Predicting stand damage and tree survival in burned forests in Catalonia (North-East Spain)

The study developed models for predicting the post-fire tree survival in Catalonia. The models are appropriate for forest planning purposes. Two types of models were developed: a stand-level model to predict the degree of damage caused by a forest fire, and tree-level models to predict the probability of a tree to survive a forest fire. The models were based on forest inventory and fire data. The inventory data on forest stands were obtained from the second (1989–1990) and third (2000–2001) Spanish national forest inventories, and the fire data consisted of the perimeters of forest fires larger than 20 ha that occurred in Catalonia between the 2nd and 3rd measurement of the inventory plots. The models were based on easily measurable forest characteristics, and they permit the forest manager to predict the effect of stand structure and species composition on the expected damage. According to the stand level fire damage model, the relative damage decreases when the stand basal area or mean tree diameter increases. Conversely, the relative stand damage increases when there is a large variation in tree size, when the stand is located on a steep slope, and when it is dominated by pine. According to the tree level survival models, trees in stands with a high basal area, a large mean tree size and a small variability in tree diameters have a high survival probability. Large trees in dominant positions have the highest probability of surviving a fire. Another result of the study is the exceptionally good post-fire survival ability of Pinus pinea and Quercus suber.