Modelling stand basal area growth for radiata pine plantations in Northwestern Spain using the GADA

A stand basal area growth system for radiata pine (Pinus radiata D. Don) plantations in Galicia (Northwestern Spain) was developed from data corresponding to 247 plots measured between one and five times. Six dynamic equations were considered for analysis and both numerical and graphical methods were used to compare alternative models. The equation that best described the data was a dynamic equation derived from the Korf growth function by the generalized algebraic difference approach (GADA) and by considering two parameters as site-specific. This equation was fitted in one stage by the base-age-invariant dummy variables method. The system also incorporated an equation for predicting initial stand basal area, expressed as a function of stand age, site index, and the number of trees per hectare. This information can be used to establish the starting point for the projection equation when no inventory data are available. The effect of thinning on stand basal area growth was also analyzed and the results showed that the same projection equation can be used to obtain reliable predictions of unit-area basal area development in thinned and unthinned stands.     

A reduced growth model based on stand basal area. A case for hybrid poplar plantations in northeast Spain

Whole-stand models normally require data on initial stand basal area and dominant height. Dominant height measurements are time-consuming and often imprecise, compromising subsequent predictions. Poplar plantations provide a special case where basal area correlates with site index; a whole-stand model could thus be based on stand basal area. We report a static model constructed by the generalized algebraic difference approach (GADA) for poplar plantations for three different hybrid poplars (Populus × euramericana (Dode) Guinier “I-214”, “MC”, and “Luisa Avanzo”) in northeast Spain. The transition function was based on current stand basal area and was fitted with data from 158 permanent plots ranging from 1- to 17-year-old plantations. Merchantable stand volume was estimated by a volume equation where height was predicted by a height–basal area relationship based on 458 temporary plots. The model differences between clones were compared using the nonlinear extra sum of squares method. Significant differences were detected, while Luisa Avanzo presented the highest merchantable volume at the end of the rotation. Errors in basal area predictions were below 20% within 6 years in the case of Luisa Avanzo and MC clones, and within 3 years in the case of I-214. Our research showed that satisfactory predictions can be obtained using GADA with a single transition function based on an easily measurable variable such as stand basal area.     

Predicting and calibrating diameter distributions of Eucalyptus grandis (Hill) Maiden plantations in Zimbabwe

Diameter distribution models for even-aged Eucalyptus grandis plantations in Zimbabwe were developed using the two-parameter Weibull function. The analysis was based on data from Correlated Curve Trend (CCT) experiments replicated on four different sites. Parameters of the Weibull distribution were predicted using stand characteristics as regressors. Two sets of parameter models were estimated: a set with and one without stand basal area as a predictor. Stand variables such as dominant height, age, site index and number of stems were used in both sets. The models were further calibrated to result in a given number of stems and stand basal area simultaneously. The usability of constructed models was tested both in prediction of yield in a stand inventory situation and in simulation of growth in connection with different growth models. The results indicated that models not including stand basal area produce considerably less precise stand volume estimates compared to models including also stand basal area. Calibration improved the accuracy of diameter distribution models. In growth simulation diameter distribution models can be connected both to single tree growth models and to stand projection models. The usability of calibration in growth simulation depends on the accuracy of the prediction of stand characteristics.     

Height-diameter curves with random intercepts and slopes for trees growing on drained peatlands

Models for predicting tree height were constructed for Scots pine (Pinus sylvestris), Norway spruce (Picea abies) and pubescent birch (Betula pubescens). The data consisted of two separate sets of permanent sample plots forming a representative sample of drained peatland stands in the whole country. A logarithmic height-diameter curve with one nonlinear parameter specific to each tree species was applied. It was assumed that the intercept and slope of the curve would vary randomly from stand to stand. Stand characteristics were used to predict the mean intercept and slope. A nonhomogeneous variance of the residual error was modelled as a function of tree diameter. A mixed linear model technique was applied to fit the models. The diameter of the tree of the median basal area, stand basal area, geographical location of the stand, and site quality were used as fixed independent variables in explaining the variation in the intercept. The diameter of the tree of the median basal area and the stand basal area were used in explaining the variation in the slope.     

基于ASD与Hyperion高光谱数据的主要针叶树种分类研究

以云南省香格里拉市为研究区,对ASD光谱仪实测的4种针叶树种光谱数据采用包络线去除法、光谱一阶微分法和光谱二阶微分法3种波段选择方法得到Hyperion高光谱影像数据的分类特征波段,采用最大似然法、支持向量机2种分类方法对所选的特征波段开展树种识别分类,对原始影像采用光谱角填图分类方法作对比实验。结果表明,基于ASD数据的光谱一阶波段选择方案的支持向量机分类方法精度最高,总体分类精度为81.95%,Kappa系数为0.725 1。采用ASD实测光谱数据能有效指导Hyperion进行树种分类,基于数据尺度和换算方式,一阶微分更适合特征波段选择;与传统的数理统计分类方法和光谱特征分类方法相比,基于机器学习的方法如支持向量机等在高光谱遥感分类中具有更大的应用潜力。     

Modeling survival in juvenile and mature loblolly pine plantations

Data from a large, region-wide set of permanent plots were used to develop equations for predicting stand-level survival in thinned and unthinned loblolly pine plantations growing in the southeastern United States. A pre-competitive model predicts survival at young ages based on age and total number of trees surviving. A post-crown closure model predicts survival after the onset of intraspecific competition based on age, site index, thinning intensity, percent hardwood basal area and number of trees surviving. Analyses of the pre-competitive data indicated that survival trends differed by drainage class and site preparation method. Thus, separate sets of coefficients were estimated for two drainage class/site preparation groups. Analyses of the post-competitive data indicated that survival trends differed for thinned and unthinned stands and were affected by the amount of hardwood competition. Therefore, the post-crown closure model was developed and parameterized to reflect the changes in survival as a result of hardwood competition and thinning from below.     

Predicting species-specific basal areas in urban forests using airborne laser scanning and existing stand register data

The aim of this work was to examine how well species-specific stand attributes can be predicted using a combination of airborne laser scanning (ALS) and existing stand register data in urban forests. In this context, the ability of three data combinations: ALS data and stand register data, ALS data and digital aerial images and all of these combined, was tested in the prediction of species-specific basal areas. We divided tree species into seven and three different tree species strata and applied two prediction methods: (1) regression method, in which the predicted total basal area was divided into tree species based on tree species proportions from stand register data, and (2) the nearest neighbour (NN) method, in which tree species proportions were used as predictor variables for species-specific basal areas. Prediction models were built based on training data of 205 field plots, and the accuracy of the models was tested based on validation data of 52 forests stands. Our results showed that species-specific predictions of seven tree species were more accurate when tree species proportions from stand register data were used in the prediction. Both the regression and the NN method provided reasonable accuracy. This study showed that tree species information from existing stand register data could be used as an alternative for aerial images in ALS-based forests inventories. The use of ALS data together with stand register data and small field data could also be economically beneficial in an inventory of urban forests.     

Accuracy of forest inventory using airborne laser scanning: evaluating the first nordic full-scale operational project

This research reports the major results from an evaluation of the first Nordic operational stand-based forest inventory using airborne laser scanner data. Laser data from a forest area of 250 km² were used to predict six biophysical stand variables used in forest planning. The predictions were based on regression equations estimated from 250 m² field training plots distributed systematically throughout the forest area. Test plots with an approximate size of 0.1–0.4 ha were used for validation. The testing revealed standard deviations between ground-truth values and predicted values of 0.36–1.37 m (1.9–7.6%) for mean height, 0.70–1.55 m (3.0–7.6%) for dominant height, 2.38–4.88 m² ha^?1 (7.8–14.2%) for basal area and 13.9–45.9 m³ ha^?1 (6.5–13.4%) for stand volume. No serious bias was detected.     

Comparison of nonspatial and spatial approaches with parametric and nonparametric methods in prediction of tree height

Nonparametric modelling has been popular in recent forestry applications. However, nonparametric modelling methods usually assume independent observations, that is, do not acknowledge the spatial relationships of most forest data sets. For these situations, mixed model and kriging approaches have been used. The aim of this paper was to compare accuracy of spatial parametric and nonparametric approaches, namely mixed models and a combination of k-nn method and mixed models, in prediction of tree height. The spatial approaches were compared to a nonspatial parametric model and k-nn method. Tree height was first modelled using either mixed model or k-nn. The residual error was divided into plot and tree effects. A nonspatial prediction was obtained using the fixed part of the models. The spatial prediction was obtained when this prediction was further adjusted using the estimates of within-plot correlation of errors and best linear predictor. The influence of the quality of modelling data was also considered. The adjustment of nonspatial estimates of both parametric and nonparametric approaches markedly improved the predictions in all study cases. For many applications, the combination of the nonparametric k-nn method for the fixed component of the model, along with random effects for spatial correlations to create a mixed model, could be used. This would allow for spatial prediction, which would likely provide improved predictions, as shown for predicting height in this paper. Also, there is the added benefit that the nonparametric k-nn does not require a particular model form.     

林分断面积组合预测模型权重确定的比较

引入组合预测方法以提高林分断面积预测的精度及2类模型(林分水平模型和单木水平模型)预测林分断面积的兼容性。组合预测法能够充分利用各单个模型的有效信息,从而提高预测精度,而单个模型权重的选取对提高组合预测法的精度至关重要。本研究基于北京山区油松连续清查数据,利用误差平方和法、方差协方差法和最优加权法确定林分断面积组合预测模型的权重。结果表明:组合预测法能够提高预测精度,同时利用最优加权法所建立的林分断面积组合预测模型其预测精度最高,方差协方差法次之,误差平方和法预测精度最低。     

Estimation of stem volume using laser scanning-based canopy height metrics

The aim of this study was to test different stem volume predictorsthat are capable of utilizing laser scanning-based canopy heightmetrics as independent variables. The three laser scanning-basedmethods compared were (1) a direct prediction model for thestem volume at plot level, (2) a volume prediction system basedon the modelled percentiles of the basal area diameter distribution,and (3) a parameter prediction method used to determinate Weibull-basedbasal area diameter distributions for the plot-level stem volumeprediction. The predicted volumes were also compared with field-measuredvolumes obtained with the Finnish conventional inventory bycompartments. The best results were obtained with the firstmethod, i.e. the model that predicts plot-level stem volumesdirectly, which is logical. Furthermore, the simulated reductionof point density of laser data had no effect on the accuracyof stem volume predictions. The percentile-based modelling ofdiameter distributions was applied, in particular, to the determinationof non-homogenous stand structure; using this method, it iseven possible to fit multimodal distributions. In terms of theaccuracy of the predicted plot-level stem volumes, the volumeprediction method based on modelled percentiles of basal areadiameter distributions was the second best, whereas the volumeprediction method based on the parameter prediction of the Weibull-basedbasal area diameter distributions resulted in slightly worseresults. However, the accuracies of the three laser-based volumeprediction methods tested were superior to the published resultsof spectral value-based remote sensing studies implemented usingdata collected from Finland. Furthermore, the accuracy of plot-levelstem volume estimates calculated from field assessments wasconsiderably weaker than the accuracy of the three volume predictionmethods that utilized measures obtained with laser scanning.     

An individual tree height increment model for mixed white spruce–aspen stands in Alberta,Canada

Individual tree-height increment models were developed for white spruce (Picea glauca (Moench) Voss) and aspen (Populus tremuloides Michx.) growing in the boreal mixed-species in Alberta. The models were formulated based on a selected base function (the Box–Lucas function), and the method of parameter prediction. Height increment was modeled as a nonlinear function of tree height, tree diameter, diameter increment, stand density, relative competitiveness of the tree in the stand, site productivity, and species composition. Since the data from permanent sample plots used in this study were time-dependent and cross-sectional, diagnostic techniques were applied to identify the models' error structure. Appropriate fits based on the identified error structure were accomplished using the nonlinear least squares procedures with a first-order autoregressive process. The models were also validated on independent testing data sets representing the population on which the models are to be used. Results showed that the average prediction biases were not significantly different from zero at α = 0.05, suggesting that the fitted models appropriately described the data and performed well when predictions were made. Biological implications of the variables that affect height increment in mixed-species stands were discussed.     

墨西哥哈里斯科(Jalisco)林分结构小面积估测

对小的生态经济区开展自然资源统计是很困难的,政府决策人员只能依靠州水平的数据库来评价一定区域或局部的自然资源（森林、牧场、草地、农田等）状况。小面积评估技术可以用于评定这些资源。然而,哪一种小面积估测法可以给出最可靠、最准确的结果还不得而知。本研究检测了小面积评估分析常用的两种方法（即综合估计法和回归估计法）的可靠性、准确性。运用这两种方法分析墨西哥哈里斯科（Jalisco）州全州的自然资源数据,从而检测每种方法对所选择的森林林分结构特征预测结果的好坏。研究表明,回归方法在多个地理尺度上,对森林林分结构特征预测的可靠性和准确性均最好。因此,推荐州或地方资源管理者,在没有其他适当的辅助信息资料的情况下,可运用回归分析法来评估小区域内自然资源状况。图4表5参14。     

角规测树方法的误差分析

本文利用414块人工落叶松林样地实测资料,探讨了角规测树的误差问题。研究结果表明,即使在排除了某些产生偏差因素的情况下,角规常数选择不当,仍然会产生偏小或偏大的误差。这说明角规常数的选择在角规测树中是至关重要的;并说明了角规常数的选择与其它误差相关联,是角规测树的中心问题。     

Quantitative validation and comparison of a range of forest growth model types

Predictions from a range of model types (simplified process-based, a statistical state space, statistical difference, and a hybrid model) were compared to 969 measurements of forest growth across an environmental gradient. The models compared were 3-PG, CANTY, CanSPBL(1.2), and CanSPBL(water). The study made an objective comparison and validation of model types, with the main criterion for comparison being each model's ability to match actual historical measurements of forest growth in an independent data set. A number of stand level forest growth variables were compared including basal area, mean top height, and stocking over 14,058 ha of plantation-grown Pinus radiata in south-eastern New Zealand. Stand variable predictions at 195 permanent plot locations covering a range of elevations from 0 to 660 m were highly correlated with field estimates derived from plot data. The hybrid model CanSPBL(water) on average was the most accurate model in the study where predictions of stocking, basal area, and mean top height were 96%, 96%, and 96% efficient. The statistical-difference equation model CanSPBL(1.2) was equally efficient but on average 3% less accurate and slightly more biased in predictions suggesting that the hybrid model explained differences in growth due to differences water availability and soil type. The process-based model 3-PG predicted stocking and basal area 89% and 88% efficiently. Finally, the statistical state-space model CANTY predicted stocking, basal area, and mean top height 96%, 87%, and 87% efficiently. Results quantify the amount of precision that can be expected from the three model types, and suggest that each approach has strengths and weaknesses.     

A review of stand basal area growth models

Growth and yield modeling has a long history in forestry. The methods of measuring the growth of stand basal area have evolved from those developed in the U.S.A. and Germany during the last century. Stand basal area modeling has progressed rapidly since the first widely used model was published by the U.S. Forest Service. Over the years, a variety of models have been developed for predicting the growth and yield of uneven/even-aged stands using stand-level approaches. The modeling methodology has not only moved from an empirical approach to a more ecological process-based approach but also accommodated a variety of techniques such as: 1) simultaneous equation methods, 2) difference models, 3) artificial neural network techniques, 4) linear/nonlinear regres-sion models, and 5) matrix models. Empirical models using statistical methods were developed to reproduce accurately and precisely field observations. In contrast, process models have a shorter history, developed originally as research and education tools with the aim of increasing the understanding of cause and effect relationships. Empirical and process models can be married into hybrid mod-els in which the shortcomings of both component approaches can, to some extent, be overcome. Algebraic difference forms of stand basal area models which consist of stand age, stand density and site quality can fully describe stand growth dynamics. This paper reviews the current literature regarding stand basal area models, discusses the basic types of models and their merits and outlines recent progress in modeling growth and dynamics of stand basal area. Future trends involving algebraic difference forms, good fitting variables and model types into stand basal area modeling strategies are discussed.     

Trends of tree growth in Swedish forests 1953–1992: An analysis based on sample trees from the national forest inventory

Average tree height and basal area growth for Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.) in Sweden were studied as functions of species, age, stand density, location and year of inventory in the period 1953–1992, on the basis of sample tree data from the National Forest Inventory. A highly significant annual increase of both height and basal area growth was found, of the magnitude 0.5–0.8%, during the 40 yr period. Possible reasons for the trend are discussed. The altered way of cutting in the early 1950s, from selective cutting to clear felling and thinning from below, has had a large impact. Also, improved regeneration methods, nitrogen fertilization and ditching have increased growth. The increasing atmospheric deposition of nitrogen is another possible factor.     

Airborne laser scanning as a method in operational forest inventory: Status of accuracy assessments accomplished in Scandinavia

Abstract

This research reports the major evaluation results from an operational stand-based forest inventory using airborne laser scanner data carried out in Norway. This is the first operational inventory in which data from two separate districts are combined. Laser data from two forest areas of 65 and 110 km² were used to predict six biophysical stand variables used in forest planning. The predictions were based on regression equations estimated from 250 m² field training plots distributed systematically throughout the two forest areas. Test plots with a size of 0.1 ha were used for validation. The testing revealed standard deviations between ground-truth values and predicted values of 0.58–0.85 m (3.4–5.6%) for mean and dominant heights, 2.62–2.87 m² ha^?1 (9.3–14.3%) for basal area, and 18.7–25.1 m³ ha^?1 (10.8–12.8%) for stand volume. No serious bias was detected. For 10 of the 12 estimated regression models there were no significant effects of district.