Developing an airborne laser scanning dominant height model from a countrywide scanning survey and national forest inventory data

Abstract

An airborne laser scanning (ALS) dominant height model was developed based on data from a national scanning survey with the aim of developing a digital terrain model (DTM) for Denmark. Data obtained in the ongoing Danish national forest inventory (NFI) were used as reference data. The data comprised a total of 2072 measurements of dominant height on NFI sample plots inventoried in 2006–2007 and their corresponding ALS data. The dominant height model included four variables derived from the ALS point cloud distribution. The variables were related to canopy height, canopy density and species composition on individual plots. The RMSE of the final model was 2.25 m and the model explained 93.9% of the variation (R ²). The model was successful in predicting dominant height across a wide range of forest tree species, stand heights, stand densities, canopy cover and growing conditions. The study demonstrated how low-density ALS data obtained in a survey not specifically aimed at forest applications may be used for obtaining biophysical forest properties such as dominant height, thereby reducing the overall forest inventory costs.     

Comparison of estimators and feature selection procedures in forest inventory based on airborne laser scanning and digital aerial imagery

Digital maps of forest resources are a crucial factor in successful forestry applications. Since manual measurement of this data on large areas is infeasible, maps must be constructed using a sample field data set and a prediction model constructed from remote sensing materials, of which airborne laser scanning (ALS) data and aerial images are currently widely used in management planning inventories. ALS data is suitable for the prediction of variables related to the size and volume of trees, whereas optical imagery helps in improving distinction between tree species. We studied the prediction of forest attributes using field data from National Forest Inventory complemented with ad hoc field plots in combination with ALS and aerial imagery data in Aland province, Finland. We applied feature selection with genetic algorithm and greedy forward selection and compared multiple linear and nonlinear estimators. Maximally around 40 features from a total of 154 were required to achieve the best prediction performances. Tree height was predicted with normalized root mean squared error value of 0.1 and tree volume with a value around 0.25. Predicting the volumes of spruce and broadleaved trees was the most challenging due to small proportions of these tree species in the study area.     

Assessing 3D point clouds from aerial photographs for species-specific forest inventories

In this study we assessed the potential of using photogrammetric data for species-specific forest inventories. The method is based on a combination of Dirichlet and ordinary linear regression models. This approach was used to predict species proportions, main tree species, total, and species-specific volume. Structural and spectral variables were used as predictors. The models were validated using 63 independent validation stands. The results from airborne laser scanning (ALS) data combined with spectral data and photogrammetric data obtained using aerial imagery with different forward overlaps of 80% and 60% were compared. The best photogrammetry-based models predicted species proportions with a relative root mean square error (RMSE) of 21.4%, classified dominant species with 79% accuracy, predicted total volume with relative RMSE of 13.4%, and predicted species-specific volume with relative RMSE of 36.6%, 46.5%, and 84.9% for spruce, pine, and deciduous species, respectively. The results were similar for the three point cloud datasets obtained from aerial imagery and ALS and the accuracies of the predictions were comparable to methods used in operational FMI. The study highlights the effectiveness of forest inventories carried out using photogrammetric data, which – differently from ALS, can include species-specific information without relying on multiple data sources.     

Comparing biophysical forest characteristics estimated from photogrammetric matching of aerial images and airborne laser scanning data

Recent development in aerial digital cameras and software facilitate the photogrammetric point cloud as a new data source in forest management planning. A total of 151 field training plots were distributed systematically within three predefined strata in a 852.6 ha study area located in the boreal forest in southeastern Norway. Stratum-specific regression models were fitted for six studied biophysical forest characteristics. The explanatory variables were various canopy height and canopy density metrics derived by means of photogrammetric matching of aerial images and small-footprint laser scanning. The ground sampling distance was 17 cm for the images and the airborne laser scanning (ALS) pulse density was 7.4 points m^–2. Resampled images were assessed to mimic acquisitions at higher flying altitudes. The digital terrain model derived from the ALS data was used to represent the ground surface. The results were evaluated using 63 independent test stands. When estimating height in young forest and mature forest on poor sites, the root mean square error (RMSE) values were slightly better using data from image matching compared to ALS. However, for all other combinations of biophysical forest characteristics and strata, better results were obtained using ALS data. In general, the best results were found using the highest image resolution.     

A comparative study of the use of laser scanner data and field measurements in the prediction of crown height in boreal forests

Abstract

This study compares the results of the prediction of crown height characteristics using airborne laser scanner (ALS) data and intensive field measurements in boreal forests. The data consisted of 31 sample plots located in Kalkkinen, southern Finland. Crown height models were constructed at both the tree and plot level. Scots pine, Norway spruce and birches were used. The models included independent variables of tree levels, such as tree height, crown area and independent plot-level variables, i.e. canopy height and density quantiles and proportion of vegetation hits. Field measurement-based models used tree height and diameter at breast height as the independent tree-level variables, whereas basal area, mean diameter and height were used as the plot-level variables. The results indicated that the ALS-based crown height models were more accurate than the field measurement-based models when plot-level information was used as independent variables. However, the field measurement-based tree-level models for Scots pine and Norway spruce were more accurate than the ALS-based models. Even so, the accuracy of the different models was very similar and the study data set was quite small. The results of this study can be used for different tree growth studies and for the assessment of tree stock quality in boreal forests.     

Assessment of handheld mobile terrestrial laser scanning for estimating tree parameters

Sustainable forest management heavily relies on the accurate estimation of tree parameters.Among others,the diameter at breast height(DBH) is important for extracting the volume and mass of an individual tree.For systematically estimating the volume of entire plots,airborne laser scanning(ALS) data are used.The estimation model is frequently calibrated using manual DBH measurements or static terrestrial laser scans(STLS) of sample plots.Although reliable,this method is time-consuming,which greatly hampers its use.Here,a handheld mobile terrestrial laser scanning(HMTLS) was demonstrated to be a useful alternative technique to precisely and efficiently calculate DBH.Different data acquisition techniques were applied at a sample plot,then the resulting parameters were comparatively analysed.The calculated DBH values were comparable to the manual measurements for HMTLS,STLS,and ALS data sets.Given the comparability of the extracted parameters,with a reduced point density of HTMLS compared to STLS data,and the reasonable increase of performance,with a reduction of acquisition time with a factor of5 compared to conventional STLS techniques and a factor of3 compared to manual measurements,HMTLS is considered a useful alternative technique.     

系统收获表研究的概念和功能(英文)

传统收获表是指某一树种在特定地区和管理体系下，以立地条件和年龄为变量描述林分平均生长过程的数表．林分密度管理图是根据林分密度效果编制的可预测林分平均收获的图．通过与传统收获表和林分密度管理图的概观比较和探讨，该文系统地阐述了系统收获表的概念和功能．系统收获表的基本特点是为预测现实林分在不同立地条件和管理体系下生长过程的计算机程序．因此，系统收获表能描述在多维变量（如年龄、立地条件、林分密度、胸径和树高）条件下的现实林分和单木的无数生长过程     

Effects of light availability and tree size on the architecture of assimilative surface in the canopy of Picea abies: variation in needle morphology

Needle dimensions, needle surface area, needle dry weight per area (LWA) and needle density (ND, needle weight per volume) were measured in terminal current-year shoots in a natural canopy of variably sized Picea abies (L.) Karst. trees growing along a light gradient. Needle shape was described as a rhomboid. Needle width (D(2)) increased with increasing diffuse site factor, a(d) (relative amount of penetrating diffuse solar radiation), whereas needle thickness (D(1)) remained nearly constant, resulting in an inverse relationship between D(1)/D(2) and a(d) and an increase in the ratio of total (TLA) to projected needle surface area (PLA) with increasing a(d). Because of the variations in needle morphology with respect to light availability, the shoot parameters used in present canopy models are also expected to be light-sensitive, and studies involving shoot morphology should also consider the variability in needle geometry. Needle dimensions and total tree height were not correlated. However, LWA increase with both increasing a(d) and total tree height. When LWA was expressed as the product of ND and needle height (NH, height of the rhomboidal transverse section of a needle), LWA appeared to increase with irradiance, because of changing NH, and with total tree height, because of changing needle density.     

Comparison between TanDEM-X- and ALS-based estimation of aboveground biomass and tree height in boreal forests

Interferometric Synthetic Aperture Radar (InSAR) data from TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X) were used to estimate aboveground biomass (AGB) and tree height with linear regression models. These were compared to models based on airborne laser scanning (ALS) data at two Swedish boreal forest test sites, Krycklan (64°N19°E) and Remningstorp (58°N13°E). The predictions were validated using field data at the stand-level (0.5–26.1 ha) and at the plot-level (10 m radius). Additionally, the ALS metrics percentile 99 (p99) and vegetation ratio, commonly used to estimate AGB and tree height, were estimated in order to investigate the feasibility of replacing ALS data with TanDEM-X InSAR data. Both AGB and tree height could be estimated with about the same accuracy at the stand-level from both TanDEM-X- and ALS-based data. The AGB was estimated with 17.2% and 14.6% root mean square error (RMSE) and the tree height with 7.6% and 4.1% RMSE from TanDEM-X data at the stand-level at the two test sites Krycklan and Remningstorp. The Pearson correlation coefficients between the TanDEM-X height and the ALS height p99 were r?=?.98 and r?=?.95 at the two test sites. The TanDEM-X height contains information related to both tree height and forest density, which was validated from several estimation models.     

天然林区小班森林资源数据的更新模型

以吉林省汪清林业局为例,根据１９９７年森林经理调查的８４８块固定样地数据,与全林整体模型方法相结合,建立了适合于天然林区林业局（场）无人为干预小班森林资源数据更新的林分级生长模型组。该组模型包括林分密度指数,平均高,断面积,形高,郁闭度等林分测算因子的生长或变化模型。     

结合自适应阈值与峰值的LiDAR林分平均高反演方法研究

随着激光雷达获取的点云密度不断增加,提取样地尺度的林分平均高成为可能。但样地尺度林分平均高的提取精度与树种之间的关系尚不明确,急需一种能适应各种树种的林分平均高提取方法。以广西国有高峰林场为例,采用机载LiDAR点云数据生成的冠层高度模型(Canopy height model,CHM),结合地面实测的201个样地数据,提出了一种结合自适应阈值与峰值的林分平均高提取算法,并分析了树种对提取精度的影响。结果表明:1)不同树种的林分平均高提取精度存在差异,杉木精度最高,而桉树和其他阔叶树种精度次之;2)自适应阈值结合峰值的算法能够较好提取林分平均高(R²=0.75,RMSE=3.11m,rRMSE=22.07%),并且对于不同的树种都有较强的稳健性;3)阔叶树种和针叶树种对不同的提取方法存在敏感性差异。研究提取的林分平均高可为森林蓄积量与生物量反演研究提供依据和参考。     

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.     

Estimation of stem attributes using a combination of terrestrial and airborne laser scanning

Properties of individual trees can be estimated from airborne laser scanning (ALS) data provided that the scanning is dense enough and the positions of field-measured trees are available as training data. However, such detailed manual field measurements are laborious. This paper presents new methods to use terrestrial laser scanning (TLS) for automatic measurements of tree stems and to further link these ground measurements to ALS data analyzed at the single tree level. The methods have been validated in six 80 × 80 m field plots in spruce-dominated forest (lat. 58°N, long. 13°E). In a first step, individual tree stems were automatically detected from TLS data. The root mean square error (RMSE) for DBH was 38.0 mm (13.1 %), and the bias was 1.6 mm (0.5 %). In a second step, trees detected from the TLS data were automatically co-registered and linked with the corresponding trees detected from the ALS data. In a third step, tree level regression models were created for stem attributes derived from the TLS data using independent variables derived from trees detected from the ALS data. Leave-one-out cross-validation for one field plot at a time provided an RMSE for tree level ALS estimates trained with TLS data of 46.0 mm (15.4 %) for DBH, 9.4 dm (3.7 %) for tree height, and 197.4 dm³ (34.0 %) for stem volume, which was nearly as accurate as when data from manual field inventory were used for training.     

Combining canopy height and tree species map information for large-scale timber volume estimations under strong heterogeneity of auxiliary data and variable sample plot sizes

A timber volume regression model applicable to the state and communal forest area of the federal German state of Rhineland-Palatinate is identified using a combination of airborne laser scanning (ALS)-derived metrics and information from a satellite-based tree species classification map available on the federal state level. As is common in many forest inventory datasets, strong heterogeneity in the ALS data due to different acquisition dates and misclassifications in the tree species classification map had noticeable effects on the regression model’s performance. This article specifically addresses techniques that improve the performance of ordinary least square regression models under such restricting conditions. We introduce a calibration technique to neutralize the effect of misclassifications in the tree species variable that originally caused a residual inflation of 0.05 in adjusted \(R^2\). Incorporating the calibrated tree species information improved the model accuracy by up to 0.07 in adjusted \(R^2\) and suggests the use of such information in forthcoming inventories. We also found that including ALS quality information as categorical variables within the regression model considerably mitigates issues with time lags between the ALS and terrestrial data acquisition and ALS quality variations (increase of 0.09 in adjusted \(R^2\)). The model achieved an adjusted \(R^2\) of 0.48 and a cross-validated root-mean-square error (RMSE\(_{\mathrm{cv}}\)) of 46.7% under incorporation of the tree species and ALS quality information and was thus improved by 0.12 in adjusted \(R^2\) (5% in RMSE\(_{\mathrm{cv}}\)) compared to the simple model only containing ALS height metrics (adjusted \(R^2=0.36\), RMSE\(_{\mathrm{cv}}=51.7\)%).     

珍贵树种多树种混交造林效果

对6年生珍贵树种多树种混交林生长情况进行调查,结果表明：与格木（Erythrophleum fordii）＋柚木（Tectona grandis）混交林、格木纯林以及降香黄檀（Dalbergia odorifera）纯林相比,多树种混交林总蓄积量能提混高13.66-49.14 m^3/hm^2,不同树种以及同一树种在不同配置模式之间的平均树高、平均胸径、平均单株材积及其年均生长量等指标差异显著。多树种混交模式中格木、降香黄檀的平均树高分别比相应纯林提高2.33%-30.23%和10.71%-19.64%,平均胸径分别提高了15.15%-27.27%和12.50%-20.83%。与格木＋柚木混交林相比,多树种混交林格木、柚木平均树高分别提高了8.70%-21.74%和8.70%,平均胸径分别提高了24.59%-37.70%和33.33%-36.46%。综合分析表明,珍贵树种多树种混交在一定程度上能够提高格木、降香黄檀、柚木等的高径生长,促进干形发育,且在促进林木胸径生长上有显著作用,该模式对于珍贵树种长周期大径材的培育切实有效,但不同树种配置及其比例的改善效果存在差异。     

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。     

Stem biomass and volume models of selected tropical tree species in West Africa

Estimating tree volume and biomass constitutes an essential part of the forest resources assessment and the evaluation of the climate change mitigation potential of forests through biomass accumulation and carbon sequestration. This research article provides stem volume and biomass equations applicable to five tree species, namely Afzelia africana Sm. (Caesalpiniaceae), Anogeissus leiocarpa (DC.) Guill. and Perr. (Combretaceae), Ceiba pentandra (L.) Gaertn. (Bombacaceae), Dialium guineense Willd. (Caesalpiniaceae), Diospyros mespiliformis Hochst. ex A.DC. (Ebenaceae) in natural protected tropical forests and, in addition, Tectona grandis L.f. (Verbenaceae) in plantations. In addition to the tree species specific equations, basic wood density, as well as carbon, nitrogen, organic matter and ash content were determined for these tree species in tropical conditions in West Africa. One hundred and sixty-two sample trees were measured through non-destructive sampling and analysed for volume and biomass. Stem biomass and stem volume were modelled as a function of diameter (at breast height; Dbh) and stem height (height to the crown base). Logarithmic models are presented that utilise Dbh and height data to predict tree component biomass and stem volumes. Alternative models are given that afford prediction based on Dbh data alone, assuming height data to be unavailable. Models that include height are preferred, having better predictive capabilities. Ranges in carbon, nitrogen and ash contents are given as well. The successful development of predictive models through the use of non-destructive methods in this study provide valuable data and tools for use in determining the contribution of these major African rainforest tree species to global carbon stocks, while ensuring the preservation of this valued African resource. This study needs to be expanded to further regions and tree species to complete a full inventory of all tree species, emphasising the relevance of African trees to carbon stocks at a global scale.     

Improving the simulation of stand structure in a forest gap model

There is currently great interest in improving the applicability of forest gap models to changing environmental conditions, in order to facilitate the assessment of possible impacts of climatic change on forest ecosystems. Moreover, for the development of mitigation strategies, it is necessary to include forest management options in the models. Both the simulation of transient effects of climatic change and of forest management regimens require a realistic representation of stand structure in gap models, since tree species respond to variations in stand density in characteristic ways, depending on their ecological strategies.In this study, we compared the effect of five different height growth functions that are sensitive to stand density on simulated stand structure of the FORSKA forest gap model. We used long term observation data from a beech thinning trial at Fabrikschleichach, Bavaria, to test the alternative functions. First, we compared simulation results of the original FORSKA model with measured stand development from 1870 to 1990. Whereas simulated stand level variables (e.g. biomass, mean diameter and height) showed good correspondence with observations, individual tree dimensions and simulated stand structure were quite unrealistic. After calibrating parameters of the height growth functions with data from a lightly thinned plot at Fabrikschleichach, we ran the model with data from a heavily thinned plot for validation. All five functions considerably improved the simulation of height/diameter relationships and stand structure. However, there were distinct differences between functions. The best correspondence with measurements was shown by a function which uses the relative radiation intensity in the centre of a tree crown as an indicator of the competition status of the tree. This function is rather simple and needs only two growth parameters, which can be derived for different functional types of species, according to their shade tolerance.With the new, flexible height growth function it should be possible to extend the applicability of gap models to more realistic simulation experiments including forest management and natural disturbance. To our knowledge, this was the first attempt to employ long term forest observation data for the calibration and validation of a forest gap model. The results suggest that such data could be very useful in model testing and improvement.     

Biomass, basic density and biomass expansion factor functions for European beech (Fagus sylvatica L.) in Denmark

The objective of this study was to analyse the within-tree allocation of biomass and to develop biomass functions for above- and below-ground components of European beech in Denmark. Separate functions were developed for stem, branches, below-ground stump and root system, total above-ground biomass and total tree biomass. For each of these components or aggregate components, models were also developed for the average basic density of wood and bark. To enhance the versatility of the models, a function for estimating the biomass expansion factor (BEF) was also developed. The functions were based on 66 trees measured for total biomass. Model performance was evaluated based on 74 trees measured only for above-ground biomass. The trees were sampled in 18 different forest stands covering a wide range of tree sizes and stand treatments. Models were estimated using a linear mixed-effects procedure to account for within-stand correlations. The functions for biomass and BEFs included only diameter at breast height and total tree height for individual trees as predictor variables. Inclusion of additional variables reflecting site quality or stand density did not improve model performance. The functions for basic density included individual tree diameter, tree height and quadratic mean diameter as predictor variables, indicating an effect of stand density on the basic density of wood and bark.     

A growth model for Pinus radiata D. Don stands in north-western Spain

A dynamic whole-stand growth model for radiata pine (Pinus radiata D. Don) stands in north-western Spain is presented. In this model, the initial stand conditions at any point in time are defined by three state variables (number of trees per hectare, stand basal area and dominant height), and are used to estimate total or merchantable stand volume for a given projection age. The model uses three transition functions derived with the generalized algebraic difference approach (GADA) to project the corresponding stand state variables at any particular time. These equations were fitted using the base-age-invariant dummy variables method. In addition, the model incorporates a function for predicting initial stand basal area, which can be used to establish the starting point for the simulation. Once the state variables are known for a specific moment, a distribution function is used to estimate the number of trees in each diameter class by recovering the parameters of the Weibull function, using the moments of first and second order of the distribution. By using a generalized height-diameter function to estimate the height of the average tree in each diameter class, combined with a taper function that uses the above predicted diameter and height, it is then possible to estimate total or merchantable stand volume.