Practical large-scale forest stand inventory using a small-footprint airborne scanning laser

Mean tree height, dominant height, mean diameter, stem number, basal area and timber volume of 116 georeferenced field sample plots were estimated from various canopy height and canopy density metrics derived by means of a small-footprint laser scanner over young and mature forest stands using regression analysis. The sample plots were distributed systematically throughout a 6500 ha study area, and the size of each plot was 232.9 m². Regressions for coniferous forest explained 60–97% of the variability in ground reference values of the six studied characteristics. A proposed practical two-phase procedure for prediction of corresponding characteristics of entire forest stands was tested. Fifty-seven test plots within the study area with a size of approximately 3740 m² each were divided into 232.9 m² regular grid cells. The six examined characteristics were predicted for each grid cell from the corresponding laser data using the estimated regression equations. Average values for each test plot were computed and compared with ground-based estimates measured over the entire plot. The bias and standard deviations of the differences between predicted and ground reference values (in parentheses) of mean height, dominant height, mean diameter, stem number, basal area and volume were ?0.58 to ?0.85 m (0.64–1.01 m), ?0.60 to ?0.99 m (0.67–0.84 m), 0.15–0.74 cm (1.33–2.42 cm), 34–108 ha^?1 (97–466 ha^?1), 0.43–2.51 m² ha^?1 (1.83–3.94 m² ha^?1) and 5.9–16.1 m³ ha^?1 (15.1–35.1 m³ ha^?1), respectively.     

基于三维激光扫描的单木胸径和树高提取

为了提高林分尺度下单木参数的识别精度,研究了基于三维激光扫描的单木胸径和树高的辨识方法。在东北林业大学实验林场,采用Trimble S60三维激光扫描仪,对104株蒙古栎进行多测站扫描,获得样本树的点云数据。在对点云数据进行配准、去噪、地形数据提取、切片栅格化等一系列处理基础上,基于霍夫变换和连续生长法分别构建了胸径和树高的提取方法,对林分尺度下单木定位识别、胸径和树高提取精度进行了对比分析。研究结果表明:所构建方法单木定位识别精度均值为87.50%,胸径和树高提取的均方根误差分别为2.88 cm、2.61 m。     

Prediction of tree height,basal area and stem volume in forest stands using airborne laser scanning

The aim of this study was to develop prediction models using laser scanning for estimation of forest variables at plot level, validate the estimations at stand level (area 0.64 ha) and test the effect of different laser measurement densities on the estimation errors. The predictions were validated using 29 forest stands (80×80 m²), each containing 16 field plots with a 10 m radius. For the best tested case, mean tree height, basal area and stem volume were predicted with a root mean square error of 0.59 m (3% of average value), 2.7 m² ha^?1 (10% of average value) and 31 m³ ha^?1 (11% of average value), respectively, at stand level. There were small differences in terms of prediction errors for different measuring densities. The results indicate that mean tree height, basal area and stem volume can be estimated in small stands with low laser measurement densities producing accuracies similar to traditional field inventories.     

Mapping and modeling forest tree volume using forest inventory and airborne laser scanning

In this paper, we present a study on the efficiency of multi-return LIDAR (Light Detection Ranging) data in the estimation of forest stem volume over a multi-layered forest area in the Italian Alps. The goals of this paper are (1) to verify the usefulness of multi-return LIDAR data compared to single-return data in forest volume estimation and (2) to define the optimal resolution of a stem volume distribution raster map over the investigated area. To achieve these goals, raw data were segmented into a net, and different cell dimensions were investigated to maximize the relationship between the LIDAR data and the ground-truth information. Twenty predicting variables (e.g., mean height, coefficient of variation) have been extracted from multi-return LIDAR data, and a multiple linear regression analysis has been used for predicting tree stem volume. Experimental results found that the optimal resolutions of the net square cells were 40 m. The analysis indicated that in a mixed multi-layered forest, characterized by a complex vertical structure, the correct selection of the map spatial resolution and the inclusion of the secondary-return data were important factors for improving the effectiveness of the laser scanning approach in forest inventories. The experimental tests showed that the chosen model is effective for the estimation of stem volume over the analyzed area, providing good results on all the three considered validation methods.     

Predicting stand-thinning maturity from airborne laser scanning data

Abstract

Airborne laser scanning (ALS) has been used in recent years to acquire accurate remote-sensing material for carrying out practical forest inventories. Still, much of the information needed in forest management planning must be collected in the field. For example, forest management proposals are often determined in the field by an expert. In the present study, statistical features extracted from ALS data were used in logistic regression models and in nonparametric k-MSN estimation to predict the thinning maturity of stands. The research material consisted of 381 treewise measured circular plots in young and advanced thinning stands from the vicinity of Evo, in southern Finland. Timing of thinning was determined in the field by an expert and coded as a binary variable. Models were developed (1) to locate stands that will reach thinning maturity within the next 10-year period and (2) for stands in which thinning should be done immediately. For comparison purposes, logistic regression models were formulated from accurately field-measured stand characteristics. Logistic regression models based on ALS features predicted the thinning maturity with a classification accuracy of 79% (1) and 83% (2). The respective percentages were 66% and 83% with models based on field-measured stand characteristics and 70% and 86% with k-MSN. The study showed that ALS data can be used to predict stand-thinning maturity in a practical way.     

Crown plasticity in mixed forests—Quantifying asymmetry as a measure of competition using terrestrial laser scanning

Interspecific competition is a key process determining the dynamics of mixed forest stands and influencing the yield of multispecies tree plantations. Trees can respond to competitive pressure from neighbors by crown plasticity, thereby avoiding competition. We employed a high-resolution ground-based laser scanner to analyze the 3-dimensional extensions and shape of the tree crowns in a near-natural broad-leaved mixed forest in order to quantify the direction and degree of crown asymmetry of 15 trees (Fagus sylvatica, Fraxinus excelsior, Carpinus betulus) in detail. We also scanned the direct neighbors and analyzed the distance of their crown centres and the crown shape with the aim to predict the crown asymmetry of the focal tree from competition-relevant attributes of its neighbors. It was found that the combination of two parameters, one summarizing the size of the neighbor (DBH) and one describing the distance to the neighbor tree (HD), was most suitable for characterizing the strength of the competitive interaction exerted on a target tree by a given neighbor. By summing up the virtual competitive pressure of all neighbors in a single competitive pressure vector, we were able to predict the direction of crown asymmetry of the focal tree with an accuracy of 96° on the full circle (360°).The competitive pressure model was equally applicable to beech, ash and hornbeam trees and may generate valuable insight into competitive interactions among tree crowns in mixed stands, provided that sufficiently precise data on the shape and position of the tree crowns is available. Multiple-aspect laser-scanning proved to be an accurate and practicable approach for analyzing the complex 3-dimensional shape of the tree crowns, needed to quantify the plasticity of growth processes in the canopy. We conclude that the laser-based analysis of crown plasticity offers the opportunity to achieve a better understanding of the dynamics of canopy space exploration and also may produce valuable advice for the silvicultural management of mixed stands.     

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.     

Identification of boreal forest stands with high herbaceous plant diversity using airborne laser scanning

Boreal forest stands with high herbaceous plant species diversity have been found to be one of the main habitats for many endangered species, but the locations and sizes of these herb-rich forest stands are not well known in many areas. Better identification of the stands could improve both their conservation and management. A new approach is proposed here for locating the mature herb-rich forest stands using airborne laser scanner (ALS) data and logistic regression, or the k-NN classifier. We show that ALS technology is capable of distinguishing the ecologically important herb-rich forests from those growing on less fertile site types, mainly on the basis of unique but quantifiable crown structure and vertical profile that characterise forests on high fertility sites. The study site, Koli National Park, is located on the border of the southern and middle boreal vegetation zones in Finland, and includes 63 herb-rich forest stands of varying sizes. The model and test data comprised 274 forest stands belonging to five forest site types varying from very fertile to poor. The best overall classification accuracy achieved with the k-NN method was 88.9%, the herb-rich forests being classified correctly in 65.0% of cases and the other forest site types in 95.7%. The best overall classification accuracy achieved with logistic regression was 85.6%, being 55.0% for the herb-rich forests and 94.3% for the other forest site types. Both methods demonstrated promising potential for separating herb-rich forests from other forest site types, although slightly better results were obtained with the non-parametric k-NN method, which was capable of utilising a higher number of explanatory variables. It is concluded that ALS-based data analysis techniques are applicable to the detection of mature boreal herb-rich forests in large-scale forest inventories.     

Assessment of standing wood and fiber quality using ground and airborne laser scanning: A review

Accurate information on the wood-quality characteristics of standing timber and logs is needed to optimize the forest production value chain and to assess the potential of forest resources to meet other services. Physical and chemical characteristics of wood vary with both tree and site characteristics. At the tree scale, crown development, stem shape and taper, branch size and branch location, knot size, type and placement, and age all influence wood properties. More broadly, at the stand level, stocking density, moisture, nutrient availability, climate, competition, disturbance, and stand age have also been identified as key determinants of wood quality. Such information is often captured in polygon based forest inventory data. Other terrain-related spatial information, such as elevation, slope and aspect, can improve assessments of site conditions and limitations upon plant growth which impact wood quality. Light Detection And Ranging (LiDAR) is an emerging technology, which directly measures the three-dimensional structure of forest canopies using ground or airborne laser instruments, and can provide highly accurate information on individual-tree and stand-level forest structure. In this paper, we explore the potential of LiDAR and other geospatial information sources to model and predict wood quality based on individual-tree and stand structural metrics. We identify a number of key wood quality attributes (i.e., basic wood density, cell perimeter, cell coarseness, fiber length, and microfibril angle) and demonstrate links between these properties and forest structure and site attributes. Finally, the potential for using LiDAR in combination with other geospatial information sources to predict wood quality in standing timber is discussed.     

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.     

Estimating single-tree branch biomass of Norway spruce with terrestrial laser scanning using voxel-based and crown dimension features

Abstract

Many remote sensing-based methods estimating forest biomass rely on allometric biomass models for field reference data. Terrestrial laser scanning (TLS) has emerged as a tool for detailed data collection in forestry applications, and the methods have been proposed to derive, e.g. tree position, diameter-at-breast-height, and stem volume from TLS data. In this study, TLS-derived features were related to destructively sampled branch biomass of Norway spruce at the single-tree level, and the results were compared to conventional allometric models with field measured diameter and height. TLS features were derived following two approaches: one voxel-based approach with a detailed analysis of the interaction between individual voxels and each laser beam. The features were derived using voxels of size 0.1, 0.2, and 0.4 m, and the effect of the voxel size was assessed. The voxel-derived features were compared to features derived from crown dimension measurements in the unified TLS point cloud data. TLS-derived variables were used in regression models, and prediction accuracies were assessed through a Monte Carlo cross-validation procedure. The model based on 0.4 m voxel data yielded the best prediction accuracy, with a root mean square error (RMSE) of 32%. The accuracy was found to decrease with an increase in voxel size, i.e. the model based on the 0.1 m voxel yielded the lowest accuracy. The model based on crown measurements had an RMSE of 34%. The accuracies of the predictions from the TLS-based models were found to be higher than from conventional allometric models, but the improvement was relatively small.     

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.     

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.     

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.     

A comparison of different methods for forest resource estimation using information from airborne laser scanning and CIR orthophotos

This article compares three methods for forest resource estimation based on remote sensing features extracted from Airborne laser scanning and CIR orthophotos. The estimation was made exemplarily for the total stem volume of trees for a given area, measured in cubic metres per hectare [m3 ha⁻¹] (as one of the most important quantitative parameters to characterise a forest stand). The following methods were compared: Regression Analysis (RA), k-NN (nearest neighbour) method and a method that utilises regional yield tables, referred to as the yield table method (YT-method). The estimation of stem volume was examined in a mixed forest in Southern Germany using 300 circular inventory plots, each with a size of 452 m2. Remote sensing features relating to vegetation height and structures were extracted and used as input variables in the different approaches. The accuracy of the estimation was analysed using scatter plots and quantified using absolute and relative root mean square errors (RMSE). The comparison was made for all plots, as well as for averaged plot values located within forest stands that have the same age class. On “plot level” the RMSE yielded 79.79 m3 ha⁻¹ (RA), 81.93 m3 ha⁻¹ (k-NN) and 81.78 m3 ha⁻¹ (YT-method) and for the averaged values 35.75 m3 ha⁻¹ (RA), 35.06 m3 ha⁻¹ (k-NN) and 42.98 m3 ha⁻¹ (YT-method). Advantages and disadvantages, as well as requirements, of the methods are discussed.     

基于地基激光数据的杨树干形分析

利用地基激光扫描技术采集泗洪陈圩林场36块样地1927株杨树的点云数据,通过软件RiSCANPro对数据进行预处理。用K均值聚类对点云数据进行林木定位分割,并提取了树木胸径、树高等测树因子,通过公式计算出4种干形参数(胸高形数、形率、高径比和形高),用SPSS软件基于4种不同造林密度由高到低(株行距配置3m×8m、5m×5m、4.5m×8m、6m×6m)对4种干形参数进行差异分析,还对干形参数进行了模型研建,主要研究结果如下:1)不同造林密度对胸高形数、形率、高径比、形高有显著影响。胸高形数、形率和高径比随密度的增大而增大,形高则随密度增大而减小,即高密度林分的树干干形更为饱满,低密度树干干形尖削。2)指数方程是胸高形数与形率关系的最优模型,适用性检验精度高,达到99.12%,模型表达式为:f1.3=0.223*exp(1.025*q2),可应用于胸高形数的预测,为分析杨树干形参数提供参考。     

Estimation of forest stem volume using multispectral optical satellite and tree height data in combination

Abstract

The accuracy of forest stem volume estimation at stand level was investigated using multispectral optical satellite and tree height data in combination. The stem volumes for the investigated coniferous stands, located in southern Sweden, were in the range of 15–585 m³ ha^?1 with an average stem volume of 266 m³ ha^?1. The results from regression analysis showed a substantial improvement for the combined stem volume estimates compared with using satellite data only. The accuracy in terms of root mean square error (RMSE) was calculated to 11.2% of the average stem volume using SPOT-4 data and tree height data in combination compared with 23.9% using SPOT-4 data only. By replacing SPOT-4 data with Landsat TM data the RMSE was improved from 25.2% to 12.2%. In addition, a sensitivity analysis was performed on the combined stem volume estimates by adding random errors, normally distributed with zero expectations, with standard deviations of 1, 1.5 and 2 m to tree height data. The results showed that the RMSE increased with increasing random tree height error to 15.4%, 18.0% and 19.9% using SPOT-4 data and 16.3%, 19.2% and 21.2% using Landsat TM data. The results imply that multispectral optical satellite data in combination with accurate tree height data could be used for standwise stem volume estimation in forestry applications.     

Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators

A conceptual model describing why laser height metrics derived from airborne discrete return laser scanner data are highly correlated with above ground biomass is proposed. Following from this conceptual model, the concept of canopy-based quantile estimators of above ground forest biomass is introduced and applied to an uneven-aged, mature to overmature, tolerant hardwood forest. Results from using the 0th, 25th, 50th, 75th and 100th percentiles of the distributions of laser canopy heights to estimate above ground biomass are reported. A comparison of the five models for each dependent variable group did not reveal any overt differences between models with respect to their predictive capabilities. The coefficient of determination (r ² ) for each model is greater than 0.80 and any two models may differ at most by up to 9%. Differences in root-mean-square error (RMSE) between models for above ground total, stem wood, stem bark, live branch and foliage biomass were 8.1, 5.1, 2.9, 2.1 and 1.1 Mg ha^?1, respectively.