Effect of tree-level airborne laser-scanning measurement accuracy on the timing and expected value of harvest decisions

The objective was to compare tree-level airborne laser-scanning (ALS) data accuracy with standwise estimation data accuracy as input data for forest planning, using tree- and stand-level simulators. The influence of the input data accuracy was studied with respect to (1) timing of the next thinning or clear-cutting and (2) the relative variation in the predicted income of the next logging expressed as the net present value (NPV). The timing and predicted NPV of thinning and clear-cutting operations were considered separately. The research was based on Monte Carlo simulations carried out with the tree- and stand-level simulators using a simulation and optimisation (SIMO) framework. The simulations used treewise measurements taken on 270 circular plots measured at the Evo Field Station, Finland, as input data. Deviations in the tree data measured were generated according to the mean standard errors found in standwise field estimation and tree-level ALS. The accuracy factors of ALS individual tree detection were based on the EUROSDR/ISPRS Tree Extraction Project. The results show that input data accuracy significantly affects both the timing and relative NPV of loggings. Tree-level ALS produces more accurate simulation results than standwise estimation with the error levels assumed. Diameter-based characteristics are the most important input data in all simulations. Accurate tree height estimates cannot be fully utilised in current simulators.     

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.     

Airborne laser scanning-based decision support for wood procurement planning

We present a decision support tool for guiding the selection of marked stands based on airborne laser scanning (ALS) data. We describe three stages, namely (1) wall-to-wall mapping of the stands matured for cutting using low-density ALS data; (2) tree-level inventory of these stands using high-density ALS data and (3) theoretical bucking of the imputed tree stems to produce detailed information on their characteristics. We tested them in a Scots pine dominated boreal forest area in Eastern Finland, where 79 sample plots were measured in the field. The detection of the stands matured for cutting had a success rate of 95% and our results demonstrated a further potential to limit the result towards stands dominated by certain species by means of intensity values derived from the low-density ALS data. The applied single-tree detection and estimation chain produced detailed tree-level information and realistic diameter distributions, yet the detection was highly emphasised on the dominant tree layer. The error levels in the estimates were generally less than standard deviations of the field attributes. Finally, plot-level accumulations of saw-log volumes were found rather similar, whether the input was based on the imputed tree data or trees measured in the field. The results are considered useful for ranking the stands based on their properties, whether the aim in the wood procurement is to focus on certain species or to select stands suitable for production needs.     

河北省主要树种单木和林分生长率模型研建

利用第六次至第九次全国森林资源清查河北省2001,2006,2011,2016年4个年度的固定样地调查数据,采用非线性回归估计方法,建立了18个树种组的单木胸径生长率和材积生长率模型,以及12个树种组的林分材积生长率模型。结果表明,单木生长率模型的平均预估误差(MPE)基本都在3%以内,而平均百分标准误差(MPSE)、胸径生长率模型大都在10%以内,材积生长率模型大都在20%左右;林分生长率模型的平均预估误差(MPE)基本都在5%以内,平均百分标准误差(MPSE)大都在25%以内。所建模型可为河北省开展森林资源年度更新提供技术支撑。     

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。     

Laser scanning of forest resources: the nordic experience

This article reviews the research and application of airborne laser scanning for forest inventory in Finland, Norway and Sweden. The first experiments with scanning lasers for forest inventory were conducted in 1991 using the FLASH system, a full-waveform experimental laser developed by the Swedish Defence Research Institute. In Finland at the same time, the HUTSCAT profiling radar provided experiences that inspired the following laser scanning research. Since 1995, data from commercially operated time-of-flight scanning lasers (e.g. TopEye, Optech ALTM and TopoSys) have been used. Especially in Norway, the main objective has been to develop methods that are directly suited for practical forest inventory at the stand level. Mean tree height, stand volume and basal area have been the most important forest mensurational parameters of interest. Laser data have been related to field training plot measurements using regression techniques, and these relationships have been used to predict corresponding properties in all forest stands in an area. Experiences from Finland, Norway and Sweden show that retrieval of stem volume and mean tree height on a stand level from laser scanner data performs as well as, or better than, photogrammetric methods, and better than other remote sensing methods. Laser scanning is, therefore, now beginning to be used operationally in large-area forest inventories. In Finland and Sweden, research has also been done into the identification of single trees and estimation of single-tree properties, such as tree position, tree height, crown width, stem diameter and tree species. In coniferous stands, up to 90% of the trees represented by stem volume have been correctly identified from canopy height models, and the tree height has been estimated with a root mean square error of around 0.6 m. It is significantly more difficult to identify suppressed trees than dominant trees. Spruce and pine have been discriminated on a single-tree level with 95% accuracy. The application of densely sampled laser scanner data to change detection, such as growth and cutting, has also been demonstrated.     

Simultaneous Estimations of Forest Parameters using Aerial Photograph Interpreted Data and the k Nearest Neighbour Method

Information about the state of the forest is of vital importance in forest management planning. To enable high-precision modelling, many forest planning systems demand input data at the single-tree level. The conventional strategy for collecting such data is a plot-wise field inventory. This is expensive and, thus, cost-efficient alternatives are of interest. During recent years, the focus has been on remote sensing techniques. The k nearest neighbour (kNN) estimation method is a way to assign plot-wise data to all stands in a forest area, using remotely sensed data in connection with a sparse sample of field reference plots. Plot-wise aerial photograph interpretations combined with information from a stand register were used in this study. Nearness to a reference plot was decided upon using a regression transform distance. Standing stem volume was estimated with a relative root mean square error (RMSE) equal to 20% at the stand level, while age could be estimated with a RMSE equal to 15%. A cost-efficient data-capturing strategy could be to assign plot data with the presented kNN method to some types of forest, while using traditional field inventories in other, more valuable, stands.     

Economic utility of 3D remote sensing data for estimation of site index in Nordic commercial forest inventories: a comparison of airborne laser scanning,digital aerial photogrammetry and conventional practices

ABSTRACT

Forest productivity is a crucial variable in forest planning, usually expressed as site index (SI). In Nordic commercial forest inventories, SI is commonly estimated by a combination of aerial image interpretation, field assessment and information obtained from previous inventories. Airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) data can alternatively be used for SI estimation, however the economic utilities of the inventory methods have not been compared. We compared seven methods of SI estimation in a cost-plus-loss analysis, by which we added the expected economic losses due to sub-optimal treatment decisions to the inventory costs. The methods comprised direct and indirect estimation from combinations of ALS, DAP and stand register data, and manual interpretation from aerial imagery supported by field assessment and information from previous inventories (conventional practices). The choice of method had great impact on both the accuracy and the economic value of the produced estimates. Direct methods using bitemporal ALS and DAP data gave the best accuracy and the smallest total cost. DAP was a suitable and low-cost data source for SI estimation. Estimation from single-date ALS and DAP data and age obtained from the stand register provided practical alternatives when applied to even-aged stands.     

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.     

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.     

Evaluation of tree and stand-level growth models using national forest inventory data

Two models, Carbware (CW) and Growfor (GF), of different resolution and based on different frameworks were evaluated in relation to stand-level forecasts of volume and basal area using Ireland’s National Forest Inventory (NFI) data. CW is a distance-independent single-tree model that is based on diameter increment. GF is a stand-level dynamic empirical model that uses the von Bertalanffy–Richards growth equation in a state-space framework. NFI data were used as input to the models, and each model’s projections were compared to NFI data at the next measurement cycle. The NFI is a permanent sampling system with the objective to assess the composition and extent of the forest estate. A subset of the NFI was used in the study, single-species even-aged plots comprising Sitka spruce and lodgepole pine. The accuracy and performance of the CW and GF models were analysed using residual analysis and standard statistical techniques. Results show that both models require improvement, though the study has raised concerns regarding the suitability of the NFI data for this type of investigation.     

Modeling individual tree height growth of Norway spruce and Scots pine from national forest inventory data in Norway

We developed individual tree height growth models for Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.) in Norway based on national forest inventory data. Potential height growth is based on existing dominant height growth models and reduced due to competition by functions developed in this study. Three spatially explicit and two spatially non-explicit competition indices were tested. Distance effects and diameter ratio effects were estimated from the data simultaneously with parameters of the potential modifier functions. Large height measurement errors in the national forest inventory data caused large residual variation of the models. However, the effects of competition on height growth were significant and plausible. The potential modifier functions show that height growth of dominant trees is largely unaffected by competition. Only at higher levels of competition, height growth is reduced as a consequence of competition. However, Scots pine also reduced height growth at very low levels of competition. Distance effects in the spatially explicit competition indices indicated that the closest neighbors are most important for height growth. However, for Scots pine also competitors at larger distance affected height growth. The five competition indices tested in this study explained similar proportions of the variation in relative height growth. Given that unbiased predictions can only be expected for the same plot size, we recommend a spatially explicit index, which describes the distance function with a negative exponential, for use in growth simulators.     

A review of the challenges and opportunities in estimating above ground forest biomass using tree-level models

Accurate biomass measurements and analyses are critical components in quantifying carbon stocks and sequestration rates, assessing potential impacts due to climate change, locating bio-energy processing plants, and mapping and planning fuel treatments. To this end, biomass equations will remain a key component of future carbon measurements and estimation. As researchers in biomass and carbon estimation, we review the present scenario of aboveground biomass estimation, focusing particularly on estimation using tree-level models and identify some cautionary points that we believe will improve the accuracy of biomass and carbon estimates to meet societal needs. In addition, we discuss the critical challenges in developing or calibrating tree biomass models and opportunities for improved biomass. Some of the opportunities to improve biomass estimate include integration of taper and other attributes and combining different data sources. Biomass estimation is a complex process, when possible, we should make use of already available resources such as wood density and forest inventory databases. Combining different data-sets for model development and using independent data-sets for model verification will offer opportunities to improve biomass estimation. Focus should also be made on belowground biomass estimation to accurately estimate the full forest contribution to carbon sequestration. In addition, we suggest developing comprehensive biomass estimation methods that account for differences in site and stand density and improve forest biomass modeling and validation at a range of spatial scales.     

林分动态生长模型的研究

The stand growth and yield dynamic models for Larch in Jilin Province were developed based on the forest growth theories with the forest continuous inventory data. The results indicated that the developed models had high precision, and they could be used for the updating data of inventory of planning and designing and optimal decision of forest management.     

Uncertainty estimation of biomass expansion factors for Norway spruce in the Czech Republic

Nation wide estimates of the changes in forest biomass are needed for the greenhouse gas (GHG) reporting under the Climate Convention. The bases for national GHG reporting concerning forest sector are the national forest inventory (NFI) programmes. Since these programmes were mostly established for monitoring of timber resources, one of the current challenges for the NFIs is the development of methodology, such as biomass expansion factors (BEFs). The methodology for carbon stock change estimation should be transparent and verifiable, but this demand is not currently met due to the fact that the source data and uncertainty in the applied BEFs are not known. Here we developed BEFs with uncertainty estimation applicable to stand wise inventory of Norway spruce forests in the Czech Republic. BEFs were constructed, based on tree wise data from permanent research plots, by applying biomass and volume models to tree-level data. These BEFs were age-dependent and their uncertainty was sensitive to the dependencies among errors. Most of the uncertainty in the BEFs was due to uncertainty in the biomass and volume models applied.     

A statistical thinning model for initialising large-scale ecosystem models

Abstract

Large-scale ecosystem models are important tools for carbon assessment at national scales. Many of these models are not initialised with known field data from any particular time, but simulate the growth of each stand from its estimated germination year up to the present or future. The models will overestimate current-day standing volume or biomass unless historic stand management (biomass removal due to thinning) is taken into account. The full management history of each stand is rarely known, and must be somehow estimated. One possibility is to build statistical thinning models based on data in a National Forest Inventory, which could then be integrated into the ecosystem models. If the harvesting model is constructed using only variables that are also used within the ecosystem model, then the management impacts can be included in the ecosystem model for the entire simulated life of the stand. In the case of most flux dynamics models, this precludes the use of the tree-level data that harvesting models have traditionally relied on. In this article, we develop a novel means to interrogate a subset of the Austrian National Forest Inventory based on deriving probability density functions for particular combinations of stand and site variables. We determine the parameters of a probabilistic model to estimate historic patterns of timber removals and validate it against inventory estimates. Our procedure can establish supportable estimates of historic management regimes suitable as input data for subsequent modelling of national-scale forest carbon stocks, sources and sinks.     

Predicting dynamic modulus of elasticity of Norway spruce structural timber by forest inventory,airborne laser scanning and harvester-derived data

Norway spruce structural timber is one of the most important products of the Norwegian sawmilling industry, and a high grade-yield of structural timber is therefore important for the economic yield. Presorting of logs suited for production of structural timber might be one option to increase the grade yield. In this study, dynamic modulus of elasticity (E_dyn) of structural timber was predicted based on forest inventory data at site level and single-tree data from airborne laser scanning (ALS) and harvester. The models were based on 611 boards from 4 sites in southeastern Norway. Important variables at site level were elevation, site index (SI), and mean stand age. However, when combining data from all information sources, mean stand age and site index were the only significant variables at site level. Tree height and variables describing the crown, like crown length and crown volume, were important vaiables extracted from ALS data. Stem diameter measures and tapering were important variables measured by the harvester. The combined model with variables from all three information sources reduced the variance the most, especially when using individual tree age instead of average stand age. However, combining all these data requires accurate positioning of the trees by the harvester.     

Assessment of bias due to random measurement errors in stem volume growth estimation by the Swedish National Forest Inventory

Abstract

We evaluated the performance of two methods for estimating stem volume increment at individual tree level with respect to bias due to random measurement errors. Here, growth is either predicted as the difference between two consecutive volume estimates where single-tree volume functions are applied to data from repeated measurements or by a regression model that is applied to data from a single survey and includes radial increment. In national forest inventories (NFIs), the first method is typically used for permanent plots, the second for temporary plots. The Swedish NFI combines estimates from both plot types to assess growth at national and regional scales and it is, therefore, important that the two methods provide similar results. The accuracy of these estimates is affected by random measurement errors in the independent variables, which may lead to systematic errors in predicted variables due to model non-linearity. Using Taylor series expansion and empirical data from the Swedish NFI we compared the expected bias in stem volume growth estimates for different diameter classes of Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.). Our results indicate that both methods are fairly insensitive to random measurement errors of the size that occur in the Swedish NFI. The empirical comparison between the two methods showed greater differences for large diameter trees of both pine and spruce. A likely explanation is that the regressions are uncertain because few large trees were available for developing the models.     

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.