Petteri Packalen

Combining ALS and NFI training data for forest management planning: a case study in Kuortane, Western Finland.

Forest inventories based on airborne laser scanning (ALS) have already become common practice in the Nordic countries. One possibility for improving their cost effectiveness is to use existing field data sets as training data. One alternative in Finland would be the use of National Forest Inventory (NFI) sample plots, which are truncated angle count (relascope) plots. This possibility is tested here by using a training data set based on measurements similar to the Finnish NFI. Tree species-specific stand attributes were predicted by the non-parametric k most similar neighbour (k-MSN) approach, utilising both ALS and aerial photograph data. The stand attributes considered were volume, basal area, stem number, mean age of the tree stock, diameter and height of the basal area median tree, determined separately for Scots pine, Norway spruce and deciduous trees. The results obtained were compared with those obtained when using training data based on observations from fixed area plots with the same centre point location as the NFI plots. The results indicated that the accuracy of the estimates of stand attributes derived by using NFI training data was close to that of the fixed area plot training data but that the NFI sampling scheme and the georeferencing of the plots can cause problems in practical applications.

A Two Stage Method to Estimate Species-specific Growing Stock

Information about tree species-specific forest characteristics is often a compulsory requirement of the forest inventory system. In Finland, the use of a combination of ALS data and orthorectified aerial photographs has been studied previously, but there are some weaknesses in this approach. First, aerial photographs need radiometric correction, and second, the ALS points and aerial photographs are not properly fused due to the radial displacement. In this study, ALS points are linked to unrectified aerial photographs of known orientation parameters, which enables better fusion. Each ALS point is mapped to several aerial photographs, and the average of DN values is utilized; this averaging is considered to be a good substitute for radiometric correction. The new two-stage method is compared to the approach in which only ALS data is used. The results show the benefits of using aerial photographs together with ALS data in order to estimate tree species-specific characteristics. Compared to earlier studies, the new two-stage method shows a considerable improvement in applicability in operational use.

Non-parametric prediction of diameter distributions using airborne laser scanner data

Abstract The aim of this study was to apply the non-parametric k-most similar neighbour (MSN) method and airborne laser scanner data to predict stand diameter distributions in a 960 km2 forest district in south-eastern Norway. The specific objectives of the study were (1) to examine the use of different dependent and independent variables in the canonical correlation analysis of MSN, and (2) to examine the influence of reduced number of training data plots by means of simulations. The reliability of the constructed diameter distributions was analysed using error indices and the accuracy of stand attributes derived from predicted diameter distributions. The study material included a total of 201 plots and they were reduced to 181, 161, … , 41 plots in the simulations. The results indicated that when selecting dependent variables in the canonical correlation analysis it is sufficient to have variables reflecting stand means and aggregated variables (sums) to obtain accurate predictions of diameter distributions. Furthermore, the prediction models should not to be too detailed, i.e. they should not include a great number of independent variables since cross-validation always tends to give too optimistic results. Validation on independent data will often show considerably poorer reliability figures. Finally, the results indicated that even such a low number of training plots as about 100 can produce accurate enough predictions of stand attributes and diameter distributions.

Effects of pulse density on predicting characteristics of individual trees of Scandinavian commercial species using alpha shape metrics based on airborne laser scanning data

Operational pulse density affects the measurements based on airborne laser scanning (ALS) data, especially at the individual tree level. The minimum density required depends on the interpretation methodology used, i.e., knowing the requirements is a prerequisite for a successful ALS data acquisition. We evaluate these requirements for a recently introduced alpha shape metrics approach in which computational volume and complexity metrics derived from ALS point clouds are utilized to produce actual tree-level characteristics. We simulated thinnings to the ALS return data using a test dataset of a total of 92 dominant or codominant trees detected and delineated manually from very high density (approximately 40 returns/m2) initial ALS data and produced species and diameter at breast height estimates with the thinned datasets. We compared the alpha shape metrics approach with alternative methods, making additional use of tree-level ALS data, and examined the sensitivity of the different methods to pulse density. The results show that in addition to the species classification possibilities recognized earlier, alpha shape metrics computed from very high density ALS data are also useful for predicting tree dimensions. Upon analysing the thinned data, the alpha shape metrics were generally discovered to suffer most from a lower pulse density. On the other hand, tree level canopy height distribution variables appeared to be more neutral for the pulse density and could be used at low density levels to complement and stabilize the alpha shape based methods for predicting both species and diameter. The results indicate that, provided individual trees can be accurately delineated, the species and diameter of mature coniferous trees in particular can be predicted using ALS data, even with a very low pulse density. As the alpha shape metrics performed well at densities that were only moderate for the individual tree delineation approach, more research is suggested to determine their full potential. Additionally, identifying trees automatically using more representative data needs to be examined to generalize the obtained result.

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.

Automatic segmentation of forest stands using a canopy height model and aerial photography

Abstract Forest management planning is based on stand-level information. The stands are typically visually delineated based on aerial photographs. Because of visual interpretation, the estimation of stand boundaries is always subjective. Moreover, there are no definite criteria for the delineation. For one forest area, several different solutions could be obtained. Automated segmentation of digital imagery offers a possible solution to these problems, by producing more objective delineation, reducing time and costs, and increasing consistency of stand delineation. The aim of this study was to evaluate the applicability of a canopy height model (CHM) in automatic segmentation of forest stands. In addition, the mosaic produced with CHM was compared with one that was produced automatically using an aerial photograph (RGB). The usefulness of combining CHM and an aerial photograph in the automatic segmentation process was also examined. The results showed that delineation based on CHM is a good option compared with aerial photographs, when aiming for homogeneity of the delineated stands. The explained proportion of the whole variability of mean diameter was 74% and of mean height 83% in the CHM mosaic, compared with 65% and 73% in the RGB mosaic, and 60% and 73% in the reference. However, a visual interpreter was able to produce delineation almost as homogeneous as the reference with respect to volume, and better with respect to mean diameter and height.

ALS-based estimation of plot volume and site index in a eucalyptus plantation with a nonlinear mixed-effect model that accounts for the clone effect

IntroductionMost airborne laser scanning (ALS) studies have been carried out in semi-natural forests, but some research has also been carried out in plantations. Results indicate that methods similar to those which are used in semi-natural forest are also usable in plantation forestry. The study was conducted in a pulpwood plantation growing Eucalyptus urograndis in Bahia State, Brazil.ObjectivesThe aims of this study are to investigate (1) how accurately the plot volume may be estimated by ALS data in eucalyptus plantations and (2) how to estimate the site index directly by combining ALS data and stand age. The plot volume and site index were estimated by means of nonlinear mixed-effect modeling in order to take into account the stand-within-clone hierarchy of the data.ResultsThe obtained accuracies are quite good if compared to those obtained in semi-natural forests. The root-mean-square error was 8.2% for plot volume and 2.7% for site index when the clone effect was used in prediction.ConclusionsPrecision forestry applied in plantations differs in many ways from the forestry practiced in a semi-natural environment. ALS-based forest inventory methods have a great deal of potential in pulpwood plantations when the unique features of plantation forestry are taken into account.

Species-Specific Management Inventory in Finland

A new remote sensing based stand management inventory system was developed and adopted to operational forestry in Finland during the years 2005–2010. The inventory is based on wall-to-wall mapping of the inventory area. The outcome of the inventory is species-specific stand attributes which are estimated with the help of ALS, aerial images and field sample plots. The new inventory system has been successful and within a few years all the actors of the practical forestry have updated their inventory and planning systems to support the new method. The new inventory system is now applied for almost 3,000,000 ha annually. This chapter presents the main properties of the system.

Using airborne laser scanning data for detecting canopy gaps and their understory type in mature boreal forest

Abstract• IntroductionCanopy gap dynamics in old-growth boreal forests is a result of tree mortality caused by insects, diseases, or meteorological phenomena. Canopy gaps improve the possibilities of natural regeneration, and concentrations of decomposed deadwood are often found in these natural openings, which provide specific habitats for many deadwood-dependent species and organisms.• MethodsDetailed monitoring setups for canopy gaps have been difficult to organize because of the expense of conventional field inventory techniques. Using three-dimensional airborne laser scanning (ALS), canopy gaps can be detected and analyzed even over large sample areas.• ResultsIn this study, we show how differences between the canopy gaps of seminatural and managed forests can be determined and how canopy gaps can be categorized using ALS data because the ALS characteristics reflect the variation of vertical structure due to different vegetation or deadwood layers in the canopy gaps.• ConclusionThe study show promising results on the applicability of ALS data for the automatic identification of canopy gap types and detection of indirect indicator characteristics usable for assessing the naturalness of boreal forests. Moreover, our method bases on the vertical distribution of laser pulses characterizing the vegetation layer, and it can therefore be applied to other vegetation zones where the ALS is applicable.

Neural Networks for the Prediction of Species-Specific Plot Volumes Using Airborne Laser Scanning and Aerial Photographs

Parametric and nonparametric modeling methods have been widely used for the estimation of forest attributes from airborne laser-scanning data and aerial photographs. However, the methods adopted suffered from complex remote-sensed data structures involving high dimensions, nonlinear relationships, different statistical distributions, and outliers. In this context, artificial neural networks (ANNs) are of interest as they have many clear benefits over conventional modeling methods and could then enhance the accuracy of current forest-inventory methods. This paper examines the ability of common ANN modeling techniques for the prediction of species-specific forest attributes, as exemplified here with the prediction stem volumes (cubic meters per hectare) at the field plot and forest stand levels. Three modeling methods were evaluated, namely, the multilayer perceptron (MLP), support vector regression (SVR), and self-organizing map, and intercompared with the corresponding nonparametric k most similar neighbor method using cross-validated statistical performance indexes. To decrease the number of model-input variables, a multiobjective input-selection method based on genetic algorithm is adopted. The numerical results obtained in the study suggest that ANNs are appropriate and accurate methods for the assessment of species-specific forest attributes, which can be used as alternatives to multivariate linear regression and nonparametric nearest neighbor models. Among the ANN models, SVR and MLP provide the best choices for prediction purposes as they yielded high prediction accuracies for species-specific tree volumes throughout.