Matti Maltamo

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.

Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forests

Experiences from Nordic countries and Canada have shown that the retrieval of the stem volume and mean tree height of a tree or at stand level from laser scanner data performs as well as, or better than, photogrammetric methods, and better than other remote sensing methods. This paper reviews the methods of small‐footprint airborne laser scanning for extracting forest inventory data, mainly in the boreal forest zone. The methods are divided into the following categories: extraction of terrain and canopy height model; feature extraction approaches (canopy height distribution and individual‐tree‐based techniques, techniques based on the synergetic use of aerial images and lidar, and other new approaches); tree species classification and forest growth using laser scanner; and the use of intensity and waveform data in forest information extraction. Despite this, the focus is on methods, some review of quality obtained, especially in the boreal forest area, is included. Several recommendations for future research are given to foster the methodology development.

Comparison of percentile based prediction methods and the Weibull distribution in describing the diameter distribution of heterogeneous Scots pine stands.

Abstract The goal of this study was to compare percentile based distribution methods and the Weibull distribution method in predicting the stand characteristics of forests with great variability in their diameter distribution. Stand structure characteristics were compared between thinned and unthinned stands dominated by Scots pine. The thinned forests were located in eastern Finland, while the unthinned natural forests were located in Republic of Karelia and Leningrad district, Russian Federation. Each data sets included 49 stands. The diameter distributions were more heterogeneous in the unthinned stands. Most of the thinned stands formed unimodal distributions. Among the unthinned stands, decreasing, multi-modal and irregular forms of diameter distributions were also found. In these data, percentile based distribution methods proved to be considerably more effective in predicting the diameter distribution than the Weibull distribution method. With the percentile based distribution method it was also possible to reproduce considerably varying shapes of diameter distributions.

Comparison of beta and weibull functions for modelling basal area diameter distribution in stands of pinus sylvestris and picea abies

The purpose of this study was to compare beta and Weibull distributions in describing basal area diameter distributions in stands dominated by Scots pine and Norway spruce. The material of the study consisted of 535 stands located in eastern Finland. Parameters for both two‐ and three‐parameter approaches of the Weibull distribution were estimated using the method of maximum likelihood. Models for these parameters were derived using regression analysis. For the beta distribution, regression models were formed for the minimum, maximum and standard deviation of diameters within individual stands. These models were used when the exponents of the beta distribution were calculated analytically. Also, some parameter models for beta and Weibull distributions from previous studies were compared with the measured diameter distributions. The distributions obtained were compared using diameter sums and an estimate of the proportion of sawtimber. The results did not reveal any major differences between the suitabilit...

Change detection techniques for canopy height growth measurements using airborne laser scanner data

This paper analyzes the potential of airborne laser scanner data for measuring individual tree height growth in a boreal forest using 82 sample trees of Scots pine. Point clouds (10 points/m 2 , beam size 40 cm) illuminating 50 percent of the treetops were acquired in September 1998 and May 2003 with the Toposys 83 kHz lidar system. The reference height and height growth of pines were measured with a tacheometer in the field. Three different types of features were extracted from the point clouds representing each tree; they were the difference between the highest z values, the difference between the DSMs of the tree crown, and the differences between the 85 th , 90 th and 95 th percentiles of the canopy height histograms corresponding to the crown. The best correspondence with the field measurements was achieved with an R 2 value of 0.68 and a RMSE of 43 cm. The results indicate that it is possible to measure the growth of an individual tree with multi-temporal laser surveys. We also demonstrated a new algorithm for tree-to-tree matching. It is needed in operational growth estimation based on individual trees, especially in dense spruce forests. The method is based on minimizing the distances between treetops in the Ndimensional data space. The experiments showed that the use of the location (derived from laser data) and height of the trees were together adequate to provide reliable tree-totree matching. In the future, a fourth dimension (the crown area) should also be included in the matching.

Forestry Applications of Airborne Laser Scanning

Airborne laser scanning (ALS) has emerged as one of the most promising remote sensing technologies to provide data for research and operational applications in a wide range of disciplines related to management of forest ecosystems. This chapter starts with a brief historical overview of the early forest-related research on airborne Light Detection and Ranging which was first mentioned in the literature in the mid-1960s. The early applications of ALS in the mid-1990s are also reviewed. The two fundamental approaches to use of ALS in forestry applications are presented – the area-based approach and the single-tree approach. Many of the remaining chapters rest upon this basic description of these two approaches. Finally, a brief introduction to the broad range of forestry applications of ALS is given and references are provided to individual chapters that treat the different topics in more depth. Most chapters include detailed reviews of previous research and the state-ofthe-art in the various topic areas. Thus, this book provides a unique collection of in-depth reviews and overviews of the research and application of ALS in a broad range of forest-related disciplines. J. Vauhkonen ( ) Department of Forest Sciences, University of Helsinki, Helsinki, Finland e-mail: jari.vauhkonen@helsinki.fi M. Maltamo School of Forest Sciences, University of Eastern Finland, Joensuu, Finland e-mail: Matti.maltamo@uef.fi R.E. McRoberts Northern Research Station, U. S. Forest Service, Saint Paul, MN, USA E. Næsset Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, Ås, Akershus, Norway e-mail: Erik.naesset@umb.no M. Maltamo et al. (eds.), Forestry Applications of Airborne Laser Scanning: Concepts and Case Studies, Managing Forest Ecosystems 27, DOI 10.1007/978-94-017-8663-8__1,

Forest stand characteristics estimation using a most similar neighbor approach and image spatial structure information

Abstract In this study, information on variograms is integrated with image interpretation of standwise volume of tree stock by using a nonparametric method based on a distance-weighted mean of most similar neighbors (MSN). The usability of the various indicator attributes, including image pixel characteristics and features derived from a variogram curve, in the interpretation is studied by analyzing the accuracy of the results at the compartment level. The accuracy of the volume estimation at the stand level was improved when the empirical variogram values were included in the set of indicator attributes in the MSN analysis. With this kind of MSN analysis, the chosen k nearest neighbors have a similar spatial variation structure in the image material as in the target stand. It was found that increasing the number of similar neighbors beyond 3 did not improve the accuracy. When the variogram information was included in the indicator attribute set, the root mean square error (RMSE) of volume estimate was at its lowest at 18% and the bias was then −0.6%. When the variogram information was not used, the RMSE was at its lowest at 24–27% and the bias was 0.2–1.8%, depending on the number of indicator variables used.

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.

Determination of the spatial distribution of trees from digital aerial photographs

Abstract This study examined the possibilities of using computerized digital aerial photograph interpretation in determining the spatial distribution of trees. The material of the study included eight mapped stands in the municipality of Hyytiala (61°50′N and 24°18′E), in southern Finland. The aerial photographs used were taken in June 1995 at a scale of 1:5000. Two approaches for determining the spatial pattern of trees were used. Firstly, in the point-process based approach used in this study, the individual trees in the digital aerial photograph were segmented by a robust segmentation method, based on recognition of the pattern of tree crowns with sub-pixel accuracy. Secondly, the crown coverage was determined by region growing segmentation combined with active surface representation. The significance of the differences in the means of image coverage pattern indices between the various spatial distribution categories was tested with one-way variance analysis. Because the process misclassified clustered spatial patterns as regular patterns, and regular patterns as random patterns, the usability of digital aerial photographs seems to be limited for the point-process based determination of the spatial pattern of trees if the scale is 1:5000 or less. When image coverage pattern indices were applied, the differences in the means of the spatial distribution categories proved not to be clearly statistically significant due to the great variation within classes. However, interpretation of crown coverage could have applications in practical forestry due to the low resolution requirements for the images used.