This article was downloaded by: [University Library Utrecht]On: 17 March 2013, At: 11:12Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: MortimerHouse, 37-41 Mortimer Street, London W1T 3JH, UK

Scandinavian Journal of Forest ResearchPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/sfor20

Laser scanning of forest resources: the nordicexperienceErik Næsset a , Terje Gobakken a , Johan Holmgren b , Hannu Hyyppä c , Juha Hyyppä d ,Matti Maltamo e , Mats Nilsson b , Håkan Olsson b , Åsa Persson f & Ulf Söderman fa Department of Ecology and Natural Resource Management, NLH, P.O. Box 5003,NO-1432, Ås, Norwayb Department of Forest Resources Management and Geomatics, SLU, SE-90183, Umeå,Swedenc Institute of Photogrammetry and Remote Sensing, Helsinki University of Technology,FI-02015, HUT, Finlandd Department of Remote Sensing and Photogrammetry, Finnish Geodetic Institute,FI-02431, Masala, Finlande Faculty of Forestry, University of Joensuu, Finlandf Department of Laser Systems, Swedish Defence Research Agency, P.O. Box 1165,SE-58111, Linköping, SwedenE-mail:Version of record first published: 02 Sep 2006.

To cite this article: Erik Næsset , Terje Gobakken , Johan Holmgren , Hannu Hyyppä , Juha Hyyppä , Matti Maltamo , MatsNilsson , Håkan Olsson , Åsa Persson & Ulf Söderman (2004): Laser scanning of forest resources: the nordic experience,Scandinavian Journal of Forest Research, 19:6, 482-499

To link to this article: http://dx.doi.org/10.1080/02827580410019553

PLEASE SCROLL DOWN FOR ARTICLE

Full terms and conditions of use: http://www.tandfonline.com/page/terms-and-conditions

This article may be used for research, teaching, and private study purposes. Any substantial or systematicreproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form toanyone is expressly forbidden.

The publisher does not give any warranty express or implied or make any representation that the contentswill be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug dosesshould be independently verified with primary sources. The publisher shall not be liable for any loss, actions,claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly orindirectly in connection with or arising out of the use of this material.

REVIEW ARTICLE

Laser Scanning of Forest Resources: The Nordic Experience

ERIK NÆSSET1, TERJE GOBAKKEN1, JOHAN HOLMGREN2, HANNU HYYPPA3,JUHA HYYPPA4, MATTI MALTAMO5, MATS NILSSON2, HAKAN OLSSON2,ASA PERSSON6 and ULF SODERMAN6

1Department of Ecology and Natural Resource Management, NLH, P.O. Box 5003, NO-1432 As, Norway, 2Department of Forest

Resources Management and Geomatics, SLU, SE-90183 Umea, Sweden, 3Institute of Photogrammetry and Remote Sensing,

Helsinki University of Technology, FI-02015 HUT, Finland, 4Department of Remote Sensing and Photogrammetry, Finnish

Geodetic Institute, FI-02431 Masala, Finland, 5Faculty of Forestry, University of Joensuu, Finland, and 6Department of Laser

Systems, Swedish Defence Research Agency, P.O. Box 1165, SE-58111 Linkoping, Sweden

Næsset, E.1, Gobakken, T.1, Holmgren, J.2, Hyyppa, H.3, Hyyppa, J.4, Maltamo, M.5, Nilsson,

M.2, Olsson, H.2, Persson, A.6 and Soderman, U.6 (1Department of Ecology and Natural

Resource Management, NLH, P.O. Box 5003, NO-1432 As, Norway, 2Department of Forest

Resources Management and Geomatics, SLU, SE-90183 Umea, Sweden, 3Institute of

Photogrammetry and Remote Sensing, Helsinki University of Technology, FI-02015 HUT,

Finland, 4Department of Remote Sensing and Photogrammetry, Finnish Geodetic Institute, FI-

02431 Masala, Finland, 5Faculty of Forestry, University of Joensuu, Finland, and 6Department

of Laser Systems, Swedish Defence Research Agency, P.O. Box 1165, SE-58111 Linkoping,

Sweden). Laser scanning of forest resources: the Nordic experience. Received Mar. 18, 2004.

Accepted Aug. 30, 2004. Scand. J. For. Res. 19: 482�/499, 2004.

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. Key words: Airborne laser scanning, canopy height, forest inventory, timber

volume, tree segmentation.

Correspondence to: E. Næsset, e-mail: erik.naesset@ina.nlh.no

INTRODUCTION

Laser altimetry can determine the distance from the

sensor to a target object using either pulse ranging

systems or continuous wave systems, i.e. the distance is

computed as the product of the speed of light and the

time taken for the light to travel from the laser to the

target object. In pulse ranging systems, the distance is

calculated by travel time of a short duration pulse

transmitted by the laser and received by the sensor,

whereas continuous wave systems use phase change in

Scand. J. For. Res. 19: 482�/499, 2004

# 2004 Taylor & Francis ISSN 0282-7581 DOI: 10.1080/02827580410019553

Dow

nloa

ded

by [

Uni

vers

ity L

ibra

ry U

trec

ht]

at 1

1:12

17

Mar

ch 2

013

a transmitted signal produced by a continuously

emitting laser to calculate the distance (Wehr &

Lohr 1999). In airborne laser scanning systems

(ALS), a scanner which distributes the transmitted

pulses across the flight direction of the platform is

attached to the laser, and such systems can measure

the three-dimensional (3D) position of points on the

ground and in vegetation canopies with an accuracy of

a few decimetres, and with up to 100 000 measure-

ments per second. Today, such systems are commer-

cially operated for terrain mapping, flood risk

mapping, creating three-dimensional (3D) city models

and planning large construction projects, such as

roads and railways. Laser scanning is also one of the

most promising technologies for retrieval of detailed

3D information about tree canopies and other char-

acteristics, such as stem volume, that are essential for

forest resource inventories. An overview of the state of

the art of airborne laser systems in general is presented

by Baltsavias (1999) and Wehr & Lohr (1999), while

Lim et al. (2003) more specifically reviewed forestry

applications.

Research related to airborne laser scanning for

forest inventory has been active in the Nordic coun-

tries for about 15 yrs. The research in Finland,

Norway and Sweden has until now developed quite

independently, with the development and application

of different sensor systems and with somewhat varied

approaches for extracting forestry relevant informa-

tion from laser point data. The development in

Norway has focused on practical procedures for

retrieval of forest data at the stand level, and since

2002 such methods have been commercially marketed

in Norway as an economically defendable method for

large-area forest surveys. The research in Finland and

Sweden has shown a large potential for extracting

accurate single-tree information from laser scanner

measurements. This review article summarizes the

research in the field of laser scanning of forest

resources in Finland, Norway and Sweden. It also

looks briefly at the status of applications of laser

scanning in forestry in the Nordic countries and some

of the challenges to future development are addressed.

EARLY STUDIES AND DEVELOPMENT OF

SYSTEMS

Beginning around 1980, a number of studies concen-

trated on the feasibility of airborne profiling laser

systems for forest inventory. To the authors’ knowl-

edge, the first published study was conducted in the

Soviet Union (Solodukhin et al. 1977). The results

demonstrated that the root mean square error

(RMSE) for laser-estimated tree heights compared

with photogrammetric estimates was 14 cm. The

Soviet study was soon followed by similar studies in

Canada and the USA (Nelson et al. 1984, Aldred &

Bonnor 1985, Maclean & Krabill 1986).

The idea of using laser-measured canopy profiles

for estimation of tree stem volume was repeated in

Sweden in 1989 using a laser mounted on a boom

truck (Nilsson 1994). In 1991, this experiment was

followed by early attempts to use a scanning distance-

measuring laser system for retrieval of forest

parameters (Nilsson 1994, 1996). Three tests were

conducted using the helicopter-borne FLASH system

(Table 1), which was developed by the Swedish

Defence Research Agency (FOA) primarily for real-

time naval applications (Steinvall et al. 1993, 1999,

Steinvall & Koppari 1996). The FLASH system

captured the full waveform of a return from a laser

pulse. The potential to estimate tree height and stem

volumes using the FLASH system was evaluated on

plots with a 10 m radius. The results showed that tree

heights were underestimated by 2.1�/3.7 m. It was also

found that the waveform area (Fig. 1) could be used in

combination with laser-measured tree heights to

estimate stem volume. Such a regression model pre-

dicted the stem volume on a plot-level basis (R2�/

0.78). Nilsson (1997) showed that laser-measured

canopy heights can complement optical satellite data

(i.e. Landsat TM data) when estimating stem volume.

He found that the RMSE for stem volume estimates

on a plot-level basis improved from 56 to 37 m3 ha�1

when using Landsat TM data in combination with

laser-measured canopy heights as opposed to field

measured tree heights.

In Finland, the first canopy profiles were acquired

in the period between 1987 and 1993 using the

helicopter-borne profiling radar HUTSCAT devel-

oped at the Helsinki University of Technology. The

system and profiling forestry applications of the

HUTSCAT are described in detail by Hyyppa

(1993). Even though the algorithms were based on

microwave radar returns, the results were generally

similar to those obtained for full-waveform lasers. The

high sensitivity modification of HUTSCAT using two

antennae of 5 GHz enabled plotwise forest inventory

with standard errors of 6.8%, 19.2% and 20.4% for

mean tree height, basal area and stem volume,

respectively. Stem volume assessment at the stand

Scand. J. For. Res. 19 (2004) Laser scanning of forests in the Nordic countries 483

Dow

nloa

ded

by [

Uni

vers

ity L

ibra

ry U

trec

ht]

at 1

1:12

17

Mar

ch 2

013

level revealed a standard error of 26.5% (Hyyppa

1993). The most significant problem encountered was

the inaccurate georeferencing due to inaccuracies in

the Global Positioning System (GPS) at that time. The

method was not used operationally at the time since

the flight costs of the radar system (1 euro ha�1) were

considered too high for practical forest inventory in

Finland.

Internationally, experimental research systems have

been developed that capture the full return waveform,

e.g. Scanning Lidar Imager of Canopies by Echo

Recovery (SLICER) (Lefsky et al. 1999) and Laser

Vegetation Imaging Sensor (LVIS) (Blair et al. 1999).

However, these full-waveform systems are often re-

ferred to as large-footprint systems, i.e. the spot size

on the ground of the transmitted light is typically 10 m

in diameter or greater. In contrast, all studies carried

out in the Nordic countries have been done with laser

systems having a beam diameter on the ground

ranging from 0.1 to 3.8 m (time-of-flight or small-

footprint systems). Although the full-waveform

FLASH system was used in the early Swedish studies,

all later studies in Finland, Norway and Sweden have

been restricted to time-of-flight systems where data

consist of one or two significant peaks in the returning

pulse, i.e. the ‘‘first return’’ and/or ‘‘last return’’ from

each laser pulse.

A problem with the early studies was the lack of

accuracy in the positioning of the laser measurements.

An important step was made when the movements of

the aircraft and the scanner instrument could be

accurately estimated through the integration of GPS

and Inertial Navigation Systems (INS). With the

integrated GPS and INS systems, the ground position

of a laser beam emitted from an airborne sensor can

most often be determined with an accuracy of 0.5 m or

better. This technical breakthrough has led to the

development of operational measurement systems. In

Sweden, the TopEye system was developed between

1993 and 1995, partly based on experiences with the

FLASH system. Since its development, eight of these

systems have been constructed, and most are still

operated by TopEye AB (http://www.topeye.com). The

system is mounted in a pod under a helicopter. The

first and last return pulses are captured, as well as the

amplitude data for these returns. The beam divergence

is adjustable between 1 and 8 mrad. Since 1999 the

system has also included an integrated digital camera.

TopEye has been used for all forestry tests in Sweden

between 1996 and 2002 and has also been tested in

Finland (Table 1).

In Norway, all forest inventory experiments based

on laser scanner data have used various systems

developed by Optech, Canada (Table 1). The first

studies were conducted in 1995 and, to the authors’

knowledge, are the first attempts to use a scanning

airborne laser to collect data for a large continuous

forest for extraction of forestry-related biophysical

Table 1. Systems used for forestry laser scanning studies in the Nordic countries

System Operator

Year used in

forestry studies

Pulse repetition

frequency (Hz) Platform Country

FLASH FOA, Sweden 1991 160 Helicopter Sweden

ALTM 1020 Fotonor AS, Norway; TopScan

GmbH, Germany

1995 2000 Fixed-wing aircraft Norway

ALTM 1210a Fotonor AS, Norway 1999�/2001 10 000 Fixed-wing aircraft Norway

ALTM 1233a BN Mapping AS, Norway 2003 33 000 Fixed-wing aircraft Norway, Sweden

ALTM 1233a BN Mapping AS, Norway 2003 33 000 Helicopter Norway

TopEye TopEye AB, Sweden 1996�/2003 7000 Helicopter Sweden, Finland

TopoSys-1 FM-Kartta Oy, Finland 1998, 2000 83 000 Fixed-wing aircraft Finland

TopoSys Falcon TopoSys, Germany 2003 83 000 Fixed-wing aircraft Finland

a The ALTM 1210 was upgraded from 10 000 Hz to 33 000 Hz in 2003 and named ALTM 1233. Fotonor was taken over by

BN Mapping in 2003.

Fig. 1. Laser return over a tree measured with the FLASH

system, which captured the full waveform. The shaded part

shows the waveform area for the tree canopy, which was used

in regressions of stem volume (from Nilsson 1996).

484 E. Næsset et al. Scand. J. For. Res. 19 (2004)

Dow

nloa

ded

by [

Uni

vers

ity L

ibra

ry U

trec

ht]

at 1

1:12

17

Mar

ch 2

013

characteristics. The studies in 1995 (Næsset 1997a ,

1997b ) were part of a promotion campaign in Norway

led by the German firm TopScan and the Norwegian

operator Fotonor. An ALTM 1020 system with a pulse

frequency of 2 kHz was used (Table 1). The system was

mounted on a fixed-wing aircraft. In 1999, Fotonor

acquired an ALTM 1210 system with a pulse fre-

quency of 10 kHz. This system was mounted in a

fixed-wing aircraft and used in six different tests

during the period between 1999 and 2001. The system

was upgraded in 2003 to a repetition frequency of

33 kHz (i.e. ALTM 1233), and conducted forestry-

related missions in Norway and Sweden during the

spring and summer of 2003. Typical flying altitudes in

all of these projects have ranged between 500 and

1000 m above ground level, and the pulse density has

been roughly 1 pulse m�2.

The research in Finland has been carried out mostly

with data from the German TopoSys-1 and TopoSys

Falcon systems. The use of TopoSys data started in

1998 within the EC-funded HIGH-SCAN project

entitled ‘‘Assessing forest stand attributes by inte-

grated use of the high-resolution satellite imagery

and laser scanner data’’. The TopoSys system was

selected to guarantee a high sampling density (83 kHz)

enabling single-tree-based solutions, an idea that was

expressed in the mid-1990s. The narrow scan angle

(7.18 at the maximum) is another characteristic of

TopoSys. In TopoSys-1, the first and last pulses had to

be recorded separately, whereas in the TopoSys

Falcon system both modes were integrated, along

with intensity. The campaigns with TopoSys-1 were

carried out in 1998 and 2000 and with the TopoSys

Falcon system in 2003. In all these campaigns,

two different flight altitudes were used, i.e. an

altitude of 400 m above ground giving pulse densities

of 8�/10 pulses m�2 and an altitude of 800 m giving

pulse densities of 4�/5 pulses m�2 for each strip.

However, owing to the overlapping of the laser strips,

the final pulse density is approximately 30�/50% higher

than these densities for each altitude. It should also

be noted that in the TopoSys system, the spacing

of the pulses on the ground is significantly higher in

the alongtrack direction than in the across-track

direction.

INVENTORY OF FOREST STANDS AND

SAMPLE PLOTS

In the Nordic countries, forest stands, typically of 1�/

10 ha in size, are the basic unit in forest management

and planning. Thus, characteristics at the stand level

are requested for strategic planning at the property

level and for tactical and operational planning of

silvicultural treatments and forest operations. Many

forest management planning systems are based on the

use of stand mean values of biophysical variables, such

as site quality, stand age, tree species, tree height,

diameter at breast height, basal area, stem number

and volume, while other systems also need informa-

tion about, for example, the diameter distribution.

In 1995, the first experiments to estimate stand

mean values of biophysical variables using laser data

were carried out in 36 coniferous stands from two

test sites in the eastern part of Norway. The main

objectives were to derive mean tree height weighted

by basal area (Næsset 1997a) and stand volume

(Næsset 1997b ) of individual stands based on laser

scanner data with a pulse density of approximately

0.1 pulses m�2. The footprint diameter was approxi-

mately 13�/16 cm and the maximum scan angle (off

nadir) was 208. Dominant tree species in the two test

sites were Scots pine (Pinus sylvestris ) and Norway

spruce (Picea abies ).

First, the mean tree heights of the test stands

were compared with the average value of all laser

pulses that were classified as canopy returns. It was

revealed that the mean value computed from the laser

data underestimated the ground measurements by

4.1�/5.5 m. A similar tendency of underestimation

has been reported in many other studies. The two

major reasons for this underestimation are (1) that the

light transmitted by the laser will usually penetrate the

outer surface of the tree crowns before a significant

return signal is recorded (Gaveau & Hill 2003), and (2)

that a large portion of the pulses will be reflected from

the lower part of the visible tree crowns.

To eliminate the impact of laser pulses that are

reflected from the lower parts of the canopy, a grid-

based approach was considered. The stands were

divided into regular grids with cells of equal size and

only the maximum laser height value within each cell

was retained. The laser mean height of a stand was

computed as the arithmetic mean of these cell max-

ima. Such computations were carried out for different

cell sizes. For cells 15�/15 m in size, the laser-based

mean height estimates were unbiased and the precision

was fairly high (7.4�/8.1%, Table 2). The grid-based

approach was also tested for estimation of stand

volume. A regression model that predicts stand

volume as a function of the grid-based mean laser

Scand. J. For. Res. 19 (2004) Laser scanning of forests in the Nordic countries 485

Dow

nloa

ded

by [

Uni

vers

ity L

ibra

ry U

trec

ht]

at 1

1:12

17

Mar

ch 2

013

Table 2. Summary of stand-level results

Study Stand variable

No. of

stands

Laser pulses

m�2Mean field-

measured value SD or RMSE (%) Bias R2 (%)

Næsset (1997a ) Mean tree height, pine 18 0.1 17.5 m 7.4 0.4

Næsset (1997a ) Mean tree height, spruce 18 0.1 14.9 m 8.1 0.1 m

Hyyppa & Inkinen (1999) Mean tree height 41 �/10 16.9 m 13.6

Næsset (2002a ) Mean tree height 61 1.2 13.9�/19.8 m 3.7�/6.3 0�/0.4 m

Holmgren (2004) Mean tree height 29 0.1�/4.3 19 m 3

Næsset (2004a ) Mean tree height 57 1.0 15.4�/20.3 m 3.7�/6.4 0.6�/0.9 m

Næsset (2004b ) Mean tree height 56 0.9 14.4�/19.6 m 2.5�/5.8 0.6�/0.8 m

Hyyppa & Inkinen (1999) Basal area 41 �/10 19.4 m2 ha�1 9.6

Næsset (2002a ) Basal area 61 1.2 19.8�/29.7 m2 ha�1 8.6�/11.5 2.3�/2.5 m2 ha�1

Holmgren (2004) Basal area 29 0.1�/4.3 29 m2 ha�1 10�/11

Næsset (2004a ) Basal area 57 1.0 21.1�/29.8 m2 ha�1 8.7�/13.2 0.4�/2.5 m2 ha�1

Næsset (2004b ) Basal area 56 0.9 19.9�/30.2 m2 ha�1 10.5�/12.1 0.7�/4.3 m2 ha�1

Næsset (1997b ) Stem volume, pine 18 0.1 191 m3 ha�1 42.7 47

Næsset (1997b ) Stem volume, spruce 18 0.1 149 m3 ha�1 20.9 84

Hyyppa & Hyyppa (1999) Stem volume 41 �/10 175 m3 ha�1 13.5

Næsset (2002a ) Stem volume 61 1.2 155�/281 m3 ha�1 11.4�/14.3 0.3�/6.2 m3 ha�1

Holmgren (2004) Stem volume 29 0.1�/4.3 269 m3 ha�1 11�/13

Næsset (2004a ) Stem volume 57 1.0 162�/287 m3 ha�1 9.3�/12.3 5.9�/16.1 m3 ha�1

Næsset (2004b ) Stem volume 56 0.9 153�/281 m3 ha�1 11.3�/16.6 1.5�/18.8 m3 ha�1

Næsset & Bjerknes (2001) Dominant height,

regeneration

12 1.2 6.6 m 8.4 0.2 m

Næsset (2002a ) Dominant height 61 1.2 16.6�/22.4 m 3.6�/8.0 0.1�/0.4 m

Næsset (2004a ) Dominant height 57 1.0 17.7�/22.9 m 3.7�/4.4 0.6�/1.0 m

Næsset (2002a ) Mean diameter 61 1.2 13.2�/21.2 cm 6.5�/12.1 0.7�/1.0 cm

Næsset (2004a ) Mean diameter 57 1.0 14.8�/22.6 cm 5.9�/15.8 0.2�/0.7 cm

Næsset (2002a ) Stem number 61 1.2 577�/1844 ha�1 16.9�/22.2 15�/103 ha�1

Næsset (2004a ) Stem number 57 1.0 680�/1655 ha�1 14.2�/29.3 34�/108 ha�1

Yu et al. (2003) Height growth 20 �/10 28 cm Max. error 8 cm

Gobakken & Næsset (2004) Diameter distributiona 54 1.0 161�/284 m3 ha�1 11.4�/24.2 20�/93

a Validated by stem volume estimated according to predicted diameter distribution.

48

6E

.N

æsset

eta

l.S

can

d.

J.F

or.

Res.

19

(20

04

)

Dow

nloa

ded

by [

Uni

vers

ity L

ibra

ry U

trec

ht]

at 1

1:12

17

Mar

ch 2

013

height and the average proportion of canopy hits

for all grid cells in a stand was developed. The results

of the regression analysis indicated that a large portion

of the variability in stand volume could be explained

for spruce forest (R2�/0.84), but not for pine forest

(R2�/0.47) (Table 2). The major conclusions of these

two initial studies were that it was possible to obtain

very precise estimates of stand height, and that it may

be possible to estimate stand volume by regression

analysis, at least for certain forest types.

Large-area forest inventory

These two initial trials motivated further studies based

on the idea that airborne laser data could be used to

inventory large forest areas provided that precisely

georeferenced field sample plots are used in a first step

as training data to develop empirical relationships

between laser data and biophysical variables (e.g.

mean tree height and stand volume). These relation-

ships are then used in a second step to predict the

characteristics for all forest stands in the area in

question.

However, of critical importance is the georeferencing

of field plots below tree canopies. Research around

1996�/1997 indicated a potential accuracy of 3�/4 m

using differential GPS measurements under tree cano-

pies (Deckert & Bolstad 1996). Positioning errors of

this magnitude will introduce a large variability in

relationships between laser data and ground data on

small sample plots owing to spatial heterogeneity

(Bolduc et al. 1999). Efforts were therefore made to

find appropriate GPS solutions that could meet the

requirements for high-precision field inventories. Sev-

eral types of GPS and Global Navigation Satellite

System (GLONASS) receivers and different measure-

ment and processing procedures were tested (Næsset

1999, 2001, Næsset et al. 2000, Næsset & Jonmeister

2002). These tests indicated that an average positional

accuracy of 0.2�/0.7 m may be obtained under tree

canopies with basal area up to about 40 m2 ha�1 using

combined GPS�/GLONASS dual-frequency measure-

ments collected for 15�/20 min (Næsset 2001). Most of

the practical laser research in Norway since then has

been based on these procedures.

The two-stage procedure for stand inventory of

large forest areas based on laser data, in which stand

estimates of biophysical variables were computed by

regression equations developed from field plots, was

first described by Næsset & Bjerknes (2001). The first

validation of the proposed inventory procedure was

accomplished in 1999. A 10 km2 study area was

measured with a density of 1 pulse m�2 using an

Optech ALTM 1210 system (Næsset 2002). In this

study, 144 sample plots of size 200 m2 were distributed

systematically throughout the entire study area. The

plots comprised both young and mature forest,

and dominant species were Norway spruce, Scots

pine and birch. For each sample plot, canopy height

distributions were derived from first and last return

laser data. Canopy densities were also computed for

different fractions of the height distributions. Regres-

sion analysis with stepwise variable selection was used

to derive regression models for mean tree height,

dominant height, mean diameter, basal area, stand

volume and stem number. It was found that the

regression models were improved by dividing the

dataset into three strata according to age and site

quality (young forest, mature forest on poor sites and

mature forest on good sites). For mean tree height,

basal area and stand volume the models explained 82�/

95%, 69�/89% and 80�/93% of the plot-level variability,

respectively (Table 3).

In the proposed inventory procedure, all forest

stands within the area in question were divided into

grid cells with size equal to the field sample plots. The

estimated regression equations were then used to

predict corresponding properties for the grid cells in

all forest stands. Finally, stand mean values were

calculated as the arithmetic mean of the individual

grid cell values within each stand. A total of 61 stands

with a size of 0.7�/11.7 ha was selected to validate the

accuracy of the estimates. The results indicated high

precision and small bias for most of the validated

characteristics (Table 2).

In 2001, a second test of the proposed procedure

was carried out in a 65 km2 study area with hilly

terrain (Næsset 2004a). The dominant tree species

were Norway spruce and Scots pine. The Optech

ALTM 1210 system was deployed in a flight plan

in such a manner as to provide laser data with an

average sampling density of 1 pulse m�2. A total of

100 sample plots (i.e. 233 m2 each) was used to

develop regression equations predicting mean tree

height, dominant height, mean diameter, basal area,

stand volume and stem number. Before the regression

equations were developed, the area was divided into

the three strata defined in the first study (Næsset 2002,

see above). The predictions were validated for 57 large

control plots with a size of about 0.4 ha. Validation

results demonstrated that the estimation accuracy was

Scand. J. For. Res. 19 (2004) Laser scanning of forests in the Nordic countries 487

Dow

nloa

ded

by [

Uni

vers

ity L

ibra

ry U

trec

ht]

at 1

1:12

17

Mar

ch 2

013

similar to that obtained in the first study. For

biophysical properties such as mean tree height, basal

area and volume, the precision was 3.7�/6.4%, 8.7�/

13.2% and 9.3�/12.3%, respectively (Table 2).

It was revealed that large variability in flying

altitudes and laser sampling density had little effect

on the precision of the procedure (Næsset 2004a ). The

stability of the estimates of biophysical properties

under variable flying altitudes was confirmed in a

second study where laser data from two different

flying altitudes (530�/540 m and 840�/850 m above

ground level) were analysed (Næsset 2004b ). However,

a mixture of coniferous and deciduous species may

degrade the precision seriously. Regression equations

for stand volume and basal area developed from a

material of coniferous sample plots tended to produce

biased estimates of stands with a large portion of

deciduous trees (Næsset 2004a), probably because the

relationships between stand volume (and basal area)

and canopy density as measured by the laser differ

between different tree species, and in particular

between conifers with narrow and conical crowns

and deciduous species, which tend to develop wide

and rounded crowns.

The grid-based approach for predicting forest vari-

ables on a stand level using laser data has also been

tested in Sweden (Holmgren 2004). This study was

carried out at the Remningstorp test site, in south�/

western Sweden, which is dominated by Norway

spruce and Scots pine. Field sample plots with a

10 m radius were used to develop regression models

that related variables derived from laser data to mean

tree height, basal area and stand volume. Regression

models for prediction of tree height, basal area and

stem volume were validated using 29 square sample

plots (80�/80 m2). The RMSE values for all variables

were quite low, both on a plot level and for the

validation data (Table 2). In addition, they were of a

magnitude similar to those reported for the Norwe-

gian studies (Næsset 2002, 2004a). The effects of

reducing the sampling density on the accuracy of the

predictions were also assessed. The initial pulse

density of 4.29 pulses m�2 was reduced to 0.55, 0.17

and 0.10 pulses m�2. The RMSE values for mean tree

height, basal area and stem volume estimations were

similar for all four measurement densities. The find-

ings in the scientific studies in Norway and Sweden, in

terms of bias and precision, were later confirmed in

the first operational forest inventory in the Nordic

countries using laser data collected over an area of

250 km2 (Næsset 2004c ).

In a Finnish study, laser-based estimates of mean

tree height and stand volume were compared with

estimates produced by other airborne (i.e. AISA

imaging spectrometer) and satellite remote sensing

data (i.e. Landsat TM, SPOT Pan and XS) (Hyyppa &

Hyyppa 1999). The first-pulse laser scanner data

collected with the TopoSys-1 system for the Kalkkinen

test site were used. The best possible predictors of the

data sources were compared using regression analysis.

The adjusted coefficient of determination, the cor-

rected standard error of the model and the standard

errors in percentage were calculated for each data

source. The data for the estimation of tree height in

decreasing order of explanatory power were (1) first-

pulse laser scanner, (2) imaging AISA, (3) Landsat

TM, (4) Spot XS and (5) Spot Pan. In the estimation

of stem volume the corresponding order was similar,

i.e. laser scanner, AISA, SPOT XS, Landsat TM and

Spot Pan. The standard errors obtained in the stem

volume estimation were 13.5% and 49.6% for the laser

scanner and Spot Pan data, respectively (Table 2). Of

the data types tested, laser scanner data were alone in

providing accuracies similar or superior to conven-

tional forest inventory methods.

The main conclusions from these trials are that the

tested procedures, although slightly different between

countries and validated with data from different laser

instruments, seem to be robust for use in practical

inventories over large areas, at least if the forest is

dominated by coniferous species. Topographic varia-

bility and variability in laser sampling density seem to

have limited impact on the applicability of the stand-

based procedures. The bias seems to be at an

acceptable level, and the precision for most of the

evaluated stand characteristics is higher than those

obtained using traditional inventory methods. The

methods are also superior to conventional inventory

methods as far as inventory costs and data utility in

economical terms are concerned. This was confirmed

in a study in which the total inventory costs and utility

losses due to erroneous data were compared for 77

sample stands (Eid et al. 2004). The authors’ revealed

that the sum of inventory costs and economic losses

was approximately 52�/56 euros ha�1 for photo-

interpretation and around 25 euros ha�1 for the

laser-based inventory (Fig. 2). The economic losses

were due to wrong timing of final harvest because of

erroneous inventory data.

488 E. Næsset et al. Scand. J. For. Res. 19 (2004)

Dow

nloa

ded

by [

Uni

vers

ity L

ibra

ry U

trec

ht]

at 1

1:12

17

Mar

ch 2

013

Table 3. Summary of plot�/level results

Study Plot variable

No. of

plots

Laser pulses

m�2Mean field-

measured value

SD or

RMSE (%) Bias R2 (%)

Nilsson (1994) Mean tree

height

28 0.1�/1.6 12.6 m 10.0�/11.2 2.1�/3.7 m

Næsset (2002a ) Mean tree

height

144 1.2 14.1�/20.3 m 6.1�/6.7 82�/95

Næsset & Økland

(2002)

Mean tree

height

10 1.4 19.5 m 7.6 91

Holmgren et al.

(2003a )

Mean tree

height

65 �/1.5 5.9�/23.1 m 10�/11 89�/91

Næsset (2004a ) Mean tree

height

100 1.0 13.0�/20.3 m 5.8�/7.2 77�/92

Næsset (2002a ) Basal area 144 1.2 19.8�/27.5 m2 ha�1 14.1�/23.0 69�/89

Næsset (2004a ) Basal area 100 1.0 22.6�/34.2 m2 ha�1 14.8�/22.5 74�/94

Nilsson (1994) Stem volume 28 0.1 118 78

Næsset (2002a ) Stem volume 144 1.2 155�/269 m3 ha�1 15.2�/24.4 80�/93

Holmgren et al.

(2003a )

Stem volume 65 �/1.5 14�/366 m3 ha�1 22 82�/90

Maltamo et al.

(2004a )

Stem volume 10 �/10 336 25 81.6 m3 ha�1

Maltamo et al.

(2004a )

Stem volume

(from tree

counts and

pdf)

10 �/10 336 16 27.6 m3 ha�1

Næsset (2004a ) Stem volume 100 1.0 175�/341 m3 ha�1 17.5�/22.5 83�/97

Næsset &

Bjerknes (2001)

Dominant

height,

regeneration

39 1.2 3.8 m 83

Næsset &

Bjerknes (2001)

Dominant

height,

regeneration

29 1.2 7.1 m 62

Næsset (2002a ) Dominant

height

144 1.2 16.3�/22.5 m 6.8�/8.5 74�/93

Næsset (2004a ) Dominant

height

100 1.0 15.8�/22.9 m 6.6�/7.5 85�/92

Næsset (2002a ) Mean diameter 144 1.2 13.3�/22.8 cm 12.5�/14.0 39�/78

Næsset (2004a ) Mean diameter 100 1.0 13.9�/23.9 cm 13.5�/20.0 55�/69

Næsset &

Bjerknes (2001)

Stem number,

regeneration

39 1.2 4197 ha�1 42

Næsset (2002a ) Stem number 144 1.2 522�/2056 ha�1 25.9�/52.2 50�/68

Maltamo et al.

(2004a )

Stem number 10 �/10 756 ha�1 74.4 464 ha�1

Maltamo et al.

(2004a )

Stem number

(from tree

counts and

pdf)

10 �/10 756 ha�1 49.2 47 ha�1

Næsset (2004a ) Stem number 100 1.0 628�/1735 ha�1 25.3�/26.2 60�/81

Næsset & Økland

(2002)

Height to

living crown

10 1.4 5.3 m 23.3 71

Næsset & Økland

(2002)

Height to

living crown

27 1.1 7.3 m 20.9 61

Næsset & Økland

(2002)

Relative height

to living crown

10 1.4 71.5% 8.8 60

Næsset & Økland

(2002)

Relative height

to living crown

27 1.1 65.3% 10.9 47

Scand. J. For. Res. 19 (2004) Laser scanning of forests in the Nordic countries 489

Dow

nloa

ded

by [

Uni

vers

ity L

ibra

ry U

trec

ht]

at 1

1:12

17

Mar

ch 2

013

Forest regeneration inventory

It has been an aim to investigate whether airborne

lasers are capable of providing much of the data

needed for forest management at all stages of stand

development in the boreal forest. For regeneration

sites (tree heights B/8�/10 m), the data requirements

are normally less than for the mature forest. Two of

the most important biophysical characteristics in

young stands are stem number and tree height.

Attempts have therefore been made to derive these

properties using airborne laser data.

In 1999, a study was carried out for 39 sample

plots (200 m2) with a dominant height between 1.8 and

6.0 m (Næsset & Bjerknes 2001). The stem number

ranged from 1650 to 7100 stems ha�1. Regression

analysis revealed that various measures of canopy

height and canopy density derived from the laser data

could explain 83% and 42% of the variability in

ground-measured dominant height and stem number,

respectively (Table 3).

The grid-based inventory procedure was also tested

for 12 young forest stands with a mean height of

6.6 m. A regression model developed from 29 sample

plots with size 236 m2 (Table 3) was used to predict

stand heights by the grid-based approach. The bias of

the predictions was 0.2 m (p �/0.05), with a precision

of 8.4% (Table 2) (Næsset & Bjerknes 2001). These

results have been confirmed by findings in another and

more extensive trial, not yet published.

OTHER BIOPHYSICAL PROPERTIES

Traditionally, forest inventories have provided data for

efficient timber production. Consequently, properties

of interest have been those that quantify the standing

timber volume and the capacity for future wood

production. An interesting family of biophysical

properties of trees and forest stands that has received

increasing attention over the past few years, comprises

properties related to the tree canopy and the tree

crowns. These properties are probably of importance

for enhanced understanding of forest ecosystem func-

tioning (Lefsky et al. 2002), but may also be useful for

Table 3 (Continued )

Study Plot variable

No. of

plots

Laser pulses

m�2Mean field-

measured value

SD or

RMSE (%) Bias R2 (%)

Yu et al. (2003) Harvested

trees

83 �/10 61 out of

83 detected

automatically

Yu et al. (2003) Height growth 2 �/10 Errors were

2 cm and

13 cm

0.0

10.0

20.0

30.0

40.0

50.0

60.0

Inventory method and site

Tota

l co

st (

EU

R h

a–1

)

Mean NPV loss 50.9 13.4 46.3 13.3

Inventory cost 5.5 11.4 5.5 11.4

Photo Laser Photo Laser

Site 1 Site 1 Site 2 Site 2

Fig. 2. Inventory costs for photo-interpretation and laser scanning and economic losses [net present value (NPV)] related to

incorrect timing of final harvests when decisions were based on erroneous inventory data for two different sites.

490 E. Næsset et al. Scand. J. For. Res. 19 (2004)

Dow

nloa

ded

by [

Uni

vers

ity L

ibra

ry U

trec

ht]

at 1

1:12

17

Mar

ch 2

013

the description of solid wood quality (Næsset &

Økland 2002).

Næsset & Økland (2002) analysed the capability for

determining wood quality-related characteristics of

individual trees and for the average of several trees

within plots from small-footprint laser data. In 1999,

laser data with a sampling density of 0.6�/

2.3 pulses m�2 were acquired over two different sites

in eastern Norway. Both sites were dominated by

Norway spruce and only mature forests were included

in the test. Canopy height distributions and corre-

sponding canopy densities were derived from the first

and last return laser data and regression analyses were

used to relate the field-measured crown properties to

the laser data. For the first test site, height distribu-

tions were derived for individual crowns as well as for

entire plots (50 m2). For the second test site, height

distributions were derived at the plot level (200 m2).

Two contrasting crown properties were assessed, i.e.

the height from the forest floor to the living crown (hc)

and the relative crown height. Relative crown height

was defined as the crown length as a percentage of

total tree height. The validation revealed large stan-

dard deviations for the differences between predicted

and observed hc of individual trees (Table 4) as well as

for the average of all trees within plots (Table 3). The

relative crown height was predicted with higher

precision. This trial, though limited in the total

number of observations, indicates that certain crown

variables can be derived from laser data with moderate

sampling density.

The stem diameter distribution is another essential

property of forest stands of significant importance in

growth and yield projections, and for estimation of

extraction costs and timber value. Gobakken &

Næsset (2004) showed that there is a strong relation-

ship between stem diameter and laser data, at least in

single-layered stands. They derived empirical diameter

and basal area distributions for 54 plots of 3740 m2

each. Regression analyses were used to relate the

Weibull distribution parameters and percentiles to

various canopy heights and canopy density metrics

derived from laser data over young and mature

coniferous forest. The plots were divided into three

strata according to age class and site quality. The

various regression models explained 20�/93% of the

variability (Table 2). Total plot volume predicted from

the estimated distributions was used to assess the

accuracy of the regressions. The standard deviations of

the differences between predicted and field measured

Table 4. Summary of results for individual trees

Study Variable

No. of

trees

Laser

pulses m�2Mean field-

measured value

SD or

RMSE

(%) Bias R2 (%)

Hyyppa & Inkinen

(1999)

Tree height 89 �/10 16.9 m 5.8 0.1 m

Næsset & Økland

(2002)

Tree height 51 1.4 17.9 m 17.7 75

Persson et al. (2002) Tree height 135 5 24.4 m 2.6

Maltamo et al. (2004b ) Tree height, pine 79 �/10 24.9 m 5.4 1.2 m

Maltamo et al. (2004b ) Tree height, spruce 77 �/10 26.9 m 3.3 0.8 m

Maltamo et al. (2004b ) Tree height, birch 109 �/10 26.0 m 9.0 0.9 m

Persson et al. (2002) Stem volume 135 5 0.96 m3 22

Persson et al. (2002) Stem number 795 5 71a

Næsset & Økland

(2002)

Height to living

crown

45 1.4 5.6 m 39.1 53

Næsset & Økland

(2002)

Relative height to

living crown

45 1.4 70.4% 14.9 51

Holmgren & Persson

(2004)

Crown base height 135 5 12.7 m 22 0.75 m

Persson et al. (2002) Crown diameter 135 5 5.0 m 12

Hyyppa et al. (2001b ) Correctly segmented

crowns

197 4 40�/50

Yu et al. (2003) Height growth 15 �/10 50 cm 36b 6 cm

a Proportion of detected trees (%).b SD given in cm.

Scand. J. For. Res. 19 (2004) Laser scanning of forests in the Nordic countries 491

Dow

nloa

ded

by [

Uni

vers

ity L

ibra

ry U

trec

ht]

at 1

1:12

17

Mar

ch 2

013

estimates of plot volume were 11.4�/24.2%. For basal

area distributions, the precision of predicted volume

was 11.4�/15.5%, which corresponds quite well with

results outlined above.

Plot-level estimates and view-angle effects

During 1997, laser data were acquired at the Re-

mningstorp test site in Sweden. Both single-tree

methods and plot-based methods were used for

estimation of mean tree height and stem volume at

the plot level (Holmgren et al. 2003a ). Mean tree

heights for plots were estimated with an RMSE of 10�/

11% of the mean tree height (Table 3). The bias was

lower for single-tree methods than for area-based

methods. Estimation of stem volume using laser-

derived mean tree heights and crown-coverage area

gave an RMSE at the plot level of 22% (Table 3),

whereas another regression function based on tree

height and results from an early stem number counting

algorithm gave an RMSE value of 26%. It was

also found that the view angle (up to 208 off nadir)

had a significant influence on canopy closure esti-

mates, but not on tree height measurements. This

observation was confirmed in a later simulation study

by Holmgren et al. (2003b ). The simulation study also

showed that the view-angle dependence was more

significant for tree species with long crowns, such as

spruce, than for those with short crowns, such as pine.

It was also revealed that the upper height percentiles

were less affected by differences in scanning angle than

the lower percentiles.

METHODS FOR EXTRACTION OF SINGLE-

TREE FEATURES

The basic idea of single-tree-based forest inventory is

that the calculation of the stand attributes for an

individual stand is based on measurements of the

position, tree height, species and crown area for

individually detected trees. All other stand variables

are derived from these basic characteristics, possibly

also in combination with field data. The position, tree

height and tree crown areas can be obtained from laser

scanner data, whereas the tree species is obtained from

image data, from laser data, or from a combination of

laser and image data.

Delineation of individual tree crowns

It has been shown by Brandtberg (1999) and

Hyyppa & Inkinen (1999) that single trees are

detectable in high-density laser data. One promising

method for the detection and measuring of single trees

has been developed at FOA in Sweden. The method

consists of three main steps: (1) a digital canopy model

(DCM) is created using an active surface algorithm;

(2) the DCM is smoothed with different scales; and (3)

the appropriate scale in different parts of the image is

determined by fitting a parabolic surface to the

canopy model (Persson et al. 2002). When the method

was validated at the Remningstorp test site, it was

found that over 70% of the trees, representing 91% of

the stem volume, were detected (Persson et al. 2002)

(Table 4).

In Finland, the first method developed for single-

tree detection from laser measurements was based on

determination of local maxima in a low-pass filtered

canopy height model followed by a segmentation

procedure similar to watershed segmentation (Hyyppa

& Inkinen 1999, Hyyppa et al. 2001a, Maltamo et al.

2004b). This segmentation algorithm was compared

with other segmentation algorithms developed at

Joanneum Research, Austria, and the University of

Freiburg, Germany (Hyyppa et al. 2001b). Three

different segmentation algorithms were applied to a

small test site in Hohentauern, Austria. For verifica-

tion purposes, 197 single trees from eight reference

areas with 15�/30 trees from each area were surveyed

by ground measurements. In general, 40�/50% of the

tree crowns were correctly segmented using different

segmentation algorithms (Finnish algorithm 40%,

German 44%, Austrian 50%). The errors in the

Finnish algorithm were mainly caused by merged

crowns due to non-detection of shadowed trees

and missing maxima caused by oversmoothing. The

German algorithm failed to segment 53 trees, the

Austrian algorithm missed one tree, whereas the

Finnish algorithm did not miss any trees. It should

be noted that the percentage of correctly segmented

crown outlines differed from the percentage of cor-

rectly detected tree tops. When two or more

trees merged into one segment, the tree height of the

highest tree within the merged segment could still be

obtained.

Measurements of individual trees

In the methods used by Hyyppa & Inkinen (1999) and

Persson et al. (2002), laser data were first used to

delineate individual tree crowns, and then the position,

height, crown diameter, stem diameter and timber

volume were determined for each tree. Stem position

492 E. Næsset et al. Scand. J. For. Res. 19 (2004)

Dow

nloa

ded

by [

Uni

vers

ity L

ibra

ry U

trec

ht]

at 1

1:12

17

Mar

ch 2

013

was set to the location of the local maximum of the

DCM, and tree height was set to the maximum height

value of the DCM. Crown diameter was calculated

using the area of a segment, assuming that the

segments have the shape of a circle. Stem diameter

was predicted using linear regression with height

and crown diameter as independent variables. Stem

volume was calculated using volume equations for

individual trees (Naslund 1947, Laasasenaho 1982),

with tree height and stem diameter as explanatory

variables.

Persson et al. (2002) validated laser data-derived

estimates of tree position, tree height and crown

diameter using field measurements of these variables

obtained at the Remningstorp test site in Sweden. The

two latter variables were estimated with an RMSE of

0.63 m (2.6%) and 0.61 m (12%), respectively (Table 4).

It was also shown by Hyyppa & Inkinen (1999) that

tree heights of 89 selected single trees in the upper

canopy could be obtained with a standard error of less

than 1 m (5.8%) (Table 4). The underestimation of tree

heights was 0.14 m. Correspondingly, in the study by

Maltamo et al. (2004b) the standard error of height

varied between 3 and 9% for different tree species,

and the underestimation of height was about 1 m

(Table 4).

In two studies by Pyysalo & Hyyppa (2002) and

Pyysalo (2000), the reconstruction of single-tree

crowns from laser scanner data was considered

(Fig. 3). Based on 50 ground-measured trees it was

found that dense laser scanner data describe in more

detail the upper canopy of the forest and therefore are

suitable for extraction of information on tree height.

The lower crown was characterized in less detail and

variables extracted for the lower canopy were less

accurate. It can be noted, however, that the obtained

canopy profile, analogous to full-waveform laser out-

puts, seemed to be indicative of the tree species (Scots

pine versus Norway spruce).

The effect of pulse density on individual tree-based

forest inventory was addressed by Hyyppa et al.

(2001b). The pulse sampling density was reduced by

randomly removing laser data from the original

Toposys dataset. Datasets with pulse densities of 1, 4

and �/10 pulses m�2 were analysed. A canopy height

model was calculated for each dataset separately, and

segmentations were carried out for each dataset. The

estimated number of stems was significantly reduced

for the datasets with successively lower pulse densities.

Thus, the number of stems estimated decreased, but

the standard deviation of the results did not change

significantly with lower pulse rates. The most likely

explanation for the results is that with a pulse density

of 1 pulse m�2, the distribution of the pulses is more

evenly spread than with higher pulse rates. For

example, with a pulse density of �/10 pulses m�2,

most of the performance is deteriorated owing to the

oversampling by the TopoSys-1 system in the along-

track direction. It seems that even with lower pulse

densities, relatively accurate volume estimates can be

collected if the systematic errors are calibrated with a

sufficient number of reference plots, as is done in area-

based methods.

Tree species classification

In Hyyppa et al. (1999), forest canopy profiles were

created using laser scanner data. It was visually

concluded the profile information includes valuable

information about tree species, e.g. discrimination

between pine/birch versus spruce. Tests have also

been performed in Sweden using laser data for species

classification of the delineated tree crowns (Holmgren

2003, Holmgren & Persson 2004). All laser points

within each segmented tree crown were grouped

together to form the point cloud belonging to each

tree (Fig. 4). The laser points were divided into ground

hits, within crown hits or DCM surface hits according

to their distances to the DCM or ground. To separate

between Norway spruce and Scots pine, features were

derived from the laser data on a single-tree level that

captured the differences in crown shape and structure.

The two species could then be discriminated from each

other with an accuracy of 95% using laser data alone

(Holmgren & Persson 2004).

Change detection

Yu et al. (2003) were the first to demonstrate the

applicability of small-footprint, high sampling density

airborne laser scanner data for boreal forest change

detection, i.e. the estimation of forest growth and

monitoring of harvested trees (see also Hyyppa et al.

2003, Yu et al. 2004). Two laser acquisitions (on 2 and 3

September 1998 and 15 June 2000) were carried out on

a test site in Kalkkinen, Finland, using a Toposys-1

laser scanner. Object-orientated algorithms were devel-

oped for detecting the removal of individual trees and

for measuring forest growth at the plot and stand levels.

Out of 83 field-checked harvested trees, 61 could be

automatically and correctly detected. All mature har-

vested trees were detected and it was mainly the smaller

Scand. J. For. Res. 19 (2004) Laser scanning of forests in the Nordic countries 493

Dow

nloa

ded

by [

Uni

vers

ity L

ibra

ry U

trec

ht]

at 1

1:12

17

Mar

ch 2

013

trees that were not found. Forest growth (Fig. 5) was

demonstrated using an object-orientated tree-to-tree

matching algorithm and statistical analysis. The preci-

sion of the estimated growth, based on field checking or

statistical analysis, was about 5 cm at the stand level

and about 10�/15 cm at the plot level.

Estimation of diameter distribution

Diameter distributions can be derived from laser data

according to an area-based approach merely using the

statistical relationships that exist between field-

measured diameters and laser data, as demonstrated

above (Gobakken & Næsset 2004). However, a dia-

meter distribution may also be estimated by using

canopy shape information about the individual trees

that are identified by a single-tree-orientated segmenta-

tion procedure. In the latter case, the height distribution

is automatically obtained and tree diameters can be

predicted using information on detected tree height and

crown characteristics.

When individual trees are recognized from a laser

image one major problem is that only the dominant

tree layer is detected. Suppressed trees may not be

found, and the detection of the shortest dominant

trees and trees that appear in closed groups is also

difficult. Therefore, the tree stock is often under-

estimated. One solution could be to combine counting

of the trees that can be detected by single-tree

segmentation methods with prediction of unseen small

trees using theoretical distribution functions.

In a study by Maltamo et al. (2004a), two different

methods were used to predict the proportion of small

trees. In the first method, the complete cumulative

Weibull distribution function was fitted to the field-

measured height distribution. The number of small

trees was then estimated by predicting the parameters

using information on the height distribution of those

trees detected using the laser scanner data. In the

second method, the two-parameter left-truncated

Weibull distribution was fitted directly to the detected

tree height distribution and the number of small trees

was predicted as in the case with the complete Weibull

distribution. The results showed that RMSE for plot

volume was about 25% when using information

obtained from laser scanning alone. The use of the

parameter prediction method to describe small trees

improved the accuracy considerably, i.e. RMSE for

Fig. 3. Three-dimensional forest model (from Pyysalo 2000).

494 E. Næsset et al. Scand. J. For. Res. 19 (2004)

Dow

nloa

ded

by [

Uni

vers

ity L

ibra

ry U

trec

ht]

at 1

1:12

17

Mar

ch 2

013

plot volume improved to 16.0%. In the case of a left-

truncated height distribution the improvement was

modest, i.e. RMSE 22.5% (Table 3). These reliability

figures for both the original laser scanning-based

estimates and the estimates that also contain small

trees are comparable to those obtained by conven-

tional standwise field inventories in Finland.

The methods used in the study by Maltamo et al.

(2004a) to describe small trees are only capable of

producing unimodal distributions. For multimodal

height distributions a more complex methodology

should be developed. It is also possible to obtain at

least some information on the occurrence of under-

storey trees by analysing the profiles of laser scanning

data or percentiles of reflected laser pulse heights (e.g.

Hyyppa et al. 2000, Næsset & Økland 2002).

PREOPERATIONAL AND OPERATIONAL

PROJECTS

The procedure for laser-based large-area forest stand

inventories that comply with the data requirements

in forest planning and management was introduced in

the commercial market in Norway in 2002. Six

operational projects were initiated in Norway in

2002�/2004, representing different types of ownership

and different forest types, ranging from typical boreal

forest within a continental climate in the inland of

eastern Norway to oceanic climate at the western coast

on the shores of the Atlantic. In the first project, aerial

photographs were acquired for 490 km2 in the Nordre

Land municipality in south�/eastern Norway, of which

most of the area is private forest land (Næsset 2004c ).

During the summer of 2003, laser scanner data with a

sampling density of about 0.7 pulse m�2 were

acquired for 250 km2 of the municipality. These data

sample forest stands that are the most productive

within the inventory area in question. The goal of the

laser scanning was to provide the forest owners with

highly accurate standwise data. The remaining part of

the forest not covered by laser data, i.e. stands with

poor site quality, high extraction costs and low timber

value, were subjected to photo-interpretation of the

required stand attributes. The major products of this

Fig. 4. Laser points of a pine, spruce and deciduous tree.

Fig. 5. Demonstration of height change. Point clouds for

selected trees. White and black dots correspond to individual

laser measurements in 1998 and 2000, respectively. The

datasets have been interactively matched using the lowest

branches.

Scand. J. For. Res. 19 (2004) Laser scanning of forests in the Nordic countries 495

Dow

nloa

ded

by [

Uni

vers

ity L

ibra

ry U

trec

ht]

at 1

1:12

17

Mar

ch 2

013

project will be forest management plans with stand

maps and orthophotographs.

Another of these six commercial projects was

initiated in the municipality of Oslo, with aerial

photographs and laser data acquired for a forest

area of 120 km2 in summer 2003. The forest is public

land owned and managed by the City of Oslo. The

Oslo area is the most densely populated area in

Norway, and the forest is a major recreational area

for a population of approximately 800 000. The forest

is, to a large extent, managed according to silvicultural

practices that maintain recreational and landscape

aesthetic qualities. Precise estimates of forest stand

characteristics are not demanded for forest manage-

ment. However, precise digital terrain models, ortho-

photographs and forest maps are considered to be

necessary for proper recreation and land-use planning

by the municipality administration. An accurate forest

inventory as derived from laser data was therefore

considered an additional benefit.

A large-area preoperational project was also in-

itiated in Sweden at the same time. In May 2003,

laser scanner data with a density of approximately

1.4 pulses m�2 were acquired for a 50 km2 area in

central Sweden. Mean tree height, stem volume, basal

area and mean stem diameter will be estimated using

laser data in conjunction with field data. The main

objective of this study is to compare laser scanning

methods for forest variable estimation with traditional

photogrammetric methods that are used currently for

estimating the timber value of the forest.

In Finland, preoperational activities are in

preparation. In 2003, laser data were collected over a

130 km2 test site near Sammatti using the Toposys

Falcon system with an average pulse density of

6.6 pulses m�2. The main objective of the campaign

was to use laser data for the verification of the

accuracy of the national digital terrain model

(DTM) and to use the accurate DTM for watershed

analysis. However, the pulse density was higher than

requested for the DTM analysis to enable large-

area forest inventory demonstrations not only tar-

geting mean values of forest stands, but also detec-

ting individual tree groups using pattern recognition

methods.

DISCUSSION

Based on the experience gained in the Nordic coun-

tries so far, there seem to be two major directions for

laser-assisted forest inventories: (1) inventory proce-

dures based on laser data with moderate sampling

density to estimate stand-level variables, and (2)

detection and measurement of characteristics of

individual trees by laser data with high sampling

density. Both directions require further development,

improvement, testing and refinement. Technological

improvements can be made by fully using the full

waveform and by integrating aerial images with laser

scanner data. In addition, research and application

development could be extended to new fields of

importance to forest- and ecosystem management

and certification, such as biodiversity assessment.

Large-area inventories

Actions taken by the commercial sector seem to

indicate that laser scanning of forests with moderate

sampling density has become an interesting alternative

to current inventory procedures. As long as the data

requirement for forest management is restricted to

mean estimates at the stand level (mean height, stand

volume, etc.), the proposed inventory procedures seem

to comply with the needs of forest management.

However, many of the management planning tools

and packages developed for users in the Nordic

countries use models requiring more detailed stand

information, such as growth models for individual

trees. For the most extensive planning systems, tree-

level data would be needed. However, in many cases

the stand diameter distribution is the link between

advanced tree-level models and the characteristics that

are actually captured in practical inventories. Based on

the preliminary works presented here, which indicate

that such distributions could be predicted from laser

data with a precision equal or superior to that of

conventional methods, the authors regard methods for

derivation of diameter distributions from laser data as

an important issue for future research. It is also

important to assess more systematically how factors

such as varying flying altitude and sampling density

affect the accuracy of the biophysical properties

derived from the laser data, especially since these

factors vary considerably in large inventories covering

up to 1000 km2 or even more and have a significant

impact on the costs of data acquisition.

Large-area inventories based on laser data can now

be taken into operational use, at least in typical boreal

forest environments with a preponderance of conifer-

ous tree species. However, the estimation accuracy for

stand characteristics derived from laser data in decid-

uous and mixed stands is more problematic. At

496 E. Næsset et al. Scand. J. For. Res. 19 (2004)

Dow

nloa

ded

by [

Uni

vers

ity L

ibra

ry U

trec

ht]

at 1

1:12

17

Mar

ch 2

013

present, deciduous forest is inventoried by conven-

tional methods. Further research should focus on

problems that arise in mixed and deciduous forest

stands. One option could be to treat deciduous stands

and mixed stands as separate strata, i.e. to separate

forest types with distinct canopy properties and to

ensure that a proper number of ground training plots

is available for these individual strata to develop

regression equations that uniquely account for the

specific canopy structures of the various forest types.

Collection of laser data during leaf-off conditions

could be another option, at least in mixed stands. The

accuracy of density-related variables such as timber

volume and basal area tends to be affected by the

proportion of deciduous species in mixed forest

(Næsset 2004a ), and one way of reducing the influence

of the deciduous tree crowns on the canopy density

estimates derived from the laser data could be to

acquire the laser data during leaf-off conditions.

In spite of the rapid technological developments and

falling prices for laser data, laser data acquisition still

accounts for a large portion of the total inventory

costs. Studies have been conducted to find a reason-

able trade-off between sampling density of laser data

and the accuracy of the inventoried properties. Re-

search has indicated that the accurate estimation of

forest stand attributes is possible, given a moderate

laser sampling density of, say, 1 pulse m�2. Efforts are

still required to determine optimal sampling strategies

for different forest types.

Reducing the field effort of laser-based methods is a

second opportunity to reduce inventory costs. In the

scientific studies accomplished so far, sample plots

have been collected locally for each trial. In regions

where the climatic conditions are similar and the trees

tend to develop similar stem and crown shapes, it may

be possible to use sample plots from adjacent areas,

e.g. a network of permanent sample plots. Thus, a

database of previously inventoried ground plots with

corresponding laser data could be a valuable asset for

future inventories. Preliminary analysis indicates that

this could be a technically and economically sound

strategy, but more extensive research under different

forest conditions is required.

Single-tree methods

Analysis at the single-tree level offers the possibility

for measuring height, crown diameter and crown

shape of most trees, and to compare their development

over time. It should be remembered that the promising

results for single-tree detection methods reviewed in

this article were carried out in coniferous-dominated

boreal forests. Results from denser forests, with more

deciduous species, are less promising (Brandtberg et

al. 2003). Tools for this are still at the research stage.

However, it can never be expected that all trees will be

detectable using laser data. Thus, procedures for using

the features extracted from laser data in combination

with field data for estimation of stand totals and

distributions require development.

Since the performance of laser scanning is increas-

ing in conjunction with a decrease in costs, a single-

tree-based approach may become commercially viable

in the latter part of this decade. It is worth determining

the level of effort required for training single-tree-

based approaches. Perhaps the level of effort required

may be substantially reduced compared with area-

based methods.

Single-tree-based concepts also require improve-

ments in the quality of segmentation, tree species

classification, integration of aerial imaging data and

conversion of crown area to stem diameter. The

analysis of how accurately the laser-derived crown

area estimates correlate with true crown area should

also continue. The optimization of laser systems and

acquisition parameters for single-tree extraction also

need to be evaluated.

CONCLUSION

This study has shown that area-based approaches to

estimate forest stand variables from laser scanner data

have matured and are now implemented in operational

projects in the Nordic countries. Further research and

development is required to provide additional biophy-

sical properties of the forest stands required in

practical forest planning, and to optimize the balance

between costs and accuracy, i.e. to determine the

optimum combination of flight parameters in laser

data acquisition, laser sampling density and ground

sampling density as the technology improves and the

cost structure shifts over time. Promising results

based on derivation of single-tree parameters has

also been presented, but a relatively high laser

sampling density is required to provide precise sin-

gle-tree estimates. However, since reduced costs for

laser data are expected in the coming years, single-

tree-based methods may be an interesting alternative

for at least certain forest inventories in the Nordic

boreal forest where highly accurate data are required.

Scand. J. For. Res. 19 (2004) Laser scanning of forests in the Nordic countries 497

Dow

nloa

ded

by [

Uni

vers

ity L

ibra

ry U

trec

ht]

at 1

1:12

17

Mar

ch 2

013

The single-tree-related research will therefore continue

in Finland, Norway and Sweden.

REFERENCES

Aldred, A. H. & Bonnor, G. M. 1985. Application of

airborne laser to forest surveys, Information Report PI-

X-51, Can. For. Serv., Petawawa Natl For. Inst., Chalk

River.

Baltsavias, E. P. 1999. Airborne laser scanning: existing

systems and firms and other resources. ISPRS J. Photo-

gramm. Remote Sens. 54: 164�/198.

Blair, J. B., Rabine, D. L. & Hofton, M. A. 1999. The laser

vegetation imaging sensor (LVIS): a medium-altitude

digitization-only, airborne laser altimeter for mapping

vegetation and topography. ISPRS J. Photogramm.

Remote Sens. 54: 115�/122.

Bolduc, P., Lowell, K. & Edwards, G. 1999. Automated

estimation of localized forest volume from large-scale

aerial photographs and ancillary cartographic informa-

tion in a boreal forest. Int. J. Remote Sens. 20: 3611�/

3624.

Brandtberg, T. 1999. Automatic individual tree-based ana-

lysis of high spatial resolution remotely sensed data.

Ph.D. Thesis, Acta Univ. Agric. Suec., Silvestria 118,

Swedish Univ. of Agric. Sci., Uppsala.

Brandtberg, T., Warner, T. A., Landenberger, R. E. &

McGraw, J. B. 2003. Detection and analysis of individual

leaf-off tree crowns in small footprint, high sampling

density lidar data from the eastern deciduous forest in

North America. Remote Sens. Environ. 85: 290�/303.

Deckert, C. & Bolstad, P. V. 1996. Forest canopy, terrain,

and distance effects on global positioning system point

accuracy. Photogramm. Engng Remote Sens. 62: 317�/

321.

Eid, T., Gobakken, T. & Næsset, E. 2004. Comparing stand

inventories based on photo-interpretation and laser

scanning by means of cost-plus-loss analyses. Scand. J.

For. Res. 19: 512�/523.

Gaveau, D. L. A. & Hill, R. A. 2003. Quantifying canopy

height underestimation by laser pulse penetration in

small-footprint airborne laser scanning data. Can. J.

Remote Sens. 29: 650�/657.

Gobakken, T. & Næsset, E. 2004. Estimation of diameter

and basal area distributions in coniferous forest by means

of airborne laser scanner data. Scand. J. For. Res. 19:

529�/542.

Holmgren, J. 2003. Estimation of forest variables using

airborne laser scanning. Ph.D. Thesis. Acta Univ. Agric.

Suec., Silvestria 278. Swedish Univ. of Agric. Sci.,

Uppsala.

Holmgren, J. 2004. Prediction of tree height, basal area and

stem volume in forest stands using airborne laser scan-

ning. Scand. J. For. Res. 19: 543�/553.

Holmgren, J. & Persson, A. 2004. Identifying species of

individual trees using airborne laser scanner. Remote

Sens. Environ. 90: 415�/423.

Holmgren, J., Nilsson, M. & Olsson, H. 2003a . Estimation

of tree height and stem volume on plots using airborne

laser scanning. For. Sci. 49: 419�/428.

Holmgren, J., Nilsson, M. & Olsson, H. 2003b. Simulating

the effects of LIDAR scanning angle for estimation of

mean tree height and canopy closure. Can. J. Remote

Sens. 29: 623�/632.

Hyyppa, J. 1993. Development and feasibility of airborne

ranging radar for forest assessment. Lab. of Space

Technol., Helsinki Univ. of Technol.

Hyyppa, H. & Hyyppa, J. 1999. Comparing the accuracy of

laser scanner with other optical remote sensing data

sources for stand attributes retrieval. Photogramm. J.

Finland 16: 5�/15.

Hyyppa, J., Hyyppa, H. & Samberg, A. 1999. Assessing

forest stand attributes by laser scanner. Laser Radar

Technol. Appl. IV 3707: 57�/69.

Hyyppa, J. & Inkinen, M. 1999. Detecting and estimating

attributes for single trees using laser scanner. Photo-

gramm. J. Finland 16: 27�/42.

Hyyppa, J., Pyysalo, U., Hyyppa, H. & Samberg, A. 2000.

Elevation accuracy of laser scanning-derived digital

terrain and target models in forest environment. 20th

EARSEL Symposium and Workshops, Dresden, Ger-

many, 14�/17 June 2000.

Hyyppa, J., Kelle, O., Lehikoinen, M. & Inkinen, M. 2001a .

A segmentation-based method to retrieve stem volume

estimates from 3-dimensional tree height models pro-

duced by laser scanner. IEEE Trans. Geosci. Remote

Sens. 39: 969�/975.

Hyyppa, J., Schardt, M., Haggren, H., Koch, B., Lohr, U.,

Scherrer, H. U., Paananen, R., Luukkonen, H., Ziegler,

M., Hyyppa, H., Pyysalo, U., Friedlander, H., Uuttera, J.,

Wagner, S., Inkinen, M., Wimmer, A., Kukko, A.,

Ahokas, A. & Karjalainen, M. 2001b. HIGH-SCAN:

the first European-wide attempt to derive single-tree

information from laserscanner data. Photogramm. J.

Finland 17 (2): 58�/68.

Hyyppa, J., Yu, X., Ronnholm, P., Kaartinen, H. & Hyyppa,

H. 2003. Factors affecting object-oriented forest growth

estimates obtained using laser scanning. Photogramm.

Journal of Finland 18 (2): 16�/31.

Laasasenaho, J. 1982. Taper curve and volume functions for

pine, spruce and birch. Communicationes Instituti Fore-

stalis Fenniae, 108, Helsinki.

Lefsky, M. A., Cohen, W. B., Acker, S. A., Parker, G. G.,

Spies, T. A. & Harding, D. 1999. Lidar remote sensing of

the canopy structure and biophysical properties of

Douglas-fir western hemlock forests. Remote Sens. En-

viron. 70: 339�/361.

Lefsky, M. A., Cohen, W. B., Parker, G. G. & Harding, D. J.

2002. Lidar remote sensing for ecosystem studies.

BioScience 52: 19�/30.

Lim, K., Treitz, P., Wulder, M., St-Onge, B. & Flood, M.

2003. LiDAR remote sensing of forest structure. Progr.

Phys. Geogr. 27: 88�/106.

Maclean, G. A. & Krabill, W. B. 1986. Gross-merchantable

timber volume estimation using an airborne lidar system.

Can. J. Remote Sens. 12: 7�/18.

498 E. Næsset et al. Scand. J. For. Res. 19 (2004)

Dow

nloa

ded

by [

Uni

vers

ity L

ibra

ry U

trec

ht]

at 1

1:12

17

Mar

ch 2

013

Maltamo, M., Eerikainen, K., Pitkanen, J., Hyyppa, J. &

Vehmas, M. 2004a . Estimation of timber volume and

stem density based on scanning laser altimetry and

expected tree size distribution functions. Remote Sens.

Environ. 90: 319�/330.

Maltamo, M., Mustonen, K., Hyyppa, J., Pitkanen, J. & Yu,

X. 2004b. The accuracy of estimating individual tree

variables with airborne laser scanning in a boreal nature

reserve. Can. J. For. Res. 34: 1791�/1801.

Næsset, E. 1997a . Determination of mean tree height of

forest stands using airborne laser scanner data. ISPRS J.

Photogramm. Remote Sens. 52: 49�/56.

Næsset, E. 1997b. Estimating timber volume of forest stands

using airborne laser scanner data. Remote Sens. Environ.

61: 246�/253.

Næsset, E. 1999. Point accuracy of combined pseudorange

and carrier phase differential GPS under forest canopies.

Can. J. For. Res. 29: 547�/553.

Næsset, E. 2001. Effects of differential single- and dual-

frequency GPS and GLONASS observations on point

accuracy under forest canopies. Photogramm. Engng

Remote Sens. 67: 1021�/1026.

Næsset, E. 2002. Predicting forest stand characteristics with

airborne scanning laser using a practical two-stage

procedure and field data. Remote Sens. Environ. 80:

88�/99.

Næsset, E. 2004a . Practical large-scale forest stand inven-

tory using small-footprint airborne scanning laser. Scand.

J. For. Res. 19: 164�/179.

Næsset, E. 2004b. Effects of different flying altitudes on

biophysical stand properties estimated from canopy

height and density measured with a small-footprint

airborne scanning laser. Remote Sens. Environ. 91:

243�/255.

Næsset, E. 2004c . Accuracy of forest inventory using

airborne laser scanning: evaluating the first Nordic full-

scale operational project. Scand. J. For. Res. 19: 554�/557.

Næsset, E., Bjerke, T., Øvstedal, O. & Ryan, L. H. 2000.

Contributions of differential GPS and GLONASS ob-

servations to point accuracy under forest canopies.

Photogramm. Engng Remote Sens. 66: 403�/407.

Næsset, E. & Bjerknes, K.-O. 2001. Estimating tree heights

and number of stems in young forest stands using

airborne laser scanner data. Remote Sens. Environ. 78:

328�/340.

Næsset, E. & Jonmeister, T. 2002. Assessing point accuracy

of DGPS under forest canopy before data acquisition, in

the field and after postprocessing. Scand. J. For. Res. 17:

351�/358.

Næsset, E. & Økland, T. 2002. Estimating tree height and

tree crown properties using airborne scanning laser in a

boreal nature reserve. Remote Sens. Environ. 79: 105�/

115.

Naslund, M. 1947. Functions and tables for computing the

cubic volume of standing trees: pine, spruce and birch in

southern Sweden and in the whole of Sweden. Report 36.

National Forest Research Institute, Stockholm.

Nelson, R., Krabill, W. & Maclean, G. 1984. Determining

forest canopy characteristics using airborne laser data.

Remote Sens. Environ. 15: 201�/212.

Nilsson, M. 1994. Estimation of tree heights and stand

volume using airborne lidar systems. Report 57. Dept of

Forest Survey, Swedish Univ. of Agric. Sci., Umea.

Nilsson, M. 1996. Estimation of tree heights and stand

volume using an airborne lidar system. Remote Sens.

Environ. 56: 1�/7.

Nilsson, M. 1997. Estimation of forest variables using

satellite image data and airborne lidar data. Ph.D. Thesis.

Acta Univ. Agric. Suec., Silvestria 17. Swedish Univ. of

Agric. Sci., Uppsala.

Persson, A., Holmgren, J. & Soderman, U. 2002. Detecting

and measuring individual trees using an airborne

laser scanner. Photogramm. Engng Remote Sens. 68:

925�/932.

Pyysalo, U. 2000. A method to create a three-dimensional

forest model from laser scanner data. Photogramm. J.

Finland 17: 34�/42.

Pyysalo, U. & Hyyppa, H. 2002. Geometric shape of the tree

extracted from laser scanning data. International Society

for Photogrammetry and Remote Sensing �/ ISPRS

Commission III Symposium (PCV’02), Graz, Austria,

9�/13 September 2002.

Solodukhin, V.I. Zukov, A.J. & Mazugin, I.N. 1977. Laser

aerial profiling of a forest. Lew. NIILKh. Leningrad.

Lesnoe Khozyaistvo 10: 53�/58 (in Russian).

Steinvall, O. & Koppari, K. 1996. Depth sounding lidar �/ an

overview of Swedish activities and with future prospects.

In Feigels, V. I. & Kopilevich, Y. I. (eds). CIS Selected

Papers on Laser Remote Sensing in Natural Waters:

From Theory to Practice. Proc. SPIE 2964: 2�/25.

Steinvall, O., Koppari, K. & Karlsson, U. 1993. Experi-

mental evaluation of an airborne depth sounding lidar.

Opt. Engng 32: 1307�/1321.

Steinvall, O., Soderman, U., Ahlberg, S., Sandberg, M.,

Letalick, D. & Jungert, E. 1999. Airborne laser radar:

systems and methods for reconnaissance and terrain

modeling. Proc. SPIE 3707: 12�/26.

Wehr, A. & Lohr, U. 1999. Airborne laser scanning �/ an

introduction and overview. ISPRS J. Photogramm. Re-

mote Sens. 54: 68�/82.

Yu, X., Hyyppa, J., Ronnholm, P., Kaartinen, H., Maltamo,

M. & Hyyppa, H. 2003. Detection of harvested trees and

estimation of forest growth using laser scanning. In

Hyyppa, J., Næsset, E., Olsson, H, Granqvist Pahlen, T.

& Reese, H. (eds). Proceedings of Scandlaser Scientific

Workshop on Airborne Laser Scanning of Forests, Umea,

3�/4 September 2003. Working Paper 112, pp. 115�/124.

Dept of Forest Resource Management and Geomatics,

Swedish Univ. of Agric. Sci.

Yu, X., Hyyppa, J., Kaartinen, H. & Maltamo, M. 2004.

Automatic detection of harvested trees and determination

of forest growth using airborne laser scanning. Remote

Sens. Environ. 90: 451�/462.

Scand. J. For. Res. 19 (2004) Laser scanning of forests in the Nordic countries 499

Dow

nloa

ded

by [

Uni

vers

ity L

ibra

ry U

trec

ht]

at 1

1:12

17

Mar

ch 2

013