SINGLE TREE FEATURE EXTRACTION FROM AIRBORNE LASER SCANNER DATA

APPLYING VECTOR MODELS

Thesis for degree of Licentiate of Science In Technology Submitted to the Department of Surveying at Helsinki University of Technology Helsinki, August 2006 Ulla Pyysalo

Supervisor: Prof., Dr. Henrik Haggrén Instructor: Docent, Dr. Hannu Hyyppä

HELSINKI UNIVERSITY OF TECHNOLOGY ABSTRACT OF THE LICENTIATE’S THESIS

Author: Name of the thesis: Date: 28.8.2006

Ulla Pyysalo Single Tree Feature Extraction from Airborne Laser Scanner Data applying Vector Models Number of pages: 78

Faculty: Department of Surveying

Professorship: Photogrammetry and Remote Sensing

Supervisor: Prof. Dr. Henrik Haggrén Instructor: D.Sc. Hannu Hyyppä

The objective of the study was to develop 3D vector models of single trees from laser scanner data in order to derive geometry features. The vector model production was implemented in the following stages: point classification, digital terrain model production, extraction of points from each tree, and vector model creation. The extracted features were tree height, crown height, trunk location, and crown profile. The method was applied at the Otaniemi test site. Reference materials from trees in the test area were acquired in field measurements by tacheometer and hypsometer. In order to derive crown shape information, a new method based on side view images of trees was tested. Trees were imaged from two directions with determined geometry and rectified to a plane parallel with the tree trunk, from which crown shape parameters were acquired. The features extracted from laser models were compared to the reference material. The results suggested that tree shape is underestimated in laser derived models in both vertical and horizontal direction. The results showed that tree location could be extracted with an accuracy of 2 m and tree heights with an accuracy of 1.5 m. Manual extraction of tree points resulted in better accuracy than segmentation-based extraction. The crown shape was sufficiently accurate at the top of the trees, but accuracy decreased significantly towards the tree base. The impact of laser scanning parameters on vector model production was considered by analysing error sources in different data sets and applying data simulations. The point density and scanning pattern were found to have the most profound effect on the segmentation and crown shape accuracy. However, the vertical feature, tree height, was obtained from all data sets to an almost similar accuracy regardless of point density.

Keywords: Laser scanning, Vector models, Side view images, 3D features

Language: English

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TEKNILLINEN KORKEAKOULU LISENSIAATTITYÖN TIIVISTELMÄ

Tekijä: Työn nimi: Päivämäärä: 28.8.2006

Ulla Pyysalo Single Tree Feature Extraction from Airborne Laser Scanner Data applying Vector Models Sivumäärä: 78

Osasto: Maanmittaus Professuuri: Fotogrammetria ja kaukokartoitus

Supervisor: Prof. Dr. Henrik Haggrén Instructor: D.Sc. Hannu Hyyppä

Lisensiaattityön tavoitteena oli kehittää menetelmä yksittäisten puiden vektorimallien muodostamiseksi piirteiden irrotusta varten. Vektorimallin muodostus tapahtui työvaiheissa, jotka olivat pisteiden luokitus, alueen korkeusmallin luonti, yhdestä puusta heijastuneiden pisteiden irrotus ympäristöstä ja vektorimallin muodostus. Malleilta irroitettiin puun geometrisiä piirteitä, jotka olivat puun ja latvuksen pituus, rungon sijainti ja latvuksen muoto. Menetelmää kokeiltiin Otaniemen koealueen puihin viidellä eri laserkeilainaineistolla. Puista kerättiin referenssiaineistoa takymetri– ja hypsometrimittauksin. Puiden latvuksen muodon määrittämiseksi kehitettiin sivuvalokuvaukseen perustuva menetelmä, jossa kuvat otetaan määrätyllä geometrialla ja oikaistaan rungon suuntaiselle tasolle mittausta varten. Vektorimalleilta irrotettujen piirteiden tarkkuutta arvioitiin vertaamalla niitä referenssiaineistoon. Tutkimuksessa selvisi, että puun muoto aliarvioidaan lasermalleilla sekä pysty- että vaakasuunnassa. Puun rungon sijainti määritettiin malleilta n. 2 m tarkkuudella ja korkeus n. 1.5 m tarkkuudella. Puun latvuksen yläosan muoto voitiin kuvata malleilla tarkasti, mutta tarkkuus huononi siirryttäessä alaspäin kohti puun juurta. Laserkeilauksen parametrien vaikutusta vektorimallien muodostukseen arvioitiin tarkastelemalla virhelähteitä eri parametreillä mitatuissa aineistoissa sekä aineiston simulaatioilla. Pistetiheydellä ja keilauskuviolla huomattiin olevan suurin vaikutus segmentointitarkkuuteen ja latvan muodon määritykseen. Puun pituus taas määritettiin eri aineistoista muodostetuilta malleilta lähes samalla tarkkuudella pistetiheydestä riippumatta.

Avainsanat: Laserkeilaus, vektorimallit, yksittäiset puut, sivukuvaus, 3D piirteet

Työn kieli: Englanti

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PREFACE

The thesis is result of work, which has been conducted in the Institute of Photogrammetry and

Remote Sensing Helsinki University of Technology between years 2000-2006. I would like to

thank my supervisor professor Henrik Haggrén for opportunity to work in the laboratory these

years and encouragement for the research scientist carrier. My instructor D.Sc. Hannu Hyyppä

I would like express my special thanks for great support, inspiring discussions and positive

energy during the research period, in addition to the trumpet-shaped chanterelle he delivered

me in autumns.

I would also like to thank professor Matti Maltamo and D.Sc. Jussi Heikkinen for helpful

comments and reviewing the thesis. Furthermore I am grateful to professor Juha Hyyppä for

helpful comments and knowledge concerning laser scanning, Arttu Soininen for possibility to

use TerraScan- software and Jaakko Järvinen and the team for carrying out field

measurements. The colleagues and friends in the Institute of Photogrammetry and Remote

Sensing I thank for the pleasant and comfortable working environment. Especially I thank my

office roommate Lic.Sc. Petteri Pöntinen for seven enjoyable years we shared the room.

During the years I received financial support from the Ministry of Agriculture and Forestry,

the Finnish Academy, the Finnish Cultural Foundation, Jenny and Antti Wihuri Foundation,

and the Nature Resource Foundation. I thank for it.

Finally, I would like to thank family and friends for support and encouragement.

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CONTENTS LIST OF ABBREVATIONS 7

LIST OF SYMBOLS 8

1 INTRODUCTION 9

1.1 Background and motivation 9

1.2 Previous studies 11

1.3 The objectives and structure of the thesis 12

2 AIRBORNE LASER SCANNER 15

2.1 Overview of laser scanning measurement 15

2.2 Laser range finder 16

2.3 Scanner 18

2.4 Positioning unit and data controlling and possessing 19

2.5 Additional components 19

2.6 Laser scanning accuracy 20

2.7 Point density in laser scanner data 21

3 MATERIALS 23

3.1 The Otaniemi test site 23

3.2 Laser scanner implementations 23

3.3 Laser scanner data sets 25

3.4 Acquisition of reference material 26

4 SIDE VIEW IMAGES 28

4.1 Motivation 28

4.2 Capturing side view images at the Otaniemi test site 29

4.3 Image preprocessing 30

4.4 Image measurements 31

4.5 Determining the trunk tilt 32

4.6 Error analysis 34

4.7 Method analysis 37

5

5 THE VECTOR MODEL PRODUCTION METHOD 38

5.1 Preprocessing of laser data 38

5.2 Point classification and DTM 39

5.3 Tree crown delineation and point extraction 41

5.4 Vector model processing method 43

5.5 Tree model feature extraction 46

6 RESULTS AND THEIR ANALYSIS 48

6.1 The evaluation of DTM accuracy 49

6.2 Single tree point extraction by tree delineation 50

6.3 Tree heights and crown heights 53

6.4 Locations of the tree trunks 56

6.5 Crown width analysis 59

7 THE IMPACT OF LASER SCANNING PARAMETERS ON THE APPLICATION

OF VECTOR MODELS 64

7.1 Point density and reconstruction 64

7.2 Effect of overlapping flight lines 66

7.3 Beam sizes and biases 67

8 SUMMARY AND CONCLUSIONS 68

8.1 Summary and conclusions 68

8.2 Discussion 70

8.3 Future work 71

REFERENCES 73

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LIST OF ABBREVATIONS

3D Three-Dimensional

ALS Airborne Laser Scanner

CHM Canopy Height Model

CW Continuous Wave

DGPS Differential Global Positioning System

DSM Digital Surface Model

DTHM Digital Tree Height Model

DTM Digital Terrain Model

GIS Geoinformation System

GPS Global Positioning System

IFOW Instantaneous Field of View

IMU Inertial Measurement Unit

KKJ Finnish National Coordinate System

LASER Light Amplification by Stimulated Emission of Radiation

PRF Pulse Repetition Frequency

STD Standard deviation

TIN Triangulated Irregular Network

VRS Virtual Reference Station

WGS World Geodetic System

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LIST OF SYMBOLS R range

Lt pulse time of flight

pφ phase difference

n number of full wavelengths

T travelling time of full wavelength

c speed of light 300 000 000 m/s

ggg zyx ,, ground co-ordinate system axes

ϖ rotation around -axis gx

φ rotation around -axis gy

κ rotation around -axis gz

x, y image co-ordinates

X, Y co-ordinates on rectification plane

21210210 ,,,,,,, ggfffeee unknown parameters of projective transformation

s scale

h point elevation from the ground

tt yx , trunk co-ordinates

r range between trunk and point

α azimuth

v trunk vector

sX1 distance between trunk and tree top in horizontal direction in image 1

sX2 distance between trunk and tree top in horizontal direction in image 2

),( 11 cc yx image plane and polygon intersection point 1

),( 22 cc yx image plane and polygon intersection point 2

dw1 distance between trunk and intersection point 1

dw2 distance between trunk and intersection point 2

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1 INTRODUCTION

1.1 Background and motivation

The rapid development of laser scanning has offered new challenges in the characterization of

the geometry of individual trees. When a forest is measured by airborne laser scanning, the

tree canopy reflects the transmitted laser pulses. Several echoes can result from one

transmitted laser pulse, since laser beams can illuminate vegetation at various distances. If the

measurement point density is as high as ten points per square meter, vegetation objects, such

as trees and bushes, can be distinguished visually from the measurement data on the basis of

their geometric form (see Figure 1.1).

Figure 1.1. Side view of the laser scanner data of a forested area.

The laser scanner point clouds collected from the air enable new approaches to forest

inventory. Conventional forest inventory methods can be roughly divided into two categories:

region-based, such as stand- and plotwise inventory, and treewise inventory, which is based

on individual trees. Traditionally airborne materials, aerial photographs or satellite images,

have been extensively applied to region-based inventory, since the advantage of these data is

the recording of large areas in a single shot. However, the disadvantage of satellite imagery is

its small scale, i.e., several individual trees mixed into one pixel, and its limited quality in

recording three-dimensional (3D) forest geometry. Today, in order to collect attributes related

to individual trees, field measurements have to be conducted. Standwise forest attributes can

be calculated on the basis of individual trees and sample plots. This approach, however, is

time-consuming and expensive.

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The airborne laser scanning of forest enables inventories of large areas while simultaneously

providing three-dimensional (3D) information about individual trees and their parameters,

such as tree height and crown width. If needed, the results from individual trees may be

generalized to stand level by applying a stand boundary map. Increasing the flying altitude

increases the width of the measurement stripe, which can be measured with single fly pass,

thus the energy of light restricts high altitude measurement. However, the disadvantage is that

the airborne view of the forest is limited, since tree crowns shadow the lower parts of the tree

and trunk (Figure 1.2). A tree is a vertically oriented object (Figure 1.3) and the majority of its

3D surface is unseen when viewed at a steep angle. In order to provide measurements from

the shadowed area, the laser light should be capable of penetrating through the canopy and

also providing energy to carry the reflected pulse back to the receiver. In reality, it is

questionable whether 3D tree geometry from the lower parts of the tree crowns is recorded by

airborne laser. In addition, laser pulses are not directed to treetops or other targets, but

distributed to the measurement stripe by means of a scanning mechanism. Therefore all tree

parameters are retrieved from laser-derived models or surfaces as secondary parameters

instead of as direct measurements. This thesis focuses on answering which individual tree

parameters may be extracted from the data, and how accurate values of parameters can be

assumed to be.

Figure 1.2. Airborne view. Figure 1.3. Terrestrial view (Rönnholm, 2004).

The approach, laser scanning assisted forest inventory, provides information that has not been

obtained previously. Tree geometry parameters below the canopy surface are problematic

even when traditional terrestrial measurements are applied. Trunk locations may be

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determined by tacheometer measurements and tree heights may be obtained by hypsometer

measurements. The crown widths and profiles, however, are not easily registered with a

tacheometer or other positioning instrument. In order to gain this same information in

terrestrial measurements, a new method, side view imaging of the trees, was tested in the

thesis. The method also provides reference material for laser-derived tree attribute

comparisons.

1.2 Previous studies

The promising nature of airborne laser scanner data has inspired several research scientists to

explore the applicability of laser material to forest inventory. The studies started around 1980

(Solodukhin et al., 1977; Nelson et al., 1984; Aldred and Bonnor, 1985; Maclean and Krabill,

1986) and concentrated on using a profiling system for forest height, stand density, tree

species, and biomass estimation. The basics of using laser measurements for forest inventory

were established at that time.

There are numerous studies concentrating on certain individual tree attributes. The most

researched parameter is tree height, which may be applied as an input parameter in timber

volume estimation models. Hyyppä and Inkinen (1999) were the first to demonstrate the

possibility of measuring treewise information using airborne laser scanning and of adopting

the retrieved parameters (height, crown width) in forest inventory calculations. Their results

indicated that the height of trees may be measured to an accuracy of better than 1 m even

though tree heights are underestimated. Similar results have been obtained later by Persson et

al. 2002, Gaveau and Hill, 2003, Leckie et al., 2003, Yu et al., 2004, Maltamo et al., 2004a. In

these studies, the typical approach has been to process the forest canopy as a surface that is

obtained from laser scanner data. The forest canopy surface is constructed as a triangulated

irregular network (TIN), contour or grid model. Magnussen and Bouldewyn (1998)

introduced a geometrical model that successfully predicted the mean difference between the

laser canopy heights and the mean tree height. The model explained why estimation of stand

heights from laser scanner data based on maximum canopy height value in each cell of a fixed

area grid (e.g. Næsset 1997b) has been successful in practice. Magnussen, Eggermont and

LaRiccia (1999) introduced two recovery models that could be used to obtain tree heights

from laser height measurements.

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In Yu et al. (2004), the capability to measure forest height growth and to detect harvested

trees from multitemporal laser surveys was demonstrated.

There are few studies focusing on tree geometry below the canopy surface. The vertical

structure of forest has been studied by Friedlaender and Koch (2000) by analysing statistically

the penetration of a laser pulse in forest canopy. In Maltamo et al. (2004), the existence and

number of suppressed trees was examined. This was carried out by analysing the height

distributions of reflected laser pulses. Height percentiles of the distribution of canopy heights

have been also used as predictors in regression models for the estimation of mean tree height,

basal area and volume (e.g. Lefsky et al., 1999; Magnussen et al., 1999; Means et al., 2000;

Naesset, 1997a,b; Naesset and Okland, 2002; Naesset, 2002). In these approaches the results

are gained for forest stands instead of individuals trees. In Pyysalo and Hyyppä (2002),

another approach was introduced to describe the tree crown using vector polygons bounding

crown sides and line estimating the shape and location of the trunk. Finding tree locations can

be also obtained by detecting image local maxima using image processing methods (e.g.

Geogeon and Moore 1989). These so-called segmentation-based methods are discussed in

more detail in chapter 5.3.

1.3 The objectives and structure of the thesis

The objective of this study was to extract features of individual trees from laser scanner data

applying vector models. Vector models were implemented in the following stages: point

classification, digital terrain model production, extraction of points from each individual tree,

and vector model creation. Different laser scanned data sets were applied in this study, which

enabled the comparison of data sets and their parameters. The sub-objectives of this thesis

were:

1. to develop a method for constructing vector models of single trees from airborne laser

scanner data

2. to develop a method for measuring reference data from side view images

3. to evaluate the accuracy of the tree geometry features derived from the vector model

and

4. to consider the effect of different laser scanning parameters in the vector model

construction process.

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The input data, workflow steps and sub-objectives are illustrated in Figure 1.4.

The topic of digital terrain model (DTM) production is discussed briefly in this thesis, since

errors in ground altitude have an impact on tree height extraction. Previous studies concerning

DTM are introduced in chapter 5.2.

Laser Sacnnerdata

Side view images

Hypsometermeasurements

Tacheometermeasurements

MAT

ERIA

LSM

ETH

OD

S

Objective 1:Applying

vector models

Objective 3:Comparison of features extracted from vector models to reference materials

Objective 4:Analysis of the impact of laser scanning parameters

Objective 2:Applying

side view image measurements

Figure 1.4. Objectives of the thesis.

However, this thesis is limited to the discussion of features that are directly measurable from

laser scanner data, and therefore tree parameters such as diameter at breast height or volume,

which are typically estimated from other parameters, are not considered.

This thesis has eight chapters and a structure with introduction, materials and methods

followed by results. The introduction in Chapter 1 includes a short description of the work,

objectives of the thesis, and a summary of previous studies. Chapter 2 presents the technical

13

description of laser scanner and system parameters. The test area, three different laser scanner

systems, and laser scanner data sets applied in the study are introduced in Chapter 3.

The first method to be explained is the side view imaging of trees for collecting reference data

in Chapter 4. The second method, vector model creation, is described in Chapter 5. These

chapters are followed by the results of the comparison of applied models to reference data and

the analysis of the results in Chapter 6. The laser scanner parameters that affect the

reconstruction are discussed in Chapter 7, and finally conclusions and discussion are provided

in Chapter 8.

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2 AIRBORNE LASER SCANNING 2.1 Overview of laser scanner measurement An airborne laser scanner (ALS) is a helicopter- or airplane-mounted measurement system

that provides 3D point clouds from the measurement area. The laser transmitter emits a laser

pulse, typically at the specific wavelength, receives the backscattered return, and determines

the distance to the object based on the time of flight. With the scanning mechanism, the area

is covered with laser pulses in the across-track direction. The ALS system also includes a

positioning system, so that measured distances can be calculated to vector origins with co-

ordinates.

Scanning angle

Distance vector

Vector origin DGPSIMU

Beam divergence angle

Figure 2.1. Principle of laser scanning

The name LASER is an acronym of the words Light Amplification by Stimulated Emission of

Radiation. Laser light has physical characteristics suitable for remote sensing ranging, as the

signal is highly coherent and therefore powerful and directional. The predecessors of today’s

laser scanners include the LIDAR (profilometer) and bathymeter, for example (Rönnholm,

2005). The first airborne lasers were adjusted for direct measurement in one stable direction

only, and therefore the measurement pattern from one flight line was a profile. In airborne

3D-profilometer a laser-camera combination was introduced; the camera captured surface

illuminated by laser line (Haggrén et al., 1995). The combination of inertia measurement and

Global Positioning System (GPS) in 1980 increased the positioning accuracy, and a scanning

mechanism to distribute pulses across the track was applied. The first laser scanner came on

the market in 1990, and since then they have been used increasingly often for operational

surveying purposes. In recent years, the development of laser scanners has introduced new

15

measurement modes, increased measurement frequency, and included digital cameras in the

systems.

The laser scanner implementation usually includes four components: (1) laser range finder

(LRF) measurement unit, (2) positioning unit, (3) scanner and (4) data control and possessing

unit (Figure 2.2) (Wehr and Lohr, 1999). In addition, digital cameras are included in several

systems. This chapter introduces these components and the principle of laser measurement.

Laser scanning accuracy and point density are considered in sections 2.6 and 2.7.

DGPS

IMU LASER CAMERA

POSITIONING UNIT

DATA CONTROLLING AND POSSESSING UNIT X,Y,Z

Figure 2.2. Laser system components (Wehr & Lohr, 1999)

2.2 Laser range finder

The laser ranging unit contains the laser transmitting and receiving components. Most

airborne lasers are pulse lasers, which emit short pulses and measure the time of flight of each

pulse. A ranging vector length is derived according to Formula 2.1. The other principle of

laser measurement consists of transmitting and receiving a continuous sinusoidal modulated

signal. In such continuous wave (CW) systems, the time of flight is derived from the phase

difference between the transmitted and received signal and the number of full wavelengths

(Formula 2.2) (Wehr and Lohr, 1999). Due to the limited amount of CW lasers used for

airborne surveying purposes, the following text concentrates on pulse laser measurement

properties.

LtcR ⋅⋅=21 (2.1)

TnTt pL +⋅=

πφ2

(2.2)

16

R range, c speed of light 300 000 000 m/s,

time of flight of the laser pulse Lt

pφ phase difference,

n number of full wavelengths T travelling time of full wavelength The emitted pulse widens during the time of flight of the laser pulse according to the beam

divergence angle and illuminates an elliptical area (equivalent to an instantaneous field of

view (IFOW)) when reaching the surface (Figure 2.3). The beam divergence typically ranges

between 0.3 and 2 mrad depending on the system (Wehr & Lohr, 1999). In some laser

scanner implementations, beam divergence is an adjustable parameter. A large IFOW is

desirable when the objects to be scanned are small in size, for example tree tops and power

lines. In contrast, the positional accuracy of a laser pulse is inversely proportional to the

illumination area, and therefore widening the IFOW will decrease the accuracy.

Beamdivergence

angle

IFOV

Pulse rise time

time

Figure 2.3. IFOV Figure 2.4. Pulse rise time.

The receiver component has been mounted so that transmitted and received pulses share the

same optical path (Wehr and Lohr, 1999). The field of view (FOV) in reception must not be

smaller than that in transmission. However, the FOW value is limited by diffraction, which

causes image blurring (Wehr and Lohr, 1999). A pulse is registered as a return signal when

the threshold of amplitude is reached or according to some other algorithm (e.g. the slope of

the return signal). The reception unit registers one or several returns from the received echo

by analyzing the return signal shape. First, last and even some middle echoes are registered by

some systems. If the whole pulse is reflected from solid surfaces perpendicular to the

direction of pulse transmission, the return signal has one steep peak and only one point is

registered (Figure 2.4). However, even a very short emitting period, such as 4 ns, produces a

17

light line of 1.2 m, and the pulse is typically reflected from multiple surfaces at different

distances and the return signal shape is more complex (Figure 2.5).

Most systems provide intensity information of the return signal. These values are typically not

absolute but scaled to a number of interval that the data possessing unit is capable of storing.

During the last few years, a new recording mode has been introduced, i.e. full waveform.

Instead of registering just one or few intensity values from the echo, for example 128 samples

from the signal are collected (Figure 2.5). With recording interval for 0.5 ns the range

between adjacent samples becomes 15 cm and the length of whole measurement vector 19.2

m. Within this space, the interaction of pulse to reflecting objects is recorded in one

waveform measurement.

Ground, only echo

Vegetation, two registered echoes

Figure 2.5. Signals from two different targets, ground only and forest canopy and ground. The horizontal red and

green lines are locations where instruments determined returned echoes.

2.3 Scanner

In order to distribute pulses across the area to be covered, a scanning mechanism is applied.

The second dimension in the imaging is achieved when the platform moves forward. The

measurement frequency refers to the pulse repetition frequency (PRF), and the scanning

frequency indicates how many across-track scans are applied during one second. The most

common scanning patterns are parallel lines (i.e. pushbroom scanning), zig-zag pattern and

elliptical scans (i.e. conical scanning with fixed incidence angle) (Figure 2.6). The technical

implementation in zig-zag scanning is accomplished by shifting the direction of pulse

transmission with swivelling mirrors or prisms, and in pushbroom scanning by transmitting

pulses through linear array fibres (Wehr & Lohr, 1999). The scanning may be one- or two-

directional depending of the deflection unit. The scanning frequency and pulse distribution

18

function are results of the beam distribution mechanism. Point density across the scan is not

constant, but increases or decreases from nadir below the platform towards the cover area

(Figure 2.6).

Figure 2.6. Left: distorted conical scanning, middle: two-directional zig-zag –pattern, right: one-dimensional

zig-zag pattern.

2.4 Positioning unit and data controlling and possessing

The positioning system of a laser scanner implementation has two units, the differential global

positioning system (DGPS) and the inertia measurement system (IMU). In addition, a DGPS

instrument should be placed near the study area or a virtual GPS solution should be used.

These together provide orientation and vector origin for each measurement. Laser range

measurements are synchronized with positioning unit values according to a time stamp

attached to each data item. The frequency of DGPS measurement is typically 1 Hz. Therefore

DGPS location measurements are supported by higher-frequency IMU. Finally the co-

ordinates for each vector origin are interpolated from the closest known locations. During the

flight, collected material is stored in the fourth component of the laser system, the data

controlling and possessing unit. Two examples of computer configuration are found in

Baltsavias (2000) and Wehr and Lohr (1999).

2.5 Additional components

Medium-format digital cameras, where image frame is for example 2032×3056 pixels, can be

included on several laser scanning implementations. Aerial digital images are taken during the

laser range capture and synchronized with location information from the positioning unit and

laser measurements using a time stamp attached to each image. In order to solve orientation of

images, co-ordinates for image capture locations are determined. A typical image capture

interval is 2-3 seconds, depending on the flying altitude and velocity.

19

2.6 Laser scanning accuracy

The accuracy of a laser-derived 3D point depends on three main factors: (1) ranging accuracy,

(2) accuracy of the distance vector origin and (3) direction of the laser pulse (Baltsavias,

2000). In addition, preprocessing steps such as strip matching and co-ordinate transformation

influence the final co-ordinates.

The ranging accuracy is influenced by the pulse transmission and receiving distance and

cover material of the objects (Baltasavias, 2000). The optimal way of transmission is to send

steep and short pulses. The pulse rise time, which is independent of the pulse width,

determines the time required for an emitted beam to increase from 10 % to 90 % of the

maximum power value (Figure 2.4). Even a short rise time, such as 1 ns, will correspond to

15 cm in range. Reflections from objects shorter than the pulse length are overlapping, and

therefore the challenge is in analysing the returning signal shape in order to determine the

return location. The power of the return signal corresponds to the distance and incident angle,

but also to the reflectivity of the surface material. Glass surfaces, for example, may reflect

pulses out of the area of reception, and a forest canopy surface may cause several weak

signals, which are mixed with background noise. According to Wehr and Lohr (1999), the

ranging accuracy depends on signal-to-noise ratio (S/N), which is affected by factors such as

the power of the received signal, input bandwidth, background radiation, amplifier noise etc.

The positional accuracy of a single laser pulse is inversely proportional to the size of the

IFOV, since all reflections from the IFOV area are considered to originate from the pulse

transmitting direction (Figure 2.3). As mentioned earlier, the beam divergence angle is

adjustable in some systems. In other systems the value is constant, and therefore the size of

the beam ellipse may only be changed by changing the flying altitude.

The accuracy of the distance vector origin is a result of the positioning and orientation

measurements, i.e. the behaviour of DGPS and IMU. According to the POS unit

manufacturer, the accuracy of their combination should be better than 0.1 m. (Wehr and Lohr,

1999). This accuracy is achieved when the GPS measurement is supported by a DGPS

receiver stationed to a local ground control point or a Virtual Reference Station (VRS) is

applied from stable GPS stations. In addition, systematic parameters must be considered for

each surveying flight. These are the three mounting angles - roll, pitch and heading - and the

20

shift between the fixed platform co-ordinate system and the positioning unit and laser (Wehr

& Lohr, 1999). Parameter values are usually solved in preprocessing by applying calibration

data, which is typically collected by flying over flat areas, i.e., football fields, airports or

water surfaces, from different directions.

The determination of the laser pulse transmitting direction also depends on the quality of INS.

The quality is affected by the INS measurement frequency, postprocessing method, and

integration of GPS. (Baltsavias, 2000)

If all error sources are combined, the typical horizontal accuracy of single laser measurement

is approximately 20-30 cm and the vertical accuracy is approximately 10 cm, when scanning

is carried out from a flying altitude of 400 m (Baltsavias, 2000). The accuracy decreases when

the flying altitude increases (Baltsavias, 2000). Recently some manufacturers have advertised

laser implementations with a positional accuracy of 42 cm when scanning is carried out from

2000 m. The accuracy is moderate compared to traditional aerial photogrammetry. However,

the power of laser scanning is not in single accurate measurements, like in photogrammetry,

but in multiple measurements, which cover the area and can, for example, be averaged to

represent surfaces. Point density is maybe the most important laser scanning data feature and

discussed in more detail in the next section.

2.7 Point density in laser scanner data

In different airborne pulse laser systems, measurement frequency varies from a few thousand

Hz up to 150 kHz. To avoid confusion, the previous transmitted pulse has to return before the

next one is emitted, and therefore measurement frequency is limited when high flying

altitudes are applied (Batsavias, 2000). Previously flying altitudes were restricted because of

the low power of returning signals rather than confusion in pulse reception. However, some

systems today are operational from up to 6 km (www.leica-geosystems.com), and in these

measurements the frequency is limited by the time of flight of pulses. The number of

transmitted pulses does not directly correspond to the number of measured points, since

several echoes are registered from one pulse. Objects and the vertical distribution of the cover

area affect the point density.

21

Pulse distributionfunction shape Pulse distribution

function values

Shadow area

Object areaPulse amount

Scanner

Figure 2.7. Laser scanning parameters and number of pulses

The connection between laser scanning parameters and point density is characterized in

Figure 2.7. The scanning frequency and scanning mechanism determine the shape of the pulse

distribution function. In conical scanning, the amount of returns increases towards swath

edges in a non-linear way. In parallel scanning, the number of pulses decreases in a linear way

towards the swath edges. The flying altitude is a scale factor; the higher the platform flies,

the wider the swath becomes and also the distance between points increases. In the flying

direction, pulse density is affected by the velocity. In order to attain a high pulse density, the

platform should fly slowly at a low altitude.

Scanning angle

Figure 2.8. Shadow area and scanning angle

Also the scanning angle has an impact on pulse distribution. Vertical objects, which beams

are not able to penetrate, cause shadow areas behind them according to the scanning angle

(Figure 2.8). There is no shadow area immediately beneath the aircraft (scanning angle 0°).

However, near the swath edges, where pulses are transmitted in an oblique direction, shadow

areas may become large. To calculate the number of pulses reflected from a certain object, the

pulse distribution function should be integrated over the object surface projection and the

shadow area, also taking into account whether the object itself is in the shadow area of some

other object.

22

3 MATERIALS

3.1 The Otaniemi test site

In the study, five different laser scanner data sets from the Otaniemi test site were applied. This

suburban 16 hectares test site (60° 11.147' lat, 24° 49.771' lon) contains a university campus,

buildings, trees, roads and a sports field and is located in the town of Espoo, southern Finland.

The active research area was rather limited, (260 m long and 265 m wide) because it had to be

covered by all laser data sets. Elevation differences in the area are small; the elevation ranges

between 0 – 12 m. The main tree species were spruce (Picea abies), pine (Pinus sylvestris) and

birch (Betula pendula).

Figure 3.1. The Otaniemi test area; ortho photo on the left, digital crown height model in the middle, and digital

terrain l model on the right.

3.2 Laser scanner implementations

The laser measurement campaigns were carried out between 2000 and 2005 with three different

laser systems, Toposys –1, TopEye MK I and TopEye MK II.

Toposys–1 was a German-manufactured laser scanner system operational from both airplane and

helicopter. The system is no longer marketed by the company, but has been replaced with second-

generation system. A scanning mechanism was carried out through 128 light fibres in an array,

resulting in a one-directional oblique line pattern (Figure 3.2). The measurement frequency was

high, 83,000 Hz. However, point spacing in the across-track direction was significantly lower

23

than in the along-track direction. When scanning was applied, the beam ellipses overlapped in the

flying direction, whereas in the same time there was still space between adjacent scan points. The

point distance was not constant during one scan, but increased towards the strip edges

(distribution function in Figure 3.3). In Toposys-1, the scanning angle varied from +7° to -7° and

either the first or last echo was registered from one flight line.

The Swedish company TopEye has two laser scanner systems, MK I and MK II. The older

model, MK I, is operated from a helicopter and pulse distribution is carried out using a rotating

mirror. The technique produces an oblique line pattern (Figure 3.2) where the scanning angle

varies from +20° to -20°. Compared to the Toposys system, instrument measurement frequency is

low, 8000 Hz, but the advantage is a more uniform sampling pattern, in which the distance

between points in the along-track direction is close to the distance between points in the across-

track direction. In this system, the point distance along the scan increases towards the strip edges

(Figure 3.3). The first, last and even five echoes (coordinates and intensity) in the middle (if they

exist) are registered by the instrument. An additional component is a Hasselblad digital camera,

which is mounted to the system and captures images at intervals of 2.5 seconds.

The MK II system is the most recent laser scanner applied in the study. The scanning pattern is

an ellipse that rotates in the flying direction (Figure 3.2). Due to the scanning pattern, every part

in the cover area is scanned twice, first when the front edge of the ellipse crosses the object and a

second time when the back edge follows. The scanning angle varies between 14° and 20°. The

difference compared to the other systems is the shape of the point distribution function (Figure

3.3). The number of pulses is lowest just below the flight line and increases towards the edges of

the strip. In MK II, the measurement frequency is 50,000 Hz and the measurement mode is the

same as in MK I: the first, last and even five echoes and intensities are registered by the

instrument. The system can also be used in the full waveform measurement mode.

24

Figure 3.2. Toposys –1 data on the left, Topeye MK II data in the middle and Topeye MK I data on the right. An

arrow expresses the flying direction.

Poin

t den

sity

PARALLEL LINEPATTERN

Strip width

Poin

t den

sity

ELLIPTICAL PATTERN

Strip width

Figure 3.3. Point distribution function for parallel line and elliptical patterns.

3.3 Laser scanner data sets

The Otaniemi test area was scanned from altitudes 200 m and 550 m using TopEye MK I

implementation, from 400 m and 800 m altitudes using Toposys implementation and from 300 m

altitude using TopEye MK II implementation. Due to the differences in flying altitude and

systems used, the beam sizes, strip widths and average point densities varied in data sets, and

they are listed in table 3.1.

Table 3.1. Laser scanner data sets and their parameters in Otaniemi test site.

Scanner Year Flying Height Beam Size Scanning Angle Average

Point Density /m2 Strip Width

TopEye MKI 2002 200 m ~ 20 cm +-20° 2.3 110 m

TopEye MKI 2002 550 m ~ 55 cm +-20° 1 360 m

TopoSys 2000 400 m ~ 20 cm +-7° 9.9 100 m

TopoSys 2000 800 m ~ 40 cm +-7° 1.8 180 m

TopEye MKII 2004 300 m ~ 30 cm +14-20° 19.6 240 m

25

3.4 Acquisition of reference material

Tacheometer measurements were carried out in summer 2002 for two purposes: to collect ground

control points for digital terrain model evaluation and to locate tree trunks. The tacheometer was

registered to a local co-ordinate system, and afterwards the points were transformed to the

Finnish National Coordinate System (KKJ 2). The 250 ground control points were measured

along four cross sections. The points were classified into three classes according to the spatial

openness of the area, meaning access from the air, and ground cover. Classes were tree-shadowed

grass area, asphalt and grass.

The trunk location measurements were carried out as illustrated in Figure 3.4. In order to derive

the trunk centre, the direction was captured in the middle of the tree trunk, but distance

measurement was carried out to prism beside the trunk. Also these points were transformed to the

KKJ 2 system.

Direction measurement

Distancemeasurement

TRUNK

PRISM

Hypsometer

Tree height

Crown height

Figure 3.4. Tacheometer trunk location measurement. Figure 3.5. Hypsometer measurement.

In addition tree heights were recorded. Fifty trees were chosen randomly from area as test trees

including spruces, pines and birches. However, requirements of the measurement were taken into

account. The measurements were performed with a hypsometer from the distances of 20 m or 15

m. The distribution of tree heights is illustrated in Figure 3.6, and information on tree species is

given in table 3.2. Also crown depth was determined as follows: the height of the branch-free

area was measured and reduced from the tree height. However, in many cases the distinction

between branch and non-branch parts was difficult, even impossible, as the trees were not

symmetrical, and therefore the accuracy of this method was low. According to the official

26

definition of lower boundary of crown, single braches, which are below two dead year branches

are not included to the crown.

In addition to tacheometer and hypsometer measurements, the side view imaging of trees was

carried out during the field measurements. The motivation for this new technique and its method

and workflow are described in more detail in the next chapter.

0

5

10

15

20

25

30

35

12 14 16 18 20 22 24 26 28 30 32

Height (m)

fi/n,

%

Figure 3.6. Distribution of tree heights; fi is frequency

in class and n total number of samples.

Table 3.2. Tree species.

Tree species Number of

samples

Birch (Betula pendula) 24

Spruce (Picea abies) 10

Pine (Pinus sylvestris) 15

27

4 SIDE VIEW IMAGES

4.1 Motivation

A traditional forest inventory applies field instruments such as hypsometers, relascope,

bussole, measurement scissors, tacheometer, and laser pointer in order to acquire information

about tree geometry. Inventory is typically carried out in systematically located circular plots

where measurement takes place in the plot center. Single tree trunk locations are determined

with respect to the plot center, whose location is measured for example with a GPS or

tacheometer, or if the inventory has taken place previously, derived from a geographic

information system (GIS) database or a tree location map. Tree heights are acquired with

hypsometer and trunk diameters at breast height are obtained by measurement scissors.

However, acquiring the crown shape by traditional methods is problematic: if tacheometer

measurements are applied, the prism should be moved around the crown sides, not just near

the lowest branches but also to the tips of the largest branches. Another approach is to apply

measurement with trigonometric methods; the tips of the branches are projected visually to

the ground level and their locations are determined. The elevations of the branch tips are

measured separately with vertical angles. Unfortunately both of these approaches are very

laborious and time-consuming. Therefore a method of side view imaging of trees was created

to collect reference material from crown shapes.

In this method, images of trees are captured with digital camera from a terrestrial platform, a

tripod, using determined imaging geometry. Afterwards images are rectified to a plane

parallel to the tree trunk. Crown profiles are acquired in the direction of the image plane. The

idea behind this approach is the assumption that the tree trunk is vertical and branches open

out from the trunk in all directions. It is also expected that the length of the branches in

different height layers does not vary and therefore branches growing towards the camera are

not shadowing the side profile captured by the central projection of the image. Tree heights,

which are traditionally measured by hypsometer, can also be extracted from the images.

28

4.2 Capturing side view images at the Otaniemi test site

The terrestrial images were captured from the Otaniemi test area trees in summer 2003.

Imaging was done with a Nikon E-10 digital camera mounted to a camera platform with

flexible elevation and rotation possibilities. The maximum resolution, 3008×2000 pixels in

image frame, was utilized and the size of the camera aperture was fixed. During the image

capture the camera was manually focused to eternity, like in camera calibration. The same

imaging geometry was applied for all camera poses for the reason that image rectification

parameters would be solved only once and applied to all images as an automatic batch

process.

Before the actual tree imaging took place, several tests were carried out in the field to find the

optimal image capture workflow. For the measurements of crown dimensions both the tree

base and the tree crown top should be visible in the same image. However, images had to be

captured as close as possible in order to do detailed measurement. During the tests it was

observed that even in sparse forests, other trees or other objects typically shadowed the target

tree if the image shooting distance was too long. The camera stand was approximately 1.5

meters high and imaged trees were between 17-27 meters in height. In the ground co-ordinate

system -axis was towards magnetic north, -axis was in gravity direction and -axis

was horizontal and perpendicular towards - and -axes. Respectively, the parameters κ, φ

and ω are rotations between the ground co-ordinate system and the camera co-ordinate

system. The applied tests and experiences resulted in the following image capture workflow:

gz gy gx

gz gy

The image capturing location was chosen at a range of 20-30 meters from the tree.

The camera stand was adjusted according to a bubble level.

The angle κ was set to 90° using a tube level (portrait image).

A reference scale bar was attached to the tree trunk.

The camera was rotated to a position in which trunk was in the middle of the image.

ω was set to 20°, angle was measured using hypsometer.

After capturing the image, the angle φ, which is the azimuth, was measured with a

compass.

The distance to the tree was acquired with a measurement tape.

29

Figure 4.1. Image capture. From left to right: image was taken in a way that the trunk was in the middle of the

image, a reference scale bar was attached to tree trunk, the camera was tilted 20°, the distance to the tree was

measured.

In order to determine crown profiles from more than one direction, an attempt was made to

image the same trees from another direction perpendicular to the first one. Unfortunately, as a

consequence of limited free viewing directions, the image optical axes were seldom at an

angle of 90°. Eventually 15 trees were imaged from one direction and 15 others from two

locations.

4.3 Image preprocessing

In the image preprocessing, lens and sensor distortion errors were removed by applying the

camera calibration. The correction caused the image area to curve towards the image center

from the edges (Figure 4.3, middle). The calibration was carried out for the shortest focal

length f utilized in image capturing. Next, images where rectified to a plane parallel to the

trunk (Figure 4.2).

ω=

Optical axisTrunk

Rectification plane

φ

Figure 4.2. Imaging geometry and rectification plane. The side view (left) and top view (right).

30

Locations of the image corner co-ordinates on the rectification plane were calculated based on

the camera opening angles and image capture geometry. These co-ordinates and traditional

least square adjustment were applied in order to determine eight parameters of projective

transformation from image plane to rectification plane, which was carried out to all side view

images (Mikhail et al, 2001) (Formulas 4.1 and 4.2).

100

111

++++

=yfxe

gyfxeX and

100

222

++++

=yfxe

gyfxeY (4.1)

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡=

⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢

⎣

⎡

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡−−−

−−

......1000

0100

2

1

2

1

0

2

1

0

YX

ggfffeee

xYyxYxxXyxXx

(4.2)

where x, y image coordinates,

X, Y co-ordinates on the rectification plane and

unknown parameters 21210210 ,,,,,,, ggfffeee

Figure 4.3. Original image (left), distortion free image (middle) and rectified image (right).

As a result of the rectification process, objects were geometrically correctly projected on the

rectification plane. The plane was vertical and in the same distance as the tree from the image

capturing position (Figures 4.2 and 4.3, right).

4.4 Image measurement

The image measurements took place in a frame where the origin was located at the tree base,

the tree height determined the maximum vertical dimension, and the maximum crown width

31

determined the horizontal dimensions (Figure 4.4). A scale parameter to transform pixel units

to metric units was obtained from a reference scale bar attached to the tree trunk. Next, tree

crown shape was determined by measuring crown width in horizontal direction. The

measurements were accomplished with 1 m intervals. In addition to the crown profile,

maximum crown width, i.e., the width of the crown projected to the ground, and tree height

were determined from the images.

Tree frame

Horizontal parallel lines

Tree height

Scale barOrigo

Maximum crown width

Figure 4.4. The tree height and crown widths were measured from two images having different viewing

directions.

4.5 Determining the trunk tilt

In forest inventory aided by remote sensing methods, tree trunks are typically assumed to be

straight. Also with instruments used in field measurement, such as hypsometers, the

assumption is the same. The method based on side view images, however, enables the

possibility of determining trunk tilt in cases where at least two tree images are captures from

different directions and the relative orientation of the images is known. Trunk tilt

determination was tested in this study.

The tree top shift sX, i.e., the horizontal deviation from the vertical line initiated from the tree

base, was determined in both images. The trunk was represented as a vector (Figure 4.5,

Formula 4.3)

kcjbiav ++= (4.3)

kcvz = (4.4)

32

where i , j and k are unit vectors in the directions of co-ordinate system axes, a and b are

unknown coefficients and c is the vertical component of the trunk vector. In the presentation

of the vertical part of the trunk vector kcvz = (Formula 4.4), c is the height of the tree. The

unknown parameters a and b are solved from an equation pair (Formula 4.5) where a and bb

are projections of shifts sX1 and sX2 to the co-ordinate system axis (Formula 4.6):

babbbabaaa

−=+= (4.5)

)2/cos(2

2 πφ −=

sXaa , )cos(

1

1φsXbb = , (4.6)

The parameters ab and ba are calculated from angles between image planes and parameters a

and b (Figure 4.5) )2/tan( 2 πφ −⋅= bab and )tan( 1φ⋅= aba . Meanwhile the trunk tilt angles

towards the image planes were calculated from vector components a, b, and c and angles 1φ

and 2φ .

φ1

φ2

aa ab

a

b

bb

ba

IMAGEPLANE 1

IMAGEPLANE 2

sX1

sX2

v

IMAGEPLANE

cv

Figure 4.5. Top view, vector projected to the ground plane (left). Side view, vector projected to the vertical plane

(right).

As expressed in Formula 4.6, determining trunk tilt requires information about the tree top

shift from both images. However, tree crowns may be wide at the top, particularly in

deciduous trees, which makes it difficult to locate tops and decreases the accuracy of

determination of trunk tilt. In addition, it is assumed in the calculation that even if the tree

trunk is in an oblique position, it is still straight. In nature this is generally not true.

Considering these limitations, trunk tilt was not determined for all trees studies in the thesis,

but applied as additional information when the results were analyzed.

33

4.6 Error analysis

In the study, the side view image measurements were compared to laser-derived vector model

features. The assumption was that image measurements were more accurate and they would

be applied as reference material. Therefore, image measurement accuracy was considered

with respect to two major error sources, scale and rotation determination errors. A simulation

was carried out to visualise the error.

During the field measurements rotations ω, φ and κ were determined with simple instruments

that have a moderate level of accuracy, namely a hypsometer, a compass, and a tube level.

The rotation ω, which is around -axis, was measured with a hypsometer. The accuracy of

hypsometer rotation measurement was estimated to be

gx

+ 1° based on the instrument ruler

index and field measurement experience. Since the image plane was rotated in preprocessing

by only 20°, the measurement plane would be at an angle of + 1° instead of in a vertical

position during the determination of crown shape.

The influence of error was simulated by rotating the image plane 1° and –1 ° around the

bottom edge of the image and then calculating the shift of each pixel compared to the correct

image. The tendency towards error behaviour is expressed in Figure 4.6. In both rotations the

effect is largest at the image edges and corners and smallest near the center of the image,

where trees were located in the capture process.

Figure 4.6. Error surfaces. Pixel dx shift for +1° ω error (up left). Pixel dy shift for +1° ω error (up right). Pixel

dx shift for -1° ω error (bottom left). Pixel dy shift for -1° ω error (bottom right).

34

The maximum errors were calculated from surface values according to Formula 4.7:

)max(2 dxDx ⋅= )max()min( dydyDy += (4.7)

)( dcfDs+

= (4.8)

D distance between projection centre and target

(f+dc) distance between projection centre and rectification plane

The maximum errors are illustrated in Table 4.1. In order to compare values with other data

sources, errors were transformed from pixels to metric values. A scale s was expressed as the

ratio of the distance between projection centre and rectification plane and the distance

between projection centre and target (Formula 4.8). During the field measurement, the image

capture distance was typically 25 meters, but all images were taken from a distance of less

than 30 meters. Maximum errors in meters for a distance of 30 m are calculated in Table 4.1.

The rotation κ (around -axis) was adjusted to 90° (portrait image) with a tube level. The

maximum error in rotation measurement was estimated to be

gz

+ 2°. Also in this simulation, the

error was considered for the worst case, which takes place when dimension measurement is

carried out from image edge to edge. For an image with a size of 3945×2928 pixels, the errors

are:

8,1)2cos(/292829284,2)2cos(/39453945

−≈−=−≈−=

dydx

The maximum errors in metric values for a distance of 30 m are calculated in Table 4.1. In

practice, trees typically filled only half or one third of the image frame and therefore the

errors were respectively smaller.

35

Table 4.1. Maximum errors.

Rotation angle Error type Error in pixels Error for distance 30 m

ω=1° dx 62 0.48 m

ω=1° dy 48 0.38 m

ω=-1° dx -62 -0.50 m

ω=-1° dy 46 -0.37 m

κ=2° dx -2.4 -0.002 m

κ=2° dy -1.8 0.014 m

κ=-2° dx -2.4 -0.002 m

κ=-2° dy -1.8 0.014 m

The optical axis of camera was adjusted in the capture process straight towards the tree trunk.

The rotation φ, which is around -axis, was measured with a compass. This method was

perhaps the most inaccurate of all methods of rotation determination, since the compass is

affected by, for example, the presence of power lines or magnetic targets in the area. The

declination between the compass north and the Finnish local co-ordinate system vertical axis

is 6° in southern Finland. However, the error in φ measurement does not affect the precision

of image measurements. The projection of objects on the rectification plane, perpendicular to

the imaging direction, are geometrically correct. The effect of error can only be noticed when

measurements are compared to other data sources. Trees with a symmetric trunk would look

the same from all directions and the error would be minimal. Unfortunately tree shape is

typically complex. The final effect of the error in φ is therefore dependent on the measured

object itself, which is beyond the scope of simulations.

gy

Finally, the error in scale measurement was considered. Images are result of perspective

projection, and therefore objects at different depths have different scales. The determined

scale s is correct only for objects located on rectification plane like the scale bar. It is assumed

that the tree crown is widest in the rectification plane. However, tree crowns are irregular in

shape. Typically the image capture distance was 25 m and the crown width was 5 m.

Therefore the distance from the crown to the camera varies from 22.5 to 27.5 m. Within this

interval the change of the scale is 0.006 to 0.0073. For example if crown width measured

from the image is 1200 pixels and its real distance to the camera is 24 m instead of 25 m, the

error in dimension is 36 cm.

36

When all error sources are taken into account, the general accuracy of this method was found

to be better than the accuracy of laser-derived vector models. The calculations in this chapter

were carried out for the worst scenario, which is not the typical situation. Therefore this

method was found to be suitable for reference material. The accuracy of this method could be

improved with more accurate rotation measurement.

4.7 Method analysis

In this chapter, a method based on side view images is suggested for measuring tree crown

dimensions. The advantage of the method is that it does not require expensive instruments,

such as terrestrial laser scanner or real-time-kinematic GPS, but relatively cheap instruments

like digital camera, camera platform, hypsometer, compass, and reference scale bar can be

applied. Collecting reference data with this method is also faster than with the traditional

method. The automation level of image preprocessing is high, because the relative orientation

between the image plane and the rectification plane is constant. Image rectification is

therefore a batch process. Image rectification does not require known points in the image area

or targeting.

However, the method based on side view images also has several limitations. The basic

problem is whether imaging from two directions is enough for crown modelling. Even if the

images are captured from two directions, the measurements are only two-dimensional. The

reason for not applying the traditional 3D stereo imaging procedure is the purpose to keep

imaging simple and efficient. Stereo measurements would require measuring corresponding

points for determining relative orientation and control points for exterior orientation. With an

irregular tree crown, these would be difficult to find. The level of tree crown shape

reconstruction may be increased if images are captured from more than two directions.

37

5 THE VECTOR MODEL PRODUCTION METHOD

This chapter introduces a method for producing vector models from original laser points and

presents features extracted from these models. The workflow steps are (1) classification of

laser points, (2) extraction of laser points from single trees, (3) vector model creation and (4)

feature calculation process. In order to acquire information about tree height, a digital

elevation model of the forest floor is required. The DTM of the cover area may already be

available from another source, such as a national elevation model or preferably derived from

laser scanning data, but since errors related to the DTM have an effect on values of the

extracted tree heights, DTM production is described as one workflow step in the thesis. For

the purpose of automating the whole process, also the issue of segmentation is considered.

The workflow is as described in Figure 5.1.

5.1 Preprocessing of airborne laser data

The preprocessing of airborne laser data is typically carried out by the company in charge of

the laser measurement campaign. Laser ranges, DGPS and IMU measurements are combined

based on a time stamp attached to each data source, and co-ordinates are calculated for laser

pulse reflection points and digital camera image capture locations. However, this so-called

direct orientation approach is affected by many errors, such as improper satellite geometry

during the laser scanning, calibration errors, and misalignment of the laser scanner and digital

camera. A strip adjustment is carried out in order to match data from adjacent laser strips and

to reduce the impact of these errors. The matching process typically applies data from

calibration areas, which are flown repeatedly during the scanning. A detailed description of

matching is found in Burman (2000). Since measurements are in the global co-ordinate

system WGS84, they are transformed to the local co-ordinate system afterwards.

In this thesis, the local co-ordinate system was the Finnish KKJ 2, and the laser data utilized

in the study was transformed to the system by applying one of the two different WGS84 –

KKJ 2 transformations. The Finnish Geodetic Institute has published one transformation

program based on equations from Ollikainen (Ollikainen, 1993). Also the National Land

Survey of Finland runs a program to transform co-ordinates from WGS 84 to KKJ either in

northern or southern Finland based on equations published in JHS 154 (JHS 154). The

transformation based on Ollikainen’s model is considered to be more accurate. Therefore

38

results from the National Land Survey transformation were adjusted to match that

transformation model. In the Otaniemi area, the difference is a simple constant shift in the x-

and y- directions; dx=0.641 and dy=0.268. The geoid fluctuates in the Otaniemi area between

–18.062 and 18.054 m (dz=0.8 cm). The variation was considered to be minor and therefore

constant shift was applied to z- co-ordinates.

Preprocessing

Classification

Digital terrain model

Digital environmental model

Digital tree height model

Segmentation surface

Tree point extraction

Vector model creation

Original points

Figure 5.1. Vector model workflow.

5.2 Point classification and DTM

Several returns are typically obtained from one emitted pulse in forested areas, since part of

the beam is reflected from the tree canopy and other parts from the forest floor. Typically the

first returns are reflections from objects close to the platform, i.e., the forest canopy, and

correspondingly the last returns are reflections from the forest floor, in case the beam has

been able to penetrate through the canopy. However, first and last echoes occasionally

originate from both ground and vegetation, which is why a classification algorithm is applied

to separate these. The common approach in classification is to determine ground points first,

since the ground is continuous surface with a smoothly varying altitude. Afterwards,

vegetation points are classified based on the distance from the ground.

Several algorithms have been developed to obtain a DTM from laser scanning point clouds.

Kraus and Pfeifer (1998) developed a DTM algorithm based on distinguishing laser points

into terrain points and non-terrain points using an iterative prediction of the DTM and weights

attached to each laser point depending on the vertical distance between the expected DTM

39

level and the corresponding laser point. Pyysalo (2000) developed a modified recursive

classification method for DTM extraction, where all points within a vertical distance of 60 cm

from the lowest expected ground level were included equally in the next DTM model

calculation. Elmqvist (2001) estimated the ground surface by employing active shape models

by means of energy minimization. The active shape model behaves like a membrane floating

up from underneath the data points. The energy function is a weighted combination of internal

and external forces. The start state is a plane below the lowest point in the data set. Sithole

(2001) and Vosselman and Maas (2001) developed a slope-based filtering technique, which

works by pushing a structuring element up vertically. In the method used by Wack (2002),

non-terrain raster elements are detected in a hierarchical approach that is loosely based on a

block-minimum algorithm.

The method applied in this thesis comes from Axelsson and has been implemented in

Terrascan software. Axelsson (1999, 2000, 2001) developed a progressive TIN densification

method where the surface was allowed to fluctuate within certain values controlled by

minimum description length, constrained spline functions, and active contour models for

elevation differences. Ground points were connected in a TIN. A sparse TIN was derived

from neighbourhood minima, and then progressively densified to the laser point cloud. During

every iteration round, points are added to the TIN, if they fall within the defined thresholds.

The algorithm was applied to create five different DTM, one from each data set. However,

before ground classification took place, low points, which are multi-path reflections, were

separated to avoid their confusing effect on algorithm behaviour. The classification was

carried out by applying parameters illustrated in table 5.1. The result was that approximately

30 % of points were defined as ground. The DTM was exported as a grid model with a pixel

size of 1 m (Figure 5.3).

Table 5.1 Ground classification parameters.

Maximum building size 60 m

Maximum terrain angle 80°

Maximum iteration angle to plane 6°

Maximum iteration distance to plane 1.4 m

Iteration angle was reduced when edge length was less than 5 m

40

Figure 5.2. Original points in TopEye MK I data. Figure 5.3. Exported DTM.

5.3 Tree crown delineation and point extraction

In the next workflow step, tree point extraction, the reflections from each tree are

geometrically separated from surrounding points. Two approaches were applied in this thesis,

namely the manual and segmentation-based approach. In both approaches, additional surfaces

were required, a digital surface model (DSM) and a canopy height model (CHM). In the DSM

each surface cell obtained its value from the highest measurement within pixel (Figure 5.4).

Altitudes were relative to the sea level. The CHM was obtained by reducing the DTM from

the DSM. In the resulting CHM, elevations were from the ground surface (Figure 5.5).

Figure 5.4. Digital surface model (DSM). Figure 5.5. Canopy height model (CHM).

In the manual extraction approach, vector polygons were drawn by hand around each tree

(Figure 5.6). The CHM was applied below the vector platform (MicroStation) in order to

visually guide delineation. After this, laser points within each vector element were exported to

separate files (TerraScan) from all different laser data sets.

41

Figure 5.6. Manual tree crown delineation

The segmentation approach performed automatic delineation of tree crowns. Segmentation is

an advanced image processing method, which has been introduced in forest inventory and

applied from large-scale aerial images up to high-resolution satellite images. The methods

used in laser scanning have been applied in similar studies using aerial imagery with a very

high resolution, with the distinction that the image is replaced by the crown DSM or the

CHM. For operational forest inventory automatic segmentation is a minimum requirement,

since the manual approach is too time-consuming.

Several algorithms have been developed for automatic tree crown segmentation. According to

Gougeon and Leckie 2003, two parts of the process are tree detection and full crown

delineation. Tree locations can be found, for example, by detecting image local maxima (e.g.

Geogeon and Moore 1989). The method provides that the filter size and image smoothing

parameters are appropriate for the tree size and image resolution. With that assumption the

approach works relatively well with coniferous trees (see also Gougeon and Leckie, 2003).

After finding the local maxima, the edge of the crown can be found using the processed

canopy height model.

Hyyppä and Inkinen (1999) were the first to demonstrate laser scanner aided forest inventory

by finding maxima of the Digital Tree Height Model (DTHM) and applying segmentation for

edge detection. The method was tested together with two other segmentation algorithms in

Finnish, Austrian, and German coniferous forests, and 40 to 50 % of the trees could be

correctly segmented. Persson et al. (2002) improved the crown delineation and could link

71% of the tree heights with the reference trees. Other attempts to use DSM or CHM for

individual tree crown isolation or crown diameter estimation have been reported by e.g.

Brandtberg et al. (2003), Leckie et al. (2003), Straub (2003), and Popescu et al. (2003).

42

Andersen et al. (2002) proposed to fit ellipsoid crown models in a Bayesian framework to the

point cloud. Morsdorf et al. (2003) presented a practical two-stage procedure where tree

locations were defined using the DSM and local maxima and crown delineation was

performed using k-means clustering in the three-dimensional point cloud. Wack et al. (2003)

first calculated the canopy height corresponding to all laser points and used the sorted list to

define a cone of the tree top. If a point was located close to the cone, it was removed. The

process was recursive. This method allowed 93 % of planted eucalyptus trees to be correctly

delineated.

The segmentation method applied in the study was the one tested by Hyyppä and Inkinen in

1999. A detailed description is found in Hyyppä & Inkinen (1999). The crown delineation is

based on a watershed algorithm that turns the CHM upside down, picks up the local minima

(tree tops) and examines the surrounding of the minima with the help of the direction and size

of the gradient. A drop of water falling on a surface follows a descending path and eventually

reaches a minimum. The pixels in which flooding ends at the same minima are included in the

segment (Figure 5.7). Because segmentation is carried out to the surface instead of the

original 3D points, the resulting areas do not overlap. In this study, the segmentation was

carried out to all five CHMs produced from different laser materials.

Figure 5.7. Segmentation surface.

5.4 Vector model processing method

In the method, each individual tree crown is bounded by parallel horizontal polygons, and the

tree trunk is formed as a vertically oriented vector from the ground surface to the tree top. The

topology information required by the method is crown/non-crown classification and polar co-

ordinates with respect to the trunk location. In the study, applied trunk locations were

43

measured by tacheometer. Another possibility is to derive trunk locations from laser point

cloud.

A crown/non-crown classification is carried out by analysing the distribution of the z co-

ordinate histogram. The reflections from ground and undervegetation can be typically

recognized as clearly distinguished maximum in the histogram (Figure 5.8), which is followed

by a minimum. The shape of the histogram is partly due to laser scanning principle and partly

due to typical tree geometry. In airborne laser scanning, pulses approach the tree from above,

and therefore the highest laser energy is applied and received from upper canopy. On the

other hand, tree branch biomass is typically less near the ground and therefore pulses have

less volume to reflect from. The lower boundary of the crown is examined by taking

histogram minima into consideration and determining the highest altitude of these minima as

the lower boundary of the crown. Points above the lower boundary of the crown are classified

as crown points and trunk location and are calculated by using the mean values of these

points.

tx ty

Figure 5.8. Three trees and their z co-ordinate histograms; on the left a birch, in the middle a pine and on the

right a spruce.

The transformation of crown points from the x, y, z co-ordinate system to the polar co-

ordinate (α,r,h) system is carried out by applying the formulas:

44

rzzh −= , ( ) ( )22tt yyxxr −+−= ,

( )r

xx t−= arccosα

(4.1)

where h point elevation from the ground

ground altitude rz

trunk co-ordinates tt yx ,

r range between trunk and point

α azimuth

The space between the lower boundary of the crown and the tree top is split in altitude layers

of 1 m in the z-direction, and points within a particular height layer are taken into account and

organized according to the ascending angle azimuth. Each point is connected to the previous

and next point with a line according to the ascending azimuth. This procedure results in a

polygon and goes through every point, i.e., polygon node, without its side edges crossing each

other (Figure 5.9). The polygon is formed on the tree model according to the layer height. The

forming and drawing of polygons is repeated for all the crown layers (Figure 5.10). Finally, to

improve the visual outlook of model, the trunk is described in straight lines from the top of

the tree to the surface of the DTM.

The vector models of 50 trees in the Otaniemi test area, which were delineated manually,

were produced from five laser scanner data sets, resulting in 250 models. However, only one

segmentation surface, CHM from Topeye 200 data, was used in automatic delineation for all

data sets in order to avoid the impact of segmentation errors on feature extraction, as

described in the following section.

45

Figure 5.9. Polygon formed with respect of trunk location

Figure 5.10. Original points (left) and formed vector model (right).

5.5 Tree model feature extraction

The formed vector models are used to provide individual tree features, which are (1) tree

height, (2) crown height, (3) location of the trunk and (4) crown shape determined by

polygons.

The tree height is calculated as the difference between the z co-ordinate of the highest crown

point and the ground altitude at trunk location. In order to get the ground height, DTM is

applied. Crown height is obtained as the difference between tree height and the height of the

lower boundary of the canopy. In order to derive trunk location two methods are possible,

either calculate the arithmetical mean of crown points or use the plane co-ordinates of highest

measurement point, which is also used in tree height extraction.

The crown polygons are utilized to provide side profiles from a given direction. The whole

crown model is split by a vertical plane, which is perpendicular towards the given direction,

46

i.e. the image capture direction (Figure 5.12). The direction measured with a compass and

trunk location co-ordinates are used to determine the cross-section. The co-ordinates for the

two intersecting points of the plane and polygons and are calculated

based on polygon edge line equations

),( 11 cc yx ),( 22 cc yx

ee bxay += , which are derived from edge points, and

the plane equation. Finally polygon widths dw1 and dw2 are calculated as the distance

between previously determined intersection points and trunk location (Figure 5.11). An

example of profiles and original points is illustrated in Figure 5.12. The profiles are formed

from crossing points.

Cross-section

dx 2

dx 1

Image plane

Figure 5.11. A vector polygon (dotted line) and

cross-section (solid line) with respect to the trunk

location (a dot). The values dw1 and dw2 are the

widths of the crown measurements.

5.12. Example of a crown profile. The red lines are

crown widths derived from intersection of polygons

and plane equation.

In this study DTM derived from Topeye 200 data set was used in the feature extraction of all

laser-derived vector models in order to avoid the impact of DTM errors and to allow the

comparison of laser data sets. The extraction was carried out on 250 trees delineated manually

and approximate 160 trees, which were detected correctly in segmentation.

47

6 RESULTS AND THEIR ANALYSIS In order to analyse the accuracy of vector models created with the method described in

chapter 5, model features were compared with the reference materials as described here. First,

altitude values from the digital surface model were compared with the tacheometer ground

control measurements, since errors in terrain model values produce errors in tree height

estimation. Next, the extraction of pulses reflected from individual trees was considered by

analyzing tree detection in segmentation surfaces and comparing them to manually delineated

tree polygons. Then tree model heights and crown model heights were compared with the

hypsometer measurements and values extracted from side view images. The determination of

tree trunk location was considered with respect to both manual and segmentation-based tree

extraction approaches. Finally, crown profiles derived from the vector model were compared

with the side view image profiles.

0 5 10 15 20 25 30 35 40 4520

25

30PROFILES 1−4

Z (

m)

Laser DTM Tacheometer DTM

0 5 10 15 20 25 30 3520

25

30

Z (

m)

0 5 10 15 20 25 30 3515

20

25

Z (

m)

0 5 10 15 20 25 3020

22

24

Measured break point (no.)

Z (

m)

Figure 6.1. Terrain profile comparisons. The length of profiles varies between 80-122 m.

The laser scanner data sets are listed in the next tables according to the laser system

implementation, which is followed by the flying height in meters.

48

6.1 The evaluation of DTM accuracy

The DTM values were compared with the tacheometer ground control measurements. Four

profiles were measured in the Otaniemi area during summer 2003. Points were classified into

three classes and outliers according to the open access from the air and ground cover type.

These classes were asphalt area, grass area, and tree-covered grass area. The classification

was carried by means of exploiting orthoimages. All five DTMs were applied. DTM profiles

from TopEye 200 data and tacheometer measured profiles are in Figure 6.1, standard

deviations and bias values in Tables 6.1 and 6.2.

Table 6.1. Standard error of differences of digital terrain model comparison.

Std (m) Topeye 200 Topeye 300 Topeye 550 Toposys 400 Toposys 800

Tree-Covered-Area 0.08 0.083 0.14 0.19 0.11 Asphalt 0.036 0.026 0.07 0.05 0.035 Grass 0.046 0.047 0.09 0.06 0.07

All except outliers 0.07 0.07 0.15 0.13 0.08

Table 6.2. Bias error of differences of digital terrain model comparison.

Bias (m) Topeye 200 Topeye 300 Topeye 550 Toposys 400 Toposys 800

Tree-Covered-Area 0.15 0.047 0.18 0.19 -0.015 Asphalt 0.05 -0.007 0.06 0.08 -0.05 Grass 0.07 -0.01 0.03 0.13 -0.02

All except outliers 0.08 0.0009 0.05 0.13 -0.013

The standard deviation of elevation comparison differences was 7 cm, when the DTM from

TopEye 200 data was applied. According to the laser system manufacturer, the accuracy of

distance measurement is as high as 6 cm. This results from the technical restriction of laser

pulse transmission. However, an accuracy of 6 cm may be achieved only if the whole pulse is

reflected from a plain surface perpendicular to the laser system. This is not the case on forest

floors, where pulses are likely to reflect from several undervegetation surfaces. The laser

measurement campaigns utilized in this study were carried out during the summer, when

49

forest ground was typically covered by grass and hay. Therefore, closer attention was paid to

ground cover in tacheometer measurement locations.

The control points on the asphalt-covered road and parking area had the smallest biases

(-0.007 - 0.08 m) and standard deviations (0.035-0.07 m) (Table 6.1). The accuracy decreased

in grass areas, even if surfaces were plain. The intensity of grass-reflected pulses was

approximately 20 % less than from asphalt areas, and it was visually observed that the

deviation of point altitude was higher in grass areas. Differences between ground control

measurements and the digital terrain model were highest in areas shadowed by trees, from

-0.015 m up to 0.19 m. These results suggests that in forested areas the ground surface is

generally higher in the laser-derived model than the real ground truth.

Results obtained in the study were similar to those suggested by previous studies. Kraus and

Pfeifer (1998) gained an RMSE of 57 cm using ALTM 1020 and average point spacing of 3.1

m in wooded areas. Hyyppä et al. (2000) reported a random error of 22 cm for modulating

forest terrain using Toposys-1 and nominal pulse density of 10 pulses per m2. Three different

DTM algorithms were compared within the EC-funded HIGH-SCAN project (1998-2001) in

Finnish (test site Kalkkinen), Austrian (Hohentauern) and Swiss (Zumikon) forests. Obtained

random errors varied between 22 and 40 cm (Hyyppä et al., 2001) using Toposys-1 and pulse

densities between 4 to 10 pulses per m2. Ahokas et al. (2002) compared three algorithms on a

forested hill in Finland and found random errors between 13 and 41 cm using Toposys-1.

Reutebuch et al. (2003) reported random errors of 14 cm for clear-cut forest, 14 cm for

heavily thinned forest, 18 cm for lightly thinned forest and 29 cm for uncut forest using

TopEye data with 4 pulses per m2. However, in dense forests, errors up to 10 to 20 m can

occur in the DTM estimation (Takeda, 2004).

6.2 Single tree point extraction by tree delineation

A tree crown delineation was performed in the study by using two approaches, manual and

segmentation-based, which were carried out to five CHMs obtained from laser data sets.

Results from manual delineation and automatic segmentation are given in Figures 6.2-6.7.

The manually drawn tree polygons, which were revised during the field inventory, were

considered as correct. At this stage, the location of tree crown borders were not considered,

only the detection of trees.

50

Figure 6.2. Manual segmentation. Figure 6.3. Segmentation, TopEye 200 derived CHM.

Figure 6.4. Segmentation, TopEye 300 CHM. Figure 6.5. Segmentation, TopEye 200 CHM.

Figure 6.6. Segmentation, Toposys 400 CHM. Figure 6.7. Segmentation, Toposys 800 CHM.

Figure 6.8. Manual (green) and segmented TopEye 200 CHM polygons (blue).

51

Firstly, the number of correctly detected trees was considered with relation to all trees (Table

6.3, row 1). Correct detection refers here to a segment that includes only one single tree, other

solutions were considered as errors (Table 6.3, row 2). Secondly, erroneous trees were taken

under closer study. Two types of errors took place. The first error consisted of merging more

than one tree crown in one segment. The other error, splitting, occurred when area containing

a single tree was divided into several segments. The proportion of these with respect to all

erroneous trees is presented in Table 6.3, rows 4 and 5.

Table 6.3. Tree detection errors applying automatic segmentation.

% Topeye 200 Topeye 300 Topeye 550 Toposys 400 Toposys 800

Correct 65.91 65.63 63.64 34.09 36.36

Error 34.09 34.38 36.36 65.91 63.64

Merged 34.09 34.38 29.55 38.64 27.27

Splitted 0.00 0.00 6.82 27.27 36.36

The segmentation results indicated what previous studies have shown, namely that the

accuracy of automatic segmentation is moderate. The proportion of correctly detected trees

varied from 34 to 66 %, and the best results were gained with CHM derived from TopEye

data flown from an altitude of 200 m. The proportion of merged tree crowns was

approximately the same in all CHM segmentations and errors were typically related to the

same trees. However, the proportion of divided tree crowns increased with rising flying

altitude and decreasing point density. All TopEye-derived CHMs were more accurately

segmented than surfaces produced from Toposys data. The reason for this was suggested to be

the scanning pattern of TopEye implementation. Regardless of the extremely high scanning

frequency of Toposys, the point distance along the scan direction is eight times larger than the

across-scan point density. Therefore, some pixels in the CHM may result from only one laser

hit that has been reflected from 1 square meter of irregularly shaped tree crown. The system

scanning angle in Toposys is also steep, which results in laser beams reflecting from crown

tops instead of crown sides. It was speculated that crown sides were moderately presented in

the CHM.

52

In both manual and segmentation-based delineation approaches, the fundamental problem was

that tree crowns were not assumed or allowed to overlap. Short trees growing under higher

trees and trees with jointed branches were therefore automatically erroneously segmented

(Figures 6.9 and 6.10). The reason for this is that the segmentation was applied to a surface

where each pixel has only one value, and the original measurement point cloud was ignored.

Correspondingly, in manual delineation, the polygon edges split the point cloud in a vertical

direction and two points with same x and y co-ordinates may not end up in two different

groups. Therefore it was concluded that delineation accuracy originates not just from the

algorithm method but also from the forest structure itself. The space between trees that

separates them from surrounding neighbours improves detection and delineation accuracy,

whereas trees growing in tight groups are likely to be detected erroneously. Similar results

have been gained by Perrsson at al (2002).

Figure 6.9. A short tree beside a tall tree. Figure 6.10. Trees with jointed crowns.

6.3 Tree heights and crown heights

The tree height comparison was carried out first using values derived from the laser model

and hypsometer measurements and then using values derived from the laser model and side

view image measurements (Figure 6.11). The tree height was calculated as the difference

between the z co-ordinate of the highest crown point and the ground altitude at tacheometer

derived trunk location. In this study, the DTM derived from the TopEye 200 data set was

applied for all five data sets in order to avoid the effect of DTM error and to allow the

comparison of different data sets. The standard deviations and biases of residuals are

presented in Tables 6.4 and table 6.5. In the study bias values were calculated as average of

differences.

53

Hypsometer

Tree height

Crown height

Figure 6.11. Hypsometer measurements, laser model and side view image were compared.

Crown heights were obtained as the difference of tree heights and the heights of the lower

boundary of the canopy. These values were studied with respect to corresponding hypsometer

and side view image measurements (Figure 6.11). The standard deviations and biases of

differences are presented in Table 6.4 and Table 6.5.

Table 6.4. The differences of laser-derived tree heights and hypsometer measurements.

Tree height Topeye 200 Topeye 300 Topeye 550 Toposys 400 Toposys 800

Std, m 1.32 1.21 1.4 1.4 1.5

Bias, m -0.44 -0.52 -0.06 -0.5 -0.005

Crown height

Std, m 4.41 5.05 4.77 5.29 3.84 Bias, m 2.68 -0.24 -1.36 -1.43 -1.92

Table 6.5. The differences of laser-derived tree heights minus and heights from side view image measurements.

Tree height Topeye 200 Topeye 300 Topeye 550 Toposys 400 Toposys 800

Std, m 1.03 1.02 1.06 1.04 1.09 Bias, m -0.33 -0.06 -0.67 -0.24 -0.77 Crown height Std, m 3.76 5.38 2.98 3.20 4.64 Bias, m -0.84 -1.16 -2.48 -1.64 -3.87

In hypsometer versus laser-derived tree height comparison, the biases varied from a couple of

centimetres to half a metre (Table 6.4). As expected, all biases were negative, which indicates

that tree heights derived from laser models are smaller than those measured by hypsometer.

The reason for this assumption is that laser pulses do not necessarily reflect from the tree

54

crown top and therefore tree height is underestimated. The DTM used in the comparison was

also laser-derived, and as mentioned in section 7.1, it was typically slightly above ground

truth in a tree-shadowed area. This causes tree heights to be underestimated. The standard

deviation of residuals was 1.2 m at minimum and increased to 1.5 m as flying height

increased and point density decreased. The accuracy was slightly worse than expected.

However, the standard deviations of residuals were 30 % smaller when tree heights measured

from side view images were applied (see Table 6.5). The results were computed with all five

data sets. Field inventory experience has shown that hypsometer measurement accuracy is

only 1 m or even worse. The measurement method is disturbed by the effect of wind bending

trees and also unclear visibility of wide crown tops. Therefore, tree heights obtained from

laser measurements and side view images were potentially more accurate than hypsometer

measurements. The differences obtained from comparison of hypsometer data were also

considered tree by tree, and it was observed that some trees had large biases but small

standard deviations. This indicated that for some trees, the heights derived from different laser

data sets were similar, but differed from tree heights acquired by hypsometer. The comparison

with side view images indicated that derived tree heights are underestimated by

approximately –0.06 m - 0.77 m and with STD of 1 m. This result was consistent with

assumptions and similar studies.

A corresponding comparison was made between laser-derived crown height and hypsometer

and side view image measurements (Tables 6.4 and 6.5). In this study, crown height was

determined as the difference between the tree top elevation and the elevation of the lower

boundary of the crown, which was defined to the minimum of the z co-ordinate histogram.

The results indicated that tree crown heights were inaccurately estimated from laser-derived

tree models. High negative biases imply that laser pulses have not reflected from the lower

branches of trees, which therefore are not visible in the models. Differences also fluctuated

heavily, resulting in standard deviations of several metres. It was visually observed that tree

crowns growing in groups were particularly underestimated as laser pulses were unable to

penetrate through the canopy of neighbouring trees. However, in open areas vector models

were more complete and residuals correspondingly smaller.

55

In addition to the lack of hits on lower branches, the results were impaired by a few cases

where the algorithm failed to determine the lower boundary of the canopy. This error took

place with trees whose crown continued all the way down to the ground surface and there was

no branch-free area (see Figure 6.12). In these cases, the z co-ordinate histogram minimum

was located somewhere in the crown. With spruces lower boundary of crown was located

typically just near the top, where crown forms a peak. Modification of the algorithm could

remove this problem, but this was not done during the study, as the number of these situations

was small, only 10 out of 250. It should also be emphasized that the lower boundary of the

crown is difficult to define even with a hypsometer in field inventory and from side view

images, since in natural conditions branches do not grow symmetrically around the tree.

Figure 6.12. A spruce with a crown continuing to the ground. Side view image on the left, laser points in the

middle, and histogram of z co-ordinates on the right, where the lower boundary of the crown algorithm has

failed.

6.4 Locations of the tree trunks

The laser-derived tree trunk locations were compared to tacheometer measurements. Trunk

locations were derived by two methods; calculating the arithmetical mean of x and y co-

ordinates of crown class points and using the plane co-ordinates of highest measurement

point. Two comparisons were carried out, laser model derived values from manually extracted

trees (Tables 6.6 and 6.7) and laser model derived values from automatically segmented trees

(Tables 6.8 and 6.9) applying the CHM from TopEye 200 data. Only correctly detected trees

56

were included in the latter comparison, which is why the number of trees here was less than in

the comparison of manually delineated trunk locations.

Table 6.6. Comparison of manually delineated trees and tacheometer measurements. Number of samples in

comparison is 50 for each data set. Derivation of trunk location has been carried out calculating average of

crown hits.

distance Topeye 200 Topeye 300 Topeye 550 Toposys 400 Toposys 800

Std, m 0.74 0.84 0.83 0.8 0.8

Bias, m 1.18 1.44 1.35 1.24 1.2

Table 6.7. Comparison of manually delineated trees and tacheometer measurements. Number of samples in

comparison is 50 for each data set. Derivation of trunk location has been carried out using plane co-ordinates of

the highest measurement point.

distance Topeye 200 Topeye 300 Topeye 550 Toposys 400 Toposys 800

Std, m 0.79 0.91 0.83 0.99 1.43

Bias, m 1.18 1.21 1.3 1.34 1.5

Table 6.8. Comparison of segmentation-delineated trees and tacheometer measurements. Number of samples in

comparison is 32 for each data set. Derivation of trunk location has been carried out calculating average of

crown hits.

Distance Topeye 200 Topeye 300 Topeye 550 Toposys 400 Toposys 800

Std, m 1.01 0.85 0.84 1.07 1.03 Bias, m 0.66 0.64 0.58 0.74 0.71

Table 6.9 Comparison of segmentation-delineated trees and tacheometer measurements. Number of samples in

comparison is 32 for each data set. Derivation of trunk location has been carried out using plane co-ordinates of

the highest measurement point.

distance Topeye 200 Topeye 300 Topeye 550 Toposys 400 Toposys 800

Std, m 0.8 1 0.93 1.14 1.16 Bias, m 1.16 1.4 1.34 1.39 1.56

The results in Tables 6.6 - 6.9 indicate that trunk locations were derived approximately with

an accuracy of 2 m. In all comparisons the biases were between 0.66 – 1.56 m and standard

57

deviation of differences between 0.74 – 1.16 m. However, the average calculation method

provided locations more accurately than using the plane co-ordinates of the highest

measurement point for both manually and segmentation delineated trees.

By using the plane co-ordinates of highest point it is assumed, that highest point and trunk

location at the breast height are in vertical line, i.e. the trunk grows straight. The trunk tilt

causes the tree crown to be shifted from the root base, which is the location that tacheometer

measurement captures. Therefore trees with the tiled trunk produce errors in the comparison.

The difference in accuracy between manually or segmentation delineated trees using plane co-

ordinates of highest point was minor, since highest point was the same using both methods.

The small difference in accuracies results from the different number of trees in a sample.

The average calculation method is also affected by several factors. The method assumes that

laser pulses have been reflected from all sides of the crown, since scanning was performed

from above. In practice, the crown side towards the scanner will probably reflect more pulses

than the opposite crown side, and therefore trunk location calculated from points is shifted

towards the scanner. In addition to pulse reflection itself, two method-dependent factors

influence the accuracy. They are single tree crown delineation and point classification. An

erroneously delineated tree crown shall have an erroneously derived trunk location, if points

from neighbouring trees are included in the calculation or, in contrast, if some part of the tree

crown is ignored. In this study, two methods were applied to delineate tree crowns. As

mentioned in section 6.2, results from the automatic segmentation were moderate from the

viewpoint of tree detection. Trees that were not correctly detected in automatic segmentation

have been left out of the trunk location experiment. The correctly detected trees were,

however, located with almost the same accuracy as the manually delineated trees. The

segmentation algorithm seems to delineate the tree crown tightly around crown sides, whereas

manually delineated crowns typically included some ground area beside the tree (Figure

6.13). Even if standard deviations of segmented tree trunks were 15 % larger, the biases were

smaller.

58

Figure 6.13. Examples of tree crown delineation with manual and segmentation approaches. Red points represent

the manual approach and cyan points the segmentation approach.

The classification of points as crown points also had an impact on the trunk location

determination. Points above the lower boundary of the crown were considered as crown

points. Section 6.3. discussed the problem of detecting the lower boundary of a crown

continuing to the ground and its effect on trunk location. The erroneously detected lower

boundary resulted in crown points being ignored in the derivation of the vertical trunk

location. However, the number of trees with this problem was rather small.

6.5 Crown width analysis

The crown widths were analyzed using two methods. At first, the maximum width of crown

acquired from images were compared to maximum crown widths derived from laser models

in the image plane direction (Figure 6.14). This phase was carried out for both image capture

directions for those trees for which a second image had been acquired. Results of the

comparison of dw1 and dw2 are listed in Tables 6.10 and 6.11. Some images were left out of

the calculations because of wind bending trees at the moment of image capture or tree fusing

merging with background.

59

Maximum crown width

dw1 dw2

Figure 6.14. Maximum crown width.

Secondly, crown profiles derived from both images and laser models were overlaid and

examined visually. The crown widths were plotted as a function of layer height with respect

to the total tree height and with respect to the viewing direction. The reason for applying the

tacheometer-acquired trunk location was to avoid the contribution of the laser-derived trunk

location error. Examples of superimposing are presented in Figures 6.15, 6.16 and 6.17.

Table 6.10. The maximum width of crown projections compared to side view image measurements.

Image 1, dw 1 Topeye 200 Topeye 300 Topeye 550 Toposys 400 Toposys 800

Std, m 1.57 1.51 1.44 1.55 1.38 Bias, m -0.19 0.46 -0.53 0.08 -0.52

Image 1, dw 2

Std, m 1.75 2.20 1.58 1.66 1.52 Bias, m -0.36 -0.06 -0.62 0.11 -0.73

60

Table 6.11. The maximum width of crown projections compared to side view image measurements.

Image 2, dw 1 Topeye 200 Topeye 300 Topeye 550 Toposys 400 Toposys 800

Std, m 2.20 2.48 2.27 1.99 2.00 Bias, m -0.28 -0.06 -0.51 -0.05 -0.85

Image 2, dw 2

Std, m 1.87 2.04 2.30 2.04 2.16 Bias, m -0.70 -0.61 -0.72 -0.55 -0.78

Figure 6.15. A spruce. The solid line is the image-derived crown profile and the dotted lines are crown profiles

derived from laser models. From left to right: TopEye 200, TopEye 300, TopEye 550, Toposys 400, Toposys

800.

Figure 6.16 A pine. The solid line is the image-derived crown profile and the dotted lines are crown profiles

derived from laser models. From left to right: TopEye 200, TopEye 300, TopEye 550, Toposys 400, Toposys

800.

61

Figure 6.17 A birch. The solid line is the image-derived crown profile and the dotted lines are crown profiles

derived from laser models. From left to right: TopEye 200, TopEye 300, TopEye 550, Toposys 400, Toposys

800.

As the results in Table 6.10 and 6.11 show, crown projection widths were derived from laser

models with an accuracy of approximately 2 m. The results were attained with both first and

second image capture locations. In almost all comparisons the biases were negative, which

indicates that the tree crown is underestimated in laser scanner derived models. This result

was consistent with tree height estimation, where biases were also negative, as well as

previous studies by Rönnholm in 2005 and Gaveau et al. in 2003. Also visual comparisons of

crown profiles showed the tree crown to be diminished in laser pulse derived vector polygons.

In most examples, the laser-acquired crown profile was within the side view image crown

shape.

The gained results were interpreted as indicating, that in addition of the uncertainty related to

laser pulses not hitting the top of the crown or tip of the branch, the algorithm registering laser

pulses tends to determine reflection inside the crown rather than on the surface. A vegetation-

originated pulse consists of the sum of multiple small surface reflections and the return signal

is broad and low instead of a clear strong peak. The recent development of laser scanner

implementations that register full waveforms has provided the opportunity to see the signal

shape of the vegetation surface. In the study a data set measured with full waveform mode in

test area in Remmingtorp, Sweden was examined. Example of two return signal that has taken

place on the side of a tree crown is shown in Figure 6.18. The return areas are wide and

smooth. For comparison, two reflections from a sand-covered road surface are shown in

Figure 6.19. The algorithms may possibly determine the pulse as returned in several positions

depending on the algorithm. With a quantisation interval of 14.4 cm, errors could easily

become large even with a small change in histogram shape.

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Figure 6.18 Vegetation signal examples. Figure 6.19 Road signal examples.

A tree delineation had impact on the crown width analysis. An example of the phenomena is

presented in Figure 6.20, where crown points from neighbouring trees have been located

within the delineation polygon, and therefore the profile derived from a laser model is wider

than the image-derived profile. This example indicates that delineation affects the horizontal

accuracy of tree geometry. Another type of error took place when the wind bent the tree

during the moment of the image capture. Figure 6.21 illustrates a birch whose laser-derived

profiles had a similar shape, which, however, was different from the image-derived profile.

Figure 6.20. Erroneously delineated spruce. The

solid line is the image-derived crown profile and

the dotted line is the crown profile derived from

laser models.

Figure 6.21. Example of a tree that has been bent by

the wind during the image capture moment. The

solid line is the image-derived crown profile and

the dotted lines are crown profiles derived from

laser models.

63

7 THE IMPACT OF LASER SCANNING PARAMETERS ON THE

APPLICATION OF VECTOR MODELS

The influence of laser scanning parameters on the modelling accuracy was taken into

consideration. Laser scanning parameters are, for example, point density, pulse transmitting

angle, and size and length of the beam. In the data sets used in the study, these parameters

had several values (Table 3.1). However, the accuracy gained from tree and crown height

comparison in addition to the estimation of trunk location was approximately the same with

all five laser data sets. The impact of each laser scanning parameter was difficult to estimate

by comparing different data results, since more than one value changed between each data

pair. In order to estimate the influence of parameters on the vector models, closer attention

was paid to the data structure and data simulation was carried out to estimate the effect of the

beam size and overlapping strips.

7.1 Point density and reconstruction

The largest difference between data sets was in point density, which varied between data sets

from 1 /m2 up to 20 / m2. The point density is a result of the measurement frequency, scanning

mechanism, and flying altitude, but the amount of pulses reflected from one object is also

affected by the geometry of the measured object, scanning angle, and object penetration. Point

density is not unique, but varies according to the pulse distribution function. Examples of

number of pulses reflected from a single tree are presented in Table 7.1 and an illustration of

one pine tree is provided in Figure 7.1. The number of pulses reflected from one tree varied

dramatically depending on the data set, tree shape itself, and location in the scanning strip.

Table 7.1. Number of pulses from single example trees in different data sets.

Number of hits Birch Spruce Pine Topeye 200 540 408 201 Topeye 300 2031 715 2440 Topeye 550 169 84 95 Toposys 400 1455 688 813 Toposys 800 248 114 154

64

Figure 7.1. From left to right: Toposys 400, Toposys 800, Topeye MK I 200, Topeye MK II 300 and Topeye

550. The single pine is in the Otaniemi area.

The high pulse amounts in trees extracted from Topeye 300 data are caused by the scanning

pattern, where each object is measured twice, but also from an oblique scanning angle. Pulses

from the shadow area of an impenetrable vertical object are reflected from the object surface,

which increases the amount. The histograms of the z co-ordinate are presented in Figure 7.2.

It may be observed that in the Toposys 400 and 800 data, the histogram maximum is near the

tree top, but in the TopEye 200, 300 and 550 data sets, pulses have also been reflected from

the lower branches (see Figure 7.1).

Figure 7.2. Histograms of z co-ordinates of tree no. 28. In vertical axes are the number of samples. Top row:

TopEye 200 and TopEye 300, bottom row from left to right: Topeye 550, Toposys 400 and Toposys 800.

65

The point density and lack of pulses reflected from low branches had an effect on the crown

profiles derived from vector models. Tree crown profiles were not properly gained from

Toposys 400, Toposys 800 and Topeye 550 data sets, even if the average point density in

Toposys 400 was 20 points per square meter. The TopEye 300 and TopEye 200 data sets

resulted in the most complete crown profiles in vector models (Figures 6.15, 6.16 and 6.17).

7.2 Effect of overlapping flight lines

The data sets applied in the study differed from each other also in terms of flying geometry.

Toposys data sets were acquired by flying only one strip, whereas TopEye data sets were

collected from multiple strips. The latter method was expected to be better, since the tree is

scanned from more than one direction. The impact of overlapping strips was compensated by

performing new single tree pulse extraction using only one measurement strip for each tree in

TopEye 200 data. The vector model reconstruction was otherwise carried out in the same way

as the other data sets were processed previously and comparison with the tacheometer and

side view image measurements was repeated.

Table 7.2. Residuals of trunk location estimation.

Derivation of trunk location has been carried out

calculating average of crown hits.

Distance TopEye 200, points from two strips

TopEye 200, points from

one strip std (m) 0.74 0.81 bias (m) 1.18 1.37

Table 7.3. Residuals of trunk location estimation.

Derivation of trunk location has been carried out

using plane co-ordinates of the highest

measurement point.

Distance TopEye 200, points from two strips

TopEye 200, points from

one strip std (m) 0.79 0.81

Bias (m) 1.18 1.17

The results (see Table 7.2) demonstrated the hypothesis that trunk location estimation was

smaller accurate for trees that have been scanned only from one strip using average of crown

hits. Both biases and standard deviations increased in this case. The impact on trunk locations,

which were acquired applying plane co-ordinates of highest measurement point was found to

be less important, since accuracy did not decrease (Table 7.3). The estimated trunk location

was changed only if highest hit was measured from the reduced flight line.

66

7.3 Beam size and biases

The impact of beam size on the modelling accuracy was carried out by reducing data from the

Toposys 400 data set to the same point density as the Toposys 800 data set. As these materials

were acquired with same instrument at the same time, the only difference in the data is the

size of the beam ellipse, which was 40 cm in the data collected at 400 m and 80 cm in the data

collected at 800 m. It was assumed that biases with large beam size would have been less

concerning tree height, since the probability that pulses are reflected just from the crown top

would be higher with larger beams. However, even though biases increased with estimation

data, they were still smaller than in results derived with Toposys 800 data (Table 7.4).

Table 7.4. Results of tree height comparison with original Toposys 400 data, Toposys estimated data and

original Toposys 800 data.

Tree height Toposys 400 original Toposys 400 sparse Toposys 800

Std (m) 1.04 1.04 1.09 Bias (m) -0.24 -0.37 -0.77

The results were not as expected. In Yu & al (2004) it was suggested that a large beam size

improved the probability of laser pulse reflection from the crown top. However, the reduction

of laser points carried out in this thesis did not produce a similar scanning pattern as that

measured from a higher flight altitude. This is due to the uneven distance in scanning and

across the scan direction. Every second measurement point in the scanning order was reduced

in order to produce a pattern in which the point distance in flying direction would stay the

same as before the reduction, but the distance in the across–scan direction would double.

Unfortunately the number of sent pulses was not the same as the number of received pulses,

which were stored in the data, since some echoes were missed by the receiver. Therefore the

scanning pattern produced by reduction was more scattered than the pattern measured from a

higher altitude.

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8 SUMMARY AND CONCLUSIONS

8.1 Summary and conclusions

The objective of this study was to provide a method for producing vector models of single

trees from laser scanner data in order to derive single tree geometry features. The method

included the following stages: point classification, digital terrain model production, extraction

of points from each tree, and vector model creation. Features extracted from laser-derived

models were tree height, crown height, trunk location, maximum crown width and crown

profile. Tree height and crown height represent the vertical geometry of a tree and trunk

location, maximum crown width, and crown profile the horizontal geometry. Since tree height

extraction requires information about the ground altitude at trunk locations, the digital terrain

model of the area was applied.

Also another method was introduced in the study, namely the side view imaging of trees for

collecting reference material for the laser-derived feature analysis. Trees in the test area were

imaged with predefined viewing geometry from two directions and the images were rectified

to a plane that is parallel to the tree trunk and perpendicular to the image capture direction.

The side view imaging method provided material that could not have been collected with

traditional forest inventory equipment. In addition to the crown and tree height, also side

profiles and the maximum width of the crown in the imaging direction were measured from

the images. If more than one image capture direction was applied, also trunk tilt could be

determined.

In order to test and evaluate the introduced methods, an experiment was carried out in the

Otaniemi study area applying five different laser data sets to reconstruct 50 trees in the area.

The features derived from laser models were compared to reference materials, which

consisted of tacheometer measurements, hypsometer measurements, and measurements

acquired from side view images.

According to the experiment, this method provided vector models of trees with the following

parameters and their accuracies: In vertical direction, tree heights were derived with a

negative bias of approximately 0.5 m and a standard deviation of 1 m. The most accurate

results were gained using TopEye 300 data, where the bias was only –0.06 m and STD was

68

1.02 m. However, crown heights were derived with only moderate accuracy; the negative

biases varied from -0.84 m to -3.84 m and standard deviations from 2.98 m to 5.38 m. These

comparisons were carried out by applying side view image measurements, since their

accuracy was found to be better than the accuracy of hypsometer measurements.

In the horizontal direction, the trunk locations were estimated from the models with a bias of

0.74 to 0.84 m and an STD of 1.18 m to 1.44 m when single tree point extraction was

performed manually. These results were obtained deriving trunk location as average of crown

class points. The other approach, the segmentation-based method, provided similar results,

except for biases that were slightly smaller, from 0.58 m to 0.74 m. The crown widths were

gained with negative biases from –0.78 to 0.46 m and STD from 1.38 to 2.48 m, which is a

moderate accuracy. The result was confirmed in a visual analysis of crown profiles compared

to the profiles derived from side view images.

The DTM accuracy was also considered, since errors in DTM emphasise errors in tree height

derivation. The experiences indicated that ground cover type and openness of the area

affected the laser-derived DTM accuracy, and that DTM is typically located slightly above the

ground truth. The worst standard deviation of the residuals was 15 cm, but the best and more

common was 7 cm. In the comparison, the DTM accuracy was highest on asphalt-paved roads

and decreased in tree-shadowed undervegetation areas.

All results indicated that tree crown shape is underestimated in the laser models. The tree and

crown heights and crown widths were negatively biased, and these results were gained with

all five different laser data sets. The reasons for underestimation were suggested to be (1)

pulse distribution of laser measurement and (2) shape of the pulse signal reflected from

vegetation.

A tree has a complicated shape. Branches grow unsymmetrically around the trunk and have

various lengths, and the trunk itself may be in an oblique position. In laser scanning, pulses

are not directed to a certain part of the measured object, but distributed in the area by a

scanning mechanism. Therefore they do not necessarily reflect from the desired part of the

surface, such as tips of the branches or top of the tree. As no data simulation is included in the

method, the tree parts that have not reflected laser pulses are not presented in the models. In

69

the visual analysis of laser-derived crown profiles versus image-derived profiles, it was

observed that crown parts with only a few laser hits were incomplete in the models. The

tendency is the same in the vertical dimension; high negative biases in crown height

estimation indicate that laser pulses have not reflected from the lower branches of the tree

crown, and therefore they are not presented in the models.

The visual analysis of return signals originated from vegetation revealed the uncertainty of

ranging measurement, since the shape of the return signal is long and varying instead of one

clear peak. When applying different algorithms, the signal could be determined as returned in

several positions and measurement vector having several lengths. Underestimation of laser-

derived tree model was interpreted so that the crown surface, which is the first to reflect the

pulses, is not presented by laser measurements, and the strongest reflection has originated

from within the crown instead of from the surface.

The laser scanning parameters affected the vector model application method. High pulse

density improves tree reconstruction and derivation of 3D geometry features. If tree height is

the only parameter to be derived, a steep scanning angle is more suitable, since shadow areas

behind impenetrable objects are small. In steep scanning, pulses are also able to reach the

ground, and these pulses are suitable for digital terrain model production. However, oblique

scanning is better than steep scanning in order to gain pulses from lower branches of the tree

in open areas, where pulses are able to reach the branches without interfering with

neighbouring tree crowns. To estimate trunk location, trees should be scanned from more than

one direction in order to make sure that both sides of the crown reflect pulses.

8.2 Discussion

The laser method introduced here provides a way to extract features of single trees from laser

scanner data. The application area of the method is individual treewise forest inventory. If a

DTM is produced from laser data, no other external information is required.

However, the method is not fully automatic. In single tree extraction, two approaches were

applied to delineate tree crowns, a manual and a segmentation-based approach. The

segmentation-based method represents a fully automatic approach, whereas manual

delineation was carried out by a human interpreting the CHM surface. As previous studies

70

have shown, segmentation is successful only if there is enough space between a tree and its

neighbours. In a dense forest, where tree crowns are joined, a single crown is either split to

several segments or merged with surrounding trees. In this study, the proportion of correctly

detected trees varied from 34 % to 64 % depending on the data set. Even with the best

results, the amount is so low that the usability of segmentation-based approach is

questionable. Therefore, manual delineation is recommended even if it is time-consuming and

expensive.

The other application area of the laser method is 3D visualisation of forest areas. Typically

visualisation is carried out for the purpose of 3D management and planning in the forest areas.

An example of one area is illustrated in Figure 8.1. Since high accuracy is not required in

visualisation, segmentation may take place and automate the whole process.

Figure 8.1. Kalkkinen test area processed with introduced laser method (Pyysalo, 1999).

8.3 Future work

This thesis was limited to the topic of single tree parameters that are available from a 3D

vector model. Thus, parameters such as timber volume or diameter at breast height were not

considered, since laser beams only rarely reflect straight from the trunk. These parameters,

however, have great economic importance in forest inventory. The next step in the tree

parameters evaluation would be to use the derived parameters as input values in timber

volume estimation models.

71

Another approach to derived single tree features would be to match 3D primitives of single

trees to a point cloud after the delineation of tree crowns. A priori knowledge of tree species

could be used, as well as information about the tree height distribution in the area. In this

study it was found that the lower parts of the tree crown are only moderately presented in the

data set. Also when measurement is carried out by applying oblique scanning, the opposite

side of the crown is shadowed by the side of the crown facing the scanner and therefore

unevenly presented in the data. The matching of trunk-symmetric 3D models would estimate

the areas where measurement information is limited.

Laser scanning implementation has developed during the preparation of this thesis. The first

data was collected with an instrument capable of registering only one echo, first or last. This

system was followed by an instrument capable of registering two echoes, first and last, and

then by an instrument registering even five echoes. The possibility of registering intensity was

also included in the system and measurement frequency has increased all the time. The latest

step in development has been the full waveform measurement mode, which has also been

discussed in this thesis. Ranging frequency with the present instrument is already high, but the

accuracy of single measurement is only moderate. The full waveform measurement provides

an opportunity to improve single point accuracy and estimate the single point quality. The

author assumes that future development concerning laser scanning applicability in tree

modelling will take into account quality-weighted laser point processing and signal analysis.

72

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