Analysis of airborne lidar data for estimation of tree height, dbh and volume

7/27/2019 Analysis of airborne lidar data for estimation of tree height, dbh and volume

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Mendel university of Brno

Faculty of forestry and wood technology

Analysis of airborne LiDAR data for estimation of tree height, DBH and

tree volume

Bachelor thesis

2013 Roberto Tjesse Beth


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Mendel university of Brno

Faculty of forestry and wood technology

Department of Geoinformation technologies

Analysis of airborne LiDAR data for estimation of tree height, DBH and

tree volume

Bachelor thesis

Supervisor: Tom Mikita RobertoTjesse Beth


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Statutory declaration

I hereby assert that I compiled the bachelor work on the topic of LiDAR data for forestmensuration variable determination by myself and have stated all sources used. I agree

to my thesis being published in accordance with 47(b) of the Act No. 111/1998 Coll. on

Higher Education Institutions including amendments to some other acts, and deposited

in the Mendel University library in Brno, accessible for study purposes in compliance

with Mendel University Chancellors decree on archiving final works in electronic form.

The qualification thesis author agrees to obtain a written statement from the University

that any license agreement with a third party on the use of copyright does not

contravene the rightful interests of the University prior to executing any such

agreement, and agrees to disburse any compensation for costs incurred in association

with the thesis compilation in compliance with the due calculation.

In Brno on the 27th of June of 2013 students signature:


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Acknowledgments

I would like to thank all the people who helped me with this project, specially to my

supervisorTom Mikita and to my girlfriend.


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Abstract:

In this bachelor work we present a study on the efficiency of multi-return LiDAR (Light

Detection and Ranging) data in the estimation of mensuration parameters in the forest of

Ktiny. The goals of this study are (1) introduce the state-of-the-art in LiDAR data uses

for forest mensuration studies, (2) define a clear how-to methodology for ALS data

assessment for mensuration purposes and (3) compare the obtained results of the

processed data with estimated information of yield tables, comparing statistical results.

Abstrakt:

Tto bakalrska prca sa zaober tdiom o innosti multi-eko LIDAR (Detekcie

svetla a rozsahu) dt v odhade meran parametrov v lese Ktiny. Ciemi tejto tdie s:

(1) predloi najvyiu rove rozvoja LIDAR dt uplatujc k tdiu merania lesa, (2)

jasne vymedzi "ako na" metodiku pre posudzovanie dt ALS (Leteckch laserovch

dajov) pre meracie ely a (3) porovna dosiahnut vsledky spracovanch dt s

odhadovanmi informciami rastovch tabuliek, porovnvajc tatistick vsledky.

Keywords: airborne laser scanning, tree height evaluation, digital elevation model,

digital surface model, Lidar Analysis, Lidar history


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Table of Contents

Introduction .......................................................................................................................... 21. Literature review ......................................................................................................... 3

1.1 Remote sensing, introduction ............................................................................. 31.2 The electromagnetic spectrum ........................................................................... 4

1.3

Electromagnetic energy interactions with atmosphere and earths surface. ........ 5

1.4 LiDAR Technology .......................................................................................... 102. Analysis of Airborne LiDAR data for mensuration parameters. .............................. 13

2.1 Area of study .................................................................................................... 132.1.1 The forest..................................................................................................... 132.1.2 Field data .................................................................................................... 132.2 Methodology .................................................................................................... 15

3 Results and conclusion .............................................................................................. 174. Citations .................................................................................................................... 205. Appendices ........................................................................................................... 22


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Introduction

Remote sensing has become a useful way of retrieving biophysical

variables. The uses in forestry are wide, resources management, fire prevention

assessment, forest mensuration, territorial planning or biocenological studies are

just some of the fields were this science can be implemented. In this work we will

focus on forest mensuration.

Individual tree identification and height determination from ALS (Airborne laser

scanning) data is particularly useful in growth and yield estimations, being now a

days an indispensable tool for any forest enterprise.

The accuracy of this measurements is high and the accuracy of the results will

depend on the knowledge in GIS . In this study we analyze the process of

variable extraction out from digital elevation models (DEM) and digital surface

models (DSM) of a forested area near Brno, Czech Republic. Simple spatial

operations such as focal statistic filtrations are used to focus and discriminate low

value points, defining a canopy height model. Results and conclusions are

presented at the end.


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1. Literature review

1.1 Remote sensing, introduction

Defined as the process or technique of obtaining information about an object, area, or

phenomenon through the analysis of data acquired by a device without being in

contact with the object, area or phenomena being studied (Chandra 2002). It consists

of the interpretation of measurements of electromagnetic energy reflected from or

emitted by a target from a vantage-point that is distant from the target (Mather,1999).

To properly understand remote sensing it is necessary some knowledge in

Electromagnetic energy, its characteristics and interactions. There are two ways of

modeling EM energy, by waves or by energy bearing particles. In the wave model,

EM energy is represented through sinusoidal waves which are propagating in space.

These waves are characterized by an electrical field (E) and a magnetic field (M)

both perpendicular to each other. The vibration of both fields is perpendicular to the

direction of the wave and propagate at the speed of light (c) which is 299.799.000

ms-1, and can be rounded off to 3x108 ms-1.

The wavelength of an electromagnetic wave is defined as the distance between

successive wave crests. Wavelength is measured in meters (m) or some fraction of

meters, such as nanometers (m, 10-9m) or micrometers (m, 10-6 m).

The frequency (v) is the number of cycles of a wave passing a fixed point over a

specific period of time. Normally measured in hertz (Hz), which is the equivalent of

one cycle per second since the speed of light is constant, wavelength and frequency

are inversely related.

v=c

Eq 1.1

Even most characteristics of electromagnetic energy can be described using this

wave model, it is important to point out the uses of the particle model. This approach

is considered when quantifying the amount of energy received by a multispectral


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sensor. The amount of energy held by a photon of a specific wavelength is given by

Q= hv

Eq.1.2

Where Q is the energy of a photon A, and h is Planck's constant (6.6262x10-34JS).

From here we can deduce that the longer the wavelength, the lower the energy

content.

Fig. 1.1 Representation of Electromagnetic energy

1.2 The electromagnetic spectrum

Electromagnetic emission is radiated by any matter with absolute temperature above

zero (0K) due to molecular agitation. This electromagnetic energy is usually in

waves of various wavelengths. The total range of wavelengths extended from gamma

rays to radio waves is commonly referred to as electromagnetic spectrum.

The amount of energy radiated depends on its temperature and emissivity. Mostmatter absorbs and reemits a a part of EM energy, usually between 80-98% of the

received energy is re-emitted and the remaining part is absorbed. We call optical part

of the electromagnetic spectrum to that part where optical laws, such as reflectance

and refraction, can be applied. This optical range extends from X-rays until far

infrared.

The visible part of the electromagnetic spectrum is commonly called light and

occupies a little part of the EM spectrum.


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Fig 1.2 The visible part of the spectrum is limited to 400 nm .

1.3 Electromagnetic energy interactions with atmosphere and earths

surface.

1.3.1 Atmospher ic in teractions

When electromagnetic energy is traveling through the atmosphere there are three

fundamental interactions; absorption, transmission and scattering. We will use the

incidence of the sun radiation to describe the electromagnetic energy interferences

through atmosphere and the interaction occurring with earth surface.

The most important absorbers of solar radiation in the atmosphere are ozone (O3),

Water vapour (H2O) and carbon dioxide (CO2).

Figure 1.3 shows the atmospheric transmission between the 0 to 70 m wavelength

region. It is appreciable that a small part of the spectrum is transmitted through theatmosphere. This is called the atmospheric window. Only the wavelength regions out

from the of the main absorption bands of atmospheric gases can be used for remote

sensing. The presence of atmospheric moisture impends the transmission of only

short wavelengths. There is a big absorption in longer wavelengths, from 33m to

1mm.


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Fig.1.3 The atmospheric window are wavelengths at which electromagnetic radiation willpenetrate the Earth's atmosphere, the chemicals notated are responsible of the respectiveretention.

Once explained absorption and transmission, we have to consider scattering.

Atmospheric scattering is defined as the influence that particles and gaseous

molecules have in the path of electromagnetic energy, deviating the initial direction.

There are 3 different kinds of scattering, Rayleigh scattering, Mie scattering and

Non-selective scattering. Factors affecting scattering are wavelength, amount of

particles and distance travelled through the atmosphere.

Rayleigh scattering: This scattering predominates when the electromagnetic

energy interacts with particles that are smaller than its own wavelength. This could

be particles of nitrogen and oxigen, for example. Shorter wavelengths are more

affected by this scattering than longer wavelengths. A good example of this kind of

this effect is the perception human eyes has of the sky. In the absence of particles, the

sky would appear black. In daytime, sun rays travel the shortest distance through the

atmosphere to the surface, causing humans eye to perceive it blue because it is the

shortest wavelength our eye can observe. But at sunrise or sunset the distance of the

sun rays through the atmosphere is longer, causing all the shorter wavelengths to be


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scattered, and only the longer wavelengths reach the surface, and as a result, the sky

appears orange or red.

In the context of satellite remote sensing the Rayleigh scattering is very important. It

causes a distortion of spectral characteristics of the reflected light when compared to

measurements taken on the ground. In general, this scattering affects the contrast of

photos, limiting the possibilities for image classification.

Mie scattering: This kind of scattering occurs when the wavelength of the

incoming radiation is similar in size to that of the atmosphere particles. Generally

restricted to the lower atmosphere, where large particles, such as aerosols, dust and

water vapour predominate. It influences the entire spectral region from near to

ultraviolet (400-200nm) including the near infrared (1um-1000 nm).

Non-selective scattering: Independent of the size of the wavelength, this

scattering happens when the size of the particles is much larger than the radiation

wavelength. The most representative effect of non-selective scattering includes the

effect of clouds (consisting of water drops). Since all wavelengths are scattered

equally a cloud appears white.

1.3.2 Ear ths sur face interactions

The incidence of Electromagnetic energy on any given feature of earth surface has

three possible interactions. Reflection, absorption and transmission. The

interrelationship between the three energy interactions can be expressed as follows.

Ei ( )= Er()+ Ea ()+ Et()

Eq. 2.1

Where Ei= The incident energy

Er= The reflected energy

Ea= The absorbed energy

Et= The transmitted energy


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The proportions between these energies vary depending on the material type and

condition of the feature. Further more, they can also vary depending on the

wavelength. These spectral variations within the visible portion of the spectrum

result in the visual effect called colour. For instance, an object will be green if it

reflects more of the green portion of the visible spectrum, and red if it reflects more

of the red portion of the visible spectrum. Remote sensing systems operate in the

wavelength regions were reflectance is dominant. Depending on the feature sensed,

different wavelengths will be used. Reflectance properties play an important role, the

modified form of Eq. 2.1 written as Eq. 2.2 shows that the reflected energy is equal

as the incident energy reduced by the sum of the absorbed energy and the transmitted

energy.

Er()= Ei() [Ea ()+ Et()]

Eq. 2.2

The geometric character of the object sensed determines the kind of reflection. There

are four kinds of reflections possible, categorized depending on the roughness of the

surface, listed as follows: specular, near-specular, near-diffuse and diffuse reflections

or also calledLambertian reflection. As a matter of fact, surfaces usually give a mix

of reflectances. Specular reflectance, which is peculiar of mirrors, do not give any

information of colour, and remote sensing focuses on the diffuse reflection of

objects.

The spectral reflectance is quantified by measuring the reflected energy expressed as

a percentage with the incident energy. (fig 2.3)

= energy of wavelength()reflected from the objectEnergy of wavelength ()incident upon the objectx100

Eq. 2.3

1.3.3 Vegetation interaction

The spectral reflectance of vegetation is distinctive and quite variable depending on

the wavelength. Fig 2.4 shows low reflectance in the blue and red region of the


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visible spectrum. This low reflectance corresponds to two chlorophyll absorption

bands. These absorption bands are centered around 0.45 m, hence a peak occurs

due to absence of chlorophyll absorption band.

There are also other pigments present in plants. Carotheses and xantophylls (yellow

pigment) are frequent, and have absorption bands in blue region. Some trees produce

anthocyanins (red) in large quantities, making them appear red.

Green vegetation is characterized by high reflectance, hight transmittance and low

absorption beyond 0.7 m as the spectrum passes from visible to infrared.

Reflectance and transmittance are around 45-50% for each and absorption in the

order of 5%.

Fig.1.4 Shows differences among reflectance between coniferous versus deciduous.

It has also been noticed that a multilayered vegetation area has higher reflectance, the

difference between multi-layered and single layered reflectance being around 85%.

This is consequence of the additive reflectance energy transmitted to the second layer

through the first layer, having significant impact on the data taken. In the middle

infrared portion spectral reflectance shows the effect of water, which has an

absorption band centered at around 1.4 m,1.9 m and 2.7 m, with some small

weak bands at 0.90 m and 1.1 m. The reflect peaks in the middle infrared occu at

1.6 m and 2,2 m. It has also been noticed that leaf-moisture has a high effect on


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reflection, decreasing the moisture content the reflectance increases.

1.4 LiDAR Technology

1.4.1 I ntroduction

LiDAR or also called LaDAR (light detection and ranging) is an active remote

sensing technique that has proven to be a key tool in modeling and mapping the

environment. Referred to the action of sensing from a certain distance in an active

way (Baltsavias 1999), the importance this application has now a days in many

ecosystem studies is unmeasurable. The possibility, for instance, to determine spatial

patterns found in vegetation on terrestrial images, opens a wide perspective to

ecological studies.

This literature review aims to define and describe basic terms used in this paper, and

add a brief historical overview if possible.

1.4.2 L iDAR state-of-the-art

The ability of LiDAR sensors to derive accurate digital terrain models (DTM),

and even model vegetation structures on forested areas has called attention of

foresters. Following the developing pace of technology, the first attempts to

measure distance by light beams were pioneered by Hulburt in the 1930s using

searchlights to measure stratospheric aerosols and molecular density. In 1937 light

pulses were used to determine the height of clouds and after the invention of the laser

in 1958 by Schawlow and Townes(fundamental work) in 1958 and Maiman

(construction) in 1960. followed by the first laser studies of the atmosphere

undertaken by Fiocco and Smullin in 1963 , not to forget mentioning the studies of

the upper region of the troposphere made by Ligda ,also in 1963.

Atmospheric LiDAR -

First uses of LiDAR technology were the detection and evaluation of densities in

different stratum of the atmosphere. Advances in wavelength selection made possible


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an exceptional variety of applications, ranging from probing of the trace-constituent

distribution as well as temperature in the upper atmosphere with Resonance

fluorescence LiDAR, to lower atmosphere constituents using differential absorption

LiDAR, boundary layers,wind and temperature with the Coherent Doppler LiDAR

and direction detection LiDAR, to airborne chlorophyl mapping of the oceans,

making use of fluorescence LiDAR

Target LiDARrange determination

In target LiDARs, we will find LiDARs for ranging, also called laser range finder

or laser altimeter and LiDARs for specie identification laser induced fluorescence

LiDAR.

LiDAR sensors determine the distance to their target by calculating the elapsed

time between the emission of a laser pulse, and the retrieval by their reflection. This

distance reflects the roundtrip of this emitted laser, thus dividing this time by two,

and multiplying it by the speed of light to the distance between the sensor and the

target, results in the distance with a high accuracy.Main differences between LiDAR sensors are related to the laser's wavelength,

power, pulse duration and repetition rate, beam size and divergence angle, the

specifics of the scanning mechanism (if any), and the information recorded for each

reflected pulse. Lasers for terrestrial applications generally have wavelengths in the

range of 9001064 nanometers, where vegetation reflectance is high. In the visible

wavelengths, vegetation absorbance is high and only a small amount of energy

would be returned to the sensor. One drawback of working in this range of

wavelengths is absorption by clouds, which impedes the use of these devices during

overcast conditions. (Lefsky et Al, 2002)


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1.4.3 Uses of Lidar in forestry, ALS scanning

In airborne laser scanning, a LiDAR sensor is placed beneath an aircraft, and

registers data during the flight. For each laser pulse the current position is registered

by means of differential GPS and IMU (Inertial Navigation Unit). Laser scanning

provides big amounts of data which requires high processing capability. The gained

data is usually processed by two methods, filtration and classification, in where the

first one refers to separating points corresponding to an object and the second one to

the separation of individual surfaces. These processes can be automatic or semi-

automatic. In forestry ALS is specially used in the following tasks:

1. tree identification

2. measurement of tree parametres

3. creation of a digital model of canopy surface and structure

When obtaining datasets in forested areas laser pulses may reflect from different

layers of vegetative covers (Maltamo et al. 2004)). A first echo or first return will

represent the first layer, the second return and following will represent the middle

layer and subsequence layers, and the last return will represent the earth ground.

Analyzing the first and last return it is possible to determine some parameters of

individual trees such as canopy structure, tree height or crop density (Mikita,

Klimnek and Cibulka et. Al ,2009)

A more recent approach when studying forest stands makes uses only of full-

waveform ALS, this means not only from discrete return and their intensities

(Heinzel and Koch 2011). By using the last return, DEM of high quality an be

interpolated with a spatial resolution of 1m and a height accuracy of ca. 0.1 to 0.20m

(Reutebuch et al 2003). A precise DEM can be intended for multiple purposes, as

new terrain classifications for forest acces roads optimizations, (Akay and sessions

2005) and increasing of Spatial Decision Support Systems (SDSS) in forestry

(Kuhmaier, M. Et Stampfer,k 2010).


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2. Analysis of Ai rborne LiDAR data for mensuration parameters.

2.1 Area of study

2.1.1 The forest

The research was done in the forest area ofKtiny, in the Training Forest Enterprise

(TFE) located North of the city of Brno, Moravia. This forest is part of the Mendel

University of Brno and is used for training purposes as well as for example purposes,

showing a silvicultural management in mostly deciduous beech forest stands. The

area studied corresponds to 1170 ha of the total amount of 10000 ha that the TFE

Ktiny holds. The compartments/stands analyzed are the 270/C, 376/D and 357A.

Each of those compartments have a high level of monospecification and are suitable

for statisical analysis due to the fact that disposed LiDAR images are not classified

by spectrum, not allowing the study of correlation between absorption

range/determination of genera. Tree genera found in this area arefagus sylvatica,

picea abies, and quercus petraea,

2.1.2 Field data

Field mensuration data was received by the department of forest management. The

file comprehends the following data: Compartment, Stand, Stand part, Storey, Age,

Species, Percentage, DBH, Height, Volume/ha, Total Volume, Number of stems and

Basimetric area.The number of stems are an estimation and are not surveyed in the forest, but

assessed from yield tables. Its accuracy is quite low and it is not suitable for

construction of spatial correlations.


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2.1.3 Aerial data

For the analysis ALS-data from GEODIS Brno s.r.o. was obtained. This scan was

done during the vegetation period, in order to define better the layers. The scanner used

was a Leica ALS50-II from a flight altitude of 1395 m with an average density of 4,3

points per square meter. The resultant point cloud was created by the combination of

several cross flights from different flight altitudes with a resultant average density of

125,6 points per square meter.


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2.2 Methodology

Data analysis

Data provided by the university were three files: the digital elevation model (DEM),

digital surface model (DSM) and a file with the classification and respective names of

compartments and tree percentage of the Ktiny area.

The program used for all the analysis was ArcGis from ESRI and the tools mainly from

the Arctoolbox..

The first step was applying a geo-referencing projection system to the data acquired.

The SJTK Krovak East North projection is the most used in Czech Republic and is theone used in our layers. Once this is done there are six processes that had to be done.

1. Automatization of individual tree delineation

2. Extraction of heights values to the identified trees, selection of the study areas.

3. Clipping of trees to selected areas and calculation of mensuration parameters.

4. Regression calculation of DBH/height relation for individual genera.

5. Calculation of volumes using formulas from slovak models.

6. Comparison of calculated volume and estimated volume.

1. The automatization of individual tree selection was done by the use of focal

statistics filtration. Focal statistics were applied to the layer of CHM05 with

rectangular and circular neighborhood selection, in a 3 cell range. Results were

named FocStat_C and FocStat_R shape files. All the following procedures

will be done with both of those files to compare differences in both delineations.

Once this was done, all the values were inverted by the math function times -1

. This is done as preparation for the hydrological function flow direction and

flow length. This functions simulate the flow of a liquid in the selected shape

files. The flowlenght function has to be set up to downstream. All this process


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provides us with the following files FD_CHMC, FD_CHMR and

FL_CHMC and FD_CHMR. Then we reclassified the output files as, 0

equals to value 1, and 0upper limit to NoData. The result is a cloud of points

in one dimension. The function raster to polygon will be used as transition to

convert this files in points with the feature to point function. At the end we

have a geo-located point file with all the trees. The final files will be names

treesC and treesR depending on their original filtration mode.

2. The extraction of multivalues to the identified points will be realized with a

bilinear interpolation and with use of the extract multiple values to points from

the filtrated CHMC/CHMR file. The following step is to select the study

polygons with the function select by attributes, selecting throught the sql

function. The result will be the *_stand files. As an example a discrimination of

areas where spruce is more than 80% and older than 110. Select by attributes/

SM>80 AND VEK>110

3. Through the clip function we add both selected trees, CHMR, and CHMC to

the *_stand layer. The output is named GeneraTrees_C/GeneraTrees_R.

Now we proceed to the following field calculator operations, calculation of DBH

out of previous regression formulas and final calculation of volume with theformulas ofPetrasa a Pajtka.


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3 Resul ts and conclusion

Results of this analysis are represented as follows, 4 tables, including 3 regression

tables for the tree genera used, one table with a summary of results and 4 figures with

some snapshots of the methodology used.

One of the most synthetizing parameters used in forestry to quantify a forest is the

density, represented in this case by the number of stems by ha. During this study we

compared two different forms of tree delineation, being the results very divergent.

Rectangular focal statistic methods are the most common used if you dont want to

make use of external software. Results in table 4 show that this method has greater

success than circular methods of filtration.. This might be due to the raster-origin kind

of file, which is written in rows and columns. Even so the number of stems is the

parameter with higher error. Causes that might have been affecting this are various, age

of the trees, incorrect estimations in the comparison yield tables or bad discrimination of

tree-value spots.

The calculation of heights, represented in table 4 by their mean, are highly

accurate. LiDAR data is a system made for distance calculations and we can not

attribute as correct the yield tables in comparison of the ALS data. Lidar scannings are

exact in their height measurements. This has a high impact on the total results of the

study, because DBH has been calculated from equations extracted from the yield tables,

and so does volume, In consequence incorrect.. As shown in table 4, heights are very

well determined with both filtration modes, concluding, that even being more successful

the identification of trees using rectangular neighborhood focal statistics, the accuracy

of heights is greater using the circular method.

The DBH estimations were calculated by appliance of the regression equation

obtained with Statistica. We classified the yield data by species and afterwards plotted

the result, adding a trend line (Table 1,2,3).

Logarithmic regressions presented problems in the generation of estimated DBH,


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maybe because of being visual basic the language used in field calculator, which

presents error in high exponential formulas. We opted for linear regression equations

rather than trying to transcript the formulas into Python. Differences of determination

coefficient were very low between linear and logarithmic and did not affect the final

DBH mean.

As shown in table 4 we get better values of DBH after a circular focal statistic filtration

in beech and in oak. This might be due to a increment in the precision of height

measurements, and in detriment of a lower stem identification. This result could lead to

the use of combinational methods for better estimation of forest existences in deciduous

species. For this purposes forest mensuration measurements should be taken in field to

corroborate yield tables and add some exactitude to the whole calculations.

Spruce shows lower values of accuracy in all the measured parameters, probably due to

the higher amount of reflectance presented by deciduous trees.

DBH values are in direct relation with the regression tables, calculated out from the

yield table estimations provided, and they have relation with height. Statistical analysis

was done throught the ArcGis field table option. The mean average of DBH is showing

an accuracy of 3 cm, and only in the case of spruce it reaches 5.9 cm difference. Thiserror is attributed nor to the equation used, but to the unreliable yield table estimates.

The estimation of volume has been done using the equations of Petra a Pajtka. Being

individual volumes quite correct, the sum of those values differs from the yield tables

(table 3. ) this is due to the amount of stems identified. We extropaleted results to the

amount of stems of the yield table and results increased accuracy. Even so, its still

below the expectations.

To finalize I would like to list the main conclusions.

Different methodologies to determine the individual stem identification have to

be tested to define a guide line for this process depending on each specie.

An exact inventory table is necessary for further comparisons, un precise

estimations lead to imprecise comparisons.


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The measurement of a specified area could be followed bya new regression

equation which could add precision to the whole estimation.


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4. Citations

Chandra, S., Ziemke, J.R., Bjartia, P.K., and Martin, R.V. (2002). Tropical

tropospheric ozone: Implications for dynamics and biomass burning,Journals of

Geophysical Research, 108, 4921, doi: 10.1029/2002JD002912

Mather, Paul M. Computer Processing of Remotely-sensed Images: An Introduction.

Chichester: Wiley, 1987. Print. ISNN: S0098300400000923

Baltsavias, E. P. 1999: Airborne laser scanning: existing systems and firms and other

resources. ISPRS Journal of Photogrammetry and Remote Sensing, 54: 164198.

(Mikita, Klimnek and Cibulka et. Al ,2009)

Heurich, M., Schneider, T., Kennel, E. 2003: Laser scanning for identification of forest

structures in the Bavarian forest national park. In: Hyypp, J., Naesset, E., Olsson, H.,

Pahln, T. G., Reese, H. (Eds.) Proceedings of the ScandLaser Scientific Workshop on

Airborne Laser Scanning of Forests. Swedish University of Agricultural Sciences in

Umea, Sweden, 98107. ISSN 1401-1204.

Maltamo, M., Eerikainen, K., Pitkanen, J., Hyypp, J., Vehmas, M. 2004: Estimation of

timber volume and stem density based on scanning laser altimetry and expected tree size

distribution functions. Remote Sensing of Environment, 90: 319330.

Lefsky, Warren B. Cohen, Geoffrey G. Parker, David J. Harding. (2002). LiDAR

Remote Sensing for Ecosystem Studies. Bioscience. 52,n1 (1), 19-33.

Heinzel, J., Koch, B. 2011: Exploring full-waveform LiDAR parameters for tree species

classification. International Journal of Applied Earth Observation and Geoinformation,

13(1): 152-160. ISSN 0303-2434.


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Holopainen, M. 2011: Effect of airborne laser scanning accuracy on forest stock and

yield estimates [doctoral dissertation]. Helsinky: Department of Surveying, Aalto

University, 160 p.


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5. Appendices

Table 1, Regression offagus sylvatica

Table 2,Regression of quercus petraea


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Table 3,Regression ofpicea abies

Compartment 357/A-4-4,fagus sylvatica,

Number ofstems Percentage DBH Height Total volume

Total volumeE.

Data_Tables 1617 77 17 14 256

Data_Lidar_R 227 14.962 12.943 26.553 189.144

Data_Lidar _C 89 15.729 13.773 12.924 234.809

Compartment 370/C-9-9, picea abies


Total volumeE

Data_Tables 389 89 33 28 417

Data_Lidar_R 185 39.539 29.519 500.977 1053.406

Data_Lidar_C 96 37.151 31.332 326.703 1323.828

Compartment 376/D-13-13, quercus p etraea


Total volumeE

Data_Tables 1458 100 34 23 1546

Data_Lidar_R 1160 29.843 21.602 907.058 1140.077

Data_Lidar _C 421 30.653 22.107 353.524 353.524

Table 4, Results


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Figure 1, Digital Surface model and identified trees. Green spots stand for

rectangular filtration and violet spots for circular filtration.

Figure 2, Identified stems of spruce after rectangular focal statistics., the background is

the inverted canopy height model.


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Figure 3, Identified stems of spruce after rectangular focal statistics.. the background is

the inverted canopy height model, circular approach.


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Figure 4, 3D representation of the CHM and the same points listed before, spruce with

both filtrations.


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Analysis of airborne lidar data for estimation of tree height, dbh and volume

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