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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
Number ofstems Percentage DBH Height Total volume
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
Number ofstems Percentage DBH Height Total volume
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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