CarboForest Romania Project Presentation

8/10/2019 CarboForest Romania Project Presentation

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ICASFOREST RESEARCH AND MANAGEMENT INSTITUTE (ICAS) - BUCURETI

FOREST B IOMASS ESTIMATION

BY THE USE OF AIRBORNE LASER SCANNING

AND IN SITU FIELDMAP MEASUREMENT

IN A SPRUCE FOREST STAND

CARBOFOREST CONFERENCE

21-23 sep tember 2011

Forest Research Institute, Skocin Stary, Poland

Authors:

Marius PETRILA

Bogdan APOSTOL

Vladimir GANCZ

Adrian LORENT

Diana SILAGHI

orestry Geomatics Laboratory


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INTRODUCTION

LiDAR technology

Laser scanning survey technology, or LiDAR (Light

Detection And Ranging), takes advantage of the

constancy of the speed of light by transmitting

laser pulses from a known source to a target and

timing the period between pulse transmission and

reception of the reflected pulse. For the aerial laser

scanning is used the term Airborne LaserScanning(ALS).


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Test site

The test site is in Romania, Vlcea county, in the area of Voineasa Forest District, within the

Lotru river valley. The prevailing species are beech (Fagus sylvatica) and spruce (Picea abies) whichare found in both pure and mixed stands. It is a mountain region, covered mostly with pasture and

forest, water bodies and different types of constructions.

Test site


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Test site

The test site is in Romania, Vlcea county, in the area of Voineasa Forest District, within the

Lotru river valley. The prevailing species are beech (Fagus sylvatica) and spruce (Picea abies) whichare found in both pure and mixed stands. It is a mountain region, covered mostly with pasture and

forest, water bodies and different types of constructions.


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ALS data

Airborne LiDAR data were used, collected in 2008-2009 by an airborne Riegl LMS-Q560device connected with a precision GPS/IMU system, which allows laser measurements to

be corrected real time. The data were provided in las LiDAR data format, in UTMcoordinate system, elevation High Above Ellipsoid (HAE). The density is 1.6 points (hits)

per square meter for one strip.

Materials

Riegl LMS-Q560


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To manage, visualize, process andanalyze airborne LiDAR data and

imagery, two software packages were

used:

MARS Explorer - function-limited 30-

day trial license - a commercialapplication developped by Merrick

Company;

Fusionforestry oriented freesoftware for managing geospatial data,

developed and maintained by the USDA

Forest Service.

Software

Materials


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Aerial Images

We used the official orthophotoimagery

provided by National Agency for Cadastre

and Land Registration, obtained from

aerial images in natural colors (collection

year 2005), 0,5 meters spatial resolution.

Materials


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GPS Measured Data

The coordinates of the plot centers

were measured using a Trimble Recon

PDA with installed Trimble Terrasync

Professional software and a Trimble

Pro XH GPS receiver, working in

double frequency L1/L2 with Zephyr

external antenna.The plot centers coordinates collected

by GPS in geographic coordinates

(Lon/Lat) on WGS 1984 ellipsoid were

transferred, corrected, reprojected in

the UTM coordinate system (the

elevation reference HAE - High Above

Ellipsoid) and exported in GIS format

with Trimble GPS Pathfinder Office

software (Fig. 2). For a better post-

processing accuracy differential

correction was performed using data

from the nearest GPS permanent

EUREF station (Deva), provided online

via Internet.

Materials


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FieldMap reference data

21 plots were set and measured

by FieldMap equipment (forestryprofessional software and

equipment for field

measurements) as reference data

for the estimation of individual tree

parameters. Tree position, height,

stem diameter and tree crown

projection were measured. All

individual trees measured in the

plots are spruce (Picea abies).

Materials


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Methods

The classification of LiDAR point clouds, DSM (which in forest areas is identical with

canopy height model - CHM) and DTM extraction were processed in MARS software. The

raw LiDAR data was provided as unclassified points. For DTM extraction we classified thelast and single returns by applying an automatic filter based on ground distance algorithm.

Four classes were created : Ground, Small Vegetation, Medium Vegetation and High

Vegetation.


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Methods

For the DTM extraction was considered only the Ground class. For the canopy height model

extraction were considered the first returns, both single and multiple echoes.


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Methods

With Fusion software the DTM and a subset of LiDAR points, were used to measure the

height of individual trees inside the plot area.

An important question was how can we be sure that the trees measured in the field would be

exactly the same that we can identify in the LIDAR point cloud.

Resolving this ambiguity was achieved by the following operations:

clipping the LiDAR data corresponding to the measured field plots

import and visualization of trees field measurements with FUSION software

import and display the Canopy Height Model in FUSION software


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Digital terrain model and LiDAR data

clipped for the 21 plot areas (FUSION)

Methods


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Methods

LDV FUSION window : field measured trees,

LiDAR point cloud, DTM for plot 5619

FUSION 3D canopy height model for the 5619 plot


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Methods

To estimate the height with Fusion software it was selected an area to see only one tree and the

height was computed as the difference between the Z-value of the highest point (local maxima)and the Z-value of the ground level (local minima). The estimation of the height for a tree is

actually the difference between CHM and DTM for that tree.


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Stem volume per plot and per hectare were determined from the field data, using

individual tree stem volume calculated by a formula according to Giurgiu:

logv = a0+ a1log d + a2 log2d + a3 log h + a4 log

2h

where:

d diameter at breast height in cm

htree height in m

v tree stem volume in m3

Coefficients a0, a1, a2, a3, a4 established for spruce (Giurgiu 2004)

Methods

Species/Coefficient a0 a1 a2 a3 a4

Spruce -4.18161 2.08131 -0.11819 0.70119 0.148181


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Biomass was calculated using three methods: two of them by calculating biomass for each

plot and the third by using Lidar-measured heights, all 3 methods taking into account only the trees with

DBH>13 cm.

A. Biomass using a series of formulas for spruce according to Wirth (based on diameter, height and age)

for branches, dry branches, stem and roots

Branches: lnWb=0+ 1lnD + 2lnH + 3(lnH)2

Dry branches: lnWd=0+ 1lnD + 2lnH + 3(lnAx lnD)

Stem: lnWs=0+ 1lnD + 2(lnD)2 + 3lnH + 4(lnH)

2+ 5lnA

Roots: lnWr=0+ 1lnD + 2(lnD)2+ 3lnA

where:

Wb= branches biomass (kg dry mass tree-1)

Wd= dry branches biomass (kg dry mass tree-1)

Ws= stem biomass (kg dry mass tree-1)

Wr= roots biomass (kg dry mass tree-1)

D = diameter at breast height (cm)

H = height of tree (m)A = age of tree (years)

Methods

Compartment 0 lnD (lnD)2 lnH (lnH)2 lnA (lnA x

lnD)

Branches -0,64565 2.85424 - -2.98493 0.41798 - -

Dry branches -1.21969 1.49138 - -1.25928 - - 0.18222

Stem -2.83958 2.55203 -0.14991 -0.19172 0.25739 -0.08278 -

Roots -8.35049 4.56828 -0.33006 - - 0.28074 -


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B. Giurgiu method for estimating total tree biomass for spruce using the following equation:

y = 44.855 - 9.8498x + 0,7929x2 ,

where

y - total biomass in kg /ha

x - diameter at breast height in cm

Methods


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C. Biomass estimation using LiDAR determined heights. This method implies a series ofpreparatory steps:

a. Computing of missing LiDAR heights using the regression equation based on all trees

for which both LiDAR and field heights were measured:

hLidar= 0.9393 hf ield+ 0.5182

b. Computing of mean hLidar

c. Computing of corrected mean height hcorusing the following regression equation

based on field and LiDAR data:

hcor =1.0067 hLidar+ 0.8278

Methods


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d. Computing of normal basal area and volume for the determined corrected mean height

hco r= hmean, according to Giurgiu:

hmean=22m

Gn = F + b1(hmean- 22) + b2(hmean- 22)2+ b3(hmean- 22)

3+ b4(hmean- 22)4

hmean=22m

Vn = C + b1(hmean- 22) + b2(hmean- 22)2+ b3(hmean- 22)

3+ b4(hmean- 22)4

where:

Gn - normal basal area for the mean height

Vn - normal volume for the mean height

Methods


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Coefficients a1

, a2

, a3

, a4

and b1

, b2

, b3

, b4

, F, C established for spruce (Giurgiu 2004):

Methods

Vn a1 a2 a3 a4

hmean22m 31.331 -0.1794 -0.0023 0.00005 531

Gn a1 a2 a3 a4

hmean22m 1.483433 -0.06672 0.002892 -0.000051 55.6


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e. Computing total volume and biomass for the determined hmean

Density index = Gn/Gtfield

where

Gtfield- total basal area measured in the field

Vt = Vn x density index,

where

Vttotal volume (m3)

Stem biomass = Vt x wood volumetric density (kg/m3)

Total biomass = stem biomass x 100 / 65 (stem biomass represent 65% percent of

total biomass (Giurgiu 2004)).

Methods


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Results

Checking the statistical coverage probability

Field measurements were summarized and were calculated statistical indicators such as

sample mean, standard deviation and coefficient of variation for all the 21 plots.

Statistical indicators of the field data

The low value of volume coefficient of variation (25%) signifies that the volumes of the 21 plotsare relatively close to each other and they are spread uniformly on the management sample,

reflecting the high degree of representativeness of its from the stand.

The aim tolerance is 10% at a statistical coverage probability of 90%. Percentage of inventory

is less than 10% (21 circular plots areas of 500 m2each), the error of representativeness (p) is

calculated with formula simplified formula:

p = t xs% / n0,5

where :

t - Student coefficient at 20 degrees of freedom (t=1.725)

s% - coefficient of volume variation (25%)

n - number of plots (21)

The representativeness error calculated with the above formula was 9.4% (smaller than 10%

tolerance) which means the number of plots areas chosen for parcel 56A is enough in order to

obtain 90% accuracy in volume and biomass estimation.

Nr. of plotsCharacteristic

considered

Sample

mean

Standard

deviation

Coefficient

of Variation

21Total basal area (m2) 3.30 0.67 20

Volume (m3) 36.35 9.15 25


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Results

Computing biomass from field data

Plot

ID

Method I - Giurgiu Method II Wirth - Biomass kg/ha

Basal

area(G)

m2/ha

Volume

(v)(m3/ha)

Biomass(kg/ha)

Branches(kg/ha)

Dry

branches(kg/ha)

Stem(kg/ha)

Roots(kg/ha)

Total(kg/ha)

561 72.82 738.13 500271.58 56726.11 14207.71 285980.21 101900.09 458814.11

562 60.62 713.21 435199.57 50359.17 10488.35 287237.29 87126.62 435211.42

563 68.42 703.69 462403.13 52442.04 13602.13 273663.47 94070.93 433778.56

564 80.96 844.16 528953.91 56585.06 15154.75 336084.40 110130.71 517954.91

565 68.59 740.81 478685.53 54184.91 13361.77 291393.28 97827.52 456767.47

566 53.05 574.92 353544.06 38274.01 9195.39 229622.50 72575.11 349667.01

567 107.86 1311.03 751396.42 82119.94 17124.86 535292.07 152496.01 787032.89

568 72.17 752.03 457633.87 46852.13 14664.72 303333.21 97731.47 462581.54

569 50.60 451.37 317838.42 68053.65 19637.10 178537.26 65926.49 332154.50

5610 57.88 503.95 343639.03 36139.62 12286.95 194922.67 73167.86 316517.09

5611 70.05 818.07 507026.18 59476.83 12592.39 324050.48 101227.56 497347.26

5612 67.37 757.95 450293.27 47552.46 12001.84 306146.30 93478.55 459179.14

5613 62.60 597.86 392990.60 40620.28 12488.05 234338.36 83344.41 370791.10

5614 73.62 819.44 484476.60 51446.28 13192.24 332061.67 100953.26 497653.46

5615 76.20 861.10 505095.70 54047.03 14094.27 349839.60 105305.08 523285.97

5616 48.42 530.75 337508.53 36480.15 8760.02 209445.27 69358.35 324043.78

5617 54.44 621.76 359301.36 36451.51 9397.78 253619.46 75519.02 374987.76

5618 56.67 609.34 386290.98 40575.92 10440.65 241493.82 80473.44 372983.83

5619 73.50 871.11 504345.99 53374.50 12661.51 354797.74 104386.74 525220.49

5620 50.85 578.00 378525.84 45698.29 9315.14 223454.74 74177.85 352646.02

5621 60.64 689.71 397494.86 42961.96 11699.25 284198.75 83134.05 421994.01

MEAN 66.06 718.49 444424.54 50020.09 12684.14 287119.64 91633.86 441457.73

% - - 11.3 2.9 65.0 20.8 100.0

First we compared the first

two terrestrial methods

that estimate biomass

(Giurgiu Wirth) using

paired samples t-test.

The significance of t-testshowed that there are no

significant differences

between them

(t(20)=0.652,p=0.522)


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ResultsCorrelation equation between height measured on the field and those measured by Lidar

IDPlot

Nr. Of

measuredtrees

Corelation

coeficient(r)

561 24 0.812

562 22 0.987

563 41 0.976

564 34 0.973

565 29 0.990

566 20 0.984

567 35 0.975

568 40 0.913

569 32 0.968

5610 47 0.932

5611 24 0.976

5612 29 0.953

5613 45 0.957

5614 37 0.956

5615 32 0.904

5616 23 0.984

5617 27 0.986

5618 27 0.943

5619 32 0.966

5620 12 0.978

5621 29 0.949

Heights determined on LIDAR data were compared with those

measured in the field and interpreted statistically to determinethe correlation coefficient between the two sets of values and

also significance of the coefficient of variation was tested. The

results show a strong linear correlation between the two sets of

measurements of height, which is a proven correlation for each

sample area.

From the table with fusioned field-LiDAR biometric

measurements we derived the correlation between heightmeasured by LiDAR (hiLidar) and real heights (hi)

hi =1.0067 hiLiDAR+ 0.8278

y = 1.0067x + 0.8278

R2= 0.9456

0

5

10

15

20

25

30

35

40

0 5 10 15 20 25 30 35 40

Height measured on LiDAR data (m)

Heightmeasuredonthefield(m)


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Results

Frequency by diameter class

In order to calculate the mean diameter, the frequency of field measured diameters

distribution was calculated to verify if is respecting the normal distribution. The frequency of

the small diameters is too high comparing to the normal, which tells us that the diameters

smaller than 13 cm should be excluded for volume and biomass determination. These trees

represent about 1% from total biomass.


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Results

Computing mean height

First step was to compute the missing adjusted LiDAR heights according to the inversefunction :

hLiDAR=0.9393 hfield+ 0.5182

Second step was to determine the mean adjusted LiDAR height corresponding to the

mean diameter class. The mean diameter of 29.5 cm belongs to the 28-30 diameter class

and the mean adjusted LiDAR height is 22.81 m.

The next step was to calculate the mean height from the mean adjusted LiDAR height by

the direct function :

hmean =1.0067 hLiDAR + 0.8278= 23.79 m


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Results

Computing normal basal area, total volume and biomass for the determined mean height

For the third method the total biomass is derived from stem biomass, which is assumed tobe 65 % of total biomass (Giurgiu 2004) which is consisted with the tree stem biomass

computed by Wirth equation

To compare the results of the third biomass estimating method with the first 2 classic ones, one-

sample t-test was used. If, when calculating the biomass, we use the general density of 372 kg/mc

(Giurgiu 2004), between the first two calculated biomasses and the third one there are significant

differences (t(20)=2.976, p=0.07; t(20)=2.605, p=0.017). This result seemed strange, because when

comparing the volumes calculated based on field data reported at hectar with the total volumedetermined with the third method, no significant differences were recorded (t(20)=1.283, p=0,214).

Based on the stem biomass computed with Wirth formula (bst) and the volumes of each tree (vst), we

determined a local regression equation of estimating stem biomass function of volume:

bst=392.797 vst+ 5.883

When applying this equation for calculation of stem biomass in the third method, the resulted total

biomass is not significantly different from the biomasses obtained using the first two methods

(t(20)=1.958, p=0.06; t(20)=1.669, p=0.111).

Vn

(m3/ha)

Gn

(m2/ha)

G

(m2/ha)

Density

index

G/Gn

Vt

(m3/ha)

Volumetric density

366 kg/m3

Volumetric density

399 kg/m3

Stem

biomass(kg/ha)

Total

biomass(kg/ha)

Stem biomass

(kg/ha)

Total biomass

(kg/ha)

586,65 58,06 66.06 1,14 667,47 244293.80 375836.62 266320,29 409723,53


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From the total of 1142 trees of the 21 sample areas, height could be measured for 641

trees representing a rate of 56%. However, the trees measured on LiDAR data, account for

90% of the biomass. Therefore it can be concluded that preliminary LiDAR data provides a

good estimation of biomass. Even though LiDAR identifiable trees data exceeds 50% of the

total number of trees, they are the dominant and codominantrees, representing most of thestand biomass. In fact, the 10% of the biomass covered by LiDAR no identifiable trees are

underdeveloped trees from the lower ceiling

The method is trying to estimate the biomass only by height measurement on LiDAR

data by comparing with widely accepted existing biomass equations for Europe and for

Romania. Good correlations between LiDAR measured height and real heights were

obtained from existing data, biomass estimation being also accurate. Is possible to derive

also a correlation between mean height and dominant height (measured on LiDAR ) or

biomass of visible trees and total biomass.

The method needs field data to obtain a good estimation of the mean height, on LiDAR

data only dominant trees being visible. This method also requires the availability of an

adequate number of LiDAR observations in different stand situations (age, density,

productivity). Local biomass equations and wood volumetric density coefficients should be

developed in order to improve the method.

Discussion

Biomass determination method Wirth

f(d, H, A)

Giurgiu

f(d)

LiDAR

f(H)

Total biomass (kg/ha) 441457.73 444424.54 409723.53

% 100 101 93

FOREST RESEARCH AND MANAGEMENT INSTITUTE (ICAS) BUCURETI


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This work presents an individual tree-based approach, developed as a method to

evaluate dry biomass of the spruce forests by combining airborne LiDAR sampling andground plots. The preliminary results proved that LiDAR data has a strong potential to

provide precise information on biomass and can offer a good estimation using only

LiDAR measured heights. Further studies will aim to more developments of the method,

in order to use less field reference data for biomass estimation and to include a crown

diameter/DBH correlation. Another topic will be the automatic tree identification and tree

heights extraction extended to all forest stand area.

Conclusions

FOREST RESEARCH AND MANAGEMENT INSTITUTE (ICAS) BUCURETI


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Thank you for your attention

ACKNOWLEDGEMENTS

The research was financed by the Romanian Ministry Of Research, under

the Nucleus Programme.

We are gratefull to Mr. Cristian Glon, the manager of SC Primul MeridianSRL company, who offered us without charge the LiDAR data for the test area.

CarboForest Romania Project Presentation

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