carboforest romania project presentation
TRANSCRIPT
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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
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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
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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.