estimating forest growth using canopy metrics derived from airborne laser scanner data
TRANSCRIPT
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sin
ase
Ter
egian
vised
airborne laser scanner data with a sampling density of 0.91.2 m
(swath width) of up to many hundred meters in one
overflight are now being used operationally in commercial
precise information about the timber resources and biomass
stocks, airborne lasers might also be considered as useful
tools in monitoring systems. At a large scale it has been
can provide useful
Remote Sensing of Environment 96stand-based forest inventories (Nsset, 2004c; Nsset et al.,1. Introduction
Airborne laser systems offer an opportunity to determine
biophysical properties of forest stands such as mean tree
height, basal area, and timber volume from detailed three-
dimensional information about tree canopies retrieved from
the laser data (e.g., Maclean & Krabill, 1986; Magnussen &
Boudewyn, 1998; Means et al., 2000; Nsset, 1997a,
1997b; Nelson et al., 1984). Scanning systems with the
ability to collect laser data along a corridor with a width
2004). Application of profiling lasers which collect a narrow
line of data beneath the platform, has already been
demonstrated as a sampling-based tool for forest and
biomass inventory in large areas, such as regions and states
(Nelson et al., 2003, 2004; Weller et al., 2003). It is
therefore evident that airborne lasers have a role to play in
resource assessment.
In an operational context in forestry, scanning and
profiling lasers have mainly been used for inventory and
assessment purposes. However, with the ability to providecollected over 133 georeferenced field sample plots and 56 forest stands located in young and mature forest. The plot size was 300400 m2
and the average stand size was 1.7 ha. Spruce and pine were the dominant tree species. Canopy height distributions were created from both
first and last pulse data. The laser data were acquired in 1999 and 2001. Height percentiles, mean and maximum height values, coefficients of
variation of the heights, and canopy density at different height intervals above the ground were computed from the laser-derived canopy
height distributions. Corresponding metrics derived from the 1999 and 2001 laser datasets were compared. Forty-five of 54 metrics derived
from the first pulse data changed their values significantly due to forest growth. The upper height percentiles increased their values more than
the field-based height growth estimates. The 50 and 90 height percentiles increased by 0.41.3 m whereas the field-estimated mean height
increased by 0.20.9 m. Metrics derived from the last pulse data were less influenced by growth.
Mean tree height (hL), basal area (G), and volume (V) were regressed against the laser-derived variables to predict corresponding values
of hL, G, and V based on the 1999 and 2001 laser data, respectively. Forest growth was estimated as the difference between the 2001 and
1999 estimates. Laser data were able to predict a significant growth in all the three biophysical variables over the 2-year period. However, the
accuracy of the predictions was poor. In most cases the predictions were biased and the precision was low. Finally, several key issues of
particular relevance to laser-based monitoring of forest growth are discussed.
D 2005 Elsevier Inc. All rights reserved.
Keywords: Forest growth; Forest monitoring; Laser scanning; Canopy height; Canopy densityCanopy height distributions were created from small-footprintAbstract
2Estimating forest growth u
from airborne l
Erik Nsset*,
Department of Ecology and Natural Resource Management, Norw
Received 10 December 2004; received in re0034-4257/$ - see front matter D 2005 Elsevier Inc. All rights reserved.
doi:10.1016/j.rse.2005.04.001
* Corresponding author. Tel.: +47 64948906; fax: +47 64948890.
E-mail address: [email protected] (E. Nsset).g canopy metrics derived
r scanner data
je Gobakken
University of Life Sciences, P.O. Box 5003, N-1432 As, Norway
form 1 April 2005; accepted 2 April 2005
(2005) 453 465
www.elsevier.com/locate/rsedemonstrated that profiling lasersinformation about the change in biomass over time (Sweda
et al., 2003). At a very local scale, it has even been shown
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temporal resolution at which reliable change estimates can
be provided. Yu et al. (2004) demonstrated that height
singgrowth of individual trees could be estimated over a 2-year
period in a pine forest using laser data with a pulse density
of 10 m2. However, they only reported the accuracy oflaser-derived height growth estimates as compared to a
ground reference for three selected sample plots. Their
findings indicated an under-estimation of height growth of
1493%, but stated this under-estimation in part was caused
by systematic differences between the two digital terrain
models used to derive the canopy heights from the laser
data. The work by Yu et al. (2004) was based on
identification of each treea feasible method due to the
high density of the laser data. In monitoring of large forest
areas, it is not economically feasible to collect data with
current scanning systems of more than, say, one laser pulse
per square meter (Nsset, 2004c). In operational large-area
monitoring programs, the estimation is most likely to be
based on a statistical approach, i.e., statistical metrics like
percentiles and mean values derived from a collection of
laser pulses for a group of trees represented by a sample plot
(Nsset, 2002a, 2004b) or a sample line (Nelson et al.,
2003) are used to estimate regression equations that relate
the laser data to field observations of the properties of
interest, for example timber volume. These equations are
then used to make predictions over the entire area of
interest. Such a statistical approach has proven to provide
precise estimates of timber volume and other biophysical
variables of interest in individual forest stands, for entire
forest properties, in municipalities, and entire states
(Holmgren, 2004; Nsset, 2002a, 2004b, 2004c; Nelson
et al., 2003, 2004).
In the present work, we analysed multi-temporal laser
data acquired for a boreal forest site in 1999 and 2001. The
laser sampling density was approximately 0.91.2 m2.The objectives of this research were (1) to assess how and to
what extent the laser-derived metrics used to estimate
biophysical forest properties were affected by forest growth
and to examine how forest type influenced on these effects,
and (2) to assess the accuracy of laser-based growth
predictions over a 2-year period of three major biophysical
properties, i.e., mean tree height, basal area, and volume.
2. Materials and methods
2.1. Study area
This study was based on data from a forest inventory inthat high-resolution laser data from scanning systems can be
used to detect growth and harvest of single trees and groups
of trees (plots) (St-Onge & Vepakomma, 2004; Yu et al.,
2004).
In monitoring it is important to find an appropriate
E. Nsset, T. Gobakken / Remote Sen454southeast Norway conducted in the municipality of Valer
(59-30VN, 10-55VE, 70120 m a.s.l.). The size of theinventory was approximately 1000 ha. The main tree
species were Norway spruce [Picea abies (L.) Karst.] and
Scots pine (Pinus sylvestris L.). Further details about the
study area can be found in Nsset (2002a).
Interpretation of aerial stereo photography was used to
delineate and classify forest stands according to the criteria
age class, site index, and tree species. The photo interpre-
tation was used as prior information in designing the
inventory. Two different ground reference datasets were
acquired; (1) one consisting of sample plots distributed
systematically throughout the entire study area, and (2) a
dataset consisting of forest stands. Both datasets were used
to analyze the effect of forest growth on the laser-derived
metrics. Both datasets were also used to assess the accuracy
of laser-based growth predictions of mean tree height, basal
area, and volume, see further details below.
2.2. Sample plots
All field data were collected during summer of 1998, see
Nsset (2002b). Since the laser data were acquired in 1999
and 2001 (see below), the area was revisited in field in
December 2001 to verify that the plots had not been subject
to any harvests or serious natural disturbances. However, it
is likely that some natural mortality had occurred during the
34-year period, although plots where it was observed that
significant mortality had taken place were discarded from
the data.
In total, 133 circular sample plots were distributed
systematically throughout the 1000-ha study area. The plots
were divided into three strata according to age class and site
quality, i.e., (1) young forest (stratum I), (2) mature forest
with poor site quality (stratum II), and (3) mature forest
with good site quality (stratum III). The plot size was 300
m2 in stratum I and 400 m2 in strata II and III. On each plot,
all trees with diameter at breast height (dbh) >4 and >10 cm
were callipered on young and mature plots, respectively,
which conforms to ordinary inventory practice in Norway.
Basal area (G) was computed as the basal area per hectare
of the callipered trees. The heights of sample trees selected
with probability proportional to stem basal area at breast
height using a relascope were measured by a Vertex
hypsometer. Mean height of each plot was computed as
Loreys mean height (hL), i.e., mean height weighted by
basal area. Total plot volume (V) was computed as the sum
of the individual tree volumes for trees with dbh>4 cm and
dbh>10 cm, respectively, using volume equations for
individual trees (Braastad, 1966; Brantseg, 1967; Vestjordet,
1967). hL, G, and V were prorated by 0.23.8 years using
growth models (Blingsmo, 1984; Braastad, 1975, 1980) to
correspond to the dates on which the 1999 and 2001 laser
data were acquired. These prorated values were used as
ground reference (Table 1).
The plot center coordinates (x and y) were determined
of Environment 96 (2005) 453465by differential Global Positioning System (GPS) and
Global Navigation Satellite System (GLONASS) using
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respectively. Sample trees were selected with probability
proportional to stem basal area. The number of sample
trees per stand ranged from 24 to 87 with an average of
44. Loreys mean height (hL) was computed as the
arithmetic mean of the sample tree heights. Stand basal
area (G) was computed as basal area per hectare for the
callipered trees. Stand volume (V) was computed accord-
ing to standard volume equations for individual trees (see
above) and diameterheight relationships derived from the
sample trees (see Nsset, 2002a for further details). hL, G,
and V were prorated according to the procedure outlined
above to correspond to the dates of the laser data
acquisition. The prorated values were used as ground
reference. A summary of the ground-truth stand data is
displayed in Table 2.
2.4. Laser scanner data
Laser scanner data for this study were acquired on 8 and
9 June 1999 (Nsset, 2002a; Nsset & Bjerknes, 2001) and
on 16 and 17 July 2001 (Nsset, 2004a) under leaf-on
canopy conditions. On both occasions, a fixed-wing Piper
PA31-310 aircraft carried the Optech ALTM 1210 laser
scanning system. The same instrument was used in 1999
and 2001. The major components of the ALTM 1210 are the
nsing of Environment 96 (2005) 453465 455Table 1
Summary of sample plot reference dataa
Characteristic 1999 Growth Mean
Range Mean 2001
Young forest-stratum I (n=53)
hL (m) 6.521.2 13.6 0.9 14.5
G (m2 ha1) 9.741.4 24.5 2.9 27.4V (m3 ha1) 36.3460.1 178.6 28.6 207.2Tree species distribution
Spruce (%) 0100 53
Pine (%) 097 34
Deciduous species (%) 069 13
Mature forest, poor site quality-stratum II (n=34)
hL (m) 11.421.5 16.1 0.2 16.4
G (m2 ha1) 9.429.5 19.1 0.8 19.9V (m3 ha1) 56.4273.8 148.7 8.3 157.0Tree species distribution
Spruce (%) 089 29
Pine (%) 0100 66
Deciduous species (%) 021 5
Mature forest, good site quality-stratum III (n=46)
hL (m) 11.425.9 20.0 0.5 20.5
G (m2 ha1) 12.148.0 28.0 1.7 29.7V (m3 ha1) 93.0555.9 271.0 21.0 292.1Tree species distribution
Spruce (%) 0100 68
Pine (%) 0100 23
E. Nsset, T. Gobakken / Remote Setwo Javad Legacy 20-channel dual-frequency receivers
observing pseudorange and carrier phase as rover and
base-station receivers. The estimated accuracy of the plot
coordinates ranged from 4 and
>10 cm were callipered on young and mature plots,
near-infrared laser (1064 nm), the scanner transmitting theDeciduous species (%) 049 9a hL=Loreys mean height, G =basal area, V =volume.Table 2
Summary of stand reference dataa
Characteristic 1999 Growth Mean
Range Mean 2001
Young forest-stratum I (n=22)
hL (m) 10.119.7 13.7 0.7 14.4
G (m2 ha1) 16.437.2 23.8 2.2 26.0V (m3 ha1) 100.1345.8 164.9 19.8 184.7Tree species distribution
Spruce (%) 9100 48
Pine (%) 086 39
Deciduous species (%) 030 13
Mature forest, poor site quality-stratum II (n=17)
hL (m) 13.617.9 16.0 0.2 16.2
G (m2 ha1) 12.630.6 19.1 0.8 19.9V (m3 ha1) 90.8257.6 145.1 7.7 152.8Tree species distribution
Spruce (%) 476 31
Pine (%) 1892 62
Deciduous species (%) 222 7
Mature forest, good site quality-stratum III (n=17)
hL (m) 15.922.7 19.2 0.4 19.6
G (m2 ha1) 17.838.8 28.5 1.7 30.2V (m3 ha1) 138.8373.4 261.6 19.2 280.8Tree species distribution
Spruce (%) 4490 70
Pine (%) 043 20Deciduous species (%) 122 10
a hL=Loreys mean height, G =basal area, V =volume.
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laser pulse and receiving the first and last echoes of each
pulse, the time interval meter measuring the elapsed time
between transmittance and receipt, the GPS airborne and
ground receivers, and the inertial reference system reporting
the aircrafts roll, pitch, and heading.
The average flight altitude was approximately 700 and
850 m a.g.l. in 1999 and 2001, respectively (Table 3). The
pulse repetition frequency was 10 kHz. First and last returns
were recorded.
In 1999, 43 flightlines were flown in a cross pattern.
Nineteen parallel flightlines with approximately 50%
overlap were flown in one direction and 24 parallel
flightlines were flown perpendicular the first 19 lines.
E. Nsset, T. Gobakken / Remote Sensing456Thus, every location in the study area was covered with
laser data from four strips. Maximum scan angle was 17-,but pulses transmitted at scan angles that exceeded 14-were excluded from the final datasets. Average footprint
diameter at the ground was 21 cm and the average pulse
density was 1.18 m2.Thirty-three parallel flightlines were flown in 2001.
Maximum scan angle was 16-, and pulses transmitted atscan angles >15- were discarded. Average footprintdiameter was 26 cm. Average pulse density was 0.87 m2.
The initial processing of the laser data was accom-
plished by the contractor (Blom Norkart Mapping, Nor-
way). Planimetric coordinates (x and y) and ellipsoidic
height values were computed for all first and last returns.
Unlike the 1999 data, a matching between swaths was
performed on the 2001 data in order to remove orientation
errors. We decided to co-register the height values of both
first and last return data from 1999 as well as 2001 to the
same terrain model to eliminate effects of systematic shifts
in the heights (the z coordinates). The last return data
acquired in 2001 were therefore used to model the ground
surface.
In a filtering operation on the last return data from 2001
undertaken by the contractor using a proprietary routine
(Anon., 2004), local maxima assumed to represent vegeta-
tion hits were discarded. A triangulated irregular network
(TIN) was generated from the planimetric coordinates and
corresponding height values of the individual terrain ground
Table 3
Summary of laser scanner data and flight parameters for the 1999 and 2001
laser data acquisitions
Parameter 1999 2001
System ALTM 1210 ALTM 1210
Repetition frequency 10 kHz 10 kHz
Scan frequency 21 Hz 30 Hz
Date 89 June 1617 July
Mean flying altitude 700 m a.g.l. 850 m a.g.l.
No. of flightlines 43 33
Max. scan angle 17- 16-
Max. processing angle 14- 15-
Mean footprint diameter 21 cm 26 cmMean pulse density 1.18 m2 0.87 m2points retained in the last pulse dataset. The ellipsoidic
height accuracy of the TIN model was expected to be
around 25 cm (Kraus & Pfeifer, 1998; Reutebuch et al.,
2003).
Four different datasets were derived from the laser data
for further analysis, i.e., first and last returns from 1999 and
2001. All first and last return observations (points) were
spatially registered to the TIN according to their coordi-
nates. Terrain surface height values were computed for each
point by linear interpolation from the TIN. The relative
height of each point was computed as the difference
between the height of the first or last return and the terrain
surface height. These datasets were spatially registered to
the sample plots and stands measured in field.
To calibrate the height values of the first and last pulse
data from 1999 and the first pulse data from 2001 according
to the TIN model derived from the 2001 last pulse data, we
identified a public road that goes through the entire study
area and divides it into two parcels of almost equal size. Six
paved road segments along the flattest part of the road were
selected. Within each segment, a square with an approxi-
mate size of 33 m was laid out in the middle of the road.Within the square we identified all the last pulses from 2001
that were node points in the TIN, i.e., they were classified as
ground points according to the TIN model. Within a search
radius of 0.5 m from each of these node points, we
identified the points that were first and last returns from
the 1999 laser data and first returns from the 2001 data. The
height values of the node points were compared with the
height values within the 0.5 m search radius. For the first
return data from 2001, no systematic shift was found. For
the first and last return data from 1999, the computed mean
differences were 13.7 and 3.9 cm, respectively, i.e., the1999 laser data were shifted downwards as compared to the
TIN surface. The standard deviations for the differences
were 5.8 and 5.7 cm, respectively. All laser pulses of the
1999 datasets were corrected according to the estimated
differences.
2.5. Computations
For each sample plot and stand inventoried in field,
height distributions were created for those laser pulses that
were considered to belong to the tree canopy, i.e., pulses
with a height value of >2 m (Nilsson, 1996). Some sample
height distributions are presented in Fig. 1. Separate
distributions were created for the first and last pulse data,
respectively, and percentiles for the canopy height for 10%
(h10), 50% (h50), and 90% (h90) were computed. In addition,
also the maximum (hmax) and mean values (hmean) and the
coefficient of variation (hcv) of the canopy height distribu-
tions were computed. Furthermore, several measures of
canopy density were derived. The range between the lowest
laser canopy height (>2 m) and the maximum canopy height
of Environment 96 (2005) 453465was divided into 10 fractions of equal length. Canopy
densities were then computed as the proportions of laser hits
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nsingFirst pulse
E. Nsset, T. Gobakken / Remote Seabove fraction #0 (>2 m), 1,. . ., 9 to total number of pulses.The densities for fraction #1 (d1), #5 (d5), and #9 (d9) were
selected for further studies.
-202468
10121416182022242628
0 30 40 50 70
19992001
Stand # = 42
-202468
10121416182022242628
0 20 40 60
Stand # = 52
-202468
10121416182022242628
0 20 60 70
Stand # = 29
Lase
r hei
ght (
m)
2010 60
Relative frequency (%)
Lase
r hei
ght (
m)
10 30 40 50
Relative frequency (%)
Lase
r hei
ght (
m)
10 30 50 70
Relative frequency (%)
Fig. 1. Height distributions (relative frequencies in 1 m height intervals) of first an
from stratum I (stand #42: age=53 years, site quality=high, 100% spruce, hLquality=poor, 92% pine, hL=16.1 m, G =13.4 m
2 ha1), and stratum III (stanG =30.1 m2 ha1).Last pulse
of Environment 96 (2005) 453465 457To assess how forest growth affected the laser-derived
metrics, differences between corresponding metrics derived
from the 2001 and 1999 laser data were computed for each
-202468
10121416182022242628
0 20 40 60
Stand # = 42
-202468
10121416182022242628
0 20 40
Stand # = 52
-202468
10121416182022242628
0 30 40 50 70
Stand # = 29 La
ser h
eigh
t (m)
10 30 50 70
Relative frequency (%)
Lase
r hei
ght (m
)
Relative frequency (%)10 20 60
Lase
r hei
ght (
m)
10 30 50 60 70
Relative frequency (%)
d last pulse laser data from 1999 and 2001 for three sample stands selected
=19.7 m, G =37.2 m2 ha1), stratum II (stand #52: age=135 years, sited #29: age=132 years, site quality=medium, 64% spruce, hL=20.3 m,
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singsample plot and forest stand. The standard deviations of the
differences were also computed to assess the stability of the
respective metrics. Separate comparisons between laser
scanner data from 1999 and 2001 were carried out for first
and last pulses, respectively.
The mean differences between data acquired in 1999 and
2001 of the investigated height and density-related metrics
were compared for different strata by means of t-tests to
assess how forest type influenced on the effects of forest
growth on the laser-derived metrics. Correspondingly, the
variances of the differences between laser scanner data from
1999 and 2001 were compared for different strata by means
of F tests. In the comparisons of the nine laser-derived
metrics between the 1999 and 2001 laser datasets within
strata and in the comparison between strata, nine t-tests or F
tests were accomplished simultaneously. In order to control
the total Type I error, Bonferroni tests were applied (Miller,
1981). Thus, the level of significance for each of the nine
tests was a / 9.To assess the accuracy of laser-based growth predictions
of mean tree height, basal area, and volume over the 2-year
period from 1999 to 2001, we followed the two-step
procedure proposed by Nsset and Bjerknes (2001) and
Nsset (2002a) to (1) relate the three biophysical properties
of interest to the laser data of the sample plots, and (2) to use
these relationships to predict corresponding values of the 56
test stands based on the 1999 and 2001 laser data,
respectively. As an additional step, (3) the growth was
estimated as the difference between the predicted values in
2001 and 1999.
Thus, in step 1, multiple regression analysis was used to
create stratum-specific relationships between the three
biophysical properties and the laser-derived metrics for
the 133 field training plots (sample plots). We only used the
1999 laser data, and not the 2001 datasets, to develop these
models to avoid effects on the growth predictions of using
different models. The estimation of regression models was
based on the height and density-related metrics derived
from the first and last pulse height distributions as
candidate explanatory variables. However, the maximum
values of the height distributions were not included as
candidates since a higher variability seems to be associated
with these variables than the other height-related metrics
(Nsset, 2004a). In the regression analysis, multiplicative
models were estimated as linear regressions in the
logarithmic variables. The linear form used in the
estimation was
lnY lnb0 b1lnh10f b2lnh50f b3lnh90f b4lnh101 b5lnh501 b6lnh901 b7lnhmeanf b8lnhmeanl b9lnhcvf b10lnhcvl b11lnd1f b12lnd5f b13lnd9f b14lnd11 b15lnd51 b16lnd91 1
where Y=field values of hL (m), G (m2 ha1), or V (m3
1
E. Nsset, T. Gobakken / Remote Sen458ha ); h10f, h50f, h90f=percentiles of the first pulse laser
canopy heights for 10%, 50%, and 90% (m); h10l, h50l,h90l=percentiles of the last pulse laser canopy heights for
10%, 50%, and 90% (m); hmeanf, hmeanl=mean of the first
and last pulse laser canopy heights (m); hcvf, hcvl=coeffi-
cient of variation of the first and last pulse laser canopy
heights (%); d1f, d5f, d9f=canopy densities corresponding
to the proportions of first pulse laser hits above fraction 1,
5, 9 to total number of first pulses; d1l, d5l, d9l=canopy
densities corresponding to the proportions of last pulse
laser hits above fraction #1, 5, 9 to total number of last
pulses. Stepwise selection was performed to select vari-
ables to be included in these models. No predictor variable
was left in the models with a partial F statistic with a
significance level greater than 0.05. The standard least-
squares method was used (Anon., 1989).
By conversion of the loglog equations to original
scale for prediction purposes a bias will be introduced in
the intercept (e.g., Goldberger, 1968). It is therefore
essential that proper corrections are undertaken to avoid
serious bias of predictions. Under given circumstances,
proper corrections may be rather complicated to accom-
plish. However, when the differences between the training
data and the data used in the predictions are small,
MSE0.5, and nk30, i.e. the number of observationsminus the number of predictor variables, an approximate
and simple correction that will introduce an error of less
than 1%, is to add half the variance to the regression
intercept before conversion (Flewelling & Pienaar, 1981).
In the present context, even a bias of 1% may have a
serious impact on the interpretation of the predictions.
Based on the training plots used in the regression analysis,
we therefore computed the corrected intercepts as the ratio
between the respective mean values of the response
variables and the mean estimated values without the
intercept.
In step 2, the estimated regression models with
adjusted intercepts were used to predict the three
biophysical properties of interest in each of the 56 large
test stands. Separate predictions were made for the laser
data acquired in 1999 and 2001, respectively. This was
done by dividing each stand into regular grid cells with a
cell size of 350 m2. Laser canopy height distributions
were created for each cell from the assigned first and last
pulse laser data, and the biophysical properties were
predicted at cell level using the estimated stratum-specific
equations and the derived laser metrics. Predicted values
at stand level were computed as mean values of the
individual cell estimates.
In step 3, the growth from 1999 to 2001 was estimated as
the difference between the predicted values of hL, G, and V
in 2001 and 1999.
Finally, to assess how the size of the target area affected
the accuracy of the growth predictions, we also made
predictions of the three biophysical properties for the 133
sample plots based on the 1999 and 2001 laser datasets, and
of Environment 96 (2005) 453465estimated the growth as the difference between the predicted
values in 2001 and 1999.
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3. Results
3.1. Height percentiles
Laser-derived canopy height and density metrics were
first computed for the 133 sample plots and 56 test stands.
For the percentiles (h10, h50, h90) of the first pulse height
distributions, all the mean differences between data acquired
in 1999 and 2001 were found to be statistically significant.
The differences for the first pulse ranged from 0.38 to 1.32
m for the sample plots (Table 4) and from 0.29 to 1.12 m for
the test stands (Table 5). The testing based on the stand
material revealed that the differences between the 1999 laser
data and the 2001 laser data were of similar magnitude for
strata I and III (Table 6). However, the increase in percentile
values was significantly greater in strata I and III than in
stratum II, indicating a higher height growth rate in the
young and highly productive forest than in the old forest on
poor sites. The standard deviations for the differences of the
percentiles between laser data acquired in 1999 and 2001
ranged from 0.34 to 0.98 m for the sample plots (Table 4)
and from 0.20 to 0.39 m for the test stands (Table 5). None
of the variances for the differences were found to be
significantly different in the statistical sense ( p >0.05) when
comparisons between forest types were made (Table 6).
When we compared the percentiles of the last pulse
height distributions acquired in 1999 and 2001, it was
revealed that the highest percentile (h90) was the only one
Table 4
Differences (D) between laser scanner data from 1999 and 2001 for laser-
derived metrics of small sample plots and standard deviation (S.D.) for the
differences for first and last pulse data, respectivelya
Metricsb D, first pulse D, last pulse
Mean S.D. Mean S.D.
Young forest-stratum I (n=53 sample plots)
h10 (m) 1.01*** 0.74 0.09 NS 1.25
h50 (m) 1.30*** 0.47 1.10*** 0.75
h90 (m) 1.32*** 0.46 1.30*** 0.49
hmax (m) 1.09*** 0.88 0.99*** 0.87
hmean (m) 1.24*** 0.43 0.90*** 0.64
hcv (%) 2.09*** 1.99 1.71* 3.87d1 (%) 0.06*** 0.04 0.03*** 0.05d5 (%) 0.11*** 0.05 0.01 NS 0.06
d9 (%) 0.01*** 0.02 0.01** 0.02
Mature forest, poor site quality-stratum II (n=34 sample plots)
h10 (m) 0.42* 0.71 0.42 NS 1.29h50 (m) 0.48*** 0.38 0.40 NS 1.06h90 (m) 0.38*** 0.34 0.42*** 0.49
hmax (m) 0.26 NS 0.74 0.08 NS 0.77
hmean (m) 0.42*** 0.31 0.14 NS 0.46hcv (%) 1.41* 2.45 3.45*** 3.86d1 (%) 0.05*** 0.05 0.04** 0.06d5 (%) 0.05*** 0.03 0.03*** 0.04
d9 (%) 0.00 NS 0.00 0.00 NS 0.00
Mature forest, poor site quality-stratum II (n=17 test stands)
h10 (m) 0.29** 0.27 0.28*** 0.21h50 (m) 0.44*** 0.25 0.16 NS 0.46h90 (m) 0.44*** 0.20 0.33*** 0.21
hmax (m) 0.28 NS 0.59 0.30* 0.38
hmean (m) 0.40*** 0.22 0.14 NS 0.27hcv (%) 0.81 NS 1.07 3.56*** 2.21d1 (%) 0.05*** 0.03 0.03* 0.04d5 (%) 0.03*** 0.02 0.01 NS 0.02d9 (%) 0.00 NS 0.00 0.00 NS 0.00
Mature forest, good site quality-stratum III (n=17 test stands)
h10 (m) 0.65*** 0.34 0.38 NS 0.53h50 (m) 0.90*** 0.36 0.35 NS 0.54
h90 (m) 0.83*** 0.36 0.66*** 0.37
hmax (m) 0.46 NS 0.78 0.58 NS 0.76
hmean (m) 0.81*** 0.32 0.23 NS 0.41
hcv (%) 1.53*** 0.74 2.32*** 1.51d1 (%) 0.05*** 0.01 0.03*** 0.02d5 (%) 0.07*** 0.03 0.00 NS 0.03
d9 (%) 0.00 NS 0.00 0.00 NS 0.00
a Level of significance (Bonferroni test, a / 9): NS=not significant(>0.05). *
-
singTable 6
Comparisons between strata of mean differences (D) and standard
deviations for the differences (S.D.) between laser derived metrics from
laser scanner data from 1999 and 2001 for the forest standsa,b,c
Metrics DI
DII
DI
DIII
DII
DIII
S.D.I
S.D.IId
S.D.I
S.D.IIId
S.D.II
S.D.IIId
First pulse data
h10 ** NS * NS NS NS
h50 *** NS ** NS NS NS
h90 *** NS ** NS NS NS
hmax NS NS NS NS NS NS
hmean *** NS *** NS NS NS
hcv ** NS NS NS NS NS
d1 NS * NS NS NS ***
d5 * NS ** ** * NS
d9 NS NS NS NS NS NS
Last pulse data
h10 NS NS NS NS NS NS
h50 *** NS NS NS NS NS
h90 *** NS * * NS NS
hmax NS NS NS *** NS NS
hmean *** NS * NS NS NS
hcv NS NS NS NS NS NS
d1 NS NS NS NS NS NS
d5 ** NS NS NS NS NS
E. Nsset, T. Gobakken / Remote Sen460all strata. For the lowest percentile (h10), only one of the six
comparisons that were made between 1999 and 2001 was
statistically significant. The comparison revealed, however,
that the differences for h10 tended to be negative, indicating
a decline in percentile values from 1999 to 2001. For h50and h90, the differences across data acquired in 1999 and
2001 differed significantly between young forest (stratum I)
and low-productive mature forest (stratum II) (Table 6).
The standard deviations for the differences were in the
range between 0.49 and 1.29 m for the plots and between
0.21 and 0.54 m for the stands. When comparisons between
forest types were made (Table 6), none of the variances for
the differences were found to deviate significantly
( p >0.05), except for the comparison of h90 between young
forest (stratum I) and mature forest dominated by pine
(stratum II).
3.2. Height maximum, mean, and variability
For the sample plots, the maximum values of the first as
well as the last pulse canopy height distributions (hmax)
differed significantly between the data acquired in 1999 and
2001 for all the comparisons that were made except for the
low-productive pine forest (stratum II) (Table 4). For the
d9 NS NS NS NS NS NS
a Roman subscript refers to stratum.b Level of significance (Bonferroni test, a / 9): NS=not significant
(>0.05). *
-
1999 and 2001 laser datasets, respectively. The growth was
computed as the difference between the predicted values for
2001 and 1999. For all the three investigated biophysical
properties a major finding was that the laser-based predic-
tions indicated either a non-significant change or a
significant and positive growth over the 2-year period
(Table 8). In four of the nine tested combinations of variable
and stratum the growth was not significant in the statistical
sense. However, the laser-predicted growth was positive for
all variables in the stratum with the highest growth rate
(stratum I).
The laser-based height growth predictions differed
significantly from the field-estimated height growth in all
three strata. The height growth was over-predicted in the
low-productive forest (stratum II) and under-predicted in the
young and productive forest (strata I and III).
Basal area growth was significantly over-predicted in the
spruce-dominated forest (strata I and III) and under-
predicted in the pine forest as compared to the field-based
estimates. For volume, the growth was significantly under-
predicted in the mature forest (strata II and III) and over-
predicted in the young forest (stratum I). In the cases where
nsing of Environment 96 (2005) 453465 461Table 7
Selected models for biophysical properties (response variables) from
stepwise multiple regression analysis of the sample plots using metrics
derived from the laser data acquired in 1999 as explanatory variables
Response variablea Expl. variablesb R2 RMSE
Young forest-stratum I (n=53 plots)
lnhL lnh10f, lnhmeanl 0.91 0.08
lnG lnh10f,lnh50f, lnd1f 0.91 0.11
lnV lnhmeanl, lnd1f 0.95 0.13
Mature forest, poor site quality-stratum II (n=34 plots)
lnhL lnh90l 0.71 0.09
lnG lnh90f, lnhcvl, lnd5l 0.78 0.14
lnV lnh50f, lnd1l 0.85 0.15
Mature forest, good site quality-stratum III (n=46 plots)
lnhL lnh10f, lnh90l, lnd5l 0.85 0.07
lnG lnhmeanf, lnd1f, lnd5l 0.86 0.12
lnV lnhmeanl, lnd1l 0.91 0.13
a hL=Loreys mean height (m), G =basal area (m2 ha1), V =volume (m3
ha1).b h10f, h50f, and h90f=percentiles of the first pulse laser canopy heights
for 10%, 50%, and 90% (m); h90l=percentile of the last pulse laser canopy
heights for 90% (m); hcvl=coefficient of variation of the last pulse laser
canopy heights (%); hmeanf and hmeanl=arithmetic mean of first or last pulse
laser canopy heights, respectively (m); d1f=canopy density corresponding
to the proportion of first pulse laser hits above fraction #1 to total number of
first pulses (see text); and d1l and d5l=canopy densities corresponding to
the proportions of last pulse laser hits above fraction #1 and 5, respectively,
to total number of last pulses.
E. Nsset, T. Gobakken / Remote Sethe last pulse data, the differences ranged from 0.04% to0.03% (Tables 4 and 5). Only the canopy density of the
lowest vertical layer (d1) differed significantly between data
acquired in 1999 and 2001 across all comparisons. The
change in density from 1999 to 2001 in the intermediate
canopy layer (d5) differed significantly between the low-
productive, mature pine forest (stratum II) and the other
forest types.
3.4. Growth predictions
To assess the accuracy of laser-based growth predictions,
stepwise regression analysis based on the 133 field training
plots was carried out to create stratum-specific relationships
between the three biophysical properties of interest (hL, G,
and V) and metrics derived from the 1999 laser data. The
selected loglog regression models explained 7195% of
the variability (Table 7). The models contained from one to
three explanatory variables. The models selected to be the
best ones for example for volume, were based on one
laser-derived variable related to canopy height and one
variable related to canopy density. Multicollinearity issues
were addressed by calculating and monitoring the size of the
condition number. None of the selected models had a
condition number greater than 6.3, indicating that there was
no serious collinearity inherent in the selected models
(Weisberg, 1985).
The selected stratum-specific regression models were
used to predict hL, G, and V for the test stands based on thea significant growth was predicted by the laser data, the
mean prediction error for basal area and volume ranged
from approximately 50% to 110%. In the cases where the
predicted growth was not significant, the relative prediction
error was greater.
The growth was also estimated from laser-based predic-
tions for the sample plots according to a similar procedure
as we followed for the test stands. In general, there was a
good correspondence between the findings for the test
stands and the results revealed for the sample plots
Table 8
Growth (19992001) of the forest stands estimated from field measure-
ments, growth predicted from laser data (19992001) using the regression
equations in Table 7, and mean difference and standard deviation for the
differences (S.D.) between laser-predicted and field-estimated growtha
Response
variablebMean field-
estimated
growth
Mean laser-
predicted
growth
Difference
Mean S.D.
Young forest-stratum I (n=22 stands)
hL (m) 0.70 0.33** 0.37*** 0.34G (m2 ha1) 2.20 3.70*** 1.50*** 0.66V (m3 ha1) 19.8 29.2*** 9.4*** 6.0
Mature forest, poor site quality-stratum II (n=17 stands)
hL (m) 0.21 0.42*** 0.21*** 0.18
G (m2 ha1) 0.81 0.37 NS 1.18*** 1.15V (m3 ha1) 7.7 1.2 NS 6.5* 10.2
Mature forest, good site quality-stratum III (n=17 stands)
hL (m) 0.44 0.19 NS 0.25* 0.47G (m2 ha1) 1.65 3.53*** 1.88*** 0.67V (m3 ha1) 19.2 1.0 NS 20.2*** 9.9a Level of significance: NS=not significant (>.05). *
-
sing4. Discussion and conclusions
The major findings of this study indicate that:(Table 9). However, the standard deviations for the
differences between the predicted and field-based growth
estimates were in general larger for the sample plots.
Table 9
Growth (19992001) of the sample plots estimated from field measure-
ments, growth predicted from laser data (19992001) using the regression
equations in Table 7, and mean difference and standard deviation for the
differences (S.D.) between laser-predicted and field-estimated growtha
Response
variablebMean field-
estimated
growth
Mean laser-
predicted
growth
Difference
Mean S.D.
Young forest-stratum I (n=53 plots)
hL (m) 0.95 0.80*** 0.15 NS 0.94G (m2 ha1) 2.92 4.23*** 1.31*** 1.49V (m3 ha1) 28.6 38.4*** 9.8*** 17.7
Mature forest, poor site quality-stratum II (n=34 plots)
hL (m) 0.23 0.50*** 0.27*** 0.47
G (m2 ha1) 0.84 1.28** 2.12*** 2.37V (m3 ha1) 8.3 1.5 NS 9.8* 24.7
Mature forest, good site quality-stratum III (n=46 plots)
hL (m) 0.48 0.25 NS 0.23 NS 1.28G (m2 ha1) 1.66 3.46*** 1.80*** 1.55V (m3 ha1) 21.0 1.4 NS 19.6*** 25.8a Level of significance: NS=not significant (>.05). *
-
nsingThe estimated change in height distribution variability
(hcv) from 1999 to 2001 gives a strong proof of the different
properties of the first and last pulse data indicated above.
For both plots and test stands, and across all forest types, the
variability of the height distributions decreased significantly
for the first pulse data and increased for the last pulse data
(Tables 4 and 5). As canopy gaps become more closed by
growth, the first pulse reflections tend to become concen-
trated (reduced variability) in the upper parts of the canopy
because of the high sensitivity of being reflected by a small
amount of biological matter obstructing the laser beams. On
the other hand, a moderate increase in biomass over a 2-year
period has little impact on beam penetration deep into the
canopy from where the last significant returns are reflected.
However, as the change in maximum height (hmax) and the
upper percentiles (h90) indicate, a small portion of even the
last returns will be reflected from the upper canopy. The last
return height distribution will therefore tend to be more
stretched (increased variability) as the growth elevates the
height of the canopy.
The second objective of this study was to assess the
accuracy of laser-based growth predictions over a short
growth period of three important biophysical properties,
namely, mean tree height, basal area, and volume. It has
been demonstrated that laser data can predict a significant
forest growth over a short period, although with substantial
deviations from what is considered to be the true
growth. However, errors are also associated with the
field-based true growth estimates since they are based
on general growth models and not measurements of the
actual growth. Ground-truth growth projections do not take
annual variations into account, such as those caused by
differences in temperature and rainfall. Errors in the
ground-truth itself can therefore in part be inherent in the
observed differences between laser-predicted and field-
estimated growth. As a matter of fact, airborne lasers may
provide a method for observing the actual growth without
having to rely on the average conditions over, for example,
a 5-year period as do the growth models.
To make precise growth predictions from laser data
over short time intervals, say 5 years or less, it is utmost
important that (1) the data acquisitions routines, (2) the
data themselves, (3) the data processing, and, (4) finally,
the growth estimation are as robust as possible, so that
the predicted growth can be attributed to the growth as
such and not to any imperfections in the procedures. The
problem of calibrating multi-temporal data from, for
example, optical remote sensing for any kind of change
detection is well-known. In airborne laser-scanning, data
acquisition parameters that seem to be critical are flying
altitude, footprint size, time of the yearespecially when
broad-leaved species are measured, laser sampling density,
and last but not least, the sensor used. Flying altitude and
footprint size as such seem to have little influence on the
E. Nsset, T. Gobakken / Remote Secharacteristics of first pulse data, at least within certain
limits (Nsset, 2004a). Last pulse data, however, aremuch more sensitive to changes in flying altitude/footprint
size, and flying altitude and/or the use of first versus last
pulse data are therefore factors that require careful
consideration in laser-based growth studies. According
to the experience gained by comparing flying altitudes of
540 and 850 m a.g.l. (Nsset, 2004a), the effects of
differences in flying altitudes in the present study (700
versus 850 m a.g.l., Table 3), are probably neglectable.
Laser sampling density, i.e., the number of laser
measurements per unit area, may affect the laser-derived
variables used to predict tree growth. The maximum height is
probably one of the most affected variables, because the
probability of hitting the tree apex of a specific tree in the
context of single-tree based approaches or the top of a
dominant tree for a certain area (e.g., sample plot or forest
stand) in statistically based methods, is a direct function of
the number of measurements per unit area. The laser
sampling density should therefore be kept stable across
acquisitions. This is particularly important in methods where
the maximum height value is an essential variable in the
growth estimation. In the present study, we did not use the
maximum values for this reason and because the maximum
value tend to be less stable than many of the other laser-
derived height metrics. The precision of the maximum values
also seems to be less improved by increasing size of the
target area (cf. Tables 4 and 5). In single-tree based methods,
however, the individual trees are usually derived from
canopy surface models created from local maxima. Growth
studies based on this approach as demonstrated by St-Onge
and Vepakomma (2004) and Yu et al. (2004), are probably
more dependant on stable laser sampling densities over time
to perform well than statistically based methods like the one
demonstrated in our study. In the present study, the average
laser sampling densities in 1999 and 2001 were 1.18 and
0.87 m2 (Table 3), respectively. The effects of suchmoderate changes in data density on laser-based predictions
of mean height, basal area, and stand volume using height
percentiles and measures of canopy density as predictor
variables are probably neglectable (Holmgren, 2004).
A major consideration in the study by St-Onge and
Vepakomma (2004) was the fact that they used different
sensors on the two occasions. The sensor-dependent effects
are probably the most difficult ones to control in multi-
temporal laser studies, especially for longer time intervals,
because under such conditions it is very likely that different
sensors will be used due to rapid technological develop-
ment. Another sensor-dependent source of uncertainty,
which has not yet received any attention is the fact that all
sensors are subject to changing physical properties over
their life-time. Although the ranging device is calibrated to
perform well when it is used for ranging of solid surfaces
such as terrain surfaces, effects of changed properties when
measuring an irregular and penetrable surface like a tree
canopy is unknown.
of Environment 96 (2005) 453465 463The most important concern regarding data processing is
the selection of a proper ground reference level that all the
-
procedure. Yu et al. (2004), however, used both separate and
common terrain models when they compared laser data for a
sing2-year growth period. They demonstrated that in certain
cases canopy height changes attributed to the use of
different terrain models were larger than the changes
attributed to canopy height growth.
In this study, we assessed the effects of growth over a
very short growth period. For boreal conifer species, it is
quite common to use 5-year periods in growth projec-
tions. Over the 2-year period assessed in the present
study, the height growth, for example, ranged from 1.2%
to 6.6% (cf. Tables 4 and 5). It is therefore a risk that
effects of imperfections in the computational procedures
and actual growth are confounded. In the present study,
we used the same equations to predict the three
biophysical variables from the laser data on the two
occasions. If different prediction models are used, it is
always a risk that different model properties may
influence on the predictions.
To conclude, the present study has indicated that forest
growth over a short growth period may affect laser-derived
metrics such as height percentiles and other canopy height
related metrics and canopy density. The effects are more
pronounced for the first pulse reflection than for the last
pulse reflection. These metrics can be used to predict a
significant growth in stand height, stand density, and
volume, even for a short growth period, at least if the site
productivity and thus the growth rate are high. However, for
short growth periods, the precision of the laser-derived
growth estimates is low.
If airborne lasers are to be considered as a useful
technology in large-area monitoring, a careful planning is
needed to control external factors across time such as data
acquisition parameters and data processing routines. A
major obstacle for detecting growth over short time periods
are probably the sensor-specific properties that affect how
the laser beams depict the various parts of a forest canopy.
Sensor-specific effects of canopy measurements by airborne
lasers and calibration of laser-derived canopy measures
across sensors are therefore important aspects that need
careful consideration prior to operational use of airborne
laser in forest monitoring.
Acknowledgments
This research has been funded by the Research Council
of Norway, the Norwegian University of Life Sciences, and
the Norwegian Institute of Land Inventory. Thanks to Blompulse measurements can be related to when the height
values of each pulse is computed. St-Onge and Vepakomma
(2004) argue that the same terrain model should be used for
both years to avoid terrain differences causing false canopy
height changes. In the present study, we followed this
E. Nsset, T. Gobakken / Remote Sen464Norkart Mapping for collection and processing of the
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E. Nsset, T. Gobakken / Remote Sensing of Environment 96 (2005) 453465 465
Estimating forest growth using canopy metrics derived from airborne laser scanner dataIntroductionMaterials and methodsStudy areaSample plotsStand inventoryLaser scanner dataComputations
ResultsHeight percentilesHeight maximum, mean, and variabilityCanopy densityGrowth predictions
Discussion and conclusionsAcknowledgmentsReferences