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Enhancement of bioenergy estimations within forests usingairborne laser scanning and multispectral line scanner data
Christoph Straub*, Barbara Koch
Department of Remote Sensing and Landscape Information Systems, University of Freiburg, Tennenbacherstr. 4, D-79106 Freiburg, Germany
a r t i c l e i n f o
Article history:
Received 14 April 2010
Received in revised form
27 April 2011
Accepted 6 May 2011
Available online 16 June 2011
Keywords:
Bioenergy
Biomass
Forest residues
Airborne laser scanning
Orthophotos
Forest inventory
Abbreviations: AGL, above ground level; Aterrain model; GIS, geographical informationsquare error; RGB, true color (red, green, blu* Corresponding author. Tel.: þ49 8161 71 58E-mail addresses: [email protected]
0961-9534/$ e see front matter ª 2011 Elsevdoi:10.1016/j.biombioe.2011.05.017
a b s t r a c t
Forest biomass is a substantial source of renewable energy and is becoming increasingly
important due to environmental and economic reasons. In Germany, several studies have
assessed the bioenergy potential for large areas, e.g. for an entire Federal state. However, in
most cases it was not possible to provide detailed maps showing the biomass and the
sustainable energy potential for individual forest stands. Thus, the aim of this study was to
develop a new and robust method that provides detailed information regarding the spatial
distribution of biomass and forest residues as a potential energy resource using a combi-
nation of remotely sensed and in situ data. A case study was carried out in a mixed forest
in Southern Germany. First, regression analyses were applied to identify relationships
between field measurements with several remote sensing metrics to estimate timber
volume, mean stem diameter and age. Cross-validation yielded relative root mean square
errors (RMSEs) of 30.20% for volume, 27.92% for diameter and 28.81% for the estimation of
the age. The absolute RMSEs were smaller than the standard deviation of the observed
variables. Next, the regression equations were used to compute attributes for individual
forest stands. Stand attributes were then used to model forest residues. To estimate the
sustainable annual potential, the actual harvest volume, as defined by forest management
planning, was included in the model. Different model parameters were analyzed and an
average potential from 0.993 to 1.181 t ha�1 a�1 was computed. The results were compared
to previous studies in Germany.
ª 2011 Elsevier Ltd. All rights reserved.
1. Introduction energy supply is produced sustainably, it can avoid and reduce
The energy and climate policy agenda of Germany promotes
the use of renewable energy sources, among these “biomass”.
Biomass plays an important role in the context of climate
change, due to the fact that it can act as a carbon sink and/or
as a substitute for fossil fuels. Assuming the biomass for
LS, airborne laser scannsystem; nDSM, normalize).75; fax: þ49 8161 71 4971.ayern.de (C. Straub), barbier Ltd. All rights reserved
CO2 emissions. The energy extracted from biomass is referred
to as “bioenergy”. It is a locally available energy source. In
Germany, the general opinion is that the utilization of
biomass, obtained from silvicultural products (fuelwood), can
be further increased [1] which opens new opportunities for
forest management.
ing; CIR, color infrared; DSM, digital surface model; DTM, digitaled DSM; RS, remote sensing; NIR, near infrared; RMSE, root mean
[email protected] (B. Koch)..
b i om a s s an d b i o e n e r g y 3 5 ( 2 0 1 1 ) 3 5 6 1e3 5 7 43562
Different types of biomass energy potential can be distin-
guished, taking into account technical, economic, regulatory
and/or social aspects [2].
1. Theoretical potential: The total woody biomass that grows
in a specific region. The availability is often given for
a specific time period.
2. Technical potential: The biomass that can actually be
supplied for energy production depending upon the access
to the biomass (relating to technical restrictions such as the
slope of the terrain).
3. Economical potential: The technical potential can be
further reduced when economical restrictions are consid-
ered (profitability regarding harvesting costs).
Information regarding the potential of bioenergy available
in a particular region and time period is necessary for policy
makers and planners, but in many cases there is a lack of
reliable data to estimate the availability of biomass. In
Germany, several studies with varying levels of detail have
been carried out to predict the bioenergy potential of forests.
These have included the whole country, federal states or
administrative districts and have implemented existing forest
inventory data, management plans and empirical data. In
Table 1 several studies [3e9] are summarized. In most cases
no information, regarding the spatial distribution of the
biomass was given, except for the studies of Refs. [8] and [9].
Some studies specified the annual bioenergy potential in
m3 ha�1 a�1 and others in t ha�1 a�1.
The data used in the studies mentioned above is usually
acquired by means of sample plot inventories supplemented
by visual assessment of individual stands by forest inventory
specialists to plan future silvicultural activities within the
next management period. Sample plot inventories have been
carried out in the German Federal state of Baden-Wurttem-
berg since 1986 [10]. Permanent geo referenced plots
Table 1 e Examples for studies that estimated the bioenergy p
Study Spatial reference
Haschke (1998) [3] German state
Dieter and Englert (2001) [4] German state
Ochs et al. (2007) [5] German state
Frommherz et al. (2000) [6] At the Federal state
level (Baden-Wurttemberg)
LWF (2000) [7] At the Federal state
level (Bavaria)
Hepperle (2007) [8] Administrative district level
(Biberach) e for
different strata)
Kay et al. (2008) [9] Administrative district
level (Goppingen) e thematic map
with 25� 25 m2 resolution
(distributed in a regular grid with 100� 200 m) are established
in state forests larger than 1500 ha. The plot data provides
statistically reliable information for the entire forest enter-
prise or a forest stratum, but the density of the plots is usually
too low to compute forest attributes for individual forest
stands. However, “stands” are the smallestmanagement units
in the forest and a numerical description would be necessary
for a critical evaluation of the bioenergy potential within
a specific region, supported by detailed maps showing the
spatial distribution of forest biomass.
To overcome this difficulty a combination of remotely
sensed and in situ data was tested in this study to model the
bioenergy potential.
During the past years, modern remote sensing (RS) tech-
niques, in particular airborne laser scanning (ALS), have been
tested to estimate dendrometrical parameters. A summary of
ALS applications for forest inventories is given in [11]. ALS is
an active RS technique that basically captures geometric data.
A LiDAR (light detection and ranging) sensor is mounted
on-board an aircraft or helicopter. During the flight, laser
beams are emitted and a strip of the terrain is scanned.
The measurement is highly accurate and provides 3D points
P¼ {p1, p2, ., pn} of the landscape with their planar coordi-
nates and height pi¼ (xi, yi, zi) i¼ 1, ., n. Additional attributes
ai, such as the echo signal intensity and the echo pulse width,
can be extracted from full-waveform analysis [12]. One of the
great advantages of ALS (compared to traditional photo-
grammetric techniques) for forestry applications is that the
laser penetrates gaps in the canopy. Thus, reflections are
obtained from the top canopy along with the terrain under-
neath and vegetation heights can be derived. Canopy height
and density metrics have been used as explanatory variables
(predictors) in regression analysis and non-parametric
methods to estimate forest attributes e.g. Refs. [13e16]. The
laser scanner can be combined with a digital camera to record
digital image data and height measurements simultaneously.
otential of forests in Germany.
Data Technical potential
m3 ha�1 a�1 t ha�1 a�1
Wood harvest statistics 2.5 e
Data of the National Forest
Inventory (NFI) and forest
management plans
e 1.31e1.54
Data of the National Forest
Inventory (NFI)
2.8 e
Forest management plans 1.3e1.6 e
Study of Nußlein (1996)
and National Forest
Inventory (NFI)
e 2.31
Forest inventory data and
forest management plans
2.1 e
Forest management plans,
existing geodata of the land
survey administration
and empirical studies
1.8 e
Fig. 1 e Tree components suitable for energy use (grey and
labeled with a box) based on a classification into small
sized logs and large sized logs.
Table 2 e Tree species composition derived from standslocated entirely within the study site.
b i om a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 3 5 6 1e3 5 7 4 3563
Hence, additional forest characteristics can be extracted e.g.
a classification of coniferous and deciduous trees as described
in Ref. [17].
The objective of this study was to develop a robust method
that allows estimating the biomass and the sustainable
energy potential for individual forest stands using a combi-
nation of ALS data, multispectral data, forest inventory data
and existing stand maps. A case study was carried out in
a mixed forest in Southern Germany. After defining potential
bioenergy sources, the study area and RS data are introduced.
The method is described, which makes use of the RS data as
auxiliary data to estimate the forest attributes of individual
forest stands. The predicted stand attributes are used to
model forest residues that can be considered to be a potential
energy resource. Computations and model predictions are
analyzed and the results are compared to other studies. Pros
and cons of the method are discussed.
1.1. Definition of potential energy resources from forestbiomass
Forest residues are considered to be a substantial potential
energy resource (Refs. [18,19]) and are classified by Ref. [20] as
“silvicultural residues”, which result from thinning of young
forest stands, and “logging residues”, which can be seen as
a by-product of conventional harvesting. In both cases, the
material is unsuitable for industrial use due to its small
dimension and/or poor quality and is usually left at the site.
Thus, forest residues are components of trees such as tops of
stems, branches, twigs and “unsuitable” stem wood (e.g.
deadwood and diseased wood).
To quantify forest residues it is necessary to distinguish
between large sized logs (logging residues) and small sized
logs (silvicultural residues). This classification is based on the
diameter at breast height (DBH). The threshold classifying
large sized logs and small sized logs can vary between regions.
Ref. [4] defined a maximum DBH of 16 cm and a minimum
DBH of 8 cm for small sized logs. For the minimum DBH it is
assumed that harvesting is still profitable. Another classifi-
cation is given by Ref. [2], which defines small sized logs as
having a DBH from 7 cm to 20 cm. The following tree compo-
nents are defined as forest residues (see also Fig. 1):
1. Large sized logs (e.g. DBH> 16 cm): stemwood above stump
height with poor quality, branches and twigs.
2. Small sized logs (e.g. 8 cm�DBH� 16 cm): total stemwood,
branches and twigs.
Due to ecological reasons (to maintain the nutrient cycle),
the foliage is not considered as biomass for energy production.
Tree species Percentage of forest area
Scots pine (Pinus sylvestris) 51%
Oak (Quercus petraea) 14%
Beech (Fagus sylvatica) 10%
Red oak (Quercus rubra) 10%
Douglas fir (Pseudotsuga menziesii) 5%
Hornbeam (Carpinus betulus) 4%
Other species 6%
Source: Forest management plan.
2. Material and methods
After an introduction into the study area, RS data and field
measurements, an explanation of the methodology is given.
First, input variables are defined and then the entire workflow
is described to estimate the energy potential for individual
forest stands. Special emphasis is placed on the extraction of
structural forest characteristics from RS data as auxiliary
variables to estimate dendrometrical parameters and the
modeling of forest residues.
2.1. Study area
The biomass energy potential was estimated for a mixed
forest with a size of 9.24 km2. The study site is located north of
the city of Karlsruhe in Southern Germany (Gauss Krueger
coordinates of the upper left corner: 3456300 (easting)/5436100
(northing) and lower right corner: 3458400 (easting)/5431700
(northing)). A total number of 108 (mixed and pure) forest
stands are located entirely within the study site. The stands
vary in their size and have a wide range of age classes. Scots
pine dominates the study site with 51% (in total 56% conif-
erous trees). The tree species composition of the study site is
listed in Table 2.
2.2. Remote sensing data
Both ALS and multispectral data were acquired in the study
site from two flights operated by TopoSys GmbH (both during
leaf-on conditions). During the first flight in August 2007, full-
waveform laser scanner dataweremeasuredwith a high point
density (�16 points/m2) using the “Harrier 56” LiDAR system
mounted on-board a helicopter. Flight and system parameters
are shown in Table 3.
Table 3 e Flight and system parameters of the flightcampaign in summer 2007 with the “Harrier 56” LiDARsystem (AGL[ above ground level).
Parameter Value
Measurement rate 100 kHz
Field of view 45�
Flying height 450 m AGL
Flying speed 30 m/s
Point density 16 points/m2
Vertical/horizontal accuracy <0.2 m/<0.5 m
Fig. 2 e nDSM with position of inventory plots and CIR
orthophoto with stand boundaries.
b i om a s s an d b i o e n e r g y 3 5 ( 2 0 1 1 ) 3 5 6 1e3 5 7 43564
Last reflections, detected in the waveforms, were used to
filter and interpolate a digital terrain model (DTM) using an
“Active Surface Algorithm”, which matches a deformable
surface to reflections considered to be terrain points, by
means of energy minimisation [21,22]. First reflections were
used to compute a digital surface model (DSM), which repre-
sents the top surface visible from above. In forests, this is an
approximation of the canopy (uppermost layer). A normalized
DSM (nDSM) that represents the height of vegetation for each
x,y position, was derived as the difference between DSM and
DTM: nDSM(x,y)¼DSM(x,y)�DTM(x,y).
During the second flight, in July 2008, multispectral data
were recorded bymeans of a RGB/NIR line scanner (integrated
in the Falcon II system, flight and technical parameters are
given in Table 4). RGB and CIR orthophotos were rectified and
geo referenced using a DSM, which was filtered from laser
scanner data (here: 6e7 points/m2) acquired at the same time
as the multispectral data.
2.3. Field measurements
A forest inventorywas conducted in the study area in summer
2006. Geo referenced field sample plots were measured by the
state forest administration of the Federal State of Baden-
Wurttemberg, Germany. A regular 100� 200 m grid was used
to spread plots systematically over the study area (Fig. 2a).
Sample plots consist of four concentric circles with the
following radii: 2 m, 3 m, 6 m and 12 m. Table 5 shows the
trees to be measured within each of the circles (dependent on
their diameter at breast height (DBH)). Concentric circles are
used to reduce the total amount of trees to bemeasured and to
thus increase the efficiency of the inventory.
Table 4 e Flight and technical parameters of the RGB/NIRline scanner of the flight in summer 2008 with the“Falcon II system” (AGL[ above ground level).
Parameter Value
Flying height 700 m AGL
Spectral channels Blue: 450e490 nm
Green: 500e580 nm
Red: 580e660 nm
Near infrared: 770e890 nm
Viewing angle 21.6�
Line rate Up to 330 Hz
Pixel per line 682
Ground sampling distance 0.4 m
In addition to the DBH, up to three tree heights in the
uppermost layer were measured (associated to two top
heights of the main crop and one top height of the dominated
crop) using a Vertex� instrument. In total, 300 plots were
selected for the computations carried out in this study (only
plots that did not intersect a stand boundary). Finally, the
following attributes were derived for each plot:
1. Standing stem volume V [m3 ha�1].
2. Mean diameter Dg [cm].
3. Age of dominant trees [years] in the uppermost layer as to
define the age class.
Forest characteristics, derived from forest inventory plots,
are given in Table 6.
2.4. Forest stand maps
Forest stand polygons, suitable for a GIS, were provided by the
forest administration (see Fig. 2b). Stands are the smallest
management units and are relatively homogeneous with
Table 5 e Concentric circles of sample plots with DBH oftrees to be measured within each circle.
Plot radius[m]
Plot size[m2]
DBH [cm] of trees to bemeasured
2 12.6 �7
3 28.3 �10
6 113.1 �15
12 452.4 �30
Source: Ref. [41], modified.
Table 6 e Forest characteristics (calculated from 300sample plots of the forest inventory).
Attribute Mean Standard deviation Min Max
Top height [m] 22.65 7.87 0 34.50
Volume [m3 ha�1] 260.67 119.19 0 610.42
Quadratic mean
diameter [cm]
27.31 10.22 0 62.16
Age [years] 68 32 0 153
b i om a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 3 5 6 1e3 5 7 4 3565
respect to certain forest characteristics (e.g. tree species
composition or age). Usually orthophotos and historical maps
are used in addition to field surveys to define the boundaries.
Forest management planning defines the sustainable
timber utilization. In Germany, sustainable timber production
is guaranteed by the forest law: sustainable forestry accounts
for economic, ecological and social aspects to maintain the
productive, protective and the recreational functions of the
forest at present and for future generations. Forest manage-
ment planning has to consider all these aspects for the
determination of the volume to be harvested, referred to as
harvest volume Vh [m3 ha�1], within the next management
period (a ten-year period). Vh was specified for each single
stand andwas assigned as an attribute to all stand geometries.
To predict the (sustainable) bioenergy potential, available
within the next management period, the harvest volume Vh
must be considered. A thematic map showing Vh for each
stand is shown in Fig. 3.
2.5. Modeling the biomass energy potential
Dendrometrical parameters are required for each forest stand
to model the biomass energy potential. Using those parame-
ters, the quantities of forest residues are estimated. The
following stand attributes are required:
Fig. 3 e Harvest volume for each forest stand within the
next forest management period as defined by forest
management planning.
1. Stem volume V [m3 ha�1] in solid cubic meter per hectare.
Defined as over bark tree stem volume of all stems and
branches with a diameter above 7 cm:
V ¼ HL$G$FA
(1)
HL¼ Lorey’s (mean) height [m], which weights each individual
tree height by its basal area. G¼ basal area per hectare [m2];
“G” refers to the German meaning “Grundflache” (basal area).
F¼ stand form factor to account for the taper of trees (often
approximated with Fw 0.5). A¼ area [ha] of the stand.
2. Quadratic mean diameter (over bark) Dg [cm] for trees with
a DBH� 7 cm, the subscript “g” again refers to the German
meaning “Grundflache”:
Dg ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1n
Xnd2i
!vuut (2)
i¼1where di is the diameter of an individual tree (measured 1.3 m
above ground level) and n is the number of trees.
3. Age: The age refers to the dominant trees in a forest stands.
Forest management in Germany uses classes with a range
of 20 years for numerical description (e.g. I: 1e20, II: 21e40
and III: 41e60)
The above-mentioned attributes (V, Dg and age) can be
used as the input variables to model the entire biomass
energy potential of forest stands. If the aim is to model the
bioenergy potential available within the next management
period, the total stem volume V must be replaced by the
harvest volumeVh, as defined by forestmanagement planning
(see Section 2.4).
The procedure was based on the study of Refs. [4] and [23],
but was modified according to the information that can be
extracted from RS data. Fig. 4 shows an overview of the entire
workflow, which can be subdivided into five main parts. The
final output of the procedure is the biomass energy potential
BE given in tons per hectare [t ha�1]. The following main pro-
cessing steps can be distinguished:
a) Classification of a) large sized logs and b) small sized logs,
see Section 1.1, to distinguish between “silvicultural” and
“logging residues”. The mean diameter Dg serves for clas-
sification. If Dg is less than the minimum DBH for small
sized logs e.g. 8 cm, it is assumed that harvesting is not
profitable and BE is set to 0.
b) The timber volume (either total stem volume or harvest
volume for the next management period) is reduced with
a reduction factor r to account for harvest loss. A reduction
of 10%was defined according to specifications given in Ref.
[24]. No bark reduction factor was applied due to the fact
that the bark can be used for energy production.
Vred ¼ V$r (3)
c) If the classification result is a) large sized logs, assortment
tables will be employed to determine the volume of
Fig. 4 e Procedure for biomass energy modelling.
b i om a s s an d b i o e n e r g y 3 5 ( 2 0 1 1 ) 3 5 6 1e3 5 7 43566
“logging residues” Vres [m3 ha�1]. These are defined as tree
components unsuitable for industrial use due to poor
quality and/or small dimension, based on the percentage
of stemwood residues Pres [%] derived from the assortment
tables:
Vres ¼ Vred$Pres=100 (4)
If the classification result is b) small sized logs, the entire
stem volume will be considered for the following computa-
tions (“silvicultural residues”). More details concerning this
step will be explained in Section 2.5.1.
d) The biomass B [t ha�1] is derived from the volume
V [m3 ha�1] after a multiplication with a conversion factor
for the wood density r [kg/m3], which depends on the tree
species composition:
B ¼ V$r (5)
e) The biomass of twigs and small branches BTþB is modeled
using expansion factors. For further details see Section
2.5.2. Finally, the biomass energy potential BE is computed
as the sum of the biomass of estimated stem wood resi-
dues Bres plus the increment for the biomass of branches
and twigs BTþB:
BE ¼ Bres þ BTþB (6)
2.5.1. Assortment tables to determine the percentage oflogging residuesAs described in Section 1.1, the modeling of stem wood resi-
dues requires the classification of large sized logs and small
sized logs. For small sized logs the total tree volume can,
theoretically, be utilized as biomass for energy production.
Whereas for large sized logs only those parts with poor quality
are considered. According to the studies of Refs. [4] and [23],
the volume is assorted based on the assortment tables
developed by Ref. [25], which are regarded to be representative
for Germany [26]. The assortment tables are given for themain
tree species. They require the mean stand diameter as entry
value and give the percentage of stem wood, pulpwood,
stacked wood andwoodwith poor quality. Due to the fact that
Scots pine (Pinus sylvestris) is the main coniferous tree species
within the study site, the assortment table of Scots pine was
used as “reference species” for coniferous trees. Accordingly,
the table of oak (Quercus petraea) was used as “reference
species” for deciduous trees.
2.5.2. Expansion factors for branches and twigsStem volume V [m3 ha�1] is defined as over bark tree stem
volume of all stems and branches with a diameter above 7 cm.
Thus, the volume does not include twigs and branches with
a diameter below 7 cm, which can also be harvested. The
volume of twigs and small branches was modeled according
to the tables given by Ref. [4], which were developed based on
the study of Ref. [27]. Table 7 shows percentages of stem
Table 7 e Percentage of above ground biomass components for scots pine and oak [t haL1].
Age classes
I II III IV V VI VII VIII IX X
Scots pine (Pinus sylvestris) Stem volume incl. bark (diam. �7 cm) [%] 0 67 75 81 81 82 85 84 e e
Branches and twigs (diam. <7 cm) incl. bark [%] 69 20 15 11 10 12 10 8 e e
Foliage [%] 31 13 10 8 9 6 5 8 e e
Oak (Quercus petraea) Stem volume incl. bark (diam. �7 cm) [%] 0 44 79 81 88 89 88 89 92 92
Branches and twigs (diam. <7 cm) incl. bark [%] 53 47 16 15 10 7 9 8 6 6
Foliage [%] 47 9 5 4 2 4 3 3 2 2
According to Ref. [4].
Fig. 5 e Example of delineated tree crowns superimposed
on a) CIR orthophoto and b) nDSM e each crown was
transformed into a circle with the same size as the original
crown segment.
b i om a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 3 5 6 1e3 5 7 4 3567
volume (diameter � 7 cm), branches and twigs (diameter <
7 cm) and foliage relating to the dry matter of the biomass. To
convert from volume V [m3] to biomass B (dry matter) [t]
conversion factors for wood density according to Ref. [4] were
used: r¼ 0.51 for Scots pine (P. sylvestris) and r¼ 0.65 for Oak
(Q. petraea).
2.6. Extraction of structural forest characteristics fromremote sensing data as auxiliary variables fordendrometrical parameter estimation
Structural forest and tree characteristics were extracted
automatically from the RS data and were tested as indepen-
dent variables to estimate stem volume, mean diameter and
age of forest stands. The following variables were regarded as
significant, reliable and robust characteristics:
1. Mean height Hm [m] e arithmetic mean of all height values
of the nDSM for a given region.
2. Top height Ht [m] e defined as the height of the one
hundred trees with largest diameter per hectare. According
to the results described in Ref. [17], the top height was
estimated using ALS data as the 90th percentile computed
from the height values of the nDSM. This value yielded
a correlation of r¼ 0.87 with the top height computed based
on field measurements (inventory plots).
3. Proportion of coniferous trees Pc and deciduous trees Pd [%]
e automatically classified based on the CIR orthophoto
mosaic (see Fig. 2b). The classification method is described
in Ref. [17].
4. Canopy cover CC [%] e defined as the ground covered by
a vertical projection of tree crowns in the uppermost layer.
Tree crown regionswere extracted using a height threshold
Dhmin applied to the nDSM. The threshold was varied for
each plot and was derived relative to the top height Ht
assuming that all height values above 50% of Ht belong to
the uppermost layer (Dhmin¼Ht$0.5).
5. Mean crown area Ca [m2] e derived from automatic single
tree segmentation. The delineation technique is a slightly
modified version of the method described in Refs. [22] and
[28]. However, in addition to ALS data also multispectral
data was used for the automatic single tree segmentation
with the objective to improve the separation of coniferous
and deciduous trees: if single trees are delineated only
based on ALS data it can happen that a coniferous tree is
merged with an adjacent deciduous tree if the crowns are
too close to each other to separate them just by their shape
and height difference. A result of the tree segmentation is
shown in Fig. 5. The most important image processing
techniques are as follows:
a) Classification of regions with coniferous and decid-
uous trees using the CIR orthophoto (see step 3). The
regions are processed separately.
b) Tree crown delineation based on nDSM using a “pour-
ing algorithm” (very similar to “watershed segmenta-
tion”, a method based on grayscale mathematical
morhology [29]) e hypothesis: each convex shape of
the surface model is predicted to be an individual tree.
Local maxima are the starting points of a “region
growing process”. The region will “grow” as long as
there are chains of pixels in which the height values
become smaller (similar to raindrops running downhill
from the maxima in all directions).
c) Refinement of delineated crowns using “ray-algo-
rithm” e hypothesis: the height change within an
individual crown must be continuous. Virtual rays
between the tree tops and border points are generated.
New border points of the tree crowns are created if
there is an interruption in the continuous height
changes.
b i om a s s an d b i o e n e r g y 3 5 ( 2 0 1 1 ) 3 5 6 1e3 5 7 43568
Finally, the (quadratic) mean crown area Ca is computed as
Ca ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1n
Xni¼1
C2a:i
!vuut (7)
where Ca.i is the crown area of an individual tree crown and
n is the number of delineated trees.
2.7. Estimation of forest stand attributes using a two-phase procedure
A practical two-phase procedure, which combines geo refer-
enced “training plots” with ALS data to estimate forest attri-
butes for individual forest stands, is suggested in Refs.
[13,14,30]. In the first phase, empirical relationships between
several laser metrics and field measurements are analyzed
and models are derived. The models are used in the second
phase to estimate a forest attribute for each individual stand
within the study site. The procedure is shown in Fig. 6. The
following processing steps can be distinguished:
a) Delineation of the forest area.
b) Geo referenced field data (here: inventory plots) and RS
data are coregistered to generate reference data with RS
features X1, X2, . ,Xn and allocated terrestrial information
Y (a forest variable measured during fieldwork): (Y, X1, X2,
. , Xn).
c) Multiple regression analysis is used to model the vari-
able of interest as a function of the RS variables Y¼ f(X1,
X2,., Xn).
d) Sub-division of the entire study area into equally-sized
squares as the inventory plots. The regression equations
are used to predict a forest attribute for each square.
Fig. 6 e Procedure to predict stand attributes with
e) Finally, the squares are intersected with stand polygons
and a stand attribute Ys is derived as area(A)-weighted
average of the predicted values Yp within the squares:
Ys ¼Xn
Yp:i$Ai=Xn
Ai (8)
i¼1 i¼1General linear regression was used to describe the rela-
tionship between forest variables measured at field plots with
the structural forest characteristics derived from RS data, as
described in Section 2.6:
Y ¼ Xbþ 3 (9)
where Y is an (n� 1) vector of the dependent variables (field
measurements). X is a (n� p) design matrix of independent
variables (structural forest characteristics derived from RS),
b is a ( p� 1) vector of model parameters (unknown coeffi-
cients) 3 is a (n� 1) vector containing errors or noise.
The model parameters b, as an estimate of b, can be solved
by ordinary least squares adjustment which minimizes the
error sum of squares 303:
b ¼ ðXX0Þ�1X0Y (10)
A stepwise selection algorithm was utilized to select only
“relevant” independent variables for the estimation of
a specific attribute. The first independent variable Xi consid-
ered for entry into themodel is the one that is most correlated
with the dependent variable Y computed as maxi rYXi,
assuming that its influence on Y is significant. Then, partial
F-test values are used to determine which variable should be
entered next and to test if any of the previously selected
variables should be deleted [31e33].
field measurements and remote sensing data.
Table 8 e Regression models derived after stepwise selection with the dependent variables stem volume V, diameter Dg
and age.
Forest Attribute Regressioncoefficients
Unstandardizedcoefficients
Std.error
Standardizedcoefficients
t Significance
1. Volume
estimation
[m3 ha�1]
(Constant) �206.621 24.335 �8.491 0.000
Hm 11.668 2.111 0.371 5.528 0.000
Pc 0.815 0.144 0.226 5.651 0.000
Ht 7.152 1.643 0.277 4.352 0.000
Ca 2.010 0.623 0.161 3.225 0.001
2. Diameter
estimation [cm]
(Constant) 2.801 3.640 0.770 0.442
Ht 1.290 0.115 0.584 11.206 0.000
CC �0.099 0.030 �0.144 �3.252 0.001
Ca 0.115 0.055 0.107 2.067 0.040
3. Estimation
of age [years]
(Constant) �93.395 16.095 �5.803 0.000
Ht 9.367 0.886 1.373 10.569 0.000
Pc �0.245 0.036 �0.257 �6.863 0.000
Hm �7.360 1.226 �0.885 �6.004 0.000
CC 0.844 0.184 0.398 4.584 0.000
Ca 0.649 0.157 0.196 4.145 0.000
b i om a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 3 5 6 1e3 5 7 4 3569
3. Results
First, results of the estimation of dendrometrical parameters
by regression analysis are given (validation of the prediction
accuracy). Then results of the biomass energy model are
shown.
3.1. Results of the regression analysis
Table 8 shows the different models derived after stepwise
regression and the sequence of selected independent vari-
ables. The strength of the relationship and the significance of
the models are given in Table 9.
Leave-one-out cross-validation (LOOCV) was used to vali-
date the prediction accuracy of the models. Each single
observation (sample plot) from the original sample was
selected as “validation data” and the remaining observations
(n� 1 out of n plots) as “training data”. The accuracy was
computed as the RMSE and Bias. The results are given in Table
10. If by�1i is a predicted value of the leave-one-out cross-vali-
dation and yi an observed value, the RMSECV and BiasCV are
computed as
RMSECV ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1n
Xni¼1
�yi � by
si
�1�2
(11)
Table 9 e Strength of the relationship and significance of the r
Forest attribute Multiple correlationcoefficient (R)
Coefdeterm
1. Volume estimation [m3 ha�1] 0.76
2. Diameter estimation [cm] 0.68
3. Estimation of age [years] 0.79
BiasCV ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1n
Xni¼1
�yi � by
si
�1�
(12)
In addition, the relative RMSECV [%] was derived, which is
the absolute RMSECV normalized to themean y of the observed
values:
RMSECV ½%� ¼ RMSECV
y(13)
The regression models were applied to predict forest attri-
butes for the entire study area (using equally-sized squares as
the inventory plots) and, finally, attributes for forest stands
were derived, as described in Section 2.7. Stand maps with
respective attributes are shown in Fig. 7. In total, 157 stands
are mapped (all stands that intersect the study site).
3.2. Results of the biomass energy model
The predicted stand attributes (V, Dg and age), as derived by
the two-phase procedure, were used as the input parameters
tomodel the quantity of forest residues as potential source for
energy production. The result is given in tons per hectare
[t ha�1]. Both the theoretical total biomass energy potential BE.t[t ha�1] and the technical potential actually available within
the next management period BE.h [t ha�1] (derived from the
harvest volume Vh) were modeled. To show the influence of
egression models.
ficient ofination (R2)
Std. error of theestimate
F Significance
0.58 78.05 100.56 0.000
0.46 7.54 84.65 0.000
0.63 19.33 100.38 0.000
Table 11 e Characteristics of the estimated bioenergypotentials (for 157 stands) based on the definition of [4],which defines the DBH as 8e16 cm.
Type ofenergy potential
Mean Standarddeviation
Min Max
a) Total biomass
energy potential
[t ha�1]
36.70 11.91 13.24 101.40
b) Biomass energy
potential available
within the next
management
period
(10 years) [t ha�1]
9.93 6.53 0.00 48.39
Table 10 e Prediction accuracy (absolute/relative RMSECVand BiasCV) for the estimation of volume, diameter andage at plot level.
Forest Attribute RMSECV RMSECV [%] BiasCV
1. Volume estimation [m3 ha�1] 78.72 30.20 0.064
2. Diameter estimation [cm] 7.62 27.92 0.008
3. Estimation of age [years] 19.69 28.81 0.003
b i om a s s an d b i o e n e r g y 3 5 ( 2 0 1 1 ) 3 5 6 1e3 5 7 43570
different model parameters, two definitions were used for
small sized logs:
1. Definition of [4], which defines the DBH as 8e16 cm.
2. Definition of [2], which defines the DBH as 7e20 cm.
The mean, standard deviation and minimum and
maximum value of the predicted attributes using the first
definition are given in Table 11 and results when using the
second definition are shown in Table 12.
As expected, the mean and maximum values of the
biomass energy potential are higher for the definition of [2],
which defines a wider range for the DBH. This results in
a higher amount of “silvicultural residues” theoretically
available for energy production. The spatial distribution of the
energy potentials based on the definition of [4] is shown with
stand maps in Fig. 8 and for the definition of [2] in Fig. 9.
Clearly visible are the higher quantities of the total
biomass energy potential in young forest stands (compared
with Fig. 7c) for both definitions, but more apparent for the
definition of [2]. For those stands, a high quantity of “silvi-
cultural residues” was computed which are theoretically
available as biomass from thinning operations.
4. Discussion
The great advantage of the suggested method is that it allows
relating biofuel estimations to geographical coordinates that
Fig. 7 e Stand maps with predicted attributes (dendrometrical p
diameter, c) age class.
cannot be achieved with in situ data alone. Thus, it allows
detailed estimations for individual forest stands which were
not possible in earlier studies. Regressions were applied to
characterize the relationship between a forest attribute, as
dependent variable, and several structural forest character-
istics extracted from the RS data, as independent variables.
Only independent variables that are apparently related to the
dependent variableswere chosen. All variableswere extracted
fully automatically from ALS data and CIR orthophotos with
the help of image processing techniques. It can be assumed
that the variables will be relatively invariant for varying flight
and system parameters. According to Ref. [34] such invariant
RS features should be favored because unstable explanatory
variables can produce biased results. As can be seen in Table 8,
the variables related to vegetation height are the most
contributive predictors to the models. Stand height estima-
tions from ALS data itself were analyzed in several studies
(e.g. Refs. [13,14,30,35,36]). The coefficients of determination
(R2) in those studies range from 0.72 to 0.95. The estimation of
top height in this study, using only one variable (90th
percentile computed from the height values of the nDSM),
yielded a correlation of r¼ 0.87 (R2¼ 0.76) [17].
arameters) a) (total) stem volume, b) quadratic mean
Table 12 e Characteristics of the estimated bioenergypotentials (for 157 stands) based on the definition of [2],which defines the DBH as 7e20 cm.
Type ofenergy potential
Mean Standarddeviation
Min Max
a) Total biomass
energy potential
[t ha�1]
41.88 18.88 10.65 134.23
b) Biomass energy
potential available
within the next
management
period
(10 years) [t ha�1]
11.81 9.67 0.00 54.13
b i om a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 3 5 6 1e3 5 7 4 3571
Nevertheless, variables related to vegetation structure
(proportion of coniferous trees, canopy cover andmean crown
area) are able to increase the strength of the relationships and,
thus, improve the estimation of stand attributes. The predic-
tion accuracy of stand attributes was validated with the help
of cross-validations (see Table 10). The observed error for stem
volume estimation (RMSECV [%]¼ 30.20) is in the range of the
errors reported by Ref. [37] (RMSE from 24.3% to 32.1%) who
used mixed-effects models in combination with forest
inventory plots as ground truth data with the same inventory
design, to estimate volume for two study sites also located in
Southern Germany. In a study from Ref. [38], ALS data and
aerial photographs were used in a boreal forest in Finland to
predict the plot volume using a non-parametric approach.
They report a relatively low RMSE of 24% for estimates of total
volume which may be explained by the high proportion of
coniferous trees in the study site (59% pine and 34% spruce).
Fig. 8 e Stand maps with predicted biomass energy potential for
logs as 8e16 cm.
Ref. [39] reports an estimation error of �25% for coniferous
trees for study sites in Austria. However, the authors assume
that the error will be higher for deciduous trees. The relatively
high scatter for estimations on “plot level” can be summarized
as follows:
e Plots from an operational inventory were used as ground
truth data. Those have known errors in their location,
which were quantified in the study from Ref. [40], with an
average deviation of 3.77 m from “true” plot locations.
Thus, an exact coregistration of RS data and field
measurements cannot be assured for all plots.
e To increase the efficiency of the inventory, concentric
circles are established in the field and, as a result, not all
trees are actually measured within a sample plot.
Consequently, modeling errors must be expected when
deriving plot attributes.
e The time difference between the inventory and aerial
survey (here: one year) may have an influence on the
relationship between RS and field data (growth of trees,
forest utilization).
Forest attribute estimation based on regression analysis
requires ground truth data. Due to the reasons listed above,
the time difference between the field survey and aerial survey
should beminimal and the position of the field plots should be
measured as accurately as possible to allow exact coregistra-
tion of RS data and field measurements. However, field
surveys are often time consuming, expensive and skilled
personnel is required. In the Federal State of Baden-Wurt-
temberg, Germany, geo referenced field sample plots from
operational inventories (as used in this study) are only avail-
able for state forests larger than 1500 ha. Often, in smaller
the definition of [4], which defines the DBH for small sized
Fig. 9 e Stand maps with predicted biomass energy potential for the definition of [2], which defines the DBH for small sized
logs as 7e20 cm.
b i om a s s an d b i o e n e r g y 3 5 ( 2 0 1 1 ) 3 5 6 1e3 5 7 43572
forest properties, as well as in private forests, sample plot
inventories have not been implemented and other techniques
are required to assess forest characteristics. Particularly for
such forests, RS can provide very valuable information and
may be the only practical way to acquire data. The advantage
of regression analysis is that forest attribute estimations can
be calibrated and the prediction accuracy quantified. When
Fig. 10 e Comparison of the estimated biofuel potential in this
Table 1). For assessment a conversion from m3haL1 aL1 to t ha
r[ 0.5675 kg/m3.
comparing Tables 6 and 10 it can be seen that the variability in
predicted stand attributes has been reduced.
One great advantage of RS is that it provides information
for large geographical areas and can locate regions with dense
woody biomass and stands with high theoretical bioenergy
potential. To estimate bioenergy for a defined time period, it is
necessary to integrate information regarding intended timber
study with the results from previous studies (as listed inL1 aL1 was carried out using an average conversion factor
b i om a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 3 5 6 1e3 5 7 4 3573
utilization. In this study, the harvest volume was derived for
each stand from the forest management plan. Technical
restrictions are already considered during the planning
process. Depending on the definition for small sized logs an
average annual bioenergy potential of 0.993 and accordingly
1.181 t ha�1 a�1 was computed for the study site. Fig. 10 shows
a comparison of the estimated energy potential to the results
of other studies carried out in Germany serving as reference
values. It is obvious that the results of the present study are
very close to the reference estimates but slightly lower than
the arithmetic mean of the reference values (1.40 t ha�1 a�1),
which confirms the applicability and plausibility of the sug-
gested method.
The biomass energy model used in this study requires
several assumptions, which were mainly derived from rele-
vant literature such as Refs. [2,4,23]. Themodelwas adapted to
the information that can be extracted automatically from RS
data, while attempting to keep the number of parameters
relatively low, to avoid making the application too complex
and thus impractical. However, some modifications are
required to allow the adaption to different situations. For this
reason the user can define the following settings:
e Definition of aminimum and amaximum threshold value
for the DBH [cm] of small sized logs, which is needed to
distinguish between “silvicultural” and “logging residues”.
This allows the adaptation of the model to different
regional definitions.
e Reduction factor r to account for harvest loss, which may
vary for different species.
e Definition of a “reference species” for coniferous and
deciduous trees for
a) the selection of respective assortment tables,
b) conversion factors for the wood density r and
c) the selection of appropriate (expansion factors)
to model the biomass of twigs and small branches
BTþB [t ha�1].
In this study, the major coniferous and deciduous species
within the study site were selected as “reference species,” as
derived from the species composition given by the forest
management plan.
e Definition of the timber assortment supplied for energy
use. In this study, only the percentage of wood with poor
quality was considered.
The model provides estimates of the technical biomass
energy potential. To further enhance the model, future
developments will integrate further technical limitations in
addition to economical restrictions. Particularly technical
restrictions, such as the slope of the terrain, which can limit
the applicability of automated harvesting methods, can be
extracted from terrain models filtered from ALS data. In
addition, automatic methods to extract the road network for
transport modeling will be developed. Further research will
also concentrate on the extraction of additional structural
forest characteristics from ALS data, e.g. vertical stand
structures (characterization of stand profiles, detection of
different layers) or horizontal variability e.g. refined
classification of tree species in the overstory using hyper-
spectral data, in order to improve the estimation of stand
attributes. Additionally the integration of additional attributes
available from full-waveformALS data, such as the echo pulse
width and amplitude for each reflection, will be tested.
Acknowledgements
The authors would like to express their gratitude to Deutsche
Bundesstiftung Umwelt (DBU) for financial support. Further-
more, we would like to thank the Forest Research Institute of
Baden-Wurttemberg (FVA), in particular Arne Nothdurft, for
providing the forest inventory data for this study.
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