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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: christoph.straub@lwf.b

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

ara.koch@felis.uni-freiburg.de (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)

i¼1

where 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 i¼1

General 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.

r e f e r e n c e s

[1] BMU. National biomass action plan. German Federal Ministryfor the Environment, Nature Conservation and NuclearSafety; 2009.

[2] Kaltschmitt M, Hartmann H, Hofbauer H. Energie ausBiomasse. Berlin, Heidelberg: Springer; 2009.

[3] Haschke P. Forstliche Energieholzpotentiale in Deutschlandund Aspekte ihrer zukunftigen Nutzung. Holz-Zentralblatt1998;53:806.

[4] Dieter M, Englert H. Abschatzung des Rohholzpotentials furdie energetische Nutzung in der BundesrepublikDeutschland. Hamburg; 2001. p. 40.

[5] Ochs T, Duschl C, Seintsch B. Regionalisierte Struktur- undMarktanalyse der ersten Verarbeitungsstufe derHolzwirtschaft. Entwicklungsperspektiven fur dieHolzindustrie. Holz-Zentralblatt; 2007:419e21.

[6] Frommherz S, Deines T, SigmundV. Viel Energieholz in Baden-WurttembergsWaldern. Holz-Zentralblatt 2000;144:1985e6.

[7] LWF. Der Energieholzmarkt in Bayern. Munchen: BayerischeLandesanstalt fur Wald- und Forstwirtschaft; 2000.

[8] Hepperle F. Energieholz aus dem Wald: Konkretisierung undLokalisierung von betrieblichen Nutzungspotentialen. DerForstbetrieb als professioneller Lieferant von Bioenergie;Freiburg 2007. p. 6.

[9] Kay S, Held M, Wagelaar R, Pelz S. GIS-basierte Ermittlungoperationaler Energieholzpotentiale im Wald. In: Strobl J,Blaschke T, Griesebner G, editors. AngewandteGeoinformatik 2008. Heidelberg: Wichmann; 2008. p. 456e65.

[10] Nothdurft A, Saborowski J, Breidenbach J. Spatial predictionof forest stand variables. European Journal of ForestResearch 2009;128:241e51.

[11] Hyyppa J, Hyyppa H, Xiaowei Y, Kaartinen H, Kukko A,Holopainen M. Forest inventory using small-footprintairborne LiDAR. Boca Raton, London, New York: CRC Press,Taylor & Francis Group; 2009.

[12] Pfeifer N, Mandlburger G. LiDAR data filtering and DTMgeneration. Boca Raton, London, New York: CRC Press, Taylor& Francis Group; 2009.

[13] Naesset E. Predicting forest stand characteristics withairborne scanning laser using a practical two-stageprocedure and field data. Remote Sensing of Environment2002;80:88e99.

[14] Naesset E. Practical large-scale forest stand inventory usinga small-footprint airborne scanning laser. ScandinavianJournal of Forest Research 2004;19:164e79.

[15] Maltamo M, Malinen J, Packalen P, Suvanto A, Kangas J.Nonparametric estimation of stem volume using airborne

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 43574

laser scanning, aerial photography, and stand-register data.Revue Canadienne De Recherche Forestiere [CanadianJournal of Forest Research] 2006;36:426e36.

[16] Hudak AT, Crookston NL, Evans JS, Hall DE, Falkowski MJ.Nearest neighbor imputation of species-level, plot-scaleforest structure attributes from LiDAR data. Remote Sensingof Environment 2008;112:2232e45.

[17] Straub C, Dees M, Weinacker H, Koch B. Using airborne laserscanner data and CIR orthophotos to estimate the stemvolume of forest stands. Photogrammetrie FernerkundungGeoinformation; 2009:277e87.

[18] Rosillo-Calle F, de Groot P, Hemstock SL, Woods J. Overviewof biomass energy. In: Rosillo-Calle F, de Groot P,Hemstock SL, Woods J, editors. The biomass assessmenthandbook. London: Earthscan; 2007. p. 1e25.

[19] Roser D, Asikainen A, Stupak I, Pasanen K. Forest energyresources and potentials. In: Roser D, Asikainen A, Raulund-Rasmussen K, Stupak I, editors. Sustainable use of forestbiomass for energy. Dordrecht: Springer; 2008. p. 9e28.

[20] Hakkila P, Parikka M. Fuel resources from the forest. In:Richardson J, Bjorheden R, Hakkila P, Lowe AT, Smith CT,editors. Bioenergy from sustainable forestry. Dordrecht:Kluwer Academic Publishers; 2002. p. 19e47.

[21] ElmqvistM, Jungert E, Lantz F, Persson A, SodermanU. Terrainmodelling and analysis using laser scanner data. InternationalArchives of the Photogrammetry, Remote Sensing and SpatialInformation Sciences 2001;34(3/W4):219e26.

[22] Weinacker H, Koch B, Heyder U, Weinacker R. Developmentof filtering, segmentation and modelling modules for LIDARand multispectral data as a fundamental of an automaticforest inventory system. International Archives of thePhotogrammetry, Remote Sensing and Spatial InformationSciences 2004;36(8/W2):90e5.

[23] Heisig H, Kuhnle C, Paredes L, Wiesel J. GIS based forestbiomass exploitation for energy supply in the northern blackforest region. Photogrammetrie FernerkundungGeoinformation 2005;4:273e80.

[24] LFV. Hilfstabellen fur die Forsteinrichtung. Stuttgart:Ministerium fur Landlichen Raum, Ernahrung,Landwirtschaft und Forsten; 1993.

[25] Schopfer W, Dauber E. Bestandessortentafeln 82/85. In:Hilfstabellen fur die Forsteinrichtung. Stuttgart: Ministeriumfur Landlichen Raum, Ernahrung, Landwirtschaft undForsten Baden Wurttemberg; 1989. p. 166e75.

[26] Rohner M, Boswald K. Forestry development scenarios:timber production, carbon dynamics in tree biomass andforest values in Germany. Silva Fennica 2001;35:277e97.

[27] Kramer K, Kruger HH. Vorrat und Nutzungsmoglichkeitenforstlicher Biomasse in der Bundesrepublik Deutschland. DerForst- und Holzwirt 1981;36:33e7.

[28] Koch B, Heyder U, Weinacker H. Detection of individual treecrowns in airborne lidar data. Photogrammetric Engineeringand Remote Sensing 2006;72:357e63.

[29] Soille P. Morphological image analysis. Berlin, Heidelberg,New York: Springer; 1999.

[30] Naesset E, Bjerknes KO. Estimating tree heights and numberof stems in young forest stands using airborne laser scannerdata. Remote Sensing of Environment 2001;78:328e40.

[31] Ryan TP. Modern regression methods. New York: John Wileyand Sons, Inc.; 1997.

[32] Draper R, Smith H. Applied regression analysis. New York:John Wiley and Sons, Inc.; 1998.

[33] Janssen J, Laatz W. Statistische Datenanlyse mit SPSS furWindows. Berlin, Heidelberg, New York: Springer; 2007.

[34] Korpela I, Koskinen M, Vasander H, Holopainen M,Minkkinen K. Airborne small-footprint discrete-return LiDARdata in the assessment of boreal mire surface patterns,vegetation, and habitats. Forest Ecology and Management2009;258:1549e66.

[35] Means JE, Acker SA, Fitt BJ, Renslow M, Emerson L,Hendrix CJ. Predicting forest stand characteristics withairborne scanning lidar. Photogrammetric Engineering andRemote Sensing 2000;66:1367e71.

[36] Rieger W, Eckmullner O, Mullner H, Reiter T. Laser-scanningfor the derivation of forest stand parameters. InternationalArchives of the Photogrammetry, Remote Sensing andSpatial Information Sciences 1999;32(3/W14):193e201.

[37] Breidenbach J. Mixed-effects models for estimating standvolume by means of small footprint airborne laser scannerdata. Photogrammetric Journal of Finland 2008;21(1):4e15.

[38] Packalen P, Maltamo M. Predicting the plot volume by treespecies using airborne laser scanning and aerialphotographs. Forest Science 2006;52:611e22.

[39] Hollaus M, Schadauer K, Dorigo W. Kommt der glaserneWald? Forstzeitung 2008;2:4e5.

[40] Breidenbach J. Regionalisierung von Waldinventuren mittelsaktiver Fernerkundungstechniken. Freiburg; 2008.

[41] Haug H, Nothdurft A. Fachkonzept BI-Teil 2; 2004.