Energy 31 (2006) 112–125
www.elsevier.com/locate/energy
Land use impact evaluation in life cycle assessment based on
ecosystem thermodynamics
Tim Wagendorp, Hubert Gulinck, Pol Coppin, Bart Muys*
Laboratory for Forest, Nature and Landscape Research, Katholieke Universiteit Leuven,
Vital Decosterstraat 102, B-3000 Leuven, Belgium
Abstract
Life Cycle Assessment (LCA) studies of products with a major part of their life cycle in biological production
systems (i.e. forestry and agriculture) are often incomplete because the assessment of the land use impact is not
operational. Most method proposals include the quality of the land in a descriptive way using rank scores for an
arbitrarily selected set of indicators.
This paper first offers a theoretical framework for the selection of suitable indicators for land use impact
assessment, based on ecosystem thermodynamics. According to recent theories on the thermodynamics of open
systems, a goal function of ecosystems is to maximize the dissipation of exogenic exergy fluxes by maximizing the
internal exergy storage under form of biomass, biodiversity and complex trophical networks. Human impact may
decrease this ecosystem exergy level by simplification, i.e. decreasing biomass and destroying internal complexity.
Within this theoretical framework, we then studied possibilities for assessing the land use impact in a more
direct way by measuring the ecosystems’ capacity to dissipate solar exergy. Measuring ecosystem thermal
characteristics by using remote sensing techniques was considered a promising tool. Once operational, it could
offer a quick and cheap alternative to quantify land use impacts in any terrestrial ecosystem of any size.
Recommendations are given for further exploration of this method and for its integration into an ISO compatible
LCA framework.
q 2005 Elsevier Ltd. All rights reserved.
1. Introduction
The environmental impact associated with land use is not addressed in many LCA studies [1]. When
performing a credible LCA study for products with a major part of their life cycle in a biological
* Corresponding author. Tel.: C32 16329726; fax: C32 16329760.
E-mail address: [email protected] (B. Muys).
0360-5442/$ - see front matter q 2005 Elsevier Ltd. All rights reserved.
doi:10.1016/j.energy.2005.01.002
T. Wagendorp et al. / Energy 31 (2006) 112–125
113
Nomenclature
a
ci
cieq
3
g
KY
K*
L[
LY
L*
f
Pi,a
R
Rn
s
S
SED
Dt
DT
T
Ts
TRN
albedo
concentration of component i in the ecosystem (mg lK1)
concentration of component i at thermodynamic equilibrium (mg lK1)
emissivity
number of genes
incoming solar radiation (0.4 and 1.1 mm) (W mK2)
net short wave radiation (0.4 and 1.1 mm) (W mK2)
outgoing long wave radiation (5 and 50 mm) (W mK2)
incoming long wave radiation (5 and 50 mm) (W mK2)
net long wave radiation (5 and 50 mm) (W mK2)
a surface slope and aspect solar gain coefficient
probability to assemble the genetic information to determine the amino acid sequences of a
living (thus subscript a) species i at thermodynamic equilibrium
gas constant (8.31451 J KK1 molK1)
net incoming radiation (W mK2)
Stefan Boltzmann constant (5.7!10K8 W mK2 KK4)
land use impact score
solar exergy dissipation (%)
time interval between two Ts measurements (s)
change of surface temperature Ts over time interval Dt (K)
absolute temperature (K)
surface temperature (K or 8C)
thermal response number (kJ mK2 CK1 or kJ mK2 KK1)
production system (such as forestry and agriculture), the evaluation of this impact is necessary. Neither a
standardized method for the land use impact category nor a database including the necessary parameters
for many types of land use is available at the time being. However, much progress was recently made in
the Society of Environmental Toxicology and Chemistry (SETAC) taskforce on resources and land [2]
and in the working group on land use of European Co-operation in the Field of Scientific and Technical
Research (COST E9) on Life Cycle Assessment for Forestry and Forest Products [3].
The simplest approach is to not consider the intensity of the land use or the original quality of the land,
but only the area and time used. In this case, land use impact can be expressed in m2 yr occupied land per
functional unit of product, and this in analogy with the sun as a power source (in W), where the use of its
energy is dependent on the time lag (in W s or J) [1].
There seems to be agreement, however, that the environmental burden is not only in the reduced
availability of land, but also, due to its use, in the reduction of its quality. Therefore, land use impact can
be expressed as the environmental impact scores multiplied by the (time!space) needed to produce one
functional unit [4]. The change in quality is relevant for permanent land occupation (e.g. agriculture) and
for land use change (e.g. afforestation, temporary exploitation of a quarry). In the case of land use change
the question arises if the burdens must be allocated to the first use (e.g. first crop, first year of
exploitation) or to all following uses.
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T. Wagendorp et al. / Energy 31 (2006) 112–125
After evaluating the existing method proposals, Heijungs et al. [1] concluded that many of them are
not compatible with the principles of LCA, are not sensitive enough for the evaluated environmental
problem, involve ambiguous or arbitrary elements, or are only marginally operational. Blonk et al. [5],
Heijungs et al. [1], Lindeijer et al. [2], and Schweinle [3] demonstrated how land use methods could be
better integrated into an LCA framework. Our paper will focus on the problem of the selection of
relevant land use indicators. Most of the published methods do not provide a theoretically sound
paradigm on which their indicator selection was based. This shows that no solid ecological basis for
choosing land use indicators exists and that the present indicators were chosen more or less arbitrarily.
Koellner [4] confirms that his environmental damage indicator choice is based on stakeholders’ values
and perception. Certain methods even use different indicators for different forms of land use [6,7] and
then apply an arbitrarily chosen impact ratio between these land use forms (e.g. a factor 10 between
forestry and intensive agriculture in INFRAS [7]). A related problem is that many of these indicators are
not quantitative.
The aim of this paper is (1) to propose a stronger theoretical background based on fundamental
ecological principles for the choice of land use impact indicators, (2) to make a critical review of the
existing land use impact evaluation methods based on this ecological concept, and (3) to develop a new
land use impact evaluation method fully compatible with the ecological concept.
2. Theory of ecosystem thermodynamics
At first glance, the build-up of complex structures in ecosystems cannot be explained by the first and
second law of thermodynamics (law of energy conservation and law of entropy). Based on the work of
Schrödinger and Prigogine [46], different authors tried to reinterpret the entropy law for open systems
[8] or even to formulate a new law of thermodynamics explaining how ecosystems can build order out of
chaos. According to this new interpretation, any open system receiving a continuous flow of high quality
energy will raise its exergy level and distance itself to a maximum extent from the thermodynamic
equilibrium [9]. Exergy is energy subtracted of its entropic content, and consequently able to do work.
Whenever several pathways for the dissipation of the induced energy gradient exist, open systems tend
to select those yielding the highest dissipation of incoming exergy [8] in order to maintain the
organization in a locally reduced entropy state. This goal function of systems far from thermodynamic
equilibrium is not in contradiction with Prigogine’s ‘minimum entropy generation’ principle, which is
only valid for systems close to equilibrium [46]. In this context, life on earth and its diversity form a
network of successful, highly evolved pathways that efficiently degrade the energy gradient induced by
the sun. The success of life on earth as an energy dissipation agent results from exergy accumulation in
the form of biomass and trophic networks (order from disorder) combined with the passing on of
successful genetic information from one generation to the next (order from order) [10]. As stated by
Schneider [8], complex ecosystems have structural and functional attributes that lead to more effective
degradation of the energy flows passing through the ecosystem. As ecosystems develop more complex
and diverse processes and structures with greater diversity and more hierarchical levels, they increase
their energy dissipation [8,11–13]. They absorb low entropy energy from solar light and emit high
entropy energy in form of dejections and heat [14]. We thus assume that the internal exergy storage level
of an ecosystem and its ability to dissipate exogenic exergy flows develop in parallel. Ecosystem exergy
storage and dissipation can be reduced by disturbances. Stable ecosystems will resist to these
T. Wagendorp et al. / Energy 31 (2006) 112–125
115
perturbations or have the resilience to adapt their structure by varying the species and processes to
maintain life support functions. They keep the ecosystem as far as possible from the thermodynamic
equilibrium. In short, the ecological integrity reflects three facets of ecosystem self-organization: (1)
current well functioning, (2) capacity to develop, regenerate and evolve and (3) resilience [15].
As a consequence, several authors proposed exergy dissipation [10,44] and maximization of internal
exergy level [16,17,44,45] as the driving forces, or in modeling terms, as the goal functions of living
systems: when an ecosystem develops and matures it becomes more effective in capturing and
dissipating the exergy of the incoming solar radiation through photosynthesis, evapotranspiration,
respiration and other ecosystem functions.
In an attempt to translate this theory in more practical terms of ecosystem state and function, we may
say that all ecosystems tend to develop towards
† the state with highest exergy level: concentration of energy, nutrients and information through buildup of biomass, horizontal and vertical structure, genetic diversity and complex interactions between
elements (‘maximum storage principle’);
† a maximal dissipation performance of exogenic exergy flows (‘maximum dissipation principle’): it
essentially means maximizing the buffering capacity of the ecosystem in the broadest sense, because
the thermodynamic laws are obviously not only valid for incoming radiation but also for outgoing
energy (e.g. thermal reradiation of the earth’s surface). As for solar radiation, being undoubtedly the
main driving factor of terrestrial ecosystem development, the analysis must by analogy apply to all
kinds of energy fluxes, such as wind energy (e.g. storm), water flow (e.g. rain as influx, runoff
and percolation as outflux), nutrient fluxes (e.g. deposition as influx, leaching as outflux), mass flow
(e.g. erosion as outflux).
It must be emphasized that state (internal exergy level) and function (exergy flux dissipation rate) of
an ecosystem are inseparably linked [44]. In theory, both variables can be measured and can hence lead
to land use impact indicators (cf. Section 3).
This whole ecosystem exergy concept is in perfect agreement with earlier concepts of ecosystem
stability as described by Daubenmire [18], Bormann and Likens [19], Packham and Harding [20] and
many others.
All what has been explained for ecosystems is valid for its components (individuals, populations) as
well. They all strive for maximal exergy dissipation, but in stable complex systems, due to ’learned’
interactions, their competition does rarely result in a lowering of the total ecosystem exergy level. As
illustrated by the wind tunnel experiments of Allen [21], immature or simplified living systems are more
prone to abiotic (e.g. storm, fire) and biotic (e.g. plagues, diseases) disturbances, which further decrease
the ecosystem exergy dissipation level by damaging or destroying ecosystem components, inhibiting
trophical networks and ecological interactions.
By analogy the development of humanity can be considered as a continuous attempt to maximize its
buffering capacity towards exogenic energy flows. Objects with economic value for humans are highly
organized, low entropy structures. In order to create them, human life feeds unavoidingly on external
sources of low entropy, just like ecosystems. But contrary to ecosystems, this low entropy is not largely
derived from solar light, but from ecosystem exergy or fossilized ecosystem exergy [14]. Human
activities will most often decrease the exergy level of natural systems due to the extraction of biotic
resources or due to degradation or simplification of the system.
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This way, human land use can be defined as a human induced disturbance influencing the exergy level
and exergy dissipation rate of an ecosystem. Human land use systems will often lead to a temporary or
permanent decrease of ecosystem exergy level, indicated by a decrease of biomass and/or canopy cover,
simplification, loss of species and a subsequent loss of ecosystem functionality indicated by, e.g., the
following entropic consequences:
† biotic deterioration and aseptization of the environment by dispersal of noxious compounds (pollution
of water, air and soil)
† loss of control by the vegetation over water and nutrient fluxes, increased run-off, loss of plant
available nutrients by leaching
† entropization of the soil conditions: oxidation of organic matter, loss of macro porosity, soil loss
through erosion, formation of toxic substances and salts, desertification
† loss of potential multiple pathways for energy degradation.
These consequences are considered undesirable because they provoke a degradation and oxidation of
the biosphere, which serves as a protective shield for the earth’s surface, an associated increase of
entropy and thus a sometimes irreversible return to the thermodynamic equilibrium. From this
perspective, and in analogy with exergy analysis in industrial LCA applications, ecosystem exergy
analysis can indicate the possibilities of thermodynamic improvement of ecosystem use and
management: human management strategies that focus on the maximal exploitation of a particular
ecosystem resource or function will always fail; only those which maintain a balanced system will
succeed [9].
It is our belief that the assessment of the environmental impacts of human land use activities
must be based on an in depth understanding of the fundamental laws of ecology and
thermodynamics, and not on stakeholders’ valuations which are time and space dependent.
Suitable indicators for measuring and monitoring land use impacts should be sensitive for changes
in ecosystem state (exergy storage level) and functionality (exergy dissipation capacity). These two
aspects of the ecosystem exergy concept coincide, respectively, with the ‘natural resources’ and
‘natural environment’ areas of protection, respectively, defined by SETAC [22] and with the
‘information and stocks’ and ‘processes’ attributes of the ecosystem quality safeguard subject
defined by Koellner [4].
3. Evaluation of the methods
Based on the above described ecosystem exergy concept the available land use impact assessment
methods proposed for use in LCA can be divided in three groups: those evaluating the state of the
ecosystem, in terms of exergy storage, compared to a reference system (‘state methods’); those
evaluating the functionality of the ecosystem, in terms of dissipation rate (‘functional methods’) and
‘hybrid methods’ [23,24]. In the following paragraphs, they are reviewed from a thermodynamic
viewpoint [25].
Most state methods choose the state with highest exergy level (the potential natural vegetation) as the
reference state. They are compatible with the exergy concept as far as the chosen state indicators
describe or quantify the system’s exergy level. Functional methods fit into the exergy framework as long
T. Wagendorp et al. / Energy 31 (2006) 112–125
117
as their indicators consider ecosystem buffer functions, whereas hybrid methods describe both
ecosystem state and functionality in relation to a reference state.
3.1. State methods
The method of Sturm and Westphal [6] estimates the hemerobia or degree of naturalness of the soil, of
the biocoenosis and of the succession, with scores on a scale, ranging from close to nature to unnatural.
This measure largely coincides with the ecosystem exergy level, but from a thermodynamic perspective,
it starts from the premise that all human interventions will lead to loss of exergy and vice versa.
Consequently, the natural ecosystem has by definition the highest exergy, which is not necessarily the
case.
The two-indicator approach of Lindeijer et al. [22] uses the plant species diversity as the information
component of the ecosystem exergy level, and the fNPP (free net primary production, it is the fraction of
net primary production which is not harvested and stays in the ecosystem and consequently is available
for life support functions and nature development) as its resource component. For both indicators, the
actual value is compared to a reference state, which is the most natural state available in the considered
physiotope. The fNPP indicator seems fully compatible with the exergy concept. The biodiversity
indicator starts from the premise that undisturbed ecosystems would have higher biodiversity. Energy
based succession models showed that ecosystem stability and biodiversity do not necessarily coincide
[19].
The method of Koellner [4] also uses species richness as an indicator, but uses the total regional
species pool as a reference, which is probably the better approach. Biodiversity will also depend on the
choice of considered taxa, which in these two methods is restricted to vascular plants. It is never possible
to assess all taxa, but as Koellner [4] states, vascular plants represent the best available data and may be a
proxy for the species richness of certain other taxa as well. Biodiversity and its related genetic
information form undoubtedly an important element of the ecosystem exergy level and can therefore be
used in a multi-indicator approach. For the above-mentioned reasons, however, it does not seem
recommended to use it as a single indicator for ecosystem exergy.
Methods like standards for Sustainable Forest Management and Environmental Management Systems
such as ISO 14000 do not only consider physically observable parameters, but also attribute indicator
scores based on management intentions [7]. Such indicators do not describe the physical reality of the
ecosystem since they are based on socio-economic and cultural values. They are related to the exergy
level of the human population, and are therefore not considered compatible with the ecosystem exergy
concept. An exception on this is the method developed by INFRAS [7], in which non-ecosystem related
indicators were excluded.
3.2. Functional methods
The method of Baitz et al. [26] attributes scores to the quality of an area using indicators,
which depend on the fulfillment of ecosystem functions in the compartments of soil, water, air,
protection of species and habitats and crop production. Functional methods fit into the ecosystem exergy
framework as long as they only consider ecosystem functions and not human functions. Most of the
indicators in Baitz et al. [26] such as erosion resistance, filtering and buffering capacity relate with
exergy dissipation and are therefore compatible with the ecosystem exergy concept. Biotic output
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(sustainable crop production potential) could be considered an anthropic function. However, it is not,
because it gives an indication of the adaptability of the ecosystem to human induced disturbances. But in
any case, it is more a resource than a function, which means that the method of Baitz et al. [26] is in fact a
hybrid method.
3.3. Hybrid methods
The LCA-based multi-indicator land use impact assessment proposed by Muys and Garcia Quijano
[27] claims to be universally applicable and uses ecosystem exergy as a conceptual framework. It
considers exergy maximization as the driving force of ecosystem succession [8]. The method is based on
a set of rather easily quantifiable indicators belonging to the four thematic categories soil, water,
vegetation structure and biodiversity. These indicators measure the integrity of an ecosystem by
comparing them to the indicator values of the reference system, which is the potential natural vegetation,
i.e. the climax vegetation, under the given environmental conditions. Part of the indicators measure the
exergy level of the system (in terms of biomass, structure and information content), other indicators
measure the level of control or buffering capacity the system has over energy and material flows.
4. Towards a new method for land use impact evaluation in LCA
To derive a better LCA method for land use impact assessment fully compatible with the exergy
theory, we must start from the question how to get a quantitative and direct measure of ecosystem exergy
storage and dissipation to describe, respectively, the structural state of the ecosystem complex (goal
function: maximum exergy storage) or the function caused by the low entropy system (goal function:
maximum exergy dissipated).
Another aspect that we should keep in mind is simplicity. Ecosystems possess an enormous
complexity, which makes it impossible to measure all the details and makes it necessary to use a holistic
approach. Therefore, the thermodynamic features of an ecosystem are appropriate to capture the global
properties of the ecosystem [8,16].
4.1. The state method approach
For ecosystem modeling purposes, Bendoricchio and Jørgensen [16] proposed an ecosystem exergy
calculation method based on the following formula
N X
ci
K ðci K cieq Þ
ci ln
(1)
ex Z RT
cieq
iZ0
where R is the gas constant, T the absolute temperature, ci the concentration in the ecosystem of
component i and cieq the corresponding concentration of component i at thermodynamic equilibrium.
They consider the concentration of the inorganic components (iZ0), the concentration of the detritus
or dead organic matter (iZ1) and the concentration of the biological components (iZ2,3,4,.,N). The
concentration cieq of a species i for example, is derived from the probability to find this species at the
thermodynamic equilibrium. This probability Pi is the probability for producing its biomass (detritus) P1
T. Wagendorp et al. / Energy 31 (2006) 112–125
119
and the probability to find the genetic code of the species Pi,a from the number of possible permutations
of 20 amino acids, knowing that each gene contains a sequence of some 700 amino acids
Pi;a Z 20K700g
(2)
where g is the number of genes.
Formula (1) distinguishes the chemical from the informational contributions to exergy [28], but does
not include the thermal, structural, mechanical and entropic part of a full exergy calculation. According
to Pueyo [29] this formula produces a strong overestimation of the thermodynamic weight of
organization. In addition to that we see a number of operational difficulties as well. The major problem
of the method is the data availability. For most species, there exists only a rough estimate for the number
of genes. Furthermore, getting an idea of the concentration of all state variables of an ecosystem,
including all taxa, is hardly possible [16]. Finally, the information in the genes is not the only
information in the ecosystem network. The phenotype of an individual is the result of the genotype in
combination with other information as the result of adaptation and learning processes.
4.2. The functional method approach
As stated by Moran [30] and Samson and Lemeur [31], the use of thermal infrared information can
play a useful role in the evaluation of ecosystem physiological activity, functioning and health. The
surface temperature of an ecosystem is believed to give a spatially integrated response of all factors,
which influence the physiological and physical canopy behavior. Several authors [21,32–37] used
surface temperature and other derived parameters as indicators for the organizational state and
functioning of ecosystems. With respect to the biological relevance of these measurements one must
take into account that the proportion of solar energy used for photosynthesis is small in relation to the
portion used by energetically more expensive processes [32]. Nutrient transport and maintenance of
turgor pressure inside the plant are energetically much more demanding processes that depend on latent
heat as energy source [31,38]. As a result of decreasing evapotranspiration due to human land use
impacts on the ecosystem, the surface temperature of an ecosystem can rise. The cooling capacity of an
ecosystem, or the loss of cooling due to disturbance, is therefore a meaningful measure of overall
ecosystem functioning and health [30].
Measurements with thermal airborne sensors in different terrestrial ecosystems testified to a trend of
decreasing surface temperature with increasing system complexity [36,37]. This relationship between
energy dissipation and thermal radiation opens perspectives for measuring the exergy of ecosystems
using remote sensing techniques from different platforms. We therefore propose a set of remote-sensingderived parameters as potential indicators of land use impact. Some of these indicators are already in use
for the study of ecosystem transpiration and hydrological balance. But their relation to man-induced
disturbances such as land use is hardly studied.
4.2.1. Calculation of surface temperature
Surface temperature of ecosystems is a well-known parameter for describing evapotranspiration and
its changes due to stress conditions [30,31,38,39]. As stated by Fraser and Kay [40] it controls major
ecosystem energy flux outputs (and hence exergy flux outputs). Papers in which surface temperature is
used as an indicator for ecosystem functionality are rare, but show clear trends, suggesting that
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T. Wagendorp et al. / Energy 31 (2006) 112–125
undisturbed natural forests dissipate solar radiation more effectively and consequently show a cooler
surface temperature during the daytime. Observing a moist tropical catchment area in Singapore in the
thermal bands of a Landsat TM satellite image, Nichol [35] found a good spatial correspondence
between surface temperature and land cover type, and a close negative relationship between temperature
and biomass. The coolest areas corresponded to mature secondary and primary rain forest, and the
warmest to urban settlements. Also, Luvall et al. [41] could detect temperature differences between a
burned area and a small patch of trees of only 15 m in diameter in a tropical forest area with an airborne
thermal sensor. The surface temperature indicator method, as implemented in these studies calculates
surface temperature from the detected long wave radiation based on the Boltzman law
rffiffiffiffiffiffiffiffiffiffiffi
L[
4
(3)
Ts Z
3 !s
where L[ is the outgoing long wave radiation as measured with remote sensing techniques; 3 the
emissivity of the land cover, s the Stefan Boltzmann constant, and Ts the surface temperature.
4.2.2. Thermal response number
Another potential indicator of ecosystem functionality in terms of dissipating solar radiation is the
thermal response number (TRN) or thermal buffer capacity. The TRN of ecosystems can be computed
from thermal remote sensing data and radiation measurements [37]
TRN Z
t2
X
Rn !Dt
t1
DT
(4)
where Rn is the net incoming radiation or the sum of K* (net short wave radiation) and L* (net long wave
radiation); Dt the time difference between two successive remote sensing images (for example one
hour); DT the change of surface temperature Ts over the time interval Dt. K* and L* are calculated as
follows
K Z ð1 K aÞfKY
(5)
where a is the albedo; f the aspect of the terrain; KY the incoming solar radiation (between 0.4 and
1.1 mm) and
L Z LYKL[ Z LYK3sTs4
(6)
where LY is the measured incoming long wave radiation (between 5 and 50 mm). This time integrating
approach facilitates a detailed study of the relationship between incoming exergy and its degradation by
the ecosystem.
4.2.3. Solar exergy dissipation
Solar exergy dissipation (SED) or the ratio of net radiation (Rn) and net shortwave radiation (K*), as
used by Luvall [37], represents the fraction of the net radiation that is dissipated into lower exergy
thermal heat [8]. It embodies the functioning and exergy degradation and storage of a system
SED Z
Rn
K
(7)
T. Wagendorp et al. / Energy 31 (2006) 112–125
121
Table 1
Land use characterization based on surface temperature (Ts), thermal response number (TRN) and solar exergy degradation
(SED) as measured at Andrews Experimental forest, Oregon (* Thermal Imaging Multispectral Sensor (TIMS) [37]), Bertem
study site in central Belgium (% Omega OS 36 infrared thermometers, Wagendorp, unpublished results) and Gorsem study site
in Northern Belgium († Digital Airborne Imaging Spectroradiometer (DAIS) [36])
Surface type
Ts (8C)
TRN (kJ mK2 CK1)
SED (%)
Forest plantation*
Douglas fir forest*
Regenerating forest*
Clear-cut*
Rock quarry*
Young forest %
Meadow %
Potato cropland %
Lawn %
Forest †
Cereal crop †
Water †
Orchard †
Grassland †
Urban †
29.5
24.7
29.4
51.8
50.7
14.2
13.8
13.3
15.7
22.4
23.5
24.0
24.2
23.4
26.4
1631
1549
788
406
168
863
502
360
318
1400
1173
1211
1154
924
309
85
90
79
65
62
89
84
83
73
67
66
65
65
66
63
Table 1 compares the thermal indicators Ts, TRN and SED for different land use types under similar
site conditions. They were obtained from airborne measurements with the Thermal Infrared
Multispectral Scanner (TIMS) and with the Digital Airborne Imaging Spectroradiometer (DAIS), and
from ground-borne measurements (Omega OS36 infrared thermometers). The results shown in Table 1
indicate that more complex undisturbed systems capture incoming exergy more efficiently resulting in
lower Ts and higher TRN and SED values. The values have to be interpreted per site, but perfectly
indicate the degree of thermal buffering or the strength of the microclimate formed by the respective land
covers.
The major advantage of TRN and SED compared to Ts is their temporal integration of radiation
characteristics during measurements, thus reducing the influence of ephemeral changes in incoming
radiation on the indicator value.
4.2.4. Compatibility with the LCA framework
Suitable thermal indicators for use in LCA should be unambiguously defined, valid for all types of
land use, based on a firm theoretical foundation and quantitative [1,4]. The proposed indicators seem to
meet entirely with these requirements. Information on the ecological meaning of these indicators and the
potential of this approach to become an operational land use impact method for LCA is being acquired
by comparing them with the results of a exergy-based multi-indicator land use impact assessment
method [27,36].
Another essential condition for compatibility with the LCA framework is the universal applicability
of the indicators. At first sight, the thermal indicators do not meet this requirement, because they are site
(soil, climate and other abiotic growth factors) specific. However, by expressing the indicator values as a
percentage of the site specific reference systems, it becomes perfectly possible to compare impact values
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T. Wagendorp et al. / Energy 31 (2006) 112–125
between sites anywhere in the world. This has been illustrated by Peters et al. [43] choosing the climax
vegetation as the site specific reference system.
4.2.5. Methodological problems
Thermal remote sensing has still a lot of methodological problems to overcome before it can be
considered a useful tool for land use impact in LCA analysis. Nichol [35] found that the edges of the
forest close to town had a slightly warmer surface temperature although they had the same biomass and
maturity as the central forest zones. This illustrates the importance of horizontal heat exchange and the
necessity to carry out the measurements at low wind speeds.
In contrast to the results in Table 1, Kutsch et al. [33] were not able to use Ts and SED for
characterizing the biological self-organization of beech forest and maize cropland in Northern Germany.
However, this might be due to methodological shortcomings. As they stated correctly, and as confirmed
by our experiments, a lot of ‘abiotic noise’ (wind, cloud cover, air temperature) might influence the Ts
measurements. The use of surface temperature measurements within the framework of a site-specific
measurement protocol, including accompanying radiation measurements, might reduce the amount of
abiotic noise and increase the overall accuracy and usefulness of thermal indicators.
Another problem mentioned [35,42] is the topography that leads to aspect-related influences on
canopy temperature. It is, however, difficult to verify without ground truthing if the temperature
differences between slopes with different exposition are due to an aspect related diurnal effect or to a
different forest composition and structure as a consequence of the different microclimate. Probably, a
correction model using a detailed Digital Elevation Model is the key to a solution.
Also, soil moisture has a significant influence on the surface temperature (drought stress results in
stomata closure and thus increase in surface temperature), but when comparing land uses with similar
soil type and precipitation, it will be mainly a result of the ecosystem exergy buffering capacity and thus
an aspect of what we want to measure.
4.2.6. Future developments
How these thermal indicator values can best be transformed into exergy scores for the land use impact
category in LCA is still an open question because they are influenced by measurement conditions, and
because the exergy of an ecosystem is not only dependent on its stability/complexity/maturity, but is also
limited by site (edaphic and climatological) factors. Measurement conditions will have to be
standardized and a reference database with exergy scores of the natural climax system and of different
land use types derived from thermal remote sensing for every edapho-climatic site class will have to be
built. In this context, it is important to mention that even in natural ecosystems, the exergy content is
never maximal over longer periods due to the natural disturbance regime. Climax forests in the
temperate zone, for example, can stay for periods of centuries in a permanent state of submaximal exergy
because of a shifting mosaic of different development stages and gaps caused by the mortality of
overmature trees [19]. Measured exergy scores for ecosystems should therefore not be compared to the
maximum but to the average exergy score of the permanent state or shifting mosaic of a natural climax
system in the considered edapho-climatic zone. It is also possible that sustainably managed forests can
reach higher exergy scores than natural systems.
At the time being, efforts are made for increasing the understanding of the measuring conditions (i.e.
viewing angle, field of view, sample density, atmospheric conditions, seasonal variations) and to study
the relationships between thermal ecosystem characteristics and other land use indicators [34,36].
T. Wagendorp et al. / Energy 31 (2006) 112–125
123
The advantage of thermal remote sensing is that it yields spatial information in a GIS environment,
which allows us to generate land use impact maps and other interesting features:
† the average impact of a production area can be calculated, but also hot spots of significant land use
impact can be spatially detected and thus more easily optimized by an adapted management
† impacts of permanent land use can be averaged over time (e.g. over one rotation period) or land use
change and restoration time can be assessed by multi-temporal monitoring.
In the near future, a higher spatial and thermal resolution, greater number of spectral bands and more
sophisticated correction for both atmosphere and emissivity will allow for a wider use of thermal
infrared information in assessing land use impact and ecosystem functioning [39]. Ongoing research that
tests both ground and airborne thermal infrared measurements (DAIS) can be seen as a preliminary study
for the use of space borne data and can play an important role in the development of detailed and
accurate land use impact assessments [36].
Finally, the partitioning of solar exergy dissipation between the different ecosystem processes and its
changes over time are still insufficiently understood. Evapotranspiration and stomatal activity [21,31,38]
and the building and maintenance of structural vegetative elements [13] are undoubtedly key factors in
this, but its relation to overall complexity needs to be further studied.
5. Conclusions
Most proposed methodologies for evaluating the land use impact in LCA use indicators that are
compatible with the ecosystem exergy concept. The problem encountered with many indicators is that
they are chosen arbitrarily, that they are not quantitative, not valid for all land uses or difficult to
measure. An alternative single indicator based on thermal (airborne) remote sensing has the potential to
offer a quick and relatively cheap value for land use impact, which is fully compatible with the exergy
concept, because it measures the ecosystem function in terms of energy dissipation in a direct and
integrated way.
Hybrid multi-indicator based land use impact assessment methods, such as the one introduced by
Muys and Garcia Quijano [27] includes exergy storage and exergy dissipation indicators within the
themes soil, water, vegetation and biodiversity. The inclusion of thermal ecosystem indicators might
contribute to the overall efficiency of the multi-indicator method since it describes in a synthetic way the
system’s energy buffering capacity (exergy dissipation).
More research is needed to make thermal land use indicators operational: ongoing research on
airborne (DAIS thermal sensor; in the framework of the EU HYSENS campaign) and ground thermal
infrared measurements in relation with existing land use impact assessment methods will provide an
insight in the thermal behaviour of a range of (semi) natural and anthropized systems [36].
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