land use impact evaluation in life cycle assessment based on ecosystem thermodynamics

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

Energy 31 (2006) 112–125
www.elsevier.com/locate/energy
0360-5442/$ - see front matter q 2005 Elsevier Ltd. All rights reserved.

doi:10.1016/j.energy.2005.01.002

* Corresponding author. Tel.: C32 16329726; fax: C32 16329760.

E-mail address: [email protected] (B. Muys).

http://www.elsevier.com/locate/energy

Nomenclature

a albedo

ci concentration of component i in the ecosystem (mg lK1)

cieq concentration of component i at thermodynamic equilibrium (mg lK1)

3 emissivity

g number of genes

KY incoming solar radiation (0.4 and 1.1 mm) (W mK2)

K* net short wave radiation (0.4 and 1.1 mm) (W mK2)

L[ outgoing long wave radiation (5 and 50 mm) (W mK2)

LY incoming long wave radiation (5 and 50 mm) (W mK2)

L* net long wave radiation (5 and 50 mm) (W mK2)

f a surface slope and aspect solar gain coefficient

Pi,a probability to assemble the genetic information to determine the amino acid sequences of a

living (thus subscript a) species i at thermodynamic equilibrium

R gas constant (8.31451 J KK1 molK1)

Rn net incoming radiation (W mK2)

s Stefan Boltzmann constant (5.7!10K8 W mK2 KK4)

S land use impact score

SED solar exergy dissipation (%)

Dt time interval between two Ts measurements (s)

DT change of surface temperature Ts over time interval Dt (K)

T absolute temperature (K)

Ts surface temperature (K or 8C)

TRN thermal response number (kJ mK2 CK1 or kJ mK2 KK1)

T. Wagendorp et al. / Energy 31 (2006) 112–125 113

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.

T. Wagendorp et al. / Energy 31 (2006) 112–125114

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

Schrodinger 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


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 build-
up 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.


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


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


(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

ex Z RTXN

iZ0

ci lnci

cieq

� �K ðci KcieqÞ

� �(1)

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


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-sensing-

derived 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


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

Ts Z

ffiffiffiffiffiffiffiffiffiffiffiL[

3!s

4

r(3)

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 ZXt2

t1

Rn !Dt

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 KaÞfKY (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 LYK3sT4s (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 ZRn

K�(7)

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* 29.5 1631 85

Douglas fir forest* 24.7 1549 90

Regenerating forest* 29.4 788 79

Clear-cut* 51.8 406 65

Rock quarry* 50.7 168 62

Young forest % 14.2 863 89

Meadow % 13.8 502 84

Potato cropland % 13.3 360 83

Lawn % 15.7 318 73

Forest † 22.4 1400 67

Cereal crop † 23.5 1173 66

Water † 24.0 1211 65

Orchard † 24.2 1154 65

Grassland † 23.4 924 66

Urban † 26.4 309 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


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].


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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