Monitoring 20 years of increased grazing impact onthe Greek island of Crete with earth observation

satellites

J. Hill*, P. Hostert*, G. Tsiourlis†, P. Kasapidis†, Th. Udelhoven* &C. Diemer*

*Remote Sensing Department & Faculty of Geography/Geosciences,University of Trier, D-54286 Trier, Germany

†Biology Department & Laboratory of Island Ecology, University of Crete,GR-711 10, Iraklion, Crete, Greece

(Received 16 June 1997, accepted 12 December 1997)

The degradation of permanent semi-natural vegetation and the resultingacceleration of soil degradation and erosion processes constitute majorelements of land degradation in the Mediterranean basin. These elements aretriggered by human activities rather than climatic conditions. The Greekisland of Crete represents a characteristic case of land degradation resultingfrom hundreds of years of intensive grazing and fires. Since Greece joined theEuropean Communities in 1981, grazing in mountainous regions has greatlyincreased due to subsidies that became available through the CommonAgricultural Policy. Within a European Research Project on the use ofsatellite remote sensing for monitoring environmental change in Medi-terranean ecosystems (DeMon-2: An Integrated Approach to Assess and MonitorDesertification Processes in the Mediterranean Basin), we have initiated a study tomonitor the impact of increased grazing pressure on two mountainousecosystems in Crete. The approach is based on describing surface conditionsand vegetation cover over time with a long-term series of earth observationsatellites.

©1998 Academic Press

Keywords: remote sensing; monitoring; land degradation; desertification;grazing; Greece; Crete; Mediterranean ecosystems

Introduction

After a long history of human pressure on land resources, land degradation resultingfrom interactions between climatic characteristics and ecologically unbalanced humaninterventions are widespread in the Mediterranean basin. Degradation of naturalvegetation has created a mosaic of ecosystems that represent different stages ofdegradation (Specht, 1969; Quezel, 1979). In Greece the natural Mediterraneansclerophyllous evergreen forest of oaks (Pinus spp.) have been replaced in most casesby matorral and garrigue or by open low systems such as phrygana (low scrub

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vegetation). The forests are restricted, and only relict patches of the indigenous coverremain.

Also today, inadequate land use practices (excessive grazing, fuelwood collection,uncontrolled fires) are the fundamental cause of further land degradation. Socio-economic impacts trigger degradation processes by disturbing the complex inter-actions of plant growth, soil formation and erosion processes. Accelerated watererosion can then become one of the most important sources of soil degradation, which,with the destruction of vegetation cover and structure, contributes greatly to thepotential increase of desertification in Mediterranean Europe. Soil and vegetation areoften connected since the degree of soil degradation in many ways reflects the state ofvegetation that covers and conditions the soil (Perez-Trejo, 1994).

From the conclusion that the most important physical indicators of land degradationin the Mediterranean basin are related to the destruction of soil and vegetation, we findthat remote sensing approaches for monitoring environmental change must mainlyconcentrate on the ability to provide a standardized characterization of surface (i.e.soil) conditions and vegetation cover over time.

Grazing impacts on Crete

The Asteroussia and Psiloriti mountains of Crete (Fig. 1) represent characteristic casesof degradation caused by intensive grazing and fires set by shepherds. Today, thewhole mountain range of Asteroussia, which reaches about 1100 m a.s.l., is dominatedby phrygana. Soil erosion is apparent in many cases, and areas that appear irreversiblydegraded (desertified) are often found. The grazing areas of the Psiloriti Mountains incentral Crete extend into higher altitudes of sub-humid and humid Mediterraneanclimates (1000–2000 m). Here, in areas often covered with matorral or mountainousphrygana, relict kermes oak forests (Quercus coccifera) are still found. But also in thisregion grazing pressure has significantly increased. (Statistical figures for some of themountainous communities show an increase of the total number of sheep and goats bymore than 200% between 1980 and 1990.)

Another element of the increasing environmental impact of grazing results from thebuilding of new access roads (Fig. 2), which are supported by the EuropeanCommunities’ Regional Development and Cohesion Funds. Areas that have alwaysbeen remote are becoming accessible by car. Animals and more food supplies can bebrought up into the mountains. As a result, more humans and their animals are presentin these ecosystems than ever before.

Degradation processes in the European Mediterranean show an enormous spatialvariability. Degraded areas are found near barely affected or even undisturbedecosystems, and both degrading and recovering systems may occur under a largevariety of climatic and physiographic conditions. In most cases we have an incompletepicture of the impact of land degradation processes on vegetation and soil resourceswithin a given period of time. Even where we have field studies, as in the Asteroussiacase (Kasapidis & Tsiourlis, in press), we do not know how findings from field studiesat the patch or slope scale can be extrapolated and upscaled to relatively large areas.We believe that remote sensing systems, and earth observation satellites in particular,can significantly contribute to solving this problem. More than providing the means fora cartographic inventory of degraded (environmentally sensitive) areas, satellite remotesensing is virtually the only data source that permits a repeated monitoring of landdegradation dynamics (Hill & Peter, 1996; Lacaze et al., 1996). But both mapping andmonitoring represent important prerequisites for drafting and implementing develop-ment plans and policy decisions for mitigating desertification and ensuring a sustaineduse of Mediterranean land resources.

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Monitoring land degradation with earth observation satellite systems

Operational earth observation satellites with multispectral sensor systems (e.g.Landsat-MSS and TM, SPOT-HRV) have high spatial (30 or 20 m) and intermediatespectral resolution (six or three bandpasses in the reflective part of the solar spectrum)but have rather large standard revisit intervals (16–26 days). The optical systemsonboard these satellites register that part of the downwelling solar radiation that hasbeen reflected by the earth’s surface and transmitted through the atmosphere. One ofthe most important issues is to select indicators for land degradation processes. Suchindicators must have some general applicability to the Mediterranean as a whole andbe observable with operational (spaceborne) remote sensing systems.

Although remote sensing provides a convenient source of information, the datacollected by these instruments do not directly correspond to the information we need.We must therefore interpret the signal that has interacted with remote objects by theproperties of these remote objects (Verstraete, 1994). Both the development ofsuitable indices and their interpretation in the thematic context of land degradationmonitoring require a conceptual framework that allows us to draw concise conclusionsabout land surface conditions (Hill et al., 1995c). Though these underlying conceptsmight vary as a function of regional ecosystem characteristics (i.e. depending on

Figure 1. The Psiloriti and Asteroussia mountain sites in Crete.

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physiographic conditions such as parent material, aridity, etc.), the results fromdifferent regions should be consistently evaluated on a higher hierarchical level so thatthe system’s susceptibility to further degradation can be assessed by using imagery-derived data and ancillary information layers. Important conclusions will neverthelessdepend on our ability to analyse multi-annual time series. For this reason retrospectivestudies are important for developing approaches for a continuous monitoring ofenvironmental changes (Graetz, 1996).

For our study on Crete, we have acquired a long-term series of Landsat-TM imagesthat cover the time span between 1984 and 1996. All images were acquired between

Figure 2. (a) Grazing in the Asteroussia Mountains at about 900 m a.s.l. and (b) new accessroads cutting into the relict forests of kermes oak (Quercus coccifera).

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late May and early June to ensure that our information was from comparablephenological phases.

Important pre-processing issues

Engineering data on detector sensitivity (i.e. calibration coefficients) allow us toreconvert encoded image grey values into measured radiances, and radiative transfercalculations (e.g. Tanre et al., 1990) can be used to correct for atmospheric effects sothat the surface-reflected radiance is restored from the satellite-measured signal.Dividing this term by the downwelling solar irradiance gives us an important primaryparameter, which is termed ‘bidirectional reflectance’r (Fig. 3). Once these primaryparameters are retrieved, precise geocoding is needed to integrate them and thederived thematic information with existing topographic references.

We further need a suitable scene model that can be used to convert multispectralreflectance into thematic information (Fig. 3). Here the question is not so much toidentify a particular surface type, but to characterize it. A variety of methods have beenproposed, ranging from empirical spectral indices to the design and inversion ofphysically-based models. While the applicability of the approaches depends on thenature and accuracy of the desired information and the availability of resources (i.e.sensor characteristics), an important prerequisite for their operational use is that theymust satisfy requirements of standardization and portability (Hill et al., 1995c).

In comparison to our previous work (Lacaze et al., 1996) we have introduced twomore steps that significantly improve our ability to use the satellite data for thematicissues. First, we have complemented the geometric rectification through a module thatcan compensate for the relief-induced image distortions, i.e. the location differencesbetween images and orthoprojected map documents are efficiently corrected (Fig. 4)(Hostert et al., 1997). Additionally, the correction of atmospheric effects has beenextended to include the compensation for illumination differences caused by terrainmorphology. The method has been described in more detail elsewhere (Hill et al.,1995b), and it may here be sufficient to show its efficiency with regard to a subset fromthe Asteroussia site (Fig. 5).

Satellite Data

Primary Parameters(e.g. ρ, albedo)

Thematic Indices

Radio/GeometricRectification

Thematic Concepts

Information Extraction

'Scene Model'

'Atmosphere/Sensor Model'

pre-processing

scene analysis

Figure 3. Conversion of satellite raw data into standardized thematic information layers.

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From field information to GIS support

Our objective has always been to use field reference data to optimize the concepts foranalysing satellite data and to provide a sound validation of concepts and results forfindings from the field. In the Asteroussia Mountains, where the main vegetation typeis phrygana, detailed vegetation surveys have already been carried out and will becontinued throughout the project (Kasapidis & Tsiourlis, in press).

Ten sampling stations were selected along three axes in the eastern, central andwestern parts of the mountain range. At each station 10 random transects (50 mlength) were selected along which plant species, plant height, the projection of canopy

Figure 4. The geometric rectification is based on a set of reliable ground control points at sealevel. In combination with the correction of relief-induced distortions through the use of digitalelevation data (see Fig. 1), we produce ortho-images that rarely exceed pixel location errors of10–15 m.

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on the transect, and maximum length and width were recorded. Species diversity wascalculated using the Shannon-Wiener index (Kasapidis & Tsiourlis, in press).

The dominant species in the study area is Sarcopoterium spinosum (present at allstations and making up 2 to 96% of the vegetation). This species develops strongdynamics in post-fire succession and in colonizing abandoned fields. For these reasonsand its low level of palatability, this type of phrygana is the most abundant species inthe excessively grazed areas of Asteroussia. Accessory species are Thymus capitatus,Calycotome villosa, Phlomis spp., Salvia fruticosa, Cistus creticus and Genista acantholada.By combining all data, the stations could be separated into four classes by plant cover

Figure 5. (a) Atmospherically corrected only and (b) atmospherically and illuminationcorrected image subset from the Asteroussia study site on Crete (Landsat ThematicMapper).

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and species diversity: (1) Phrygana with high plant cover ( > 45%) and relatively highspecies diversity ( > 0.4); (2) Phrygana with moderate plant cover (30–45%) andmoderate to high diversity ( > 0.4); (3) Phrygana with high plant cover ( > 45%) butlow diversity ( < 0.4); and (4) Phrygana with moderate to low plant cover ( < 45%) andlow species diversity ( < 0.4).

These four categories, according to plant cover and species diversity, represent asequence of grazed but still well-developed phrygana communities to overgrazed andbadly deteriorated ones. The results conform to other Greek area studies that have alsoshown that grazing reduces plant cover and species diversity (Arianoutsou-Faragitaki,1985; Tsiourlis, 1988; Pantis & Mardiris, 1992). Meanwhile a network of 20 morestations has been set up in the Psiloriti Mountains. These stations include higheraltitude ecosystems (pine forest and kermes oak matorral).

Results so far clearly show that proportional cover is a prime indicator for thedegradational stage of plant communities. This is an important finding because coveris one of the most compatible parameters with remote sensing (Graetz, 1990).However, sparse vegetation also transmits the colour of the soil beneath, which meansthat the soil surface itself should be as much an object of attention as is the vegetation(Graetz, 1990; Hill et al., 1995a).

The key issue is how to accurately estimate green vegetation abundance without thebias of the spectral contribution of background components (i.e. soils and rockoutcrops). Methodological procedures for deriving vegetation structural parametersfrom spectral variables range from empirical approaches to the inversion of physically-based models (see Lacaze, 1996; Pinty et al., 1996). The difficult problem ofestimating vegetation cover of sparse canopies has been addressed by alternativemethods based on spectral mixture analysis (SMA). These methods have proven to beuseful (e.g. Smith et al., 1990, 1994; Gong et al., 1994; Lacaze et al., 1996) and aredescribed in more detail later.

We need information on the spectral reflectance characteristics of both plant and soilmaterials. This type of information is being acquired in the context of the DeMon-2project on Crete (Fig. 6). The huge amount of spectra collected in the field (and

Figure 6. An ASD Fieldspec II spectoradiometer set up for measuring the spectralcharacteristics of plant species in the Psiloriti Mountains of Crete.

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partially in the laboratory) will be organized in a spectral library database, supportedby the DeMon-2 project team from the European Commission’s Joint ResearchCentre at Ispra, Italy (EMAP unit). In the context of a dedicated geographicinformation system prepared at the University of Trier we can then relate thesemeasurements to specific field locations. More information on the spatial variability ofvegetation communities, bedrock lithology and soils can then be used in an advancedanalysis of the images (Fig. 7). Specific soil parameters will be provided from theproject partners at the University of Utrecht, The Netherlands.

Standardized approaches for mapping surface characteristics

The most important issue in satellite remote sensing is how to conceptualize andstreamline the data analysis so that we can obtain consistent indicators for landdegradation. This issues implies the developing of standardized processing schemes(e.g. Hill et al., 1995c; Pickup & Chewings, 1996) that can be efficiently applied to avariety of landscape types in the Mediterranean basin.

Figure 7. Detail from the Geological Map 1:50,000, covering the Kofinas (1230 m a.s.l.) areain the Asteroussia Mountains, Crete, with spectral measurements from various samplinglocations (spectra collected with the ASD Fieldspec II instrument of the University of Trier).

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The spectral mixing paradigm — a conceptual framework for analysing vegetation and soilconditions

From our conceptual considerations we need information extraction methods that givelargely unbiased estimates for green vegetation cover, permit the identifying of soil-related spectral information and allow enough standardization for multi-temporalmonitoring. Traditional multispectral classification approaches and most vegetationindices are not ideally suited to fulfil these requirements (Hill et al., 1995c). Since theinversion of physically-based directional reflectance models against satellite data is notreally feasible with currently available data sets, we wish to draw the attention tosuitable semi-empirical models.

One of the most promising approaches has become known as spectral mixtureanalysis (SMA) (e.g. Adams et al., 1989; Smith et al., 1990). SMA assumes that mostof the spectral variation in multispectral images is caused by mixtures of a limitednumber of surface materials, and it attempts to model the multispectral reflectance ras a mixture of representative ‘prototype’ spectra, the so-called spectral endmembers(i.e. vegetation, soil and bedrock components, shade as an illumination component,etc.). Linear mixing within the footprint of a multichannel spectrometer or imagingsensor further assumes that the surface components are large or opaque enough toallow photons to interact with only one component, i.e. the radiative transfer processesare additive. Compositive spectra can then be unmixed by inverting the linear mixingequation:

Ri = Σj = 1

n

Fj · REij + εi Σj = 1

n

Fj = 1(Eqn 1)

through a least-squares regression, while constraining the sum of fractions (i.e. theproportional weights) to one. Ri denotes the reflectance of the mixed spectrum in bandi; REij is the reflectance of the endmember spectrum j in band i; Fj is the fraction ofendmember j; and εi is the residual error in band i. A unique solution is possible as longas the number of spectral components (endmembers) does not exceed the number ofbands plus one.

The mixing paradigm provides a type of image enhancement that is not only intensebut also physically meaningful (Craig, 1994). Its objective is to isolate the spectralcontributions of important surface materials (endmember abundance) before these areedited and recombined to produce thematic maps (Adams et al., 1989).

An essential part of SMA consists of identifying optimized spectral endmember setsthat are representative of the major variations of regional soil and vegetationconditions. Though endmembers can be retrieved from the image itself, we prefer tochoose them from collections of spectroradiometric field and/or laboratory measure-ments, the so-called spectral libraries (see previous section of this paper: From fieldinformation to GIS support).

Hill et al. (1995c) have successfully adopted the spectral mixing paradigm to analysethe spectral information content related to the erosional state of soils (Fig. 8) and toderive precise maps of soil conditions and improved estimates of green vegetationabundance from various types of multispectral images (see also Hill, 1993). Whenapplying this approach to multispectral images (e.g. Landsat-TM), we require that thedata already be corrected for atmospheric effects (see previous section of this paper:Important pre-processing issues). In this case the same reference endmembers as in thelaboratory can be used, providing a fully standardized analysis.

Given the increased robustness against soil colour differences and its ability topartially compensate for effects related to illumination and shade, we found that SMAcan also give better estimates of vegetation abundance than conventional vegetation

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indices (Lacaze et al., 1996). First experiments with Landsat Thematic Mapper imagesfrom our study site show that these findings also seem to hold for the study sites inCrete (Fig. 9).

Monitoring capabilities

An essential requirement for the general applicability of remote sensing-basedapproaches is that suitable processing chains can be transferred and applied tolocations with a wider range of climatic and lithologic conditions without changing toomany of the processing parameters. Because spectral unmixing approaches correspondalmost perfectly to these requirements, we expect to provide in the DeMon-2 projecta consistent retrospective analysis of vegetation dynamics throughout the last 200years. To cover this time span, which extends to periods before the Landsat ThematicMapper system became available, requires incorporating other satellite systems intothe analysis. In particular, we thought of using MSS images of Europe since 1976. Thechallenge we face in this aspect is to cope with the slightly different spectral (fourreflective spectral bands instead of six from the Thematic Mapper) and spatialresolution (80 m pixel size instead of 30 m) of this system. We expect that spectral

Soils

0

25

50

75

100

100

75

50

25

0

1000 25 50 75Marls Limestone

Soil conditionsIIIIIIIVaIVb

GER-SIRIS measurements,sampled to Landsat-TM spectral bands

Figure 8. Ternary plot of about 100 field and lab measured soil spectra from a study site insouthern France at Landsat-TM spectral resolution, where the mixing volume is defined by onesoil (vertic cambisol) and two rock spectra (marls and limestone). Soil conditions range fromundisturbed soils (I) to substrates that are increasingly affected by erosion (II–IV) (Hill et al.,1995c).

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unmixing is to be considered one of the few approaches that might producecomparable vegetation abundance estimates for the two systems.

Conclusions

In the context of the DeMon-2 project we wish to emphasize the importance of aretrospective analysis of earth observation satellite images that have been available foralmost 20 years. The integrated interpretation of satellite-derived information layers,available climatic records and results from detailed field studies is expected to give anew perspective for understanding land degradation processes in the EuropeanMediterranean.

Long-term retrospective monitoring of environmental change during the past 20years appears feasible by integrating earth observation satellite images from theLandsat Thematic Mapper (available since 1984) and Landsat-MSS systems (since1976). Although these systems have slightly different technical characteristics, we hopeto provide consistent indicators based on thorough preprocessing strategies (highprecision geometric and radiometric corrections) and the use of dedicated models fordata interpretation.

The actual data analysis employs linear spectral mixture analysis as a core element.SMA provides an intense but also physically meaningful image enhancement thatideally complements the conceptual framework we have introduced. Selective editingof the resulting fractional images permits an efficient separation of vegetation- and soil-related spectral information. This separation of information is an important advantagefor obtaining more objective estimates of green vegetation abundance and for mappingsubstrate-related spectral soil properties more independently from the disturbinginfluence of sparse vegetation cover or soil moisture and illumination differences. Thismethod further holds the potential to be largely standardized for required processingparameters. The inputs from individual analysts can be minimized, which is anadvantage for designing operational processing schemes.

Clearly, such an approach needs support and validation data from related fieldstudies. We are fortunate that such records will be available for both study sites in

Figure 9. Vegetation abundance (per cent cover) estimate for a section of the AsteroussiaMountains in southern Crete, derived from Landsat-5 Thematic Mapper data by inverting aspectral mixing model incorporating vegetation, soil (lithic leptosol), bedrock (limestone) andshade endmembers. Grey values correspond to cover proportions ranging from 0 (black) to 50%(white).

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Crete. By combining field studies and the interpretation of satellite images we candescribe more precisely the dynamics of ecosystems with respect to managementpractices and gain a more precise understanding of desertification processes and theirdynamics in the Mediterranean. Such an understanding is a prerequisite for draftingand implementing efficient mitigation and land management strategies.

The DeMon-2 project includes teams from the University of Trier (Remote Sensing Dept.,project coordination) in Germany, the CNRS (Centre d’Ecologie Fonctionnelle et Evolutive) inMontpellier, France, the University of Utrecht (Institute for Geographical Research) in TheNetherlands, the University of Valencia (Dept. of Thermodynamics) in Spain, the EuropeanCommission’s Joint Research Centre (Space Applications Institute) in Ispra, Italy, and theUniversity of Crete (Biology Department) in Iraklion.

The project is carried out with financial support of the European Commission, DirectorateGeneral XII for Science, Research and Development in the ENVIRONMENT & CLIMATEProgramme (ENV4-CT95-0166). This support is gratefully acknowledged.

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