Observation and longterm monitoring of Mediterranean ecosystems with satellite remote sensing and GIS
Identifieur interne : 001C39 ( Istex/Corpus ); précédent : 001C38; suivant : 001C40Observation and longterm monitoring of Mediterranean ecosystems with satellite remote sensing and GIS
Auteurs : J. Hill ; P. Hostert ; A. RderSource :
- Management of Environmental Quality: An International Journal [ 1477-7835 ] ; 2003-03-01.
Abstract
The importance of thoroughly monitoring the state of the environment in Mediterranean ecosystems has long been recognised. With regard to the spatial extension of large areas threatened by various degradation processes it becomes obvious that terrestrial observation alone is hardly able to cope with this task. Remote sensing with air or spaceborne sensor systems provides a comprehensive spatial coverage, is intrinsically synoptic, and collects objective, repetitive data and is thus ideally suited for monitoring environmentally sensitive areas. The major problem associated with its use is to quantitatively interpret a measured signal that has interacted with remote objects in terms of the properties of these objects. In parallel to the advances in remote sensing geographical information systems GIS have emerged as a fully functional support for resource management tasks. As an example for tracing and analysing environmental change with coupled remote sensing and GIS approaches we present a case study on the island of Crete which was carried out in the framework of research programmes supported by the European Union. Although it is known that grazing in Crete dramatically increased during the last two decades, it was not well understood how grazing pressure differs spatially and in how far it altered the landscape of Crete. One of the major rangeland areas of central Crete, the Psiloritis Mountains, have been selected to serve as a test site for answering these questions. On the basis of an extended LandsatTM and MSS data set acquired between 1977 and 1996 it has been shown that time series analysis techniques based on vegetation fractions derived from spectral unmixing can substantiate a spatiotemporal interpretation of degradation processes. In areas under massive grazing pressure such processes can be linked to the respective driving forces by GISbased analyses of natural and socioeconomic boundary conditions.
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DOI: 10.1108/14777830310460388
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With regard to the spatial extension of large areas threatened by various degradation processes it becomes obvious that terrestrial observation alone is hardly able to cope with this task. Remote sensing with air or spaceborne sensor systems provides a comprehensive spatial coverage, is intrinsically synoptic, and collects objective, repetitive data and is thus ideally suited for monitoring environmentally sensitive areas. The major problem associated with its use is to quantitatively interpret a measured signal that has interacted with remote objects in terms of the properties of these objects. In parallel to the advances in remote sensing geographical information systems GIS have emerged as a fully functional support for resource management tasks. As an example for tracing and analysing environmental change with coupled remote sensing and GIS approaches we present a case study on the island of Crete which was carried out in the framework of research programmes supported by the European Union. Although it is known that grazing in Crete dramatically increased during the last two decades, it was not well understood how grazing pressure differs spatially and in how far it altered the landscape of Crete. One of the major rangeland areas of central Crete, the Psiloritis Mountains, have been selected to serve as a test site for answering these questions. On the basis of an extended LandsatTM and MSS data set acquired between 1977 and 1996 it has been shown that time series analysis techniques based on vegetation fractions derived from spectral unmixing can substantiate a spatiotemporal interpretation of degradation processes. In areas under massive grazing pressure such processes can be linked to the respective driving forces by GISbased analyses of natural and socioeconomic boundary conditions.</div></front></TEI><istex><corpusName>emerald</corpusName><author><json:item><name>J. Hill</name><affiliations><json:string>Remote Sensing Department, University of Trier, Trier, Germany</json:string></affiliations></json:item><json:item><name>P. Hostert</name><affiliations><json:string>Remote Sensing Department, University of Trier, Trier, Germany</json:string></affiliations></json:item><json:item><name>A. Rder</name><affiliations><json:string>Remote Sensing Department, University of Trier, Trier, Germany</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>Geographical information systems</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Environment</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Greece</value></json:item></subject><language><json:string>eng</json:string></language><originalGenre><json:string>e-technical-paper</json:string></originalGenre><abstract>The importance of thoroughly monitoring the state of the environment in Mediterranean ecosystems has long been recognised. With regard to the spatial extension of large areas threatened by various degradation processes it becomes obvious that terrestrial observation alone is hardly able to cope with this task. Remote sensing with air or spaceborne sensor systems provides a comprehensive spatial coverage, is intrinsically synoptic, and collects objective, repetitive data and is thus ideally suited for monitoring environmentally sensitive areas. The major problem associated with its use is to quantitatively interpret a measured signal that has interacted with remote objects in terms of the properties of these objects. In parallel to the advances in remote sensing geographical information systems GIS have emerged as a fully functional support for resource management tasks. As an example for tracing and analysing environmental change with coupled remote sensing and GIS approaches we present a case study on the island of Crete which was carried out in the framework of research programmes supported by the European Union. Although it is known that grazing in Crete dramatically increased during the last two decades, it was not well understood how grazing pressure differs spatially and in how far it altered the landscape of Crete. One of the major rangeland areas of central Crete, the Psiloritis Mountains, have been selected to serve as a test site for answering these questions. On the basis of an extended LandsatTM and MSS data set acquired between 1977 and 1996 it has been shown that time series analysis techniques based on vegetation fractions derived from spectral unmixing can substantiate a spatiotemporal interpretation of degradation processes. 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With regard to the spatial extension of large areas threatened by various degradation processes it becomes obvious that terrestrial observation alone is hardly able to cope with this task. Remote sensing with air or spaceborne sensor systems provides a comprehensive spatial coverage, is intrinsically synoptic, and collects objective, repetitive data and is thus ideally suited for monitoring environmentally sensitive areas. The major problem associated with its use is to quantitatively interpret a measured signal that has interacted with remote objects in terms of the properties of these objects. In parallel to the advances in remote sensing geographical information systems GIS have emerged as a fully functional support for resource management tasks. As an example for tracing and analysing environmental change with coupled remote sensing and GIS approaches we present a case study on the island of Crete which was carried out in the framework of research programmes supported by the European Union. Although it is known that grazing in Crete dramatically increased during the last two decades, it was not well understood how grazing pressure differs spatially and in how far it altered the landscape of Crete. One of the major rangeland areas of central Crete, the Psiloritis Mountains, have been selected to serve as a test site for answering these questions. On the basis of an extended LandsatTM and MSS data set acquired between 1977 and 1996 it has been shown that time series analysis techniques based on vegetation fractions derived from spectral unmixing can substantiate a spatiotemporal interpretation of degradation processes. In areas under massive grazing pressure such processes can be linked to the respective driving forces by GISbased analyses of natural and socioeconomic boundary conditions.</p></abstract><textClass><keywords scheme="keyword"><list><head>keywords</head><item><term>Geographical information systems</term></item><item><term>Environment</term></item><item><term>Greece</term></item></list></keywords></textClass><textClass><keywords scheme="Emerald Subject Group"><list><label>cat-PPEM</label><item><term>Public policy & environmental management</term></item><label>cat-EISS</label><item><term>Environmental issues</term></item><label>cat-ENVM</label><item><term>Environmental management</term></item><label>cat-EHLT</label><item><term>Environmental health</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="2003-03-01">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/document/44A81780E4093F19A0F7F2E189F8F5FE81DA1340/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="corpus emerald not found" wicri:toSee="no header"><istex:xmlDeclaration>version="1.0" encoding="UTF-8"</istex:xmlDeclaration><istex:document><!-- Auto generated NISO JATS XML created by Atypon out of MCB DTD source files. Do Not Edit! --><article dtd-version="1.0" xml:lang="en" article-type="e-technical-paper"><front><journal-meta><journal-id journal-id-type="publisher-id">meq</journal-id><journal-id journal-id-type="doi">10.1108/meq</journal-id><journal-title-group><journal-title>Management of Environmental Quality: An International Journal</journal-title></journal-title-group><issn pub-type="ppub">1477-7835</issn><publisher><publisher-name>MCB UP Ltd</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.1108/14777830310460388</article-id><article-id pub-id-type="original-pdf">0830140104.pdf</article-id><article-id pub-id-type="filename">0830140104</article-id><article-categories><subj-group subj-group-type="type-of-publication"><compound-subject><compound-subject-part content-type="code">e-technical-paper</compound-subject-part><compound-subject-part content-type="label">Technical paper</compound-subject-part></compound-subject></subj-group><subj-group subj-group-type="subject"><compound-subject><compound-subject-part content-type="code">cat-PPEM</compound-subject-part><compound-subject-part content-type="label">Public policy & environmental management</compound-subject-part></compound-subject><subj-group><compound-subject><compound-subject-part content-type="code">cat-EISS</compound-subject-part><compound-subject-part content-type="label">Environmental issues</compound-subject-part></compound-subject><subj-group><compound-subject><compound-subject-part content-type="code">cat-EHLT</compound-subject-part><compound-subject-part content-type="label">Environmental health</compound-subject-part></compound-subject></subj-group></subj-group><subj-group><compound-subject><compound-subject-part content-type="code">cat-ENVM</compound-subject-part><compound-subject-part content-type="label">Environmental management</compound-subject-part></compound-subject></subj-group></subj-group></article-categories><title-group><article-title>Observation and long‐term monitoring of Mediterranean ecosystems with satellite remote sensing and GIS</article-title></title-group><contrib-group><contrib contrib-type="author"><string-name><given-names>J.</given-names> <surname>Hill</surname></string-name><aff>Remote Sensing Department, University of Trier, Trier, Germany</aff></contrib><x></x><contrib contrib-type="author"><string-name><given-names>P.</given-names> <surname>Hostert</surname></string-name><aff>Remote Sensing Department, University of Trier, Trier, Germany</aff></contrib><x></x><contrib contrib-type="author"><string-name><given-names>A.</given-names> <surname>Röder</surname></string-name><aff>Remote Sensing Department, University of Trier, Trier, Germany</aff></contrib></contrib-group><pub-date pub-type="ppub"><day>01</day><month>03</month><year>2003</year></pub-date><volume>14</volume><issue>1</issue><fpage>51</fpage><lpage>68</lpage><permissions><copyright-statement>© MCB UP Limited</copyright-statement><copyright-year>2003</copyright-year><license license-type="publisher"><license-p></license-p></license></permissions><self-uri content-type="pdf" xlink:href="14777830310460388.pdf"></self-uri><abstract><p>The importance of thoroughly monitoring the state of the environment in Mediterranean ecosystems has long been recognised. With regard to the spatial extension of large areas threatened by various degradation processes it becomes obvious that terrestrial observation alone is hardly able to cope with this task. Remote sensing with air‐ or spaceborne sensor systems provides a comprehensive spatial coverage, is intrinsically synoptic, and collects objective, repetitive data and is thus ideally suited for monitoring environmentally sensitive areas. The major problem associated with its use is to quantitatively interpret a measured signal that has interacted with remote objects in terms of the properties of these objects. In parallel to the advances in remote sensing geographical information systems (GIS) have emerged as a fully functional support for resource management tasks. As an example for tracing and analysing environmental change with coupled remote sensing and GIS approaches we present a case study on the island of Crete which was carried out in the framework of research programmes supported by the European Union. Although it is known that grazing in Crete dramatically increased during the last two decades, it was not well understood how grazing pressure differs spatially and in how far it altered the landscape of Crete. One of the major rangeland areas of central Crete, the Psiloritis Mountains, have been selected to serve as a test site for answering these questions. On the basis of an extended Landsat‐TM and ‐MSS data set acquired between 1977 and 1996 it has been shown that time series analysis techniques based on vegetation fractions derived from spectral unmixing can substantiate a spatio‐temporal interpretation of degradation processes. In areas under massive grazing pressure such processes can be linked to the respective driving forces by GIS‐based analyses of natural and socio‐economic boundary conditions.</p></abstract><kwd-group><kwd>Geographical information systems</kwd><x>, </x><kwd>Environment</kwd><x>, </x><kwd>Greece</kwd></kwd-group><custom-meta-group><custom-meta><meta-name>peer-reviewed</meta-name><meta-value>no</meta-value></custom-meta><custom-meta><meta-name>academic-content</meta-name><meta-value>yes</meta-value></custom-meta><custom-meta><meta-name>rightslink</meta-name><meta-value>included</meta-value></custom-meta></custom-meta-group></article-meta><ack><p>The Crete case study has been carried out with financial support of the Commission of the European Communities, Directorate General XII for Science, Research and Development, in the frame of the Environment and Climate Programme (1994‐1998) under contract no. ENV4‐CT95‐0166 (DG 12 – DTEE): DeMon‐II – An Integrated Approach to Assess and Monitor Desertification Processes in the Mediterranean Basin. This support is gratefully acknowledged.</p></ack></front><body><sec><title>Introduction</title><p>Land degradation processes that imply a reduction of the potential productivity of the land (e.g. soil degradation and accelerated erosion, reduction of the quantity and diversity of natural vegetation) are widely spread in the Mediterranean basin. As a continuation of the long history of human pressure upon land resources, the main environmental impact originates from interactions between climatic characteristics and ecologically unbalanced human interventions. An overview of the ecological, physical, social, economic and cultural issues that are collectively contributing to the increasing risk of further degradation of Mediterranean lands has been presented by <xref ref-type="bibr" rid="b28">Perez‐Trejo (1994)</xref>. However, the extent and spatial distribution of environmentally endangered ecosystems in the Mediterranean basin is only roughly known, and the knowledge on the current land degradation status or the magnitude of the potential hazard is for the most part incomplete and fragmented, or may even be entirely lacking (<xref ref-type="bibr" rid="b4">Boer, 1999</xref>). This makes it extremely difficult to design and implement mitigation, reclamation and prevention measures.</p><p>The productivity of dry Mediterranean ecosystems largely depends on surface properties which, as they control water availability, the spontaneous emergence and development of new plants and dust production during wind storms, frequently dominate climatic variables (e.g. <xref ref-type="bibr" rid="b19">Hill, 2000</xref>). It emerges from this conceptual framework that surface properties should be considered more indicative than long‐term climatic conditions. Evidently, the ability to draw concise conclusions with respect to land resources and environmental change depends on the capability to assess specific surface characteristics (i.e. vegetation cover, structure and composition, properties of parent material and soils, including effects of mineralogical and biologic crusting), a task which, for many aspects, can be efficiently achieved with satellite remote sensing. The advantages result from its synoptic nature, comprehensive spatial information and objective, repetitive coverage. While remote sensing has initially been primarily used for resource mapping and inventory, it turns out that retrospective analysis, monitoring and predictive modelling is becoming increasingly important. Because of the availability of long data records and methodological advances, remote sensing with earth observation satellites has in fact become an irreplaceable component of resource assessments and monitoring strategies for observing and quantifying environmental change.</p><p>Growing consensus is emerging that the coupling between remote sensing and geographic information systems is so strong that the potential contribution of each cannot be realized without continued and, finally, complete integration of the two endeavours (<xref ref-type="bibr" rid="b6">Ehlers <italic>et al.</italic>, 1993</xref>; <xref ref-type="bibr" rid="b10">Estes and Star, 1997</xref>; <xref ref-type="bibr" rid="b11">Franklin, 2001</xref>). In fact, the reality today is that almost every remote sensing image or image product will reside and find application at some point of its lifetime in a GIS environment. Although there is no doubt that updating a GIS with remote sensing information continues to be an important and complex application area, it is now also understood that GIS and remote sensing integration goes both ways: many remote sensing scientists have recognised that many image analysis tasks can be improved with access to other spatial data (<xref ref-type="bibr" rid="b11">Franklin, 2001</xref>). Finally, the complementarity between GIS and remote sensing is increasing our capabilities for many types of environmental modelling and integrated analysis approaches (e.g. <xref ref-type="bibr" rid="b37">Wilkinson, 1996</xref>)</p></sec><sec><title>Instrumental opportunities and methodological issues</title><p>When compared to the early 1970s when the first Landsat system was placed into a space orbit remote sensing systems meanwhile exhibit an enormous diversity in terms of spectral, spatial and temporal parameters (<xref ref-type="bibr" rid="b23">Kramer, 1996</xref>), and it depends on the user requirements which system is to be used. If a frequent repetitive coverage with relatively low spatial resolution is desired (e.g. for meteorology) one would certainly be inclined to base the approach on the AVHRR system available from the polar‐orbiting satellites of the NOAA series, SPOT VEGETATION, polar‐orbiting platforms such as EOS‐1 Terra (e.g. MODIS) or the European ENVISAT (MERIS), or even on data from geostationary satellites (METEOSAT or GOES) Alternatively, one might look for the highest spatial and spectral resolution available, even at the expense of relatively low repetition rates, and would thus choose one of the available earth observation satellite systems (Landsat, SPOT, IRS, IKONOS).</p><p>However, although it is agreed that remote sensing provides a convenient source of information, the problem is that the data collected by these instruments do not directly correspond to the information we need. We must therefore interpret the signal which has interacted with remote objects in terms of the properties of these remote objects (<xref ref-type="bibr" rid="b36">Verstraete, 1994</xref>). Depending on the scientific perspectives, quite some variety of approaches and strategies has been proposed for assessing environmental characteristics of arid, semi‐arid and sub‐humid ecosystems based on remote sensing (see, for example, <xref ref-type="bibr" rid="b17">Hill and Peter, 1996</xref>). The different perspectives expressed therein are not only emphasising methodological preferences but, more fundamentally, also reflect important paradigms of scientific disciplines.</p><sec><title>Remotely‐sensed primary parameters, thematic models and derived indices</title><p>Before discussing appropriate scene models one needs to understand that the initial part of the processing chain (i.e. data pre‐processing) deals with the geometric rectification of digital imagery and with turning uncalibrated image grey values into physical quantities. Engineering data about the detector sensitivity (i.e. calibration coefficients) permit to reconvert encoded DN into measured radiance, and radiative transfer calculations can be used to correct for atmospheric effects, such that the surface‐reflected radiance is restored from the satellite‐measured signal. Dividing this term by the downwelling solar irradiance provides us with an important primary parameter which is termed bi‐directional reflectance (<xref ref-type="fig" rid="F_0830140104004">Figure 1</xref>); albedo and surface temperature are other primary parameters which, as a result of similar processing chains, can be derived from optical remote sensing systems. Here, not much emphasis will be given to the radiometric processing of satellite images nor to geometric rectification issues because both involve routine operations which are described elsewhere (e.g. <xref ref-type="bibr" rid="b15">Hill, 1993</xref>a; <xref ref-type="bibr" rid="b16">Hill <italic>et al.</italic>, 1995</xref>).</p><p>In order to assess land resources or land degradation processes it is necessary to define diagnostic indicators. These may be primary indicators (e.g. high salt content in salinised soils) or secondary indicators which are produced by the problem (e.g. reduced vigour of vegetation). The most useful indicators are those which have distinct spectral signatures and are unique to a particular issue (<xref ref-type="bibr" rid="b22">Johnston and Barson, 1990</xref>). Although of relevance to the global radiation budget, albedo and reflectance changes <italic>per se</italic> are not direct indicators of land degradation processes, in particular when we consider spatially complex areas like the European Mediterranean. We therefore need to infer the environmental impact of altered reflectance properties by characterising their physical nature in terms of land surface conditions, which requires appropriate scene model that can be used to convert multi‐spectral reflectance into thematic information (<xref ref-type="fig" rid="F_0830140104004">Figure 1</xref>). A variety of methods have been proposed which range from empirical spectral indices to the design and inversion of physically‐based models. While the applicability of the various approaches depends on the nature and accuracy of the desired information and the availability of resources (i.e. sensor characteristics), an important prerequisite for their operational use is that they must also satisfy specific requirements in terms of standardisation and portability.</p><p>Both the development of suitable indices and their interpretation in the thematic context of land degradation monitoring requires a conceptual framework which allows to draw concise conclusions about the land surface conditions. Though these underlying concepts might vary as a function of regional ecosystem characteristics (i.e. depending on physiographic conditions such as parent material, aridity, etc.), we request that the results from different regions can be consistently evaluated on a higher hierarchical level, such that the system's susceptibility to further degradation can be assessed by using image‐derived, but also ancillary, information layers. Important conclusions will nevertheless depend on the capability to analyse multi‐annual time series, and it is for this reason that retrospective studies are so important with respect to developing approaches for a continuous monitoring of environmental changes (<xref ref-type="bibr" rid="b13">Graetz, 1996</xref>).</p></sec><sec><title>A conceptual and methodological framework for the analysis of vegetation and soil conditions</title><p>Scene models (<xref ref-type="fig" rid="F_0830140104004">Figure 1</xref>) involve thematic concepts which provide the rational for translating remote measurements of primary parameters (e.g. <italic>ρ</italic>) into relevant thematic information about vegetation and soil conditions, and information extraction techniques that can be used to support the conceptual framework. Ideally, both should be valid for any location, but in reality some adaptations might be necessary in order to account for regional variations.</p><p><italic>Conceptual issues</italic>. Eroded soils are often recognised through typical soil colour changes which are due to the removed topsoil. It is nevertheless difficult to define universally applicable concepts which account for a variability of soil types and the corresponding sequence of pedogenetic horizons. An very useful approach refers to basic concepts which consider soil development to be either progressive or regressive with time (<xref ref-type="bibr" rid="b3">Birkeland, 1990</xref>). Under progressive development, soils become better differentiated by horizons, and horizon contrasts become stronger. In contrast, regressive pedogenesis refers to the addition of material to the surface at a rate that suppresses soil formation (i.e. eolian dunes, glacial moraines, distal fans, etc.) or the suppression of pedogenesis and the truncation of soil horizons by surface erosion (<xref ref-type="fig" rid="F_0830140104005">Figure 2</xref>). Both, progressive and regressive pedogenesis cause alterations of the soil surface which, due to corresponding colour changes, are detectable through the wavelength‐dependant variations of <italic>ρ</italic> (e.g. <xref ref-type="bibr" rid="b2">Baumgardner <italic>et al.</italic>, 1985</xref>; <xref ref-type="bibr" rid="b8">Escadafal, 1993</xref>). The intensity of brunification and rubification, and the organic matter content of the topsoil material thus provide important diagnostic features for the spectral identification of a majority of undisturbed Mediterranean soils (e.g. cambisols, fluvisols, luvisols, vertisols, rendzinas). Compared to that, soil erosion produces truncated soil profiles which are characterised by decreasing amounts of iron oxides and organic carbon, while the proportion of parent material increases (e.g. lithosols, regosols). Most parent materials differ spectrally from developed soil substrates, in particular due to specific spectral absorption features and increased albedo levels.</p><p>The resulting concept, which is based on the spectral contrast between developed substrates and parent materials, seems to provide a widely applicable framework for relating spectrally detectable surface phenomena to Mediterranean soil conditions, thereby satisfying an important requirement for the successful application of remote sensing techniques (<xref ref-type="bibr" rid="b16">Hill <italic>et al.</italic>, 1995</xref>). However, the validity of such concepts has to be carefully analysed in the context of the specific physiographic conditions under which they should be applied. Modifications might be required, for example, in cases of extreme aridity where soil forming processes do not permit the accumulation of noticeable amounts of organic components (e.g. <xref ref-type="bibr" rid="b9">Escadafal <italic>et al.</italic>, 1994</xref>).</p><p>Vegetation attributes are usually described by structure, dynamics and taxonomic composition, of which taxonomy is the least important of the three. The classification which is most compatible with remote sensing relates to the projected foliage cover (PFC, or cover) and the life form of the tallest vegetation stratum (<xref ref-type="bibr" rid="b12">Graetz, 1990</xref>). However, about 70 per cent of the earth's surface is covered by sparse vegetation which transmits the colour of the soil beneath, i.e. the projected foliage cover (PFC) is below 1. In particular semi‐arid ecosystems, such as the Mediterranean, are dominated by such vegetation communities. For these, the soil surface itself should be as much an object of attention as is the vegetation (<xref ref-type="bibr" rid="b12">Graetz, 1990</xref>), and the key issue is therefore to provide accurate estimates of green vegetation abundance which are not biased by the spectral contribution of background components (i.e. soils and rock outcrops). Attention should also be given to the spectral characteristics of non‐green components of plant canopies and associated plant litters, which largely contribute to the reflectance of terrestrial surfaces in semi‐arid ecosystems (<xref ref-type="bibr" rid="b7">Elvidge, 1990</xref>). Although we know that the spectral resolution of earth observation satellite systems is not adequate to consistently differentiate between dry plant components and soils, efforts have to be made to resolve ambiguities from the image context (<xref ref-type="bibr" rid="b34">Smith <italic>et al.</italic>, 1990</xref>).</p><p><italic>Advanced data interpretation: the spectral mixing paradigm</italic>. This results from our conceptual considerations that we require information extraction methods which provide largely unbiased estimates for green vegetation cover, equally permit the identification of soil related spectral information, and allow sufficient standardisation for multi‐temporal monitoring. Traditional multi‐spectral classification approaches as well as most vegetation indices are not ideally suited to fulfill these requirements (<xref ref-type="bibr" rid="b16">Hill <italic>et al.</italic>, 1995</xref>). Since the inversion of physically‐based bidirectional reflectance models against satellite data is not easily feasible with currently available data sets, we wish to draw the attention on suitable semi‐empirical models.</p><p>Uncertainties in measuring vegetation abundance with optical sensors can be minimised by accounting for the reflective properties of sometimes highly variable background materials (e.g. <xref ref-type="bibr" rid="b33">Siegal and Goetz, 1977</xref>; <xref ref-type="bibr" rid="b29">Price, 1993</xref>; <xref ref-type="bibr" rid="b16">Hill <italic>et al.</italic>, 1995</xref>). Conversely, natural vegetation can significantly mask and alter the spectral response of the ground. Therefore, instead of attempting to develop separate soil‐ or vegetation‐related indices which are based on spectral reflectance measurements in single or multiple bands it seems more appropriate to use dedicated spectral decomposition techniques prior to developing thematic indices based on multispectral measurements.</p><p>One of the most promising approaches for computing the proportional abundance of materials which occur within a specific surface area (i.e. pixel) is based on computationally decomposing multispectral measurements with reference to a finite number of pure spectral components, i.e. endmembers (<xref ref-type="fig" rid="F_0830140104006">Figure 3</xref>). The method has become known as “spectral mixture analysis” (SMA) (e.g. <xref ref-type="bibr" rid="b1">Adams <italic>et al.</italic>, 1993</xref>; <xref ref-type="bibr" rid="b34">Smith <italic>et al.</italic>, 1990</xref>), and it assumes that most of the spectral variation in multispectral images is due to mixtures of a limited number of surface materials, and that these mixtures can, in first approximation, be described as a result of additive (linear) spectral mixing (i.e. where each photon contacts only type of surface material). Multiple scattering would need to be either accounted for with non‐linear models, or the variables of the linear mixing equation would need to be delinearised by using, for example, single scattering albedo instead of reflectance (<xref ref-type="bibr" rid="b21">Johnson <italic>et al.</italic>, 1983</xref>). The computation of proportional abundances can then always be solved with a system of linear equations. In matrix notation one can write:<xref ref-type="fig" rid="F_0830140104001">Equation 1</xref>where A is the m·n matrix of spectral endmember signatures (usually derived from so‐called spectral libraries, i.e. a suitable collection of laboratory and field measurements), with each column containing one of the endmember spectral vectors. X denotes the n•1 unknown vector of abundances, and B the m•1 observed data vector (i.e. measured reflectance of one pixel). The unknown vector of abundances is computed with:<xref ref-type="fig" rid="F_0830140104002">Equation 2</xref>i.e. essentially by inverting the endmember matrix. Evidently, a unique solution is possible as long as the number of spectral endmembers corresponds to the number of spectral bands. Also, if the problem is underdetermined, i.e. the number of unknown fraction components (i.e. abundances) exceeds the number of useful spectral bands by one, a solution can still be obtained by assuming that the set of endmembers is exhaustive<xref ref-type="fn" rid="fn1">[1]</xref> (i.e. the sum of the computed endmember fractions is equal to 1). However, the typical case encountered in spectral unmixing (in particular when hyperspectral data are used) is that the number of bands is greater than the number of endmember spectra. The linear mixing model then becomes overdetermined such that the endmember matrix can not be inverted. In this case a solution can be obtained through the pseudo‐inverse:<xref ref-type="fig" rid="F_0830140104003">Equation 3</xref>which minimises the mean‐squared error in fitting the abundance estimates to the data, and renders the computation of abundance estimates equivalent to a rotational transform of the image (<xref ref-type="bibr" rid="b32">Schowengerdt, 1997</xref>). The added term <italic>ε</italic> represents the residual error of the model.</p><p>The mixing paradigm provides a type of image enhancement that is not only intense but also physically meaningful. Its objective is of course to isolate the spectral contributions of important surface materials (“endmember abundance”) before these are edited and recombined to produce thematic maps. <xref ref-type="bibr" rid="b16">Hill <italic>et al.</italic> (1995)</xref> have successfully adopted this approach to analyse the spectral information content related to the erosional state of soils, and to derive precise maps of soil conditions and improved estimates of green vegetation abundance from various types of multispectral images (e.g. <xref ref-type="bibr" rid="b16">Hill <italic>et al.</italic>, 1995</xref>). Given the increased robustness against soil colour differences and ist ability to at least partially compensate effects related to illumination and shade it has been shown that Spectral Mixture Modelling can also provide better estimates of vegetation abundance (i.e. proportional cover) than conventional vegetation indices (e.g. <xref ref-type="bibr" rid="b24">Lacaze <italic>et al.</italic>, 1995</xref>). In particular the incorporation of variable background signals through multiple endmember sets has indicated quite promising results (<xref ref-type="bibr" rid="b16">Hill <italic>et al.</italic>, 1995</xref>). Whether, and to which extent these methods can be applied to process and interpret long time series of satellite imagery, was meanwhile illustrated in more case studies.</p></sec></sec><sec><title>The Crete case study</title><p>Crete is a region that, during the past decades, has experienced an increasing pressure on natural resources through excessive grazing activities in mountainous ecosystems, mainly triggered by an increasing demand in animal products. One of the major rangelands of central Crete has been selected to serve as a test site for answering these questions: the Psiloritis Mountains, the main mountain range in the heart of Crete. The Psiloritis range is an extensively grazed area and its history of human influence dates back to Minoan times and earlier. Livestock husbandry can be regarded as the main economic activity and almost all areas not suitable for agriculture are under grazing regimes (<xref ref-type="bibr" rid="b26">Lyrintzis and Papanastasis, 1995</xref>).</p><p>An additional element of the environmental impact related to grazing results from the construction of new access roads which is supported through the Regional Development and Cohesion Fund of the European Union, a funding source which became available after Greece joined the EU in 1981. Areas that traditionally had been too remote became rapidly accessible by vehicle. Animals, together with additional food supplies, could then be transported into the mountainous rangelands, with the result that the presence of man and roaming animals became there more frequent than ever before. It was therefore believed that a retrospective analysis of vegetation dynamics in the mountainous ecosystems of Crete provides one of the few test cases to assess the evidence of grazing‐dependant degradation processes in the Mediterranean (<xref ref-type="bibr" rid="b18">Hill <italic>et al.</italic>, 1998</xref>). While many case studies have shown at plot scale level that there is a strong link between landscape development and human influence, it is often not straightforward to monitor degradation processes that operate on a larger spatial or time scale. One way of overcoming such restrictions is an analysis strategy based on retrospective satellite remote sensing and GIS, as proposed in the frame of DeMon‐I and DeMon‐II (<xref ref-type="bibr" rid="b25">Lacaze <italic>et al.</italic>, 1996</xref>; <xref ref-type="bibr" rid="b18">Hill <italic>et al.</italic> 1998</xref>).</p><p>The Asteroussia and Psiloriti Mountains of Crete (<xref ref-type="fig" rid="F_0830140104007">Figure 4</xref>) are hence known for the fact that the grazing pressure has significantly increased during the past decades, in particular since Greece joined the European Communities. Official statistics indicate that the number of sheep and goats in the townships of the Psiloriti region has grown by more than 150 per cent since 1980 (<xref ref-type="bibr" rid="b38">Zioganas <italic>et al.</italic>, 1998</xref>; <xref ref-type="bibr" rid="b5">Dubost, 1998</xref>), resulting in a significant decrease of woody cover leading to desertification (<xref ref-type="bibr" rid="b27">Lyrintzis <italic>et al.</italic>, 1998</xref>). Notwithstanding the somewhat questionable reliability of the statistical figures, it is beyond any doubt that grazing pressure has been continuously high during the past decades. But, has this really triggered a significant loss of biomass, and, if so, does it affect the complete mountain range or specific areas only?</p><sec><title>Extended time series of earth observation images</title><p>The analysis was based on a set of nine Landsat‐5 TM (1984‐1996) and five Landsat MSS scenes (1977‐1988), all recorded in late spring. Auxiliary data comprised a SPOT‐derived DEM of central Crete and a corresponding orthophoto in UTM coordinates with a geometric resolution of 20m and 10m, respectively. The DEM was employed to derive maps of aspect and slope. A geological map of the same area was digitized from 1:50,000 scale maps. Field based data on vegetation cover and species distribution were provided for several plots and transects. Numerous hyperspectral measurements of soils, rocks and vegetation (photosynthetic and non‐photosynthetic active) were collected during three field campaigns, georeferenced with GPS and stored in a GIS (<xref ref-type="bibr" rid="b20">Hostert, 2001</xref>).</p></sec><sec><title>Methods</title><p>The images were geometrically rectified to the orthophoto taking into account relief displacement on the basis of the corresponding DEM. Elevation induced pixel shifts yielding maximum values of about 250m in mountainous terrain have been corrected to sub‐pixel accuracy. The radiometric correction was based on a modified 5S‐code (<xref ref-type="bibr" rid="b14">Hill and Sturm, 1991</xref>). Variable atmospheric constituents have been estimated from the respective images over water surfaces by making use of a dark target approach (<xref ref-type="bibr" rid="b35">Teillet and Fedosejevs, 1995</xref>). A topographic normalization based on the separation of direct and diffuse light was conducted using the DEM. Moreover, isotrop and anisotrop diffuse light were taken into account for an exact description of topography induced errors in radiometry (<xref ref-type="bibr" rid="b16">Hill <italic>et al.</italic>, 1995</xref>). Due to missing calibration constants for the MSS images, a cross‐calibration approach was introduced, based on synoptically recorded Landsat‐TM and ‐MSS data from 1988 (<xref ref-type="bibr" rid="b31">Röder <italic>et al.</italic>, 2001</xref>).</p><p>SMA was employed to calculate the abundance of green vegetation and the dominant soil and rock surfaces in each pixel (<xref ref-type="fig" rid="F_0830140104008">Figure 5</xref>). To support the definition of suitable endmember models, the geological map and available data on vegetation have been used to stratify the test sites into ecologically and spectrally homogeneous units. A four‐endmember model comprising a Luvisol, limestone (with lichen), photosynthetic active vegetation, and shade was selected based on a Pixel Purity Index. The spectrally lower dimensioned MSS data have been analyzed with a three‐endmember model, where soil and bedrock spectra formed a mixed background endmember.</p><p>By using locally‐optimised sets of endmember spectra it was possible to estimate vegetation cover comparable to the ground‐based mapping of vegetation communities performed by the DeMon‐II project partners from the University of Iraklion. A correspondingly adapted endmember matrix was used for the analysis of the Landsat‐MSS data from earlier years, and the remaining systematic differences between the TM‐ and MSS‐based cover estimates were successfully adjusted based on a transfer function derived from simultaneously acquired TM and MSS images from June 1988.</p><p>The trend analysis of vegetation change could thus be based on a homogeneous data set that encompassed 13 carefully calibrated and processed images, covering a time span from 1976 to 1996. The stacked vegetation fraction images have been analysed with a linear regression on a per‐pixel basis (<xref ref-type="fig" rid="F_0830140104009">Figure 6</xref>). Thus, it was possible to derive not only mean values of vegetation abundances, but also spatially explicit trend functions indicating the development of grazed vegetation during the monitored 20‐year period (<xref ref-type="bibr" rid="b20">Hostert, 2001</xref>).</p></sec><sec><title>Results</title><p>Vegetation development was categorized into changes taking place gradually and those occurring on a much shorter time scale. The latter have mainly been caused by singular events like wildfires, intentional burning or mechanical clearings, for example to create horticultures or olive plantations. Developments over several years or even over two decades may rather be related to processes like grazing and the associated socio‐economic triggers such as changes in infrastructure, subsidies, etc.</p><p>The retrospective assessment of vegetation dynamics has cast some doubt on the general idea that, starting with the EU membership of Greece, most of the mountainous ecosystems in Crete have been entering a phase of accelerated degradation triggered by intensified grazing. Of course, herding has been encouraged by European Community subsidies and, although the official figures seem somewhat inflated, the number of browsing animals has certainly increased (e.g. <xref ref-type="bibr" rid="b30">Rackham and Moody, 1996</xref>). However, based on sufficiently long time series of Earth Observation Satellite imagery, it could here be demonstrated that degradation trends are in fact restricted to specific areas, while extended parts of the highlands in central Crete remained largely unaffected. This appears to be in good agreement with the fact that most of the increasing grazing activities are, and have been, relying on the use of access roads for transporting animals and additional food supplies.</p><p>While the singular disturbances were characterized by mapping their year of occurrence and spatial extension, the long‐term trends have been more closely investigated by combining the direction of the trend with the magnitude and the average level on which it manifests. To support a synoptic interpretation of these information layers, a degradation index has been defined on a scale ranging from −6 to +6, representing different degrees of degradation starting from a significant decrease on a low level of vegetation cover to a significant increase. Extreme levels of degradation were found along the northern plateau of the Psiloritis massif in elevations between 800m and 1,500m.</p><p>A GIS‐based analysis of boundary conditions reveals that vegetation degradation does not occur in areas where stress levels tend to be higher such as on south‐facing slopes or in elevated areas with short vegetation periods. Decreasing vegetation cover rather is typical for sites with good water supply, developed soils and a moderate local climate. Apparently, favorable natural boundary conditions coincide with degrading vegetation levels. On the other hand, these areas correspond very well with the main grazing zones that are centered around a few communities in this part of the Psiloritis Mountain. A comparison of livestock husbandry statistics on community level reveals that about 60 per cent of the animals in Psiloritis are concentrated in four communities. These villages at the same time represent the core area of vegetation degradation processes (<xref ref-type="fig" rid="F_0830140104010">Figure 7</xref>).</p><p>It can be concluded, that the significant increase in the number of grazing animals along with a highly improved accessibility of formerly remote mountainous areas has resulted in an increased grazing pressure, which is locally reflected by a degradation of vegetation. By incorporating village boundaries and available livestock statistics, information on degradation can be related to administrative units, which is mandatory for the definition of concrete management suggestions to support a sustainable utilisation of natural resources for grazing. Moreover, the pattern of decline, increase and stability of vegetation originating from the trend analysis clearly underlines that degradation monitoring needs to be based on a spatially differentiated approach.</p><p>The method is limited to areas where monitored resources differ from resources influenced by overgrazing, e.g. when grazing regimes mainly rely on vegetation under forest cover. In such areas additional sources beyond remote sensing data are needed to meet the terms of the monitoring task. Furthermore, detailed data on animal distributions in the course of the year and information about roaming of stocks beyond administrative boundaries is needed to study the underlying processes in more detail. With such information it will be possible to relate causes of degradation processes and their consequences through integrated models. From this point of view respective analysis turns into a powerful prospective planning tool.</p></sec></sec><sec><title>Conclusions</title><p>The advantages of satellite remote sensing result from its synoptic nature, comprehensive spatial information and objective, repetitive coverage. It has been illustrated how remotely sensed primary parameters, such as the spectral surface reflectance <italic>ρ</italic>, can be converted into a standardised characterisation of soil conditions and vegetation abundance. In this context, the term of thematic concepts has been introduced, by which we understand the conceptual background for identifying functional links between surface reflectance and vegetation and soil characteristics. Such concepts are primarily based on research in geosciences and ecology, and it is important to keep strong links between these disciplines and remote‐sensing specialists. While remote sensing has initially been used primarily for resource mapping and inventory it turns out that monitoring and predictive modelling is becoming more important and successful. However, no matter whether one intends to map surface properties or the objective is to estimate fluxes based on remote sensing data requires that the primary parameters, such as the spectral reflectance or surface temperature, are first retrieved with adequate precision. This raises immediately the issue of adequate radiometric corrections. While atmospheric effects have been a significant factor in the failure of scene models (in particular for monitoring concepts), much progress has been achieved in recent years, and we are now in the position where large parts of the radiometric pre‐processing are considered a routine operation, similar to the geometric rectification. Presently, the remaining problems in retrieving surface reflectance from satellite data appear more related to the absolute radiance calibration of the sensor systems than to methodological drawbacks. This ensures that more advanced scene models, such as the spectral mixing paradigm or invertible physically‐based analytical models, can be used to derive quantitative estimates and improved indicators for land resources and degradation processes.</p><p>Concerning the case studies presented in this paper, a synoptic interpretation of all processed data has allowed to draw first conclusions concerning the dynamic development of selected Mediterranean vegetation communities. The fact that it has been possible to identify a degradational reduction of vegetative cover in specific locations with continued grazing pressure, implies that remote sensing can provide substantial contributions to a more conscious management of precious lands: available resources are limited, and man has already frequently crossed sensible thresholds without taking note in time. It is believed that thorough assessments of available resources, the implementation of adequate management strategies and efficient approaches to monitor and control the state of the environment are core elements on which to build efficient strategies to mitigate land degradation and desertification risks.</p><p>We have shown that both inventory and mapping is required to define the current status of soil and vegetation resources and provide a baseline for monitoring, and that surveys must be repeatable and comparable, and therefore demand a standardised methodological framework. Although it is unrealistic that remote sensing will replace traditional sources of data for inventory and monitoring there is, without any doubt, an obvious role it has to play in assessing and monitoring the state of the environment. It thus forms the basis for drafting and implementing efficient land management plans which are needed to avoid land degradation under inadequate management practises.</p></sec><sec><fig position="float" id="F_0830140104001"><caption><p>Equation 1</p></caption><graphic xlink:href="0830140104001.tif"></graphic></fig></sec><sec><fig position="float" id="F_0830140104002"><caption><p>Equation 2</p></caption><graphic xlink:href="0830140104002.tif"></graphic></fig></sec><sec><fig position="float" id="F_0830140104003"><caption><p>Equation 3</p></caption><graphic xlink:href="0830140104003.tif"></graphic></fig></sec><sec><fig position="float" id="F_0830140104004"><label><bold>Figure 1<x> </x></bold></label><caption><p>The conversion of satellite raw data into standardised thematic information layers</p></caption><graphic xlink:href="0830140104004.tif"></graphic></fig></sec><sec><fig position="float" id="F_0830140104005"><label><bold>Figure 2<x> </x></bold></label><caption><p>The concept of progressive (top) and regressive soil formation (bottom)</p></caption><graphic xlink:href="0830140104005.tif"></graphic></fig></sec><sec><fig position="float" id="F_0830140104006"><label><bold>Figure 3<x> </x></bold></label><caption><p>The concept of spectral unmixing, illustrated using an example of a sensor system with six spectral bands and five endmember materials</p></caption><graphic xlink:href="0830140104006.tif"></graphic></fig></sec><sec><fig position="float" id="F_0830140104007"><label><bold>Figure 4<x> </x></bold></label><caption><p>Rangeland monitoring sites in the mountainous ecosystems of Crete, and the increase of ruminants in the Psiloriti region</p></caption><graphic xlink:href="0830140104007.tif"></graphic></fig></sec><sec><fig position="float" id="F_0830140104008"><label><bold>Figure 5<x> </x></bold></label><caption><p>The spectral unmixing concept (upper left) and the estimated abundances of green vegetation, soil and limestone for one pass of Landsat‐TM over the Psiloriti study region</p></caption><graphic xlink:href="0830140104008.tif"></graphic></fig></sec><sec><fig position="float" id="F_0830140104009"><label><bold>Figure 6<x> </x></bold></label><caption><p>Regression‐based trend analysis of satellite‐derived estimates of proportional vegetative cover for selected reference areas in central Crete</p></caption><graphic xlink:href="0830140104009.tif"></graphic></fig></sec><sec><fig position="float" id="F_0830140104010"><label><bold>Figure 7<x> </x></bold></label><caption><p>GIS‐based identification of the rural communities (Prines, Margarites, Agios Mammas, Kolyros, Anogia) with increasing livestock numbers and decreasing vegetation over time, i.e. negative trend</p></caption><graphic xlink:href="0830140104010.tif"></graphic></fig></sec></body><back><fn-group><title>Note</title><fn id="fn1"><p>This assumption, however, is not unproblematic since it is difficult to be sure that a sufficient number of spectral endmembers has been defined for a 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With regard to the spatial extension of large areas threatened by various degradation processes it becomes obvious that terrestrial observation alone is hardly able to cope with this task. Remote sensing with air or spaceborne sensor systems provides a comprehensive spatial coverage, is intrinsically synoptic, and collects objective, repetitive data and is thus ideally suited for monitoring environmentally sensitive areas. The major problem associated with its use is to quantitatively interpret a measured signal that has interacted with remote objects in terms of the properties of these objects. In parallel to the advances in remote sensing geographical information systems GIS have emerged as a fully functional support for resource management tasks. As an example for tracing and analysing environmental change with coupled remote sensing and GIS approaches we present a case study on the island of Crete which was carried out in the framework of research programmes supported by the European Union. Although it is known that grazing in Crete dramatically increased during the last two decades, it was not well understood how grazing pressure differs spatially and in how far it altered the landscape of Crete. One of the major rangeland areas of central Crete, the Psiloritis Mountains, have been selected to serve as a test site for answering these questions. On the basis of an extended LandsatTM and MSS data set acquired between 1977 and 1996 it has been shown that time series analysis techniques based on vegetation fractions derived from spectral unmixing can substantiate a spatiotemporal interpretation of degradation processes. In areas under massive grazing pressure such processes can be linked to the respective driving forces by GISbased analyses of natural and socioeconomic boundary conditions.</abstract><subject><genre>keywords</genre><topic>Geographical information systems</topic><topic>Environment</topic><topic>Greece</topic></subject><relatedItem type="host"><titleInfo><title>Management of Environmental Quality: An International Journal</title></titleInfo><genre type="journal">journal</genre><subject><genre>Emerald Subject Group</genre><topic authority="SubjectCodesPrimary" authorityURI="cat-PPEM">Public policy & environmental management</topic><topic authority="SubjectCodesSecondary" authorityURI="cat-EISS">Environmental issues</topic><topic authority="SubjectCodesSecondary" authorityURI="cat-ENVM">Environmental management</topic><topic authority="SubjectCodesSecondary" authorityURI="cat-EHLT">Environmental health</topic></subject><identifier type="ISSN">1477-7835</identifier><identifier type="PublisherID">meq</identifier><identifier type="DOI">10.1108/meq</identifier><part><date>2003</date><detail type="volume"><caption>vol.</caption><number>14</number></detail><detail type="issue"><caption>no.</caption><number>1</number></detail><extent unit="pages"><start>51</start><end>68</end></extent></part></relatedItem><identifier type="istex">44A81780E4093F19A0F7F2E189F8F5FE81DA1340</identifier><identifier type="DOI">10.1108/14777830310460388</identifier><identifier type="filenameID">0830140104</identifier><identifier type="original-pdf">0830140104.pdf</identifier><identifier type="href">14777830310460388.pdf</identifier><accessCondition type="use and reproduction" contentType="copyright">© MCB UP Limited</accessCondition><recordInfo><recordContentSource>EMERALD</recordContentSource></recordInfo></mods></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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