The potential of remote sensing for monitoring rural land use changes and their effects on soil conditions
Identifieur interne : 001754 ( Istex/Corpus ); précédent : 001753; suivant : 001755The potential of remote sensing for monitoring rural land use changes and their effects on soil conditions
Auteurs : S. Sommer ; J. Hill ; J. MégierSource :
- Agriculture, Ecosystems and Environment [ 0167-8809 ] ; 1998.
Abstract
The paper reviews the principles of existing remote sensing techniques and new methods considered particularly suitable for monitoring rural land use changes and their effects on soil conditions. Conventional classification methods in combination with local field surveys are operationally used in national as well as in supra-national environmental and agricultural inventories established by the EU such as the European Commission's CORINE programme (Coordination of Information on the Environment) and the MARS project (Monitoring Agriculture with Remote Sensing). The `Environmental Mapping and Modelling Unit' (EMAP) of the EC Joint Research Centre, in cooperation with other partners, is investigating the use of operational earth observation satellites and airborne hyperspectral data (imaging spectrometry) for mapping and monitoring of vegetation and soil characteristics. In the context of previous experiments, approaches such as spectral mixture analysis have been developed which can already be routinely applied to large regions. Problems related to the standardised retrieval of remotely sensed primary parameters (such as reflectance), concepts for the thematic interpretation of reflectance data, and the definition of satellite-derived land degradation indices are discussed. On this basis, the requirements for the design of an operational satellite observatory for environmental monitoring are presented.
Url:
DOI: 10.1016/S0167-8809(97)00119-9
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<front><div type="abstract" xml:lang="en">The paper reviews the principles of existing remote sensing techniques and new methods considered particularly suitable for monitoring rural land use changes and their effects on soil conditions. Conventional classification methods in combination with local field surveys are operationally used in national as well as in supra-national environmental and agricultural inventories established by the EU such as the European Commission's CORINE programme (Coordination of Information on the Environment) and the MARS project (Monitoring Agriculture with Remote Sensing). The `Environmental Mapping and Modelling Unit' (EMAP) of the EC Joint Research Centre, in cooperation with other partners, is investigating the use of operational earth observation satellites and airborne hyperspectral data (imaging spectrometry) for mapping and monitoring of vegetation and soil characteristics. In the context of previous experiments, approaches such as spectral mixture analysis have been developed which can already be routinely applied to large regions. Problems related to the standardised retrieval of remotely sensed primary parameters (such as reflectance), concepts for the thematic interpretation of reflectance data, and the definition of satellite-derived land degradation indices are discussed. On this basis, the requirements for the design of an operational satellite observatory for environmental monitoring are presented.</div>
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<notesStmt><note type="content">Fig. 1: Characteristic soil bidirectional reflectance spectra after Baumgardner et al. (1985). Curve (A) developed fine textured soils with high (>2%) organic matter content; (B) undeveloped soils with low (<2%) organic matter and low (<1%) iron oxide content; (C) developed soil with low (<2%) organic matter and medium (1–4%) iron oxide content; (D) moderately course textured soils with high (>2%) organic matter content and low (<1%) iron-oxide content; (E) fine textured soils with high (>4%) iron oxide content.</note>
<note type="content">Fig. 2: Process flow-chart for the production of accurate land cover maps and statistical inventories with multi-temporal earth observation satellite images (Hill, 1993a).</note>
<note type="content">Fig. 3: The conversion of satellite raw data into standardised thematic information layers (Hill et al., 1995b).</note>
<note type="content">Fig. 4: Ternary plot of about 100 field- and lab-measured soil spectra at Landsat-TM spectral resolution, where the mixing volume is defined by one soil (vertic cambisol) and two bedrock spectra (marls and limestone). Soil conditions range from undisturbed soils (I) to substrates that are increasingly affected by erosion (II–IVa/b) (Hill et al., 1995b).</note>
<note type="content">Fig. 5: Spectral endmembers of the expanded linear mixing model in the spectral resolution of an imaging spectrometer (AVIRIS, NASA/JPL) and the Landsat-5 TM system (Hill et al., 1995a).</note>
<note type="content">Fig. 6: Degradation of satellite detectable soil conditions in a Mediterranean test site (Xilokastron, NE-Peloponnesos, Greece) between 1985 and 1990, mapped with SMA from Landsat TM images. The black parts are masked areas with more than 50% vegetation cover. Soil conditions are mapped as grey values ranging from I (undisturbed soils), II (moderately degraded), III (severely degraded) and IV (denuded parent material/rock) (Hill, 1993b).</note>
<note type="content">Fig. 7: Classification of spectral anomalies of rape and rye grown on a filled waste deposit. The classification was performed according to the spectral match of GER 63 band imaging spectrometer pixels with heavy metal affected reference spectra. The highest spectral anomalies, marked with the white pointers, are related to Zinc anomalies. The factor of the grey scale is related to the standard deviation of pixel and reference spectra (Rothfuss, 1994).</note>
<note type="content">Fig. 8: Processing scheme for deriving standardised land degradation indices from earth observation satellite data (Hill et al., 1995b).</note>
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<note type="correspondence"><p>Corresponding author. Tel.: +39-332-789631; fax: +39-332-789469; e-mail: stefan.sommer@jrc.it</p>
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<abstract xml:lang="en"><p>The paper reviews the principles of existing remote sensing techniques and new methods considered particularly suitable for monitoring rural land use changes and their effects on soil conditions. Conventional classification methods in combination with local field surveys are operationally used in national as well as in supra-national environmental and agricultural inventories established by the EU such as the European Commission's CORINE programme (Coordination of Information on the Environment) and the MARS project (Monitoring Agriculture with Remote Sensing). The `Environmental Mapping and Modelling Unit' (EMAP) of the EC Joint Research Centre, in cooperation with other partners, is investigating the use of operational earth observation satellites and airborne hyperspectral data (imaging spectrometry) for mapping and monitoring of vegetation and soil characteristics. In the context of previous experiments, approaches such as spectral mixture analysis have been developed which can already be routinely applied to large regions. Problems related to the standardised retrieval of remotely sensed primary parameters (such as reflectance), concepts for the thematic interpretation of reflectance data, and the definition of satellite-derived land degradation indices are discussed. On this basis, the requirements for the design of an operational satellite observatory for environmental monitoring are presented.</p>
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<istex:document><converted-article version="4.5.2" docsubtype="fla"><item-info><jid>AGEE</jid>
<aid>1263</aid>
<ce:pii>S0167-8809(97)00119-9</ce:pii>
<ce:doi>10.1016/S0167-8809(97)00119-9</ce:doi>
<ce:copyright year="1998" type="full-transfer">Elsevier Science B.V.</ce:copyright>
</item-info>
<head><ce:title>The potential of remote sensing for monitoring rural land use changes and their effects on soil conditions</ce:title>
<ce:author-group><ce:author><ce:given-name>S.</ce:given-name>
<ce:surname>Sommer</ce:surname>
<ce:cross-ref refid="AFF1">a</ce:cross-ref>
<ce:cross-ref refid="CORR1">*</ce:cross-ref>
</ce:author>
<ce:author><ce:given-name>J.</ce:given-name>
<ce:surname>Hill</ce:surname>
<ce:cross-ref refid="AFF2">b</ce:cross-ref>
</ce:author>
<ce:author><ce:given-name>J.</ce:given-name>
<ce:surname>Mégier</ce:surname>
<ce:cross-ref refid="AFF1">a</ce:cross-ref>
</ce:author>
<ce:affiliation id="AFF1"><ce:label>a</ce:label>
<ce:textfn>SAI/EMAP, European Commission, Joint Research Centre, Space Applications Institute, Ispra I-21020, Italy</ce:textfn>
</ce:affiliation>
<ce:affiliation id="AFF2"><ce:label>b</ce:label>
<ce:textfn>University of Trier, FB VI Geography, Remote Sensing Dept., Trier D-54286, Germany</ce:textfn>
</ce:affiliation>
<ce:correspondence id="CORR1"><ce:label>*</ce:label>
<ce:text>Corresponding author. Tel.: +39-332-789631; fax: +39-332-789469; e-mail: stefan.sommer@jrc.it</ce:text>
</ce:correspondence>
</ce:author-group>
<ce:abstract><ce:section-title>Abstract</ce:section-title>
<ce:abstract-sec><ce:simple-para>The paper reviews the principles of existing remote sensing techniques and new methods considered particularly suitable for monitoring rural land use changes and their effects on soil conditions. Conventional classification methods in combination with local field surveys are operationally used in national as well as in supra-national environmental and agricultural inventories established by the EU such as the European Commission's CORINE programme (Coordination of Information on the Environment) and the MARS project (Monitoring Agriculture with Remote Sensing). The `Environmental Mapping and Modelling Unit' (EMAP) of the EC Joint Research Centre, in cooperation with other partners, is investigating the use of operational earth observation satellites and airborne hyperspectral data (imaging spectrometry) for mapping and monitoring of vegetation and soil characteristics. In the context of previous experiments, approaches such as spectral mixture analysis have been developed which can already be routinely applied to large regions. Problems related to the standardised retrieval of remotely sensed primary parameters (such as reflectance), concepts for the thematic interpretation of reflectance data, and the definition of satellite-derived land degradation indices are discussed. On this basis, the requirements for the design of an operational satellite observatory for environmental monitoring are presented.</ce:simple-para>
</ce:abstract-sec>
</ce:abstract>
<ce:keywords class="keyword"><ce:section-title>Keywords</ce:section-title>
<ce:keyword><ce:text>Soil degradation</ce:text>
</ce:keyword>
<ce:keyword><ce:text>Environmental monitoring</ce:text>
</ce:keyword>
<ce:keyword><ce:text>Earth observation satellites</ce:text>
</ce:keyword>
<ce:keyword><ce:text>Airborne hyperspectral data</ce:text>
</ce:keyword>
<ce:keyword><ce:text>Spectral mixture analysis</ce:text>
</ce:keyword>
<ce:keyword><ce:text>Environmental satellite observatory</ce:text>
</ce:keyword>
</ce:keywords>
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<affiliation>SAI/EMAP, European Commission, Joint Research Centre, Space Applications Institute, Ispra I-21020, Italy</affiliation>
<description>Corresponding author. Tel.: +39-332-789631; fax: +39-332-789469; e-mail: stefan.sommer@jrc.it</description>
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<name type="personal"><namePart type="given">J.</namePart>
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<copyrightDate encoding="w3cdtf">1998</copyrightDate>
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<abstract lang="en">The paper reviews the principles of existing remote sensing techniques and new methods considered particularly suitable for monitoring rural land use changes and their effects on soil conditions. Conventional classification methods in combination with local field surveys are operationally used in national as well as in supra-national environmental and agricultural inventories established by the EU such as the European Commission's CORINE programme (Coordination of Information on the Environment) and the MARS project (Monitoring Agriculture with Remote Sensing). The `Environmental Mapping and Modelling Unit' (EMAP) of the EC Joint Research Centre, in cooperation with other partners, is investigating the use of operational earth observation satellites and airborne hyperspectral data (imaging spectrometry) for mapping and monitoring of vegetation and soil characteristics. In the context of previous experiments, approaches such as spectral mixture analysis have been developed which can already be routinely applied to large regions. Problems related to the standardised retrieval of remotely sensed primary parameters (such as reflectance), concepts for the thematic interpretation of reflectance data, and the definition of satellite-derived land degradation indices are discussed. On this basis, the requirements for the design of an operational satellite observatory for environmental monitoring are presented.</abstract>
<note type="content">Fig. 1: Characteristic soil bidirectional reflectance spectra after Baumgardner et al. (1985). Curve (A) developed fine textured soils with high (>2%) organic matter content; (B) undeveloped soils with low (<2%) organic matter and low (<1%) iron oxide content; (C) developed soil with low (<2%) organic matter and medium (1–4%) iron oxide content; (D) moderately course textured soils with high (>2%) organic matter content and low (<1%) iron-oxide content; (E) fine textured soils with high (>4%) iron oxide content.</note>
<note type="content">Fig. 2: Process flow-chart for the production of accurate land cover maps and statistical inventories with multi-temporal earth observation satellite images (Hill, 1993a).</note>
<note type="content">Fig. 3: The conversion of satellite raw data into standardised thematic information layers (Hill et al., 1995b).</note>
<note type="content">Fig. 4: Ternary plot of about 100 field- and lab-measured soil spectra at Landsat-TM spectral resolution, where the mixing volume is defined by one soil (vertic cambisol) and two bedrock spectra (marls and limestone). Soil conditions range from undisturbed soils (I) to substrates that are increasingly affected by erosion (II–IVa/b) (Hill et al., 1995b).</note>
<note type="content">Fig. 5: Spectral endmembers of the expanded linear mixing model in the spectral resolution of an imaging spectrometer (AVIRIS, NASA/JPL) and the Landsat-5 TM system (Hill et al., 1995a).</note>
<note type="content">Fig. 6: Degradation of satellite detectable soil conditions in a Mediterranean test site (Xilokastron, NE-Peloponnesos, Greece) between 1985 and 1990, mapped with SMA from Landsat TM images. The black parts are masked areas with more than 50% vegetation cover. Soil conditions are mapped as grey values ranging from I (undisturbed soils), II (moderately degraded), III (severely degraded) and IV (denuded parent material/rock) (Hill, 1993b).</note>
<note type="content">Fig. 7: Classification of spectral anomalies of rape and rye grown on a filled waste deposit. The classification was performed according to the spectral match of GER 63 band imaging spectrometer pixels with heavy metal affected reference spectra. The highest spectral anomalies, marked with the white pointers, are related to Zinc anomalies. The factor of the grey scale is related to the standard deviation of pixel and reference spectra (Rothfuss, 1994).</note>
<note type="content">Fig. 8: Processing scheme for deriving standardised land degradation indices from earth observation satellite data (Hill et al., 1995b).</note>
<subject><genre>Keywords</genre>
<topic>Soil degradation</topic>
<topic>Environmental monitoring</topic>
<topic>Earth observation satellites</topic>
<topic>Airborne hyperspectral data</topic>
<topic>Spectral mixture analysis</topic>
<topic>Environmental satellite observatory</topic>
</subject>
<relatedItem type="host"><titleInfo><title>Agriculture, Ecosystems and Environment</title>
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<titleInfo type="abbreviated"><title>AGEE</title>
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<originInfo><dateIssued encoding="w3cdtf">199802</dateIssued>
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<identifier type="ISSN">0167-8809</identifier>
<identifier type="PII">S0167-8809(00)X0044-8</identifier>
<part><date>199802</date>
<detail type="volume"><number>67</number>
<caption>vol.</caption>
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<detail type="issue"><number>2–3</number>
<caption>no.</caption>
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<extent unit="issue pages"><start>97</start>
<end>302</end>
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<extent unit="pages"><start>197</start>
<end>209</end>
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