Mapping Complex Patterns of Erosion and Stability in Dry Mediterranean Ecosystems
Identifieur interne : 001391 ( Istex/Corpus ); précédent : 001390; suivant : 001392Mapping Complex Patterns of Erosion and Stability in Dry Mediterranean Ecosystems
Auteurs : Joachim Hill ; Brigitta SchüttSource :
- Remote Sensing of Environment [ 0034-4257 ] ; 2000.
Abstract
Parametrizing soil reflectance spectra with variables related to specific shape characteristics of the spectral profile permits organic carbon concentrations in soils to be estimated on the basis of regionally validated regression models. An important feature of the approach is that it can not only be applied to continuous spectra but, without notable loss in accuracy, also to the spectral resolution of operational earth observation satellites such as the Landsat-TM or -ETM systems. Using this type of imagery, it can also be shown that soil organic matter is positively correlated to growth conditions for cereal crops in dryland agriculture. Strong correlations with qualitative erosion indicators that can be derived through spectral unmixing approaches demonstrate that soil organic matter is an important indicator for assessing land degradation processes in dry ecosystems from space.
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DOI: 10.1016/S0034-4257(00)00146-2
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<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title>Mapping Complex Patterns of Erosion and Stability in Dry Mediterranean Ecosystems</title><author><name sortKey="Hill, Joachim" sort="Hill, Joachim" uniqKey="Hill J" first="Joachim" last="Hill">Joachim Hill</name><affiliation><mods:affiliation>E-mail: hillj@uni-trier.de</mods:affiliation></affiliation><affiliation><mods:affiliation>Remote Sensing Department, University of Trier, Trier, Germany</mods:affiliation></affiliation></author><author><name sortKey="Schutt, Brigitta" sort="Schutt, Brigitta" uniqKey="Schutt B" first="Brigitta" last="Schütt">Brigitta Schütt</name><affiliation><mods:affiliation>Department of Physical Geography, University of Trier, Trier, Germany</mods:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:602959A5CCE532637426B9E430D1F7442CF77610</idno><date when="2000" year="2000">2000</date><idno type="doi">10.1016/S0034-4257(00)00146-2</idno><idno type="url">https://api.istex.fr/document/602959A5CCE532637426B9E430D1F7442CF77610/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">001391</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Corpus" wicri:corpus="ISTEX">001391</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a">Mapping Complex Patterns of Erosion and Stability in Dry Mediterranean Ecosystems</title><author><name sortKey="Hill, Joachim" sort="Hill, Joachim" uniqKey="Hill J" first="Joachim" last="Hill">Joachim Hill</name><affiliation><mods:affiliation>E-mail: hillj@uni-trier.de</mods:affiliation></affiliation><affiliation><mods:affiliation>Remote Sensing Department, University of Trier, Trier, Germany</mods:affiliation></affiliation></author><author><name sortKey="Schutt, Brigitta" sort="Schutt, Brigitta" uniqKey="Schutt B" first="Brigitta" last="Schütt">Brigitta Schütt</name><affiliation><mods:affiliation>Department of Physical Geography, University of Trier, Trier, Germany</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j">Remote Sensing of Environment</title><title level="j" type="abbrev">RSE</title><idno type="ISSN">0034-4257</idno><imprint><publisher>ELSEVIER</publisher><date type="published" when="2000">2000</date><biblScope unit="volume">74</biblScope><biblScope unit="issue">3</biblScope><biblScope unit="page" from="557">557</biblScope><biblScope unit="page" to="569">569</biblScope></imprint><idno type="ISSN">0034-4257</idno></series><idno type="istex">602959A5CCE532637426B9E430D1F7442CF77610</idno><idno type="DOI">10.1016/S0034-4257(00)00146-2</idno><idno type="PII">S0034-4257(00)00146-2</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0034-4257</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Parametrizing soil reflectance spectra with variables related to specific shape characteristics of the spectral profile permits organic carbon concentrations in soils to be estimated on the basis of regionally validated regression models. An important feature of the approach is that it can not only be applied to continuous spectra but, without notable loss in accuracy, also to the spectral resolution of operational earth observation satellites such as the Landsat-TM or -ETM systems. Using this type of imagery, it can also be shown that soil organic matter is positively correlated to growth conditions for cereal crops in dryland agriculture. Strong correlations with qualitative erosion indicators that can be derived through spectral unmixing approaches demonstrate that soil organic matter is an important indicator for assessing land degradation processes in dry ecosystems from space.</div></front></TEI><istex><corpusName>elsevier</corpusName><author><json:item><name>Joachim Hill</name><affiliations><json:string>E-mail: hillj@uni-trier.de</json:string><json:string>Remote Sensing Department, University of Trier, Trier, Germany</json:string></affiliations></json:item><json:item><name>Brigitta Schütt</name><affiliations><json:string>Department of Physical Geography, University of Trier, Trier, Germany</json:string></affiliations></json:item></author><language><json:string>eng</json:string></language><originalGenre><json:string>Full-length article</json:string></originalGenre><abstract>Parametrizing soil reflectance spectra with variables related to specific shape characteristics of the spectral profile permits organic carbon concentrations in soils to be estimated on the basis of regionally validated regression models. An important feature of the approach is that it can not only be applied to continuous spectra but, without notable loss in accuracy, also to the spectral resolution of operational earth observation satellites such as the Landsat-TM or -ETM systems. Using this type of imagery, it can also be shown that soil organic matter is positively correlated to growth conditions for cereal crops in dryland agriculture. Strong correlations with qualitative erosion indicators that can be derived through spectral unmixing approaches demonstrate that soil organic matter is an important indicator for assessing land degradation processes in dry ecosystems from space.</abstract><qualityIndicators><score>6.572</score><pdfVersion>1.2</pdfVersion><pdfPageSize>594 x 792 pts</pdfPageSize><refBibsNative>true</refBibsNative><keywordCount>0</keywordCount><abstractCharCount>895</abstractCharCount><pdfWordCount>6936</pdfWordCount><pdfCharCount>44549</pdfCharCount><pdfPageCount>13</pdfPageCount><abstractWordCount>131</abstractWordCount></qualityIndicators><title>Mapping Complex Patterns of Erosion and Stability in Dry Mediterranean Ecosystems</title><pii><json:string>S0034-4257(00)00146-2</json:string></pii><refBibs><json:item><host><author></author></host></json:item><json:item><author><json:item><name>J.B Adams</name></json:item><json:item><name>M.O Smith</name></json:item><json:item><name>A.R Gillespie</name></json:item></author><host><pages><last>166</last><first>145</first></pages><author></author><title>Topics in Remote Sensing IV</title></host><title>Imaging spectroscopy</title></json:item><json:item><author><json:item><name>A.H Al-Abbas</name></json:item><json:item><name>M.F Baumgardner</name></json:item></author><host><volume>114</volume><pages><last>485</last><first>477</first></pages><author></author><title>Soil Sci.</title></host><title>Relating organic matter and clay content to the multispectral radiance of soils</title></json:item><json:item><author><json:item><name>M.F Baumgardner</name></json:item><json:item><name>L.F Silva</name></json:item><json:item><name>L.L Biehl</name></json:item><json:item><name>E Stoner</name></json:item></author><host><volume>38</volume><pages><last>44</last><first>1</first></pages><author></author><title>Adv. 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Soil Sci.</title></host><title>Organic matter and water-stable aggregates in soil</title></json:item><json:item><author><json:item><name>B.G Volk</name></json:item><json:item><name>H Loeppert</name></json:item></author><host><pages><last>268</last><first>211</first></pages><author></author><title>Handbook of Soils and Climate in Agriculture</title></host><title>Soil organic matter</title></json:item></refBibs><genre><json:string>research-article</json:string></genre><host><volume>74</volume><pii><json:string>S0034-4257(00)X0071-5</json:string></pii><pages><last>569</last><first>557</first></pages><issn><json:string>0034-4257</json:string></issn><issue>3</issue><genre><json:string>journal</json:string></genre><language><json:string>unknown</json:string></language><title>Remote Sensing of Environment</title><publicationDate>2000</publicationDate></host><categories><wos><json:string>science</json:string><json:string>remote sensing</json:string><json:string>imaging science & photographic technology</json:string><json:string>environmental sciences</json:string></wos><scienceMetrix><json:string>applied sciences</json:string><json:string>engineering</json:string><json:string>geological & geomatics engineering</json:string></scienceMetrix></categories><publicationDate>2000</publicationDate><copyrightDate>2000</copyrightDate><doi><json:string>10.1016/S0034-4257(00)00146-2</json:string></doi><id>602959A5CCE532637426B9E430D1F7442CF77610</id><score>1.8023851</score><fulltext><json:item><extension>pdf</extension><original>true</original><mimetype>application/pdf</mimetype><uri>https://api.istex.fr/document/602959A5CCE532637426B9E430D1F7442CF77610/fulltext/pdf</uri></json:item><json:item><extension>zip</extension><original>false</original><mimetype>application/zip</mimetype><uri>https://api.istex.fr/document/602959A5CCE532637426B9E430D1F7442CF77610/fulltext/zip</uri></json:item><istex:fulltextTEI uri="https://api.istex.fr/document/602959A5CCE532637426B9E430D1F7442CF77610/fulltext/tei"><teiHeader><fileDesc><titleStmt><title level="a">Mapping Complex Patterns of Erosion and Stability in Dry Mediterranean Ecosystems</title></titleStmt><publicationStmt><authority>ISTEX</authority><publisher>ELSEVIER</publisher><availability><p>©2000 Elsevier Science Inc.</p></availability><date>2000</date></publicationStmt><notesStmt><note type="content">Figure 1: Location of the Cañada Hermosa study site in the Guadalentin Basin in SE Spain. a) Landsat TM image (25 July 1992, 10.06 h GMT, Path/Row 199-34, N37.5°/E358.3°, Sun elevation/azimuth: 57.2°/114.3°) of the limestone/marl areas south of Zarzilla de Ramos (Lorca); the study site is indicated by the rectangle. b) Index map showing the location of the study site (shaded rectangle)</note><note type="content">Figure 2: Causal-loop diagram illustrating the feedback relationships in aggregate growth from primary particles (Imeson et al., 1996)</note><note type="content">Figure 3: Characteristic soil bidirectional reflectance spectra (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 coarse 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">Figure 4: Sampling transects in the Cañada Hermosa study site</note><note type="content">Figure 5: Original and peak-normalized spectral reflectance of continuous (left) and TM-resolved spectra (right) in comparison to the corresponding third-order polynomials [0.45–1.676 μm] for selected samples from the Cañada Hermosa site. Soil organic carbon concentrations range from 0.08 to 1.57 [wt % Corg]. The different line signatures indicate: original spectra, peak-normalized spectra, polynomial fit. The □ - symbols indicate supplementary anchor points in TM resolution</note><note type="content">Figure 6: Scatter-plots of the cross validation between modeled and laboratory-measured soil organic carbon concentrations (ln Corg [wt %]) as derived from continuous (Model 1.0, left), and TM-resolved spectra (Model 2.0, right)</note><note type="content">Figure 7: Landsat-TM average reflectance of the calibration target (7/89 and 7/92) in comparison to mean and ±1 S.D. of the corresponding field measurements (left). The 4/92 scene (sparse vegetation cover within the primary reference area) has been intercalibrated to the image from 7/92 based on bright and dark reference targets with negligible (i.e., ⩽5%) vegetation cover (right)</note><note type="content">Figure 8: Spectral endmembers used for the forward/inverse modeling of spectral mixtures of the Landsat TM images included in this study</note><note type="content">Figure 9: Color composites (RGB=bands 4-5-3) of the original Landsat-TM reflectance image from April 1992 (left) and its “defoliated” version where areas with Fveg>0.5 have been masked in white (right). The imaged area is outlined in Figure 1</note><note type="content">Figure 10: Average soil organic carbon concentrations (Corg) derived from applying Model 2.0 to three Landsat Thematic Mapper images (7/89, 4/92, and 7/92) of the Cañada Hermosa Study Site. The histogram upper right shows the distribution of Corg values in the imaged area; the gray scale relates image gray tones to Corg values</note><note type="content">Figure 11: SMA-derived map of qualitative soil erosion indicators, displaying the average proportion (in %) of carbonate parent material exposed at the surface (i.e., a maximum value composite derived from three Landsat images). The scatter plots, obtained from selected reference areas in the farmed areas, document the strong correlation which exists between SMA-derived qualitative erosion indicators and the quantified soil organic carbon concentrations derived from the Landsat TM images</note><note type="content">Figure 12: Normalized difference vegetation index (NDVI) computed for the Landsat Image from April 1992 (left). The scatter plots, obtained from selected reference areas within the farmed areas, clearly emphasize the positive correlation between the vegetative growth of the cereal crop and the soil organic carbon concentrations derived from the Landsat TM images</note><note type="content">Table 1: Comparison of Different Methodological Approaches Predicting Soil Organic Matter by Spectral Reflectance Features, Generated by 100% of Total Data (n=91)</note></notesStmt><sourceDesc><biblStruct type="inbook"><analytic><title level="a">Mapping Complex Patterns of Erosion and Stability in Dry Mediterranean Ecosystems</title><author xml:id="author-1"><persName><forename type="first">Joachim</forename><surname>Hill</surname></persName><email>hillj@uni-trier.de</email><affiliation>Address correspondence to J. Hill, Remote Sensing Dept., Univ. of Trier, 54286 Trier, Germany.</affiliation><affiliation>Remote Sensing Department, University of Trier, Trier, Germany</affiliation></author><author xml:id="author-2"><persName><forename type="first">Brigitta</forename><surname>Schütt</surname></persName><affiliation>Department of Physical Geography, University of Trier, Trier, Germany</affiliation></author></analytic><monogr><title level="j">Remote Sensing of Environment</title><title level="j" type="abbrev">RSE</title><idno type="pISSN">0034-4257</idno><idno type="PII">S0034-4257(00)X0071-5</idno><imprint><publisher>ELSEVIER</publisher><date type="published" when="2000"></date><biblScope unit="volume">74</biblScope><biblScope unit="issue">3</biblScope><biblScope unit="page" from="557">557</biblScope><biblScope unit="page" to="569">569</biblScope></imprint></monogr><idno type="istex">602959A5CCE532637426B9E430D1F7442CF77610</idno><idno type="DOI">10.1016/S0034-4257(00)00146-2</idno><idno type="PII">S0034-4257(00)00146-2</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2000</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="en"><p>Parametrizing soil reflectance spectra with variables related to specific shape characteristics of the spectral profile permits organic carbon concentrations in soils to be estimated on the basis of regionally validated regression models. An important feature of the approach is that it can not only be applied to continuous spectra but, without notable loss in accuracy, also to the spectral resolution of operational earth observation satellites such as the Landsat-TM or -ETM systems. Using this type of imagery, it can also be shown that soil organic matter is positively correlated to growth conditions for cereal crops in dryland agriculture. Strong correlations with qualitative erosion indicators that can be derived through spectral unmixing approaches demonstrate that soil organic matter is an important indicator for assessing land degradation processes in dry ecosystems from space.</p></abstract></profileDesc><revisionDesc><change when="2000-05-10">Modified</change><change when="2000">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/document/602959A5CCE532637426B9E430D1F7442CF77610/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Elsevier, elements deleted: ce:floats; body; tail"><istex:xmlDeclaration>version="1.0" encoding="utf-8"</istex:xmlDeclaration><istex:docType PUBLIC="-//ES//DTD journal article DTD version 4.5.2//EN//XML" URI="art452.dtd" name="istex:docType"><istex:entity SYSTEM="gr1" NDATA="IMAGE" name="gr1"></istex:entity><istex:entity SYSTEM="gr2" NDATA="IMAGE" name="gr2"></istex:entity><istex:entity SYSTEM="gr3" NDATA="IMAGE" name="gr3"></istex:entity><istex:entity SYSTEM="gr4" NDATA="IMAGE" name="gr4"></istex:entity><istex:entity SYSTEM="gr5" NDATA="IMAGE" name="gr5"></istex:entity><istex:entity SYSTEM="fx1" NDATA="IMAGE" name="fx1"></istex:entity><istex:entity SYSTEM="fx2" NDATA="IMAGE" name="fx2"></istex:entity><istex:entity SYSTEM="fx3" NDATA="IMAGE" name="fx3"></istex:entity><istex:entity SYSTEM="gr6" NDATA="IMAGE" name="gr6"></istex:entity><istex:entity SYSTEM="gr7" NDATA="IMAGE" name="gr7"></istex:entity><istex:entity SYSTEM="gr8" NDATA="IMAGE" name="gr8"></istex:entity><istex:entity SYSTEM="gr9" NDATA="IMAGE" name="gr9"></istex:entity><istex:entity SYSTEM="gr10" NDATA="IMAGE" name="gr10"></istex:entity><istex:entity SYSTEM="gr11" NDATA="IMAGE" name="gr11"></istex:entity><istex:entity SYSTEM="gr12" NDATA="IMAGE" name="gr12"></istex:entity></istex:docType><istex:document><converted-article version="4.5.2" docsubtype="fla"><item-info><jid>RSE</jid><aid>5441</aid><ce:pii>S0034-4257(00)00146-2</ce:pii><ce:doi>10.1016/S0034-4257(00)00146-2</ce:doi><ce:copyright type="full-transfer" year="2000">Elsevier Science Inc.</ce:copyright></item-info><head><ce:title>Mapping Complex Patterns of Erosion and Stability in Dry Mediterranean Ecosystems</ce:title><ce:author-group><ce:author><ce:given-name>Joachim</ce:given-name><ce:surname>Hill</ce:surname><ce:cross-ref refid="AFF1">*</ce:cross-ref><ce:cross-ref refid="CORR1">*</ce:cross-ref><ce:e-address>hillj@uni-trier.de</ce:e-address></ce:author><ce:author><ce:given-name>Brigitta</ce:given-name><ce:surname>Schütt</ce:surname><ce:cross-ref refid="AFF2">†</ce:cross-ref></ce:author><ce:affiliation id="AFF1"><ce:label>*</ce:label><ce:textfn>Remote Sensing Department, University of Trier, Trier, Germany</ce:textfn></ce:affiliation><ce:affiliation id="AFF2"><ce:label>†</ce:label><ce:textfn>Department of Physical Geography, University of Trier, Trier, Germany</ce:textfn></ce:affiliation><ce:correspondence id="CORR1"><ce:label>*</ce:label><ce:text>Address correspondence to J. Hill, Remote Sensing Dept., Univ. of Trier, 54286 Trier, Germany.</ce:text></ce:correspondence></ce:author-group><ce:date-received day="27" month="9" year="1999"></ce:date-received><ce:date-revised day="10" month="5" year="2000"></ce:date-revised><ce:abstract><ce:section-title>Abstract</ce:section-title><ce:abstract-sec><ce:simple-para>Parametrizing soil reflectance spectra with variables related to specific shape characteristics of the spectral profile permits organic carbon concentrations in soils to be estimated on the basis of regionally validated regression models. An important feature of the approach is that it can not only be applied to continuous spectra but, without notable loss in accuracy, also to the spectral resolution of operational earth observation satellites such as the Landsat-TM or -ETM systems. Using this type of imagery, it can also be shown that soil organic matter is positively correlated to growth conditions for cereal crops in dryland agriculture. Strong correlations with qualitative erosion indicators that can be derived through spectral unmixing approaches demonstrate that soil organic matter is an important indicator for assessing land degradation processes in dry ecosystems from space.</ce:simple-para></ce:abstract-sec></ce:abstract></head></converted-article></istex:document></istex:metadataXml><mods version="3.6"><titleInfo><title>Mapping Complex Patterns of Erosion and Stability in Dry Mediterranean Ecosystems</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>Mapping Complex Patterns of Erosion and Stability in Dry Mediterranean Ecosystems</title></titleInfo><name type="personal"><namePart type="given">Joachim</namePart><namePart type="family">Hill</namePart><affiliation>E-mail: hillj@uni-trier.de</affiliation><affiliation>Remote Sensing Department, University of Trier, Trier, Germany</affiliation><description>Address correspondence to J. Hill, Remote Sensing Dept., Univ. of Trier, 54286 Trier, Germany.</description><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Brigitta</namePart><namePart type="family">Schütt</namePart><affiliation>Department of Physical Geography, University of Trier, Trier, Germany</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="research-article" displayLabel="Full-length article"></genre><originInfo><publisher>ELSEVIER</publisher><dateIssued encoding="w3cdtf">2000</dateIssued><dateModified encoding="w3cdtf">2000-05-10</dateModified><copyrightDate encoding="w3cdtf">2000</copyrightDate></originInfo><language><languageTerm type="code" authority="iso639-2b">eng</languageTerm><languageTerm type="code" authority="rfc3066">en</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Parametrizing soil reflectance spectra with variables related to specific shape characteristics of the spectral profile permits organic carbon concentrations in soils to be estimated on the basis of regionally validated regression models. An important feature of the approach is that it can not only be applied to continuous spectra but, without notable loss in accuracy, also to the spectral resolution of operational earth observation satellites such as the Landsat-TM or -ETM systems. Using this type of imagery, it can also be shown that soil organic matter is positively correlated to growth conditions for cereal crops in dryland agriculture. Strong correlations with qualitative erosion indicators that can be derived through spectral unmixing approaches demonstrate that soil organic matter is an important indicator for assessing land degradation processes in dry ecosystems from space.</abstract><note type="content">Figure 1: Location of the Cañada Hermosa study site in the Guadalentin Basin in SE Spain. a) Landsat TM image (25 July 1992, 10.06 h GMT, Path/Row 199-34, N37.5°/E358.3°, Sun elevation/azimuth: 57.2°/114.3°) of the limestone/marl areas south of Zarzilla de Ramos (Lorca); the study site is indicated by the rectangle. b) Index map showing the location of the study site (shaded rectangle)</note><note type="content">Figure 2: Causal-loop diagram illustrating the feedback relationships in aggregate growth from primary particles (Imeson et al., 1996)</note><note type="content">Figure 3: Characteristic soil bidirectional reflectance spectra (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 coarse 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">Figure 4: Sampling transects in the Cañada Hermosa study site</note><note type="content">Figure 5: Original and peak-normalized spectral reflectance of continuous (left) and TM-resolved spectra (right) in comparison to the corresponding third-order polynomials [0.45–1.676 μm] for selected samples from the Cañada Hermosa site. Soil organic carbon concentrations range from 0.08 to 1.57 [wt % Corg]. The different line signatures indicate: original spectra, peak-normalized spectra, polynomial fit. The □ - symbols indicate supplementary anchor points in TM resolution</note><note type="content">Figure 6: Scatter-plots of the cross validation between modeled and laboratory-measured soil organic carbon concentrations (ln Corg [wt %]) as derived from continuous (Model 1.0, left), and TM-resolved spectra (Model 2.0, right)</note><note type="content">Figure 7: Landsat-TM average reflectance of the calibration target (7/89 and 7/92) in comparison to mean and ±1 S.D. of the corresponding field measurements (left). The 4/92 scene (sparse vegetation cover within the primary reference area) has been intercalibrated to the image from 7/92 based on bright and dark reference targets with negligible (i.e., ⩽5%) vegetation cover (right)</note><note type="content">Figure 8: Spectral endmembers used for the forward/inverse modeling of spectral mixtures of the Landsat TM images included in this study</note><note type="content">Figure 9: Color composites (RGB=bands 4-5-3) of the original Landsat-TM reflectance image from April 1992 (left) and its “defoliated” version where areas with Fveg>0.5 have been masked in white (right). The imaged area is outlined in Figure 1</note><note type="content">Figure 10: Average soil organic carbon concentrations (Corg) derived from applying Model 2.0 to three Landsat Thematic Mapper images (7/89, 4/92, and 7/92) of the Cañada Hermosa Study Site. The histogram upper right shows the distribution of Corg values in the imaged area; the gray scale relates image gray tones to Corg values</note><note type="content">Figure 11: SMA-derived map of qualitative soil erosion indicators, displaying the average proportion (in %) of carbonate parent material exposed at the surface (i.e., a maximum value composite derived from three Landsat images). The scatter plots, obtained from selected reference areas in the farmed areas, document the strong correlation which exists between SMA-derived qualitative erosion indicators and the quantified soil organic carbon concentrations derived from the Landsat TM images</note><note type="content">Figure 12: Normalized difference vegetation index (NDVI) computed for the Landsat Image from April 1992 (left). The scatter plots, obtained from selected reference areas within the farmed areas, clearly emphasize the positive correlation between the vegetative growth of the cereal crop and the soil organic carbon concentrations derived from the Landsat TM images</note><note type="content">Table 1: Comparison of Different Methodological Approaches Predicting Soil Organic Matter by Spectral Reflectance Features, Generated by 100% of Total Data (n=91)</note><relatedItem type="host"><titleInfo><title>Remote Sensing of Environment</title></titleInfo><titleInfo type="abbreviated"><title>RSE</title></titleInfo><genre type="journal">journal</genre><originInfo><dateIssued encoding="w3cdtf">200012</dateIssued></originInfo><identifier type="ISSN">0034-4257</identifier><identifier type="PII">S0034-4257(00)X0071-5</identifier><part><date>200012</date><detail type="volume"><number>74</number><caption>vol.</caption></detail><detail type="issue"><number>3</number><caption>no.</caption></detail><extent unit="issue pages"><start>327</start><end>640</end></extent><extent unit="pages"><start>557</start><end>569</end></extent></part></relatedItem><identifier type="istex">602959A5CCE532637426B9E430D1F7442CF77610</identifier><identifier type="DOI">10.1016/S0034-4257(00)00146-2</identifier><identifier type="PII">S0034-4257(00)00146-2</identifier><accessCondition type="use and reproduction" contentType="copyright">©2000 Elsevier Science Inc.</accessCondition><recordInfo><recordContentSource>ELSEVIER</recordContentSource><recordOrigin>Elsevier Science Inc., ©2000</recordOrigin></recordInfo></mods></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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