Geostatistical interpolation and classification of remote sensing data from ice surfaces
Identifieur interne : 002542 ( Main/Merge ); précédent : 002541; suivant : 002543Geostatistical interpolation and classification of remote sensing data from ice surfaces
Auteurs : U. C. Herzfeld [Allemagne, États-Unis]Source :
- International journal of remote sensing [ 0143-1161 ] ; 1999.
Descripteurs français
- Pascal (Inist)
- Glace, Télédétection spatiale, Classification, Interpolation, Géostatistique, Extrapolation, Krigeage, Optimisation, Analyse moindres carrés, Variogramme, Glaciologie, Altimétrie, Antarctique, Radar antenne synthétique, Rétrodiffusion, Déformation, Carte topographique, Crue glaciaire, Glaciodynamique, ERS-1, Radar imageur synthèse ouverture, ICECLASS.
- Wicri :
- topic : Glace, Classification.
English descriptors
- KwdEn :
Abstract
Geostatistical methods for interpolation and extrapolation of irregularly spaced data are kriging methods, a family of methods based on the least-squares optimization principle and usually introduced in a probabilistic framework ('geostatistical estimation'). The difference between kriging and numerical interpolation methods lies in the exploitation of a spatial structure function, the variogram, in the optimization. Usefulness of geostatistical interpolation for glaciological data analysis has been demonstrated a number of times. Application of ordinary kriging to satellite radar altimeter data from Antarctic ice streams yields maps of 3-km resolution that facilitate glaciodynamic investigations. The technique is exemplified here for 1995 ERS-1 data, and the resultant new map of the entire Lambert Glacier/Amery Ice Shelf area including upper Lambert Glacier and other tributaries of (lower) Lambert Glacier (59°- 79'E/68' 75 S) is presented. While interpolation utilizes the primary information in the data, a geostatistical surface classification method is developed to derive secondary information from elevation and backscatter data. Based on quantitative properties of the variogram, elements of surface structures are used for mapping and segmentation of an area into provinces homogeneous in surface characteristics. A critical issue in the analysis of satellite Synthetic Aperture Radar (SAR) data is the availability of ground truthing to distinguish between intensity variations caused by subscale-resolution geophysical variability and noise, and to determine small-scale sources of variations in backscattering. During the 1993-1995 surge of Bering Glacier, Alaska, GPS-located video data were collected from small aircraft and analyzed systematically with the geostatistical ice surface classification system ICECLASS. The objectives are to (a) help understand the relations between ice velocities, surface strain states, and progression of deformation processes during the surge. and (b) provide a technique for surface classification based on video data, SAR data, or image data in general.
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Pascal:99-0135196Le document en format XML
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Herzfeld</name><affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Geomathematik, Fachbereich Geographie/Geowissenschaften, Universität Trier</s1><s2>54286 Trier</s2><s3>DEU</s3><sZ>1 aut.</sZ></inist:fA14><country>Allemagne</country><wicri:noRegion>54286 Trier</wicri:noRegion><wicri:noRegion>Universität Trier</wicri:noRegion><wicri:noRegion>54286 Trier</wicri:noRegion></affiliation><affiliation wicri:level="2"><inist:fA14 i1="02"><s1>Institute of Arctic and Alpine Research, University of Colorado</s1><s2>Boulder, CO 80309-0450</s2><s3>USA</s3><sZ>1 aut.</sZ></inist:fA14><country>États-Unis</country><placeName><region type="state">Colorado</region></placeName></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">INIST</idno><idno type="inist">99-0135196</idno><date when="1999">1999</date><idno type="stanalyst">PASCAL 99-0135196 INIST</idno><idno type="RBID">Pascal:99-0135196</idno><idno type="wicri:Area/PascalFrancis/Corpus">001047</idno><idno type="wicri:Area/PascalFrancis/Curation">001747</idno><idno type="wicri:Area/PascalFrancis/Checkpoint">000D90</idno><idno type="wicri:explorRef" wicri:stream="PascalFrancis" wicri:step="Checkpoint">000D90</idno><idno type="wicri:doubleKey">0143-1161:1999:Herzfeld U:geostatistical:interpolation:and</idno><idno type="wicri:Area/Main/Merge">002542</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a">Geostatistical interpolation and classification of remote sensing data from ice surfaces</title><author><name sortKey="Herzfeld, U C" sort="Herzfeld, U C" uniqKey="Herzfeld U" first="U. 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Herzfeld</name><affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Geomathematik, Fachbereich Geographie/Geowissenschaften, Universität Trier</s1><s2>54286 Trier</s2><s3>DEU</s3><sZ>1 aut.</sZ></inist:fA14><country>Allemagne</country><wicri:noRegion>54286 Trier</wicri:noRegion><wicri:noRegion>Universität Trier</wicri:noRegion><wicri:noRegion>54286 Trier</wicri:noRegion></affiliation><affiliation wicri:level="2"><inist:fA14 i1="02"><s1>Institute of Arctic and Alpine Research, University of Colorado</s1><s2>Boulder, CO 80309-0450</s2><s3>USA</s3><sZ>1 aut.</sZ></inist:fA14><country>États-Unis</country><placeName><region type="state">Colorado</region></placeName></affiliation></author></analytic><series><title level="j" type="main">International journal of remote sensing</title><title level="j" type="abbreviated">Int. j. remote sens.</title><idno type="ISSN">0143-1161</idno><imprint><date when="1999">1999</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">International journal of remote sensing</title><title level="j" type="abbreviated">Int. j. remote sens.</title><idno type="ISSN">0143-1161</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Antarctica</term><term>Space remote sensing</term><term>altimetry</term><term>backscattering</term><term>classification</term><term>deformation</term><term>extrapolation</term><term>geostatistics</term><term>glacier surges</term><term>glaciology</term><term>ice</term><term>interpolation</term><term>kriging</term><term>least-squares</term><term>optimization</term><term>synthetic aperture radar</term><term>topographic maps</term><term>variograms</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Glace</term><term>Télédétection spatiale</term><term>Classification</term><term>Interpolation</term><term>Géostatistique</term><term>Extrapolation</term><term>Krigeage</term><term>Optimisation</term><term>Analyse moindres carrés</term><term>Variogramme</term><term>Glaciologie</term><term>Altimétrie</term><term>Antarctique</term><term>Radar antenne synthétique</term><term>Rétrodiffusion</term><term>Déformation</term><term>Carte topographique</term><term>Crue glaciaire</term><term>Glaciodynamique</term><term>ERS-1</term><term>Radar imageur synthèse ouverture</term><term>ICECLASS</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Glace</term><term>Classification</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Geostatistical methods for interpolation and extrapolation of irregularly spaced data are kriging methods, a family of methods based on the least-squares optimization principle and usually introduced in a probabilistic framework ('geostatistical estimation'). The difference between kriging and numerical interpolation methods lies in the exploitation of a spatial structure function, the variogram, in the optimization. Usefulness of geostatistical interpolation for glaciological data analysis has been demonstrated a number of times. Application of ordinary kriging to satellite radar altimeter data from Antarctic ice streams yields maps of 3-km resolution that facilitate glaciodynamic investigations. The technique is exemplified here for 1995 ERS-1 data, and the resultant new map of the entire Lambert Glacier/Amery Ice Shelf area including upper Lambert Glacier and other tributaries of (lower) Lambert Glacier (59°- 79'E/68' 75 S) is presented. While interpolation utilizes the primary information in the data, a geostatistical surface classification method is developed to derive secondary information from elevation and backscatter data. Based on quantitative properties of the variogram, elements of surface structures are used for mapping and segmentation of an area into provinces homogeneous in surface characteristics. A critical issue in the analysis of satellite Synthetic Aperture Radar (SAR) data is the availability of ground truthing to distinguish between intensity variations caused by subscale-resolution geophysical variability and noise, and to determine small-scale sources of variations in backscattering. During the 1993-1995 surge of Bering Glacier, Alaska, GPS-located video data were collected from small aircraft and analyzed systematically with the geostatistical ice surface classification system ICECLASS. The objectives are to (a) help understand the relations between ice velocities, surface strain states, and progression of deformation processes during the surge. and (b) provide a technique for surface classification based on video data, SAR data, or image data in general.</div></front></TEI><affiliations><list><country><li>Allemagne</li><li>États-Unis</li></country><region><li>Colorado</li></region></list><tree><country name="Allemagne"><noRegion><name sortKey="Herzfeld, U C" sort="Herzfeld, U C" uniqKey="Herzfeld U" first="U. C." last="Herzfeld">U. C. Herzfeld</name></noRegion></country><country name="États-Unis"><region name="Colorado"><name sortKey="Herzfeld, U C" sort="Herzfeld, U C" uniqKey="Herzfeld U" first="U. C." last="Herzfeld">U. C. Herzfeld</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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