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Per-Field Irrigated Crop Classification in Arid Central Asia Using SPOT and ASTER Data

Conrad, Christopher und Fritsch, Sebastian und Zeidler, Julian und Rücker, Gerd und Dech, Stefan (2010) Per-Field Irrigated Crop Classification in Arid Central Asia Using SPOT and ASTER Data. Remote Sensing, 2 (4), Seiten 1035-1056. Multidisciplinary Digital Publishing Institute (MDPI). DOI: 10.3390/rs2041035

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Offizielle URL: http://www.mdpi.com/2072-4292/2/4/1035/


Abstract: The overarching goal of this research was to explore accurate methods of mapping irrigated crops, where digital cadastre information is unavailable: (a) Boundary separation by object-oriented image segmentation using very high spatial resolution (2.5–5 m) data was followed by (b) identification of crops and crop rotations by means of phenology, tasselled cap, and rule-based classification using high resolution (15–30 m) bi-temporal data. The extensive irrigated cotton production system of the Khorezm province in Uzbekistan, Central Asia, was selected as a study region. Image segmentation was carried out on pan-sharpened SPOT data. Varying combinations of segmentation parameters (shape, compactness, and color) were tested for optimized boundary separation. The resulting geometry was validated against polygons digitized from the data and cadastre maps, analysing similarity (size, shape) and congruence. The parameters shape and compactness were decisive for segmentation accuracy. Differences between crop phenologies were analyzed at field level using bi-temporal ASTER data. A rule set based on the tasselled cap indices greenness and brightness allowed for classifying crop rotations of cotton, winter-wheat and rice, resulting in an overall accuracy of 80 %. The proposed field-based crop classification method can be an important tool for use in water demand estimations, crop yield simulations, or economic models in agricultural systems similar to Khorezm.

Titel:Per-Field Irrigated Crop Classification in Arid Central Asia Using SPOT and ASTER Data
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iD
Conrad, Christopher christopher.conrad@uni-wuerzburg.deNICHT SPEZIFIZIERT
Fritsch, Sebastiansebastian.fritsch@uni-wuerzburg.deNICHT SPEZIFIZIERT
Zeidler, Julianjulian.zeidler@uni-wuerzburg.deNICHT SPEZIFIZIERT
Rücker, Gerdgerd.ruecker@dlr.deNICHT SPEZIFIZIERT
Dech, Stefanstefan.dech@dlr.deNICHT SPEZIFIZIERT
Datum:8 April 2010
Erschienen in:Remote Sensing
Referierte Publikation:Ja
In Open Access:Ja
In ISI Web of Science:Ja
DOI :10.3390/rs2041035
Seitenbereich:Seiten 1035-1056
Verlag:Multidisciplinary Digital Publishing Institute (MDPI)
Stichwörter:object-based classification; segmentation; tasselled cap; Uzbekistan; irrigated agriculture; multi-sensor
HGF - Forschungsbereich:Verkehr und Weltraum (alt)
HGF - Programm:Weltraum (alt)
HGF - Programmthema:W EO - Erdbeobachtung
DLR - Schwerpunkt:Weltraum
DLR - Forschungsgebiet:W EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):W - Vorhaben Geowissenschaftl. Fernerkundungs- und GIS-Verfahren (alt)
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Deutsches Fernerkundungsdatenzentrum > Landoberfläche
Deutsches Fernerkundungsdatenzentrum
Hinterlegt von: Zeidler, Julian
Hinterlegt am:03 Feb 2011 21:01
Letzte Änderung:08 Mär 2018 18:49

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