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

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

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Official 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.

Item URL in elib:https://elib.dlr.de/67174/
Document Type:Article
Title:Per-Field Irrigated Crop Classification in Arid Central Asia Using SPOT and ASTER Data
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Conrad, Christopher christopher.conrad (at) uni-wuerzburg.deUNSPECIFIED
Fritsch, Sebastiansebastian.fritsch (at) uni-wuerzburg.deUNSPECIFIED
Zeidler, Julianjulian.zeidler (at) uni-wuerzburg.deUNSPECIFIED
Rücker, Gerdgerd.ruecker (at) dlr.deUNSPECIFIED
Dech, Stefanstefan.dech (at) dlr.deUNSPECIFIED
Date:8 April 2010
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
DOI :10.3390/rs2041035
Page Range:pp. 1035-1056
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Keywords:object-based classification; segmentation; tasselled cap; Uzbekistan; irrigated agriculture; multi-sensor
HGF - Research field:Aeronautics, Space and Transport (old)
HGF - Program:Space (old)
HGF - Program Themes:W EO - Erdbeobachtung
DLR - Research area:Space
DLR - Program:W EO - Erdbeobachtung
DLR - Research theme (Project):W - Vorhaben Geowissenschaftl. Fernerkundungs- und GIS-Verfahren (old)
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface
German Remote Sensing Data Center
Deposited By: Zeidler, Julian
Deposited On:03 Feb 2011 21:01
Last Modified:14 Dec 2019 04:25

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