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Remote Sens. 2010, 2(4), 1035-1056; doi:10.3390/rs2041035

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

Remote Sensing Unit, University of Wuerzburg, Am Hubland, 97074 Wuerzburg, Germany
German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Oberpfaffenhofen, 82234 Wessling, Germany
Author to whom correspondence should be addressed.
Received: 17 February 2010 / Revised: 22 March 2010 / Accepted: 23 March 2010 / Published: 8 April 2010
(This article belongs to the Special Issue Global Croplands)
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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. View Full-Text
Keywords: object-based classification; segmentation; tasselled cap; Uzbekistan; irrigated agriculture; multi-sensor object-based classification; segmentation; tasselled cap; Uzbekistan; irrigated agriculture; multi-sensor

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Conrad, C.; Fritsch, S.; Zeidler, J.; Rücker, G.; Dech, S. Per-Field Irrigated Crop Classification in Arid Central Asia Using SPOT and ASTER Data. Remote Sens. 2010, 2, 1035-1056.

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