Per-Field Irrigated Crop Classification in Arid Central Asia Using SPOT and ASTER Data
Zitieren Sie bitte immer diese URN: urn:nbn:de:bvb:20-opus-68630
- 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, wasThe 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.…
Autor(en): | Christopher Conrad, Sebastian Fritsch, Julian Zeidler, Gerd Rücker, Stefan Dech |
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URN: | urn:nbn:de:bvb:20-opus-68630 |
Dokumentart: | Artikel / Aufsatz in einer Zeitschrift |
Institute der Universität: | Philosophische Fakultät (Histor., philolog., Kultur- und geograph. Wissensch.) / Institut für Geographie und Geologie |
Sprache der Veröffentlichung: | Englisch |
Erscheinungsjahr: | 2010 |
Originalveröffentlichung / Quelle: | In: Remote Sensing (2010) 2, 1035-1056; DOI: doi:10.3390/rs2041035 |
Allgemeine fachliche Zuordnung (DDC-Klassifikation): | 5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften |
Normierte Schlagworte (GND): | Geologie |
Freie Schlagwort(e): | Uzbekistan; irrigated agriculture; multi-sensor; object-based classification; segmentation; tasselled cap |
Datum der Freischaltung: | 12.11.2012 |
Lizenz (Deutsch): | CC BY: Creative-Commons-Lizenz: Namensnennung |