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首页> 外文会议>Remote sensing for agriculture, ecosystems, and hydrology XIII >Potentials of RapidEye time series for improved classification of crop rotations in heterogeneous agricultural landscapes: Experiences from irrigation systems in Central Asia
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Potentials of RapidEye time series for improved classification of crop rotations in heterogeneous agricultural landscapes: Experiences from irrigation systems in Central Asia

机译：RapidEye时间序列对改善异种农业景观中作物轮作分类的潜力：中亚灌溉系统的经验

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摘要

In Central Asia, more than eight Million ha of agricultural land are under irrigation. But severe degradation problems and unreliable water distribution have caused declining yields during the past decades. Reliable and area-wide information about crops can be seen as important step to elaborate options for sustainable land and water management. Experiences from RapidEye classifications of crop in Central Asia are exemplarily shown during a classification of eight crop classes including three rotations with winter wheat, cotton, rice, and fallow land in the Khorezm region of Uzbekistan covering 230,000 ha of irrigated land. A random forest generated by using 1215 field samples was applied to multitemporal RapidEye data acquired during the vegetation period 2010. But RapidEye coverage varied and did not allow for generating temporally consistent mosaics covering the entire region. To classify all 55,188 agricultural parcels in the region three classification zones were classified separately. The zoning allowed for including at least three observation periods into classification. Overall accuracy exceeded 85 % for all classification zones. Highest accuracies of 87.4 % were achieved by including five spatiotemporal composites of RapidEye. Class-wise accuracy assessments showed the usefulness of selecting time steps which represent relevant phenological phases of the vegetation period. The presented approach can support regional crop inventory. Accurate classification results in early stages of the cropping season permit recalculation of crop water demands and reallocation of irrigation water. The high temporal and spatial resolution of RapidEye can be concluded highly beneficial for agricultural land use classifications in entire Central Asia.
机译：在中亚，超过800万公顷的农业用地受到灌溉。但是在过去的几十年中，严重的退化问题和不可靠的水分配导致了单产下降。关于作物的可靠和全地区信息可以被视为制定可持续土地和水管理方案的重要步骤。在对八种作物进行分类的过程中，示例性地展示了中亚RapidEye作物分类的经验，其中包括乌兹别克斯坦Khorezm地区的冬小麦，棉花，水稻和休耕地三个轮作，覆盖了23万公顷的灌溉土地。通过使用1215个野外样本生成的随机森林应用于2010年植被时期采集的多时相RapidEye数据。但是RapidEye的覆盖范围有所不同，无法生成覆盖整个区域的时间一致的镶嵌图。为了对区域中的所有55,188个农业地块进行分类，分别对三个分类区域进行了分类。分区允许将至少三个观察期包括在内。所有分类区域的总体准确度均超过85％。通过包含五个RapidEye的时空复合材料，可以实现最高的准确度87.4％。逐级准确性评估表明，选择代表植被时期相关物候阶段的时间步长很有用。提出的方法可以支持区域作物清单。播种季节早期的准确分类结果可以重新计算作物需水量并重新分配灌溉水。可以断定RapidEye的高时空分辨率对于整个中亚的农业土地利用分类非常有利。

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  • 会议地点 Prague(CZ)
  • 作者

    Christopher Conrad; Miriam Machwitz; Gunther Schorcht; Fabian Loew; Sebastian Fritsch; Stefan Dech;

  • 作者单位

    University of Wuerzburg, Institute of Geography and Geology, Dept. of Remote Sensing, Am Hubland, 97074 Wuerzburg, Germany;

    Center for Development Research (ZEF), Walter-Flex-Strasse 3, 53113 Bonn, Germany;

    University of Wuerzburg, Institute of Geography and Geology, Dept. of Remote Sensing, Am Hubland, 97074 Wuerzburg, Germany;

    University of Wuerzburg, Institute of Geography and Geology, Dept. of Remote Sensing, Am Hubland, 97074 Wuerzburg, Germany;

    University of Wuerzburg, Institute of Geography and Geology, Dept. of Remote Sensing, Am Hubland, 97074 Wuerzburg, Germany,Center for Development Research (ZEF), Walter-Flex-Strasse 3, 53113 Bonn, Germany;

    University of Wuerzburg, Institute of Geography and Geology, Dept. of Remote Sensing, Am Hubland, 97074 Wuerzburg, Germany,German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Postbox 1116, 82234 Wessling, Germany;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 环境遥感;
  • 关键词

    rapideye; time series; crop mapping; central asia; irrigated agriculture; field-based classification; classification concepts;

    机译：急眼时间序列;作物图中亚;灌溉农业基于现场的分类；分类概念;

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