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Classification of irrigated and non-irrigated cropland using object-based image analysis: A case study in south-central Nebraska

机译:使用基于对象的图像分析的灌溉和非灌溉农作物的分类:内部内布拉斯加州中部的案例研究

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Detailed and accurate delineation of irrigated and non-irrigated land is critical to water resource management in arid and semi-arid areas, where dependence on groundwater irrigation is high. However, there is no such information available in the national land use and land cover databases such as National Land Cover Dataset and Cropland Data Layer. This study proposed an object-based image classification method to delineate the irrigated and non-irrigated cropland using remote sensing indices, evapotranspiration and other supplemental information. The method has been tested in South-Central Nebraska, and the results showed that the method produced accurate account of irrigated and non-irrigated land classification. The method is expected to be applicable in other arid and semi-arid areas.
机译:对灌溉和非灌溉土地的详细和准确划分对干旱和半干旱地区的水资源管理至关重要,其中依赖地下水灌溉很高。但是,国家土地利用和土地覆盖数据库等国家土地使用和土地覆盖数据层等信息。本研究提出了一种基于对象的图像分类方法,用于使用遥感指数,蒸发术和其他补充信息来描绘灌溉和非灌溉的农作物。该方法已在内布拉斯加州南部进行测试,结果表明,该方法对灌溉和非灌溉土地分类进行了准确的叙述。该方法预计将适用于其他干旱和半干旱地区。

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