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A method for mapping corn using the US Geological Survey 1992 National Land Cover Dataset

机译:一种使用1992年美国地质调查局国家土地覆盖数据集绘制玉米图的方法

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Long-term exposure to elevated nitrate levels in community drinking water supplies has been associated with an elevated risk of several cancers including non-Hodgkin's lymphoma, colon cancer, and bladder cancer. To estimate human exposure to nitrate,specific crop type information is needed as fertilizer application rates vary widely by crop type. Corn requires the highest application of nitrogen fertilizer of crops grown in the Midwest US. We developed a method to refine the US Geological Survey National Land Cover Dataset (NLCD) (including map and original Landsat images) to distinguish corn from other crops. Overall average agreement between the resulting corn and other row crops class and ground reference data was 0.79 kappa coefficient with individual Landsat images ranging from 0.46 to 0.93 kappa. The highest accuracies occurred in Regions where corn was the single dominant crop (greater than 80.0%) and the crop vegetation conditions at the time of image acquisition were optimum for separation of corn from all other crops. Factors that resulted in lower accuracies included the accuracy of the NLCD map, accuracy of corn areal estimates, crop mixture, crop condition at the time of Landsat overpass, and Landsat scene anomalies.
机译:长期接触社区饮用水中硝酸盐水平升高与多种癌症(包括非霍奇金淋巴瘤,结肠癌和膀胱癌)的风险升高有关。为了估算人类对硝酸盐的暴露程度,需要特定的作物类型信息,因为肥料的施用率因作物类型而异。玉米需要在美国中西部种植的农作物中氮肥施用量最高。我们开发了一种方法,可以完善美国地质调查局国家土地覆盖数据集(NLCD)(包括地图和原始Landsat图像),以区分玉米与其他作物。所得玉米与其他行作作物类别和地面参考数据之间的总体平均一致性为0.79 kappa系数,各个Landsat影像的范围为0.46至0.93 kappa。精度最高的地区是玉米为单一优势作物(大于80.0%),并且在采集图像时的作物植被条件最适合于将玉米与所有其他作物分开。导致较低准确度的因素包括NLCD地图的准确性,玉米面积估计的准确性,作物混合物,Landsat立交时的作物状况以及Landsat场景异常。

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