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Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program

机译:监视美国农业:美国农业部,国家农业统计局,农田数据层计划

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The National Agricultural Statistics Service (NASS) of the US Department of Agriculture (USDA) produces the Cropland Data Layer (CDL) product, which is a raster-formatted, geo-referenced, crop-specific, land cover map. CDL program inputs include medium resolution satellite imagery, USDA collected ground truth and other ancillary data, such as the National Land Cover Data set. A decision tree-supervised classification method is used to generate the freely available state-level crop cover classifications and provide crop acreage estimates based upon the CDL and NASS June Agricultural Survey ground truth to the NASS Agricultural Statistics Board. This paper provides an overview of the NASS CDL program. It describes various input data, processing procedures, classification and validation, accuracy assessment, CDL product specifications, dissemination venues and the crop acreage estimation methodology. In general, total crop mapping accuracies for the 2009 CDLs ranged from 85% to 95% for the major crop categories.View full textDownload full textKeywordscropland classification, agriculture, Advanced Wide Field Sensor, crop estimatesRelated var addthis_config = { ui_cobrand: "Taylor & Francis Online", services_compact: "citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,more", pubid: "ra-4dff56cd6bb1830b" }; Add to shortlist Link Permalink http://dx.doi.org/10.1080/10106049.2011.562309
机译:美国农业部(USDA)的国家农业统计服务(NASS)制作了农田数据层(CDL)产品,该产品是栅格格式的,地理参考的,特定于作物的土地覆盖图。 CDL计划的输入包括中等分辨率的卫星图像,USDA收集的地面真相和其他辅助数据,例如“国家土地覆被数据”集。决策树监督的分类方法用于生成可免费获得的州级作物覆盖度分类,并根据CDL和NASS 6月农业调查的基本情况向NASS农业统计委员会提供作物种植面积估算。本文概述了NASS CDL程序。它描述了各种输入数据,处理程序,分类和确认,准确性评估,CDL产品规格,传播场所和农作物面积估算方法。一般而言,2009年CDL的主要作物类别的总作物制图准确度从85%到95%不等。查看全文下载全文关键词scropland分类,农业,先进的宽视野传感器,作物估计相关var addthis_config = {ui_cobrand:“ Taylor&Francis在线”,services_compact:“ citeulike,netvibes,twitter,technorati,可口,linkedin,facebook,stumbleupon,digg,google,更多”,pubid:“ ra-4dff56cd6bb1830b”};添加到候选列表链接永久链接http://dx.doi.org/10.1080/10106049.2011.562309

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