基于地理空间耕地数据层的自动分层新方法在NASS区域框架构建中的实现

C. Boryan, Zhengwei Yang
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引用次数: 7

摘要

利用美国农业部国家农业统计局(NASS)地理空间耕地数据层(CDLs)的一种新的自动分层方法最近在NASS业务中实施。最近的研究结果表明,使用CDL分层方法而不是传统的卫星图像和航空摄影目视解译方法来定义耕地面积百分比,可以在降低成本的同时提高构建的区域采样帧(Area Sampling Frames, ASF)的准确性、客观性和效率[3]。本文描述了一种可操作的ASF构建过程,该过程将自动CDL分层结果与传统的编辑/审查程序相结合,这是一种混合方法。新的2013/2014年南达科他州和俄克拉何马州的asf使用新的操作流程成功构建,并在框架精度、操作效率和成本方面取得了显着改善。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementation of a new automatic stratification method using geospatial cropland data layers in NASS area frame construction
A new automatic stratification method utilizing USDA National Agricultural Statistics Service (NASS) geospatial Cropland Data Layers (CDLs) was recently implemented in NASS operations. Recent research findings indicated that using the CDL stratification method rather than visual interpretation of satellite imagery and aerial photography (traditional method) to define percent cultivation of land areas resulted in Area Sampling Frames (ASF) constructed with improved accuracy, objectivity and efficiency at reduced cost [3]. This paper describes an operational ASF construction process that integrates the automated CDL stratification results with traditional editing/review procedures, a hybrid approach. New 2013/2014 ASFs for South Dakota and Oklahoma were successfully built using the new operational process and illustrated significant improvements in frame accuracy, operational efficiency, and cost.
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