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Evaluating the impact of training data pixel level buffering on area sampling frame stratification results and crop estimates

机译:评估训练数据像素级别缓冲对区域采样帧分层结果和作物估计的影响

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Area Sampling Frames are used for surveys including crop acreage and yield, forests, and natural resource inventories and are the foundation of the statistical program of the USDA National Agricultural Statistics Service (NASS) and many statistical survey programs around the world. An automated area frame stratification method was recently implemented into NASS operations, which is based on the objective calculation of percent cultivation derived from the NASS geospatial Cropland Data Layers (CDLs). While autostratification consistently outperforms manual stratification in cultivated areas, we found that CDL-based pixel counting estimation consistently underestimated crop acreage. Previous research indicates that CDL classification accuracy is affected by training data pixel level buffering. We hypothesize that training data pixel level buffering will also affect the CDL based auto-stratification results and crop acreage estimation. This paper evaluates the impact of training data buffering on area frame stratification results and crop estimates. Preliminary results indicate that the crop acreage underestimation can be directly attributed to the training data pixel level buffering procedure.
机译:区域抽样框用于包括作物种植面积和产量,森林和自然资源清单在内的调查,并且是美国农业部国家农业统计局(NASS)统计计划和全球许多统计调查计划的基础。 NASS操作中最近实现了一种自动区域框架分层方法,该方法基于客观计算得出的NASS地理空间农田数据层(CDL)的耕种百分比。尽管在耕种地区,自动分层始终优于手动分层,但我们发现基于CDL的像素计数估算始终低估了农作物的种植面积。先前的研究表明,CDL分类准确性受训练数据像素级缓冲的影响。我们假设训练数据像素级缓冲也会影响基于CDL的自动分层结果和农作物面积估计。本文评估了训练数据缓冲对区域框架分层结果和作物估计的影响。初步结果表明,作物种植面积低估可以直接归因于训练数据像素级缓冲过程。

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