Crop sequence boundaries using USDA national agricultural statistics service historic cropland data layers

Kevin A. Hunt, Jonathon Abernethy, Peter C. Beeson, Maria Bowman, Steven Wallander, Ryan Williams
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Abstract

Gridded landcover datasets like the NASS Cropland Data Layer (CDL) provide a useful resource for analyses of cropland management. However, many farm operation decisions are made at the field level, not the pixel level. To capture relationships between land cover and field characteristics – size, contiguity, etc. – some method is needed to aggregate gridded data into crop fields. To provide a uniform and consistent approach for aggregation of gridded data at the field level over a series of years, this research project developed a set of Crop Sequence Boundaries (CSBs), which are polygons that delineate areas of homogeneous cropping sequences for the contiguous US. The CSBs are open-sourced algorithm-based, geospatial polygons derived using historic CDLs together with road and rail networks to capture areas with common cropping sequences. The CSB approach used geospatial functions in Google Earth Engine (GEE) and in the ArcGIS Pro application. These geospatial functions are run in parallel by sub-dividing the contiguous US into smaller regions based on road and rail boundaries to prevent overlaps or gaps in the data. As a new set of algorithmically delineated field polygons, the CSBs enhance applications requiring large-scale crop mapping with vector-based data.
利用美国农业部国家农业统计服务历史耕地数据层划分作物序列边界
网格土地覆盖数据集(如 NASS 耕地数据层 (CDL))为耕地管理分析提供了有用的资源。然而,许多农场经营决策是在田地层面而非像素层面做出的。为了捕捉土地覆被与田地特征(面积、毗连性等)之间的关系,需要采用某种方法来汇总网格。- 需要采用某种方法将网格数据汇总到作物田中。为了提供一种统一、一致的方法来汇总多年来田块级的网格数据,该研究项目开发了一套作物序列边界(CSBs),这是一种多边形,用于划定美国毗连地区的同质作物序列区域。CSB 是基于开源算法的地理空间多边形,利用历史 CDL 以及公路和铁路网络来捕捉具有共同种植序列的区域。CSB 方法使用了谷歌地球引擎 (GEE) 和 ArcGIS Pro 应用程序中的地理空间功能。这些地理空间功能并行运行,根据公路和铁路边界将美国毗连区细分为更小的区域,以防止数据中出现重叠或空白。作为一套新的算法划定的田间多边形,CSB 增强了需要使用基于矢量数据的大规模作物制图的应用。
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