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Crop Field Boundary Delineation using Historical Crop Rotation Pattern

机译：使用历史作物轮作模式的作物田间边界定界

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摘要

GIS data layer on crop field boundary has many applications in agricultural research, ecosystem study, crop monitoring, and land management. Crop field boundary mapping through field survey is not time and cost effective for vast agriculture areas. Onscreen digitization on fine-resolution satellite image is also labor-intensive and error-prone. The recent development in image segmentation based on their spectral characteristics is promising for cropland boundary detection. However, processing of large volume multi-band satellite images often required high-performance computation systems. This study utilized crop rotation information for the delineation of field boundaries. In this study, crop field boundaries of Iowa in the United States are extracted using multi-year (2007-2018) CDL data. The process is simple compared to boundary extraction from multi-date remote sensing data. Although this process was unable to distinguish some adjacent fields, the overall accuracy is promising. Utilization of advanced geoprocessing algorithms and tools on polygon correction may improve the result significantly. Extracted field boundaries are validated by superimposing on fine resolution Google Earth images. The result shows that crop field boundaries can easily be extracted with reasonable accuracy using crop rotation information.
机译：作物田间边界上的GIS数据层在农业研究，生态系统研究，作物监测和土地管理中有许多应用。对于广大的农业地区而言，通过田间调查进行作物田间边界制图既不节省时间又不节省成本。高分辨率卫星图像上的屏幕数字化也需要大量劳动并且容易出错。基于光谱特征的图像分割技术的最新发展有望用于农田边界的检测。但是，处理大量的多波段卫星图像通常需要高性能的计算系统。这项研究利用作物轮作信息来划定田间边界。在这项研究中，使用多年期（2007-2018）CDL数据提取了美国爱荷华州的作物田间边界。与从多日期遥感数据中进行边界提取相比，该过程很简单。尽管此过程无法区分一些相邻的字段，但总体准确性是有希望的。在多边形校正中使用先进的地理处理算法和工具可以显着改善结果。通过在高分辨率的Google Earth图像上叠加来验证提取的场边界。结果表明，利用作物轮作信息可以很容易地以合理的精度提取作物田间边界。

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