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首页> 外文学位 >Principal Component Analysis and Spatial Regression Techniques to Model and Map Corn and Soybean Yield Variability with Radiometrically Calibrated Multitemporal and Multispectral Digital Aerial Imagery
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Principal Component Analysis and Spatial Regression Techniques to Model and Map Corn and Soybean Yield Variability with Radiometrically Calibrated Multitemporal and Multispectral Digital Aerial Imagery

机译：主成分分析和空间回归技术，通过辐射校准的多时相和多光谱数字航空影像对玉米和大豆的产量变异性进行建模和映射

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摘要

Remotely sensed data has been discussed as a possible alternative to the standard precision agriculture systems of combine-mounted yield monitors because of the burden, cost, end of season use, and inherent errors that are associated with these systems. Due to the potential quantitative use of remote sensing in precision agriculture, the primary focus of this study was to test the relationship between multitemporal/multispectral digital aerial imagery with corn (Zea mays L.) and soybean (Glycine max L.) yield. Digital aerial imagery was gathered on nine different dates throughout the 2015 growing season from two fields (one corn and one soybean) located on a farm in Story County, Iowa. To begin assessing this relationship, the digital aerial imagery was radiometrically calibrated. The radiometric calibration process used calibration tarps with known reflectance values (3, 6, 12, 22, 44, and 56 percent). The calibrated imagery was then used to calculate and output 12 different vegetation indices (VIs) and three calibrated wavebands (red, green, and near-infrared).;Next, the calibrated VIs and wavebands from the 2015 growing season were used to examine their relationship with the corn and soybean yield data collected from a combine yield monitor system. This relationship between multitemporal/multispectral digital aerial imagery with corn and soybean yield was investigated with principal component analysis and spatial modeling techniques. The results from spatial modeling of corn revealed that VIs utilizing the green waveband performed strongly. VIs such as, chlorophyll index-green, chlorophyll vegetation index, and green normalized difference vegetation index accounted for 81.6, 83.0, and 82.4 percent of the yield variability, respectively. Strong modeling relationships were also found in soybean using just the near-infrared waveband or VIs that utilized the near-infrared waveband. The near-infrared waveband captured 89.1 percent of the yield variation, while VIs such as, difference vegetation index, triangular vegetation index, soil adjusted vegetation index, and optimized soil adjusted vegetation index accounted for 87.3, 87.3, 83.9, and 83.8 percent of soybean yield variability, respectively. The temporal assessment of the remotely sensed data also identified certain VIs and wavebands that captured pivotal growth stages for detecting potential yield limiting factors. These specific growth stages varied for different VIs and wavebands for both corn and soybean. Overall, the results from this study identified that mid-to-late vegetative growth stages (prior to tasseling) and late-season reproductive stages were important parameters that provided unique information in the modeling of corn yield variability, while the later reproductive stages (just prior to senescence) were essential to capturing soybean yield variability.;Lastly, this research produced corn and soybean yield maps from the digital aerial imagery. The digital aerial imagery yield maps were then compared with maps that used kriging interpolation of the combine yield monitor data gathered from the same corn and soybean fields. The results indicated that both corn and soybean yield maps produced with multitemporal/multispectral digital aerial imagery were comparable with a standard method of kriging interpolation from yield monitor data.
机译：由于与这些系统相关的负担，成本，季节结束使用和固有错误，已经讨论了遥感数据可以作为组合式产量监测仪的标准精密农业系统的一种可能替代方法。由于精确农业中遥感技术的潜在定量应用，本研究的主要重点是检验玉米（Zea mays L.）和大豆（Glycine max L.）产量的多时相/多光谱数字航空影像之间的关系。在整个2015年生长季节的9个不同日期收集了数字航空影像，这些数字来自位于爱荷华州斯托特县的一个农场的两个田地（一个玉米和一个大豆）。为了开始评估这种关系，对数字航空影像进行了辐射校准。辐射校准过程使用具有已知反射率值（3％，6％，12％，22％，44％和56％）的校准油布。然后使用校准后的图像计算并输出12种不同的植被指数（VI）和三个校准后的波段（红色，绿色和近红外）;接着，使用2015年生长期的校准后的VI和波段来检查其与从联合收割机监控系统收集的玉米和大豆收成数据的关系。利用主成分分析和空间建模技术研究了玉米与大豆产量之间的多时相/多光谱数字航空影像之间的关系。玉米空间建模的结果表明，利用绿色波段的VI表现出色。叶绿素指数绿色，叶绿素植被指数和绿色归一化差异植被指数等VI分别占产量变异的81.6％，83.0％和82.4％。在仅使用近红外波段或利用了近红外波段的VI的大豆中也发现了很强的建模关系。近红外波段捕获了89.1％的产量变化，而差异植被指数，三角植被指数，土壤调整后的植被指数和优化土壤调整后的植被指数等VI分别占大豆的87.3、87.3、83.9和83.8％产量差异。对遥感数据的时间评估还确定了某些VI和波段，这些VI和波段捕获了关键的生长阶段，用于检测潜在的产量限制因素。这些特定的生长阶段因玉米和大豆的不同VI和波段而异。总体而言，这项研究的结果表明，中晚期营养生长阶段（去雄之前）和后期生殖阶段是重要的参数，可为建模玉米产量变异提供独特的信息，而后期生殖阶段（仅衰老之前）对于捕获大豆产量的变化至关重要。最后，这项研究从数字航空影像中绘制了玉米和大豆的产量图。然后将数字航空影像产量图与使用克里格插值法对从相同玉米和大豆田收集的联合收成监测器数据进行插值的图进行比较。结果表明，用多时相/多光谱数字航拍图像制作的玉米和大豆产量图均与标准的克里格插值法（根据产量监测数据）可比。

著录项

  • 作者

    Pritsolas, Joshua.;

  • 作者单位

    Southern Illinois University at Edwardsville.;

  • 授予单位 Southern Illinois University at Edwardsville.;
  • 学科 Agriculture.;Remote sensing.;Geographic information science and geodesy.
  • 学位 M.S.
  • 年度 2018
  • 页码 258 p.
  • 总页数 258
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 世界各国经济概况、经济史、经济地理;
  • 关键词

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