A Comparison of UAV- and TLS-derived Plant Height for Crop Monitoring: Using Polygon Grids for the Analysis of Crop Surface Models (CSMs)
A Comparison of UAV- and TLS-derived Plant Height for Crop Monitoring: Using Polygon Grids for the Analysis of Crop Surface Models (CSMs)
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DOI：
10.1127/pfg/2016/0289
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发表时间：
2016-01-01
影响因子：
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通讯作者：
Bolten, Andreas
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文献类型：
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作者：
Bareth, Georg;Bendig, Juliane;Bolten, Andreas
多时相作物表面模型（csm）是一种可靠的农作物监测方法。它们提供了作物冠层的三维表示，最好作为多时间数据集提供。利用csm可以推导出植物高度的空间分布。csm的数据通过遥感方法捕获，包括地面激光扫描（TLS）和结合计算机视觉技术的无人驾驶飞行器（uav）图像。以前的研究强调了这两种方法的适用性。然而，这两种方法是否能提供实际可比的资料仍然是一个悬而未决的问题。我们假设两个传感器的不同视角会影响最终的CSM，并且由于传感器的最低点位置，基于无人机的CSM包含作物密度信息。因此，我们期望在基于无人机的CSM中有较低的平均植物高度和较高的变化。对两种方法的植物高度相关性进行分析，并利用多边形网格进行空间分析。多边形网格通过区域统计为每个栅格单元提供描述性统计，以研究数据作为密度度量的潜力。通过这种分析，可以最大限度地提取更大网格单元的空间信息，尽管它不能与标准重采样方法相比。我们分析了一个大麦试验田生长早期、中期和晚期的csm，发现两种方法得出的株高具有很高的相关性（R-2 = 0.91）。在各个生育期，无人机衍生株高普遍低于tls衍生株高。然而，与预期相反，变异系数在TLS数据集中更高。
Multi-temporal crop surface models (CSMs) are a reliable method for agricultural crop monitoring. They provide 3-dimensional representations of crop canopies, preferably available as a multi-temporal dataset. From the CSMs the spatial distribution of plant height can be derived. The data for the CSMs are captured by remote sensing methods including terrestrial laser scanning (TLS) and imagery from unmanned aerial vehicles (UAVs) combined with computer vision techniques. Previous studies underlined the suitability of both methods. However, it remained an open question if both methods provide actually comparable information. We assume that the differing viewing angles of both sensors influence the resulting CSM and that the UAV-based CSMs contain crop density information due to the nadir sensor position. Therefore, we expect a lower mean plant height and higher variation in the UAV-based CSM. The correlation between plant heights from both methods was analyzed and complemented by using polygon grids for spatial analysis. The polygon grids provide descriptive statistics for each raster cell by zonal statistics to investigate the data's potential as a density measure. Through this analysis it is possible to maximize the extraction of spatial information for larger grid cells though it is not comparable to standard resampling methods. We analyzed CSMs at early, middle, and late growth stages from a barley experiment field and found a high correlation (R-2 = 0.91) in plant height derived from both methods. The UAV-derived plant height was generally lower than the TLS-derived plant height at all growth stages. However, contrary to the expectations the coefficient of variation was higher in the TLS data set.