Crop height is useful to monitor the effects of cropland reclamation. Unmanned aircraft system (UAS)-based observation through digital cameras has exhibited significant potential as a cost-effective way to predict crop height. However, the performance of UAS-based multispectral images has not been adequately analyzed in the context of estimating crop height. Therefore, our study investigates the performance of UAS-based multispectral images in terms of modeling and extracting crop height by considering a region of reclaimed cropland as an example. A framework is developed to model and extract the height of wheat and corn plants, the point cloud of varying densities is generated using the images, and geometric and physical outliers are removed from the point cloud. The flight altitude, threshold of extraction, and format of the data are examined to accurately describe and verify the canopy structure. The results indicate that UAS-based multispectral images can be used to estimate crop height. The relative accuracy of the estimated height of corn (<6 % ) was higher than that of wheat (>9 % ), but the opposite result was obtained in terms of absolute accuracy. The estimated crop height was significantly correlated with the measured above-ground biomass and can be used to monitor typical reclaimed cropland.

中文翻译：

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使用来自小型无人机系统的多光谱图像估计开垦农田中小麦和玉米作物的冠层表面高度

作物高度可用于监测农田开垦的效果。通过数码相机进行的基于无人机系统 (UAS) 的观察已显示出作为预测作物高度的经济有效方式的巨大潜力。然而，在估计作物高度的背景下，基于 UAS 的多光谱图像的性能尚未得到充分分析。因此，我们的研究以开垦的农田区域为例，研究了基于 UAS 的多光谱图像在建模和提取作物高度方面的性能。开发了一个框架来建模和提取小麦和玉米植物的高度，使用图像生成不同密度的点云，并从点云中去除几何和物理异常值。飞行高度，提取阈值，检查数据的格式和格式以准确描述和验证冠层结构。结果表明，基于 UAS 的多光谱图像可用于估计作物高度。玉米估计高度的相对精度（<6%）高于小麦（>9%），但在绝对精度方面得到了相反的结果。估计的作物高度与测量的地上生物量显着相关，可用于监测典型的开垦农田。