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The evaluation of unmanned aerial systems-based photogrammetry and terrestrial laser scanning to generate DEMs of agricultural watersheds

机译:基于无人机的摄影测量和地面激光扫描生成农业流域的DEM的评估

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摘要

Agricultural watersheds tend to be places of intensive farming activities that permanently modify their microtopography. The surface characteristics of the soil vary depending on the crops that are cultivated in these areas. Agricultural soil microtopography plays an important role in the quantification of runoff and sediment transport because the presence of crops, crop residues, furrows and ridges may impact the direction of water flow. To better assess such phenomena, 3-D reconstructions of high-resolution agricultural watershed topography is essential. Fine-resolution topographic data collection technologies can be used to discern highly detailed elevation variability in these areas. Knowledge of the strengths and weaknesses of existing technologies used for data collection on agricultural watersheds may be helpful in choosing an appropriate technology. This study assesses the suitability of terrestrial laser scanning (TLS) and unmanned aerial system (UAS) photogrammetry for collecting the fine-resolution topographic data required to generate accurate, high-resolution digital elevation models (DEMs) in a small watershed area (12 ha). Because of farming activity, 14 TLS scans (≈ 25 points m− 2) were collected without using high-definition surveying (HDS) targets, which are generally used to mesh adjacent scans. To evaluate the accuracy of the DEMs created from the TLS scan data, 1,098 ground control points (GCPs) were surveyed using a real time kinematic global positioning system (RTK-GPS). Linear regressions were then applied to each DEM to remove vertical errors from the TLS point elevations, errors caused by the non-perpendicularity of the scanner’s vertical axis to the local horizontal plane, and errors correlated with the distance to the scanner’s position. The scans were then meshed to generate a DEMTLS with a 1 × 1 m spatial resolution. The Agisoft PhotoScan and MicMac software packages were used to process the aerial photographs and generate a DEMPSC (Agisoft PhotoScan) and DEMMCM (MicMac), respectively, with spatial resolutions of 1 × 1 m. Comparing the DEMs with the 1,098 GCPs showed that the DEMTLS was the most accurate data product, with a root mean square error (RMSE) of 4.5 cm, followed by the DEMMCM and the DEMPSC, which had RMSE values of 9.0 and 13.9 cm, respectively. The DEMPSC had absolute errors along the border of the study area that ranged from 15.0 to 52.0 cm, indicating the presence of systematic errors. Although the derived DEMMCM was accurate, an error analysis along a transect showed that the errors in the DEMMCM data tended to increase in areas of lower elevation. Compared with TLS, UAS is a promising tool for data collection because of its flexibility and low operational cost. However, improvements are needed in the photogrammetric processing of the aerial photographs to remove non-linear distortions.
机译:农业流域往往是集约化农业活动的场所,永久改变其微观形貌。土壤的表面特性因这些地区种植的农作物而异。农业土壤的微观形貌在径流和泥沙输送的量化中起着重要作用,因为农作物,农作物残留物,犁沟和山脊的存在可能影响水流的方向。为了更好地评估此类现象,高分辨率农业流域地形的3D重建至关重要。可以使用高分辨率的地形数据收集技术来识别这些区域中高度详细的高程变化。了解用于农业流域数据收集的现有技术的优缺点可能有助于选择合适的技术。这项研究评估了地面激光扫描(TLS)和无人机系统(UAS)摄影测量法在收集小流域(12公顷)中生成精确的高分辨率数字高程模型(DEM)所需的高分辨率的地形数据时的适用性)。由于耕作活动,在不使用通常用于对相邻扫描进行网格划分的高清测量(HDS)目标的情况下,收集了14次TLS扫描(≈25点m-2)。为了评估由TLS扫描数据创建的DEM的准确性,使用实时运动学全球定位系统(RTK-GPS)对1,098个地面控制点(GCP)进行了调查。然后将线性回归应用于每个DEM,以消除TLS点高程中的垂直误差,由扫描仪的垂直轴与本地水平面的非垂直度引起的误差以及与到扫描仪位置的距离相关的误差。然后将扫描结果网格化以生成具有1×1 m空间分辨率的DEMTLS。使用Agisoft PhotoScan和MicMac软件包处理航空照片并分别生成空间分辨率为1×1 m的DEMPSC(Agisoft PhotoScan)和DEMMCM(MicMac)。将DEM与1,098个GCP进行比较表明,DEMTLS是最准确的数据产品,均方根误差(RMSE)为4.5 cm,其次是DEMMCM和DEMPSC,RMSE值分别为9.0和13.9 cm。 。 DEMPSC沿研究区域边界的绝对误差为15.0至52.0 cm,表明存在系统误差。尽管导出的DEMMCM准确无误,但沿样条线进行的误差分析表明,在DEMMCM数据中,误差较低的区域倾向于增加。与TLS相比,UAS具有灵活性和较低的运营成本,是一种很有前途的数据收集工具。然而,在航空照片的摄影测量处理中需要改进以消除非线性失真。

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