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Remote Sensing for Natural Resources    2023, Vol. 35 Issue (1) : 115-122     DOI: 10.6046/zrzyyg.2021461
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A method for soil roughness measurement based on UAV point cloud data
ZHANG Tian1,2,3(), ZHOU Zhongfa1,2,3(), WANG Lingyu1,2,3, ZHAO Xin1,2,3, ZHANG Wenhui1,2,3, ZHANG Shu1,2,3, WANG Yu1,2,3
1. School of Geography and Environmental Science, Institute of Karst Science, Guizhou Normal University, Guiyang 550001,China
2. State Key Laboratory Incubation Base for Mountain Ecology Environment of Guizhou Province, Guiyang 550001,China
3. State Engineering Technology Institute for Karst Desertification Control, Guiyang 550001, China
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Abstract  

The soil roughness of cultivated land is an important element affecting the monitoring of agricultural information, such as soil moisture, microwave remote sensing observation, and plant growth. Soil roughness is generally interpreted according to field photos. However, such interpretation suffers some shortcomings such as low efficiency and anthropogenic effects on processing results. UAV low-altitude remote sensing is sensitive to surface relief. To explore the precision of the soil roughness determined using UAV data, this study employed UAV photogrammetry to photograph the surface and then compared the photogrammetry results with the data obtained using a gauging plate for soil roughness. The results show that the close-range photogrammetry had mean absolute errors of mainly 0.4~1.2 cm, a mean relative error of 6.16%, and a root mean square error of 0.40 cm. Therefore, UAV-based point cloud photogrammetry could be effectively applied to the measurement of surface roughness, and a smaller sampling area is associated with more accurate soil roughness.
Keywords soil roughness      point cloud data      close-range photogrammetry      three-dimensional modeling     
ZTFLH:  P237  
Issue Date: 20 March 2023
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Tian ZHANG
Zhongfa ZHOU
Lingyu WANG
Xin ZHAO
Wenhui ZHANG
Shu ZHANG
Yu WANG
Cite this article:   
Tian ZHANG,Zhongfa ZHOU,Lingyu WANG, et al. A method for soil roughness measurement based on UAV point cloud data[J]. Remote Sensing for Natural Resources, 2023, 35(1): 115-122.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021461     OR     https://www.gtzyyg.com/EN/Y2023/V35/I1/115
Fig.1  Data sampling diagram
类别 参数 参数值
无人机参数 无人机质量/g 907(起飞重量)
最大上升速度/(m/s) 5
最大下降速度/(m/s) 3
相机参数 相机型号 L1D-20C_10.3
焦距/mm 10
相片ISO曝光范围 100~3 200(自动)
相机分辨率/像素 5 472×3 648
飞行参数 飞行高度/m 10
航向/旁向重叠度/% 75/75
拍摄模式 悬停拍摄
地面分辨率/cm 0.43
照片数量/张 94
Tab.1  UAV, camera and flight parameters
Fig.2  Technical roadmap
Fig.3  Schematic diagram of block sampling
测量板法 无人机点云摄影测量法
RMSH ACL RMSH ACL
平均值 1.08 44.04 1.09 42.40
方差 0.06 53.00 0.05 41.41
最大值 1.39 55.00 1.48 49.00
最小值 0.80 23.00 0.60 24.00
Tab.2  Comparison of accuracy verification of surface roughness parameter measurement results (cm)
Fig.4  Result comparison between measuring boards method and close-range photogrammetry
Fig.5  Comparison of the same soil transect obtained by different measurement methods
Fig.6  UAV photogrammetry calculation results
采样面积/
(cm×cm)		 RMSH/cm ACL/cm 与上位测量结果之差
RMSH/cm ACL/cm
100×100 0.16 1.44 — —
80×80 0.13 1.00 0.03 0.44
60×60 0.11 0.53 0.02 0.47
40×40 0.07 0.89 0.04 -0.36
20×20 0.04 0.26 0.03 0.63
10×10 0.02 0.14 0.02 0.12
Tab.3  Measurement results of soil roughness parameters under different sampling area
Fig.7  Soil surface models of different sampling area
方法 外业数据
采集/s		 内业处理
与计算/min		 所需
设备		 RMSE/
cm		 测量
维度
测量板法 60 10 测量板 1.08 2D
无人机点云摄影测量法 2 20 无人机 0.32 3D
Tab.4  Comparison of the required time and accuracy of the two methods
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