A method for soil roughness measurement based on UAV point cloud data

ZHANG Tian; ZHOU Zhongfa; WANG Lingyu; ZHAO Xin; ZHANG Wenhui; ZHANG Shu; WANG Yu

doi:10.6046/zrzyyg.2021461

2023 Vol. 35, No. 1

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Article navigation > Remote Sensing for Natural Resources > 2023 > 35(1): 115-122

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ZHANG Tian, ZHOU Zhongfa, WANG Lingyu, ZHAO Xin, ZHANG Wenhui, ZHANG Shu, WANG Yu. 2023. A method for soil roughness measurement based on UAV point cloud data. Remote Sensing for Natural Resources, 35(1): 115-122. doi: 10.6046/zrzyyg.2021461

Citation:

ZHANG Tian, ZHOU Zhongfa, WANG Lingyu, ZHAO Xin, ZHANG Wenhui, ZHANG Shu, WANG Yu. 2023. A method for soil roughness measurement based on UAV point cloud data. Remote Sensing for Natural Resources, 35(1): 115-122. doi: 10.6046/zrzyyg.2021461

A method for soil roughness measurement based on UAV point cloud data

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

Received Date: 27 December 2021

Revised Date: 15 March 2023

Publish Date: 20 March 2023

Abstract

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.
- soil roughness /
- point cloud data /
- close-range photogrammetry /
- three-dimensional modeling

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References

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