Optimal Parameter Configuration of UAV for Measuring Gully Morphology in Black Soil Region of Northeast China

TANG Jie; XIE Yun; LIU Chuan; ZHANG Yan

doi:10.13870/j.cnki.stbcxb.2024.05.015

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Optimal Parameter Configuration of UAV for Measuring Gully Morphology in Black Soil Region of Northeast China

更新时间：2025-02-18

- Optimal Parameter Configuration of UAV for Measuring Gully Morphology in Black Soil Region of Northeast China
- Vol. 38, Issue 5, Pages: 49-58(2024)
- 作者机构：
  
  1. 北京师范大学环境与生态前沿交叉研究院,广东,珠海,519087
  2. 北京师范大学地理科学学部,北京,100875
  3. 北京师范大学文理学院,广东,珠海,519087
  4. 北京林业大学水土保持学院,北京,100083
- 作者简介：
- 基金信息：
- DOI：10.13870/j.cnki.stbcxb.2024.05.015
  CLC： S157.1
- Published：2024
- 稿件说明：
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TANG Jie, XIE Yun, LIU Chuan, et al. Optimal Parameter Configuration of UAV for Measuring Gully Morphology in Black Soil Region of Northeast China[J]. 2024, 38(5): 49-58.
DOI：

TANG Jie, XIE Yun, LIU Chuan, et al. Optimal Parameter Configuration of UAV for Measuring Gully Morphology in Black Soil Region of Northeast China[J]. 2024, 38(5): 49-58. DOI： 10.13870/j.cnki.stbcxb.2024.05.015.

摘要

[目的] 东北黑土区切沟侵蚀不断蚕食耕地资源

威胁我国粮食安全。无人机技术的发展为沟蚀监测提供有效手段

但获取数据分辨率越高耗时越长

如何配置无人机参数

使其既能满足精度要求又能提高航摄效率是当前需要解决的问题。[方法] 选择东北漫川漫岗区的鹤北流域为研究区

以实地断面测量数据为验证值

通过评估不同分辨率、不同飞行航向及不同无人机数据提取的切沟参数精度

探究不同无人机参数配置的适用条件。[结果] (1)与实测值相比

分辨率1 cm数据提取的切沟参数精度最高

所有参数的平均百分误差<5.0%

适用于典型切沟发展过程监测;分辨率3

5 cm数据提取的切沟宽度平均误差百分比<10.0%

提取的切沟深度平均误差随切沟深度变小而增大

切沟深度>1 m时

平均百分误差<10.0%

适用于区域范围快速抽样调查;8

10 cm分辨率提取的切沟深度和截面积平均百分误差>40.0%

适用于切沟分布位置和平面参数提取;(2)固定翼无人机垂直地面获取的正摄数据虽在提取切沟二维形态特征指标方面效果略好

但在三维形态特征指标方面不如多旋翼无人机倾斜摄影测量获取的数据;(3)单航向倾斜摄影测量数据提取的切沟平均深度和截面积平均百分误差是交叉双航向数据平均百分误差的1.7

1.9倍。交叉航向获取的无人机数据提取切沟参数精度更高

且细节更丰富。[结论] 倾斜摄影测量和交叉双航向航测能显著提高获取的切沟三维参数精度

1~3

3~5 cm分辨率可分别用于典型切沟监测和大范围切沟侵蚀快速调查。单航向正摄摄影测量获取的5 cm以上分辨率数据适用于提取切沟位置和平面参数。

Abstract

[Objective]Severe gully erosion in the black soil region of Northeast China is continuously eroding arable land resources

posing a threat to the country's food security. The development of unmanned aerial vehicle (UAV) technology provides an effective way for monitoring gully erosion

but the higher the resolution of data obtained

the longer it takes. The challenge lies in configuring UAV parameters to simultaneously meet accuracy requirements and enhance aerial photography efficiency. [Methods]The Hebei catchment in the rolling hills region of Northeast China was chosen as the study area. Using actual cross-section measurement data as a validation value

the accuracy of gully parameters extracted from UAV data at different resolutions

flight directions

and types was assessed to explore the suitable conditions for various UAV parameter configurations. [Results] (1) Compared with field measurement data

data at a 1 cm resolution extracted the gully parameters with the highest accuracy

the average percentage error for all parameters was less than 5.0%

suitable for monitoring typical gully development processes. Data extracted at 3 cm and 5 cm resolutions had an average percentage error of less than 10.0% for gully width

and the average error increased as gully depth decreased. For gullies deeper than 1 m

the average percentage error was less than 10.0%

suitable for rapid regional sampling surveys. Data extracted at resolutions of 8 cm and 10 cm had average percentage errors greater than 40.0% for gully depth and cross-sectional area

suitable for extracting gully distribution locations and planar parameters. (2) Although fixed-wing UAV obtaining data slightly outperformed in extracting two-dimensional gully features

multirotor UAV using oblique photogrammetry were better in extracting three-dimensional gully features. (3) The average percentage errors for gully depth and cross-sectional area extracted from single-direction oblique photogrammetry data were 1.7 and 1.9 times those from cross-direction data. UAV data obtained from cross-direction flights provided higher accuracy in gully parameter extraction and richer details. [Conclusion] Setting UAV parameters to a resolution of less than 5 cm and obtaining cross-direction oblique aerial photography data can meet the accuracy requirements for monitoring gully morphology in the black soil region of Northeast China.

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Keywords

references

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