Correcting Elevation Error of ASTER GDEM Using Random Forest Regression Algorithm

YU Tingting; DONG Youfu

doi:10.13203/j.whugis20190245

Volume 46 Issue 7

Jul. 2021

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Geomatics and Information Science of Wuhan University > 2021 > 46(7): 1098-1105. > DOI: 10.13203/j.whugis20190245

YU Tingting, DONG Youfu. Correcting Elevation Error of ASTER GDEM Using Random Forest Regression Algorithm[J]. Geomatics and Information Science of Wuhan University, 2021, 46(7): 1098-1105. DOI: 10.13203/j.whugis20190245

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Correcting Elevation Error of ASTER GDEM Using Random Forest Regression Algorithm

YU Tingting^1,,
DONG Youfu^1, ,

1.
School of Geomatics Science and Technology, Nanjing Tech University, Nanjing 211816, China

Funds:

The National Natural Science Foundation of China 41131360

The National Natural Science Foundation of China 41871324

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Author Bio:
YU Tingting, master, specializes in digital elevation model uncertainty. E-mail: 15850736616@163.com
Corresponding author:
DONG Youfu, PhD, professor. E-mail: dongyoufu@163.com
Received Date: October 09, 2019
Published Date: July 09, 2021

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Abstract

Abstract

Objectives Effective correction of ASTER GDEM (advanced spaceborne thermal emission and reflection radiometer global digital elevation model) elevation error is of great significance to the quality and application of ASTER GDEM data. Terrain parameters such as slope and aspect derived from digital elevation model (DEM) data have a significant impact on the accuracy of the data. Therefore, the relationship model between the ASTER GDEM elevation error and each influencing factor can be constructed to effectively correct its elevation accuracy.
Methods Four geomorphological types of Changwu, Yijun, Ganquan and Yanchuan in the Loess Plateau of northern Shaanxi are selected. With 1∶50 000 DEM as reference data, after data preprocessing, the elevation error values and related topographic factors and the surface coverage index of each point are calculated, by extracting a certain number of sampling points and check points, a random forest regression algorithm is introduced to establish an elevation error prediction model to correct the elevation accuracy, and compared with multiple regression model.
Results ASTER GDEM elevation error characteristics are closely related to the terrain conditions; the contribution of each impact factor to the model is different in different areas; the random forest regression prediction model is better than the multiple regression model overall, and has good applicability and error correction effect. It can reduce the error mean of Changwu, Yijun, Ganquan and Yanchuan by 3.08 m, 3.00 m, 3.61 m and 6.95 m.
Conclusions This research is helpful to understand the ASTER GDEM elevation error characteristics and its influencing factors in different geomorphological areas of the Loess Plateau in northern Shaanxi. At the same time, suitable regression models can be selected for areas with different terrain conditions to effectively correct the error values, further improve the application accuracy of ASTER GDEM data.
- ASTER GDEM,
- elevation error,
- random forest regression algorithm

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References

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