Name
Title | ||
|---|---|---|
Choosing the Right Horizontal Resolution for Gully Erosion Susceptibility Mapping Using Machine Learning Algorithms: A Case in Highly Complex Terrain |
Abstract | ||
|---|---|---|
Gully erosion susceptibility (GES) maps are essential for managing land resources and erosion control. Choosing the optimal horizontal resolution in GES mapping is a challenge. In this study, the optimal resolution for GES mapping in a complex loess hilly area on the Chinese Loess Plateau was tested using two machine learning algorithms. Unmanned aerial vehicle (UAV) images with a 9 cm resolution and GNSS RTK field-measured data were employed as base datasets, and 11 factors were used in the machine learning models. A series of horizontal resolutions, from 0.5-30 m, was used to determine which was the optimal level and how the resolution influenced the GES mapping. The results showed that the optimal resolution for GES mapping was 2.5-5 m in the loess hilly area, for both the random forest (RF) and extreme gradient-boosting (XGBoost) machine learning algorithms employed in this study. High resolutions overestimated the probability of gully erosion in stable regions, and it became difficult to identify gully and non-gully regions at too-coarse resolutions. The variable importance for GES mapping changed with the resolution and varied among variables. Slope gradient, land use, and contributing area were, in general, the three most critical factors. Land use remained an important factor at all the tested resolution levels. The importance of the slope gradient was underestimated at coarse resolutions (10-30 m), and the importance of the contributing area was underestimated at resolutions that were comparatively fine (0.5-1 m). This study provides an essential reference for selecting the optimal resolution for gully mapping, and thus, offers support for approaches attempting to map gullies using UAV. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3390/rs14112580 | REMOTE SENSING |
Keywords | DocType | Volume |
gully erosion, resolution, machine learning, influencing factor, Loess Plateau | Journal | 14 |
Issue | ISSN | Citations |
11 | 2072-4292 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Annan Yang | 1 | 0 | 0.68 |
| Chunmei Wang | 2 | 0 | 0.34 |
| Qinke Yang | 3 | 17 | 6.46 |
| Guowei Pang | 4 | 0 | 0.34 |
| Yongqing Long | 5 | 0 | 0.68 |
| Lei Wang | 6 | 65 | 54.21 |
| Lijuan Yang | 7 | 0 | 0.34 |
| Richard M. Cruse | 8 | 0 | 0.34 |