Simulation and Spatio-Temporal Analysis of Soil Erosion in the Source Region of the Yellow River Using Machine Learning Method
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Source and Processing
2.2.1. Measured Data Acquisition and Processing
2.2.2. Environmental Variable Data Acquisition and Processing
- Soil factors: 5 variables, including clay, sand, silt, soil organic carbon (SOC), and soil erodibility (K) factor;
- Meteorological factors: 8 variables, including precipitation, temperature, rainfall erosivity (R) factor, wind, illumination, freeze–thaw (FT), nighttime land surface temperature (LsTN), and daytime land surface temperature (LsTD);
- Topographic factors: longitude, latitude, elevation, slope, aspect, and length–slope (LS) factor;
- Remote sensing indices: 15 variables, including enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), coverage, leaf area index (LAI), modified soil adjusted vegetation index (MSAVI), normalized difference vegetation green index (NDVGI), soil adjusted vegetation index (SAVI), difference vegetation index (DVI), normalized difference soil index (NDSI), normalized difference water index (NDWI), optimized soil adjusted vegetation index (OSAVI), ratio vegetation index (RVI), soil adjusted total vegetation index (SATVI), soil color index (SCI), transformed vegetation index (TVI), and coverage.
2.3. Methods and Modeling
2.3.1. RUSLE Model
2.3.2. RWEQ Model
2.3.3. Machine Learning Model
2.4. Trend Analysis
3. Results
3.1. RUSLE Model Results
3.2. REWQ Model Results
3.3. RF Model Results
3.3.1. Variable Selection
3.3.2. Model Results and Accuracy Evaluation
3.4. Spatial Distribution of Soil Erosion in the SRYR
3.5. Temporal Distribution of Soil Erosion in the SRYR
4. Discussion
4.1. Model Comparisons and Effectiveness
4.2. Influence Factors
4.3. Implications for Soil Erosion Prevention and Control in the SRYR
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Variable Types | Data Sources | Data Processing |
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Soil factors | accessed on 1 April 2024 https://data.isric.org/ | - |
Meteorological factors | accessed on 1 April 2024 http://data.cma.cn https://data.tpdc.ac.cn/zh-hans/ | Meteorological station point data were interpolated into raster data format with a 1 km spatial resolution using ANUSPLIN tool. |
Topographic factors | accessed on 15 April 2024 http://srtm.csi.cgiar.org/ | Calculated using the DEM and adjusted to a 1 km spatial resolution. |
Remote sensing indices | accessed on 28 April 2024 https://code.earthengine.google.com/ | - |
Z Value | Trends in Soil Erosion | |
---|---|---|
≥0.001 | ≥1.96 | Significantly worsened |
≥0.001 | −1.96–1.96 | Slightly worsened |
−0.001–0.001 | −1.96–1.96 | Stable |
≤−0.001 | −1.96–1.96 | Slightly improved |
≤−0.001 | ≤−1.96 | Significantly improved |
County | Max (t·ha−1·y−1) | Min (t·ha−1·y−1) | Mean (t·ha−1·y−1) | Total (106 t·y−1) | Proportion (%) |
---|---|---|---|---|---|
Maduo | 33.52 | 14.62 | 25.63 | 52.18 | 23.17 |
Maqin | 33.03 | 14.40 | 19.06 | 25.36 | 11.26 |
Qumalai | 33.60 | 15.18 | 28.89 | 22.90 | 10.17 |
Dari | 32.30 | 14.09 | 17.69 | 20.46 | 9.09 |
Maqu | 30.73 | 13.57 | 15.53 | 14.75 | 6.55 |
Gande | 32.90 | 14.35 | 16.45 | 12.02 | 5.34 |
Chindu | 33.04 | 15.06 | 23.76 | 10.91 | 4.84 |
Xinghai | 32.76 | 14.74 | 19.97 | 10.59 | 4.70 |
Ruoregai | 23.05 | 12.73 | 14.96 | 10.12 | 4.49 |
Hongyuan | 28.98 | 12.08 | 13.58 | 9.14 | 4.06 |
Jiuzhi | 32.05 | 13.44 | 15.19 | 9.11 | 4.05 |
Tongde | 32.56 | 14.59 | 17.96 | 8.13 | 3.61 |
Henan | 25.04 | 14.51 | 15.75 | 7.75 | 3.44 |
Zeku | 32.17 | 14.79 | 16.23 | 6.87 | 3.05 |
Aba | 20.47 | 12.79 | 14.04 | 4.90 | 2.18 |
Land Cover Type | Max (t·ha−1·y−1) | Min (t·ha−1·y−1) | Mean (t·ha−1·y−1) | Total (106 t·y−1) | Proportion (%) |
---|---|---|---|---|---|
Alpine steppe | 33.52 | 12.08 | 24.74 | 34.68 | 15.64 |
Alpine meadow | 33.50 | 12.23 | 18.49 | 135.39 | 61.08 |
Mountain meadow | 25.95 | 12.14 | 14.31 | 15.60 | 7.04 |
Cropland | 32.53 | 13.43 | 17.10 | 4.13 | 1.86 |
Wetland | 33.35 | 12.34 | 16.98 | 12.70 | 5.73 |
Barren | 33.60 | 14.25 | 28.15 | 17.83 | 8.04 |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Su, J.; Tang, R.; Lin, H. Simulation and Spatio-Temporal Analysis of Soil Erosion in the Source Region of the Yellow River Using Machine Learning Method. Land 2024, 13, 1456. https://doi.org/10.3390/land13091456
Su J, Tang R, Lin H. Simulation and Spatio-Temporal Analysis of Soil Erosion in the Source Region of the Yellow River Using Machine Learning Method. Land. 2024; 13(9):1456. https://doi.org/10.3390/land13091456
Chicago/Turabian StyleSu, Jinxi, Rong Tang, and Huilong Lin. 2024. "Simulation and Spatio-Temporal Analysis of Soil Erosion in the Source Region of the Yellow River Using Machine Learning Method" Land 13, no. 9: 1456. https://doi.org/10.3390/land13091456