Mapping Groundwater Prospective Areas Using Remote Sensing and GIS-Based Data Driven Frequency Ratio Techniques and Detecting Land Cover Changes in the Yellow River Basin, China
Abstract
:1. Introduction
2. Study Area
3. Data and Methods
4. Results
4.1. Soil
4.2. Elevation
4.3. Aspects
4.4. Topography Roughness Index (TRI)
4.5. Depressions/Sinks
4.6. Lineaments
4.7. Distance to River
4.8. Rainfall Data
4.9. Drainage Density (Dd)
5. Groundwater Potential Mapping and Validation of the Built Models
6. Changes Detection of in Land Use Land Cover
7. Discussion
8. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Elevation | No. Pixels in Domain | Domain % | No. Wells | No. Wells % | FR |
---|---|---|---|---|---|
1087 to 1276 | 1,016,102 | 0.19 | 38 | 0.38 | 1.97 |
1276 to 1479 | 1,641,795 | 0.31 | 20 | 0.2 | 0.64 |
1479 to 1705 | 1,315,398 | 0.25 | 24 | 0.24 | 0.96 |
1705 to 1998 | 942,575 | 0.18 | 15 | 0.15 | 0.84 |
1998 to 2941 | 349,645 | 0.07 | 3 | 0.03 | 0.45 |
TRI classes | |||||
0.614 to 0.889 | 542,030 | 0.10 | 12 | 0.12 | 1.17 |
0.535 to 0.614 | 1,244,420 | 0.24 | 21 | 0.21 | 0.89 |
0.461 to 0.535 | 1,655,667 | 0.31 | 27 | 0.27 | 0.86 |
0.379 to 0.461 | 1,299,650 | 0.25 | 24 | 0.24 | 0.97 |
0.111 to 0.379 | 533,086 | 0.10 | 16 | 0.16 | 1.58 |
Dd reclasses | |||||
5.31 to 31.10 | 6537 | 0.12 | 6 | 0.06 | 0.49 |
31.20 to 43.10 | 15,167 | 0.29 | 26 | 0.26 | 0.91 |
43.20 to 54.30 | 15,598 | 0.29 | 30 | 0.3 | 1.02 |
54.40 to 66.70 | 11,203 | 0.21 | 24 | 0.24 | 1.14 |
66.80 to 101 | 4551 | 0.09 | 14 | 0.14 | 1.63 |
Soil | |||||
Calcic xerosols | 13,224 | 0.261334 | 21 | 0.21 | 0.80 |
Luvic xerosols | 332 | 0.006561 | 0 | 0 | 0.00 |
Lithosols | 26,848 | 0.530572 | 35 | 0.35 | 0.66 |
Eutric Gleysols | 9759 | 0.192858 | 43 | 0.43 | 2.23 |
Calcic cambisols | 439 | 0.008676 | 1 | 0.01 | 1.15 |
Dist River | |||||
1480 to 1750 | 1,870,410 | 0.046373 | 0 | 0 | 0 |
1110 to 1480 | 5,139,914 | 0.127435 | 12 | 0.12 | 0.94 |
740 to 1110 | 8,467,401 | 0.209934 | 13 | 0.13 | 0.62 |
370 to 740 | 11,778,792 | 0.292033 | 32 | 0.32 | 1.10 |
0 to 370 | 13,077,205 | 0.324225 | 43 | 0.43 | 1.33 |
Depression | |||||
0 to 1 | 5,041,843 | 0.957521 | 95 | 0.95 | 0.99 |
1 to 3.8 | 144,133 | 0.027373 | 4 | 0.04 | 1.46 |
3.8 to 72 | 79,539 | 0.015106 | 1 | 0.01 | 0.66 |
ASPECT | |||||
Flat (−1) | 13,758 | 0.002613 | 0 | 0 | 0 |
North (0–22.5) | 444,969 | 0.084506 | 20 | 0.2 | 2.37 |
Northeast (22.5–67.5) | 766,094 | 0.145493 | 20 | 0.2 | 1.37 |
East (67.5–112.5) | 578,467 | 0.10986 | 4 | 0.04 | 0.36 |
Southeast (112.5–157.5) | 542,320 | 0.102995 | 6 | 0.06 | 0.58 |
South (157.5–202.5) | 636,523 | 0.120885 | 11 | 0.11 | 0.91 |
Southwest (202.5–247.5) | 680,426 | 0.129223 | 9 | 0.09 | 0.70 |
West (247.5–292.5) | 594,492 | 0.112903 | 12 | 0.12 | 1.06 |
Northwest (292.5–337.5) | 654,449 | 0.12429 | 13 | 0.13 | 1.05 |
North (337.5–360) | 354,017 | 0.067233 | 5 | 0.05 | 0.74 |
Lineaments | |||||
0 to 3.3 | 14,814 | 0.29 | 34 | 0.34 | 1.18 |
3.3 to 8.04 | 15,780 | 0.31 | 30 | 0.3 | 0.98 |
8.04 to 13.30 | 13,530 | 0.26 | 23 | 0.23 | 0.88 |
13.30 to 21.10 | 6090 | 0.12 | 10 | 0.1 | 0.85 |
21.10 to 40.87 | 1312 | 0.03 | 3 | 0.03 | 1.18 |
Precipitation | |||||
234 to 288 | 4585 | 0.28 | 34 | 0.34 | 1.21 |
288 to 338 | 4168 | 0.26 | 26 | 0.26 | 1.01 |
338 to 394 | 4408 | 0.27 | 25 | 0.25 | 0.92 |
394 to 477 | 3094 | 0.19 | 15 | 0.15 | 0.79 |
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Li, S.; Abdelkareem, M.; Al-Arifi, N. Mapping Groundwater Prospective Areas Using Remote Sensing and GIS-Based Data Driven Frequency Ratio Techniques and Detecting Land Cover Changes in the Yellow River Basin, China. Land 2023, 12, 771. https://doi.org/10.3390/land12040771
Li S, Abdelkareem M, Al-Arifi N. Mapping Groundwater Prospective Areas Using Remote Sensing and GIS-Based Data Driven Frequency Ratio Techniques and Detecting Land Cover Changes in the Yellow River Basin, China. Land. 2023; 12(4):771. https://doi.org/10.3390/land12040771
Chicago/Turabian StyleLi, Shuhang, Mohamed Abdelkareem, and Nassir Al-Arifi. 2023. "Mapping Groundwater Prospective Areas Using Remote Sensing and GIS-Based Data Driven Frequency Ratio Techniques and Detecting Land Cover Changes in the Yellow River Basin, China" Land 12, no. 4: 771. https://doi.org/10.3390/land12040771
APA StyleLi, S., Abdelkareem, M., & Al-Arifi, N. (2023). Mapping Groundwater Prospective Areas Using Remote Sensing and GIS-Based Data Driven Frequency Ratio Techniques and Detecting Land Cover Changes in the Yellow River Basin, China. Land, 12(4), 771. https://doi.org/10.3390/land12040771