Model Prediction of the Soil Moisture Regime and Soil Nutrient Regime Based on DEM-Derived Topo-Hydrologic Variables for Mapping Ecosites
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Sample Plots
2.3. Predictors for Modeling
2.4. Artificial Neural Network Models
2.5. Model Calibration, Validation, and Assessment
2.6. Mapping Forest Ecosites
3. Results
3.1. SMR Model
3.2. SNR Model
3.3. Mapped Forest Ecosite
4. Discussion
4.1. The Performance of SMR and SNR Models and Ecosite Maps
4.2. Effects of Coarse-Resolution Soil Maps and High-Resolution DEM-Derived Maps
4.3. Limitations and Future Improvements
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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No. Variable | RMSE | R2 * | OA (%) | The Best Combined DEM-Derived Variables | |
---|---|---|---|---|---|
SMR | 1 | 1.34 | 0.53 | 41 | TPI |
2 | 1.19 | 0.63 | 45 | TPI, SDR | |
3 | 1.07 | 0.68 | 45 | TPI, SDR, slope | |
4 | 1.09 | 0.67 | 48 | TPI, SDR, slope, FD | |
5 | 1.00 | 0.73 | 52 | TPI, SDR, slope, FD, STF | |
6 | 0.98 | 0.75 | 56 | TPI, SDR, slope, FL, aspect, PSR | |
7 | 0.95 | 0.78 | 61 | TPI, SDR, slope, FL, aspect, PSR, VSP | |
8 | 0.97 | 0.75 | 56 | TPI, SDR, slope, FL, aspect, PSR, VSP, FD | |
9 | 0.98 | 0.75 | 55 | TPI, SDR, slope, FL, aspect, PSR, VSP, FD, STF | |
SNR | 1 | 0.73 | 0.52 | 55 | TPI |
2 | 0.65 | 0.64 | 66 | TPI, aspect | |
3 | 0.58 | 0.74 | 72 | TPI, FL, slope | |
4 | 0.57 | 0.76 | 74 | TPI, aspect, slope, FL | |
5 | 0.55 | 0.78 | 76 | TPI, aspect, slope, FL, SDR | |
6 | 0.55 | 0.78 | 78 | TPI, aspect, slope, FL, SDR, FD | |
7 | 0.54 | 0.78 | 78 | TPI, aspect, slope, FL, SDR, FD, VSP, | |
8 | 0.51 | 0.81 | 82 | TPI, aspect, slope, FL, SDR, FD, VSP, PSR | |
9 | 0.53 | 0.80 | 79 | TPI, aspect, slope, FL, SDR, FD, VSP, PSR, STF |
SMR Model-Estimation | Producer’s Accuracy * (%) | |||||||||||
VD | D | F | FM | M | MW | W | Total | OA | OA ±1 Class | |||
Acadian- region | field assessment | VD | 4 | 3 | 4 | 0 | 0 | 0 | 0 | 11 | 36 | 64 |
D | 0 | 40 | 38 | 11 | 0 | 1 | 0 | 90 | 45 | 87 | ||
F | 2 | 21 | 523 | 83 | 24 | 9 | 3 | 665 | 79 | 94 | ||
FM | 1 | 3 | 80 | 163 | 26 | 18 | 0 | 291 | 56 | 92 | ||
M | 0 | 2 | 13 | 79 | 67 | 27 | 14 | 202 | 33 | 86 | ||
MW | 0 | 0 | 4 | 23 | 35 | 57 | 10 | 129 | 44 | 79 | ||
W | 0 | 1 | 3 | 6 | 16 | 34 | 59 | 119 | 50 | 78 | ||
Total | 7 | 70 | 665 | 365 | 168 | 146 | 86 | 1507 | 61 | 90 | ||
SNR Model-Estimation | Producer’s Accuracy (%) | |||||||||||
VP | P | M | R | VR | Total | OA | OA ±1 Class | |||||
Mar.-Bor.-region | field assessment | VP | 31 | 36 | 3 | 1 | 0 | 71 | 44 | 95 | ||
P | 9 | 321 | 65 | 3 | 0 | 398 | 81 | 99 | ||||
M | 5 | 58 | 739 | 12 | 2 | 816 | 91 | 99 | ||||
R | 0 | 7 | 62 | 136 | 1 | 206 | 66 | 67 | ||||
VR | 0 | 1 | 1 | 7 | 7 | 16 | 44 | 88 | ||||
Total | 45 | 423 | 870 | 159 | 10 | 1507 | 82 | 99 |
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Zhao, Z.; Yang, Q.; Ding, X.; Xing, Z. Model Prediction of the Soil Moisture Regime and Soil Nutrient Regime Based on DEM-Derived Topo-Hydrologic Variables for Mapping Ecosites. Land 2021, 10, 449. https://doi.org/10.3390/land10050449
Zhao Z, Yang Q, Ding X, Xing Z. Model Prediction of the Soil Moisture Regime and Soil Nutrient Regime Based on DEM-Derived Topo-Hydrologic Variables for Mapping Ecosites. Land. 2021; 10(5):449. https://doi.org/10.3390/land10050449
Chicago/Turabian StyleZhao, Zhengyong, Qi Yang, Xiaogang Ding, and Zisheng Xing. 2021. "Model Prediction of the Soil Moisture Regime and Soil Nutrient Regime Based on DEM-Derived Topo-Hydrologic Variables for Mapping Ecosites" Land 10, no. 5: 449. https://doi.org/10.3390/land10050449
APA StyleZhao, Z., Yang, Q., Ding, X., & Xing, Z. (2021). Model Prediction of the Soil Moisture Regime and Soil Nutrient Regime Based on DEM-Derived Topo-Hydrologic Variables for Mapping Ecosites. Land, 10(5), 449. https://doi.org/10.3390/land10050449