Extraction of Kenyan Grassland Information Using PROBA-V Based on RFE-RF Algorithm
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Datasets and Processing
2.2.1. PROBA-V Image Data
2.2.2. SRTMGL1_003 Data
2.2.3. Sample Point Data
2.3. Methods
2.3.1. Spectral and Index Features
2.3.2. Topographic Features
2.3.3. Feature Optimization Methods
2.3.4. Classification Methods
2.3.5. Accuracy Verification Methods
3. Results
3.1. Feature Optimization Results
3.2. Classification Results and Analysis
4. Discussion
4.1. Effect of Timing on Classification
4.2. Effect of Auxiliary Data on Classification
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Ground Class Code | Land Class Name | Number of Samples |
---|---|---|
1 | Water | 121 |
2 | Savanna | 192 |
3 | Woody savanna | 237 |
4 | Closed shrubland | 263 |
5 | Barren | 268 |
6 | Grassland | 360 |
7 | Cropland | 545 |
8 | Forest | 695 |
9 | Open shrubland | 1098 |
PROBA-V Spectral Bands | Centred at (nm) | Width Span (nm) |
---|---|---|
BLUE | 463 | 46 |
RED | 655 | 79 |
NIR | 85 | 144 |
SWIR | 1600 | 73 |
Features Type | Original Features | Number | Optimized Features | Number |
---|---|---|---|---|
Spectral features | BLUE, RED, NIR, SWIR | 4 | BLUE, RED, SWIR | 3 |
Index features | NDVI, EVI, LSWI, RVI | 4 | NDVI, EVI, LSWI, RVI | 4 |
Topographic features | Elevation, Aspect, Slope, Hillshade | 4 | Elevation | 1 |
Total | 12 | 8 |
Confusion Matrix | Forecast Category | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | Total | PA | ||
Category | 1 | 314 | 5 | 3 | 5 | 11 | 3 | 0 | 0 | 0 | 341 | 0.92 |
2 | 8 | 223 | 0 | 17 | 0 | 5 | 0 | 0 | 0 | 253 | 0.88 | |
3 | 8 | 0 | 112 | 4 | 0 | 0 | 0 | 0 | 0 | 124 | 0.90 | |
4 | 1 | 31 | 5 | 478 | 0 | 0 | 30 | 0 | 2 | 547 | 0.87 | |
5 | 13 | 0 | 0 | 0 | 115 | 0 | 0 | 0 | 0 | 128 | 0.89 | |
6 | 4 | 7 | 0 | 0 | 0 | 84 | 0 | 0 | 0 | 95 | 0.88 | |
7 | 0 | 4 | 0 | 54 | 0 | 0 | 119 | 0 | 5 | 182 | 0.65 | |
8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 60 | 0 | 60 | 1 | |
9 | 0 | 0 | 0 | 3 | 0 | 0 | 5 | 0 | 115 | 123 | 0.93 | |
Total | 348 | 270 | 120 | 561 | 126 | 92 | 154 | 60 | 122 | 1853 | ||
UA | 0.90 | 0.82 | 0.93 | 0.85 | 0.91 | 0.91 | 0.77 | 1 | 0.94 | |||
OA | 0.87 | |||||||||||
Kappa | 0.85 |
Confusion Matrix | Forecast Category | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | Total | PA | ||
Category | 1 | 301 | 2 | 10 | 7 | 10 | 0 | 0 | 0 | 0 | 330 | 0.91 |
2 | 7 | 247 | 0 | 38 | 1 | 10 | 0 | 0 | 0 | 303 | 0.81 | |
3 | 8 | 0 | 118 | 9 | 0 | 0 | 0 | 0 | 0 | 135 | 0.87 | |
4 | 4 | 25 | 8 | 478 | 0 | 3 | 33 | 0 | 0 | 551 | 0.86 | |
5 | 17 | 0 | 0 | 0 | 81 | 0 | 0 | 0 | 0 | 98 | 0.82 | |
6 | 5 | 9 | 0 | 2 | 0 | 74 | 0 | 0 | 0 | 90 | 0.82 | |
7 | 2 | 4 | 0 | 87 | 0 | 0 | 99 | 0 | 4 | 196 | 0.50 | |
8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 60 | 0 | 60 | 1 | |
9 | 0 | 0 | 0 | 4 | 0 | 0 | 5 | 0 | 128 | 137 | 0.93 | |
Total | 344 | 287 | 136 | 625 | 92 | 87 | 137 | 60 | 132 | 1900 | ||
UA | 0.87 | 0.86 | 0.86 | 0.76 | 0.88 | 0.85 | 0.72 | 1 | 0.96 | |||
OA | 0.83 | |||||||||||
Kappa | 0.79 |
Confusion Matrix | Forecast Category | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | Total | PA | ||
Category | 1 | 325 | 5 | 4 | 0 | 9 | 0 | 0 | 0 | 0 | 343 | 0.94 |
2 | 7 | 237 | 0 | 21 | 0 | 6 | 4 | 0 | 0 | 275 | 0.86 | |
3 | 13 | 0 | 103 | 3 | 0 | 0 | 0 | 0 | 0 | 119 | 0.86 | |
4 | 5 | 16 | 13 | 519 | 0 | 0 | 30 | 0 | 0 | 583 | 0.89 | |
5 | 15 | 0 | 0 | 0 | 98 | 0 | 0 | 0 | 0 | 113 | 0.86 | |
6 | 12 | 17 | 0 | 0 | 0 | 79 | 0 | 0 | 0 | 108 | 0.73 | |
7 | 0 | 3 | 58 | 0 | 0 | 0 | 113 | 0 | 3 | 177 | 0.63 | |
8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 57 | 0 | 57 | 1 | |
9 | 0 | 0 | 0 | 6 | 0 | 0 | 8 | 0 | 138 | 152 | 0.90 | |
Total | 377 | 278 | 178 | 549 | 107 | 85 | 155 | 57 | 141 | 1927 | ||
UA | 0.86 | 0.85 | 0.57 | 0.94 | 0.91 | 0.92 | 0.72 | 1 | 0.97 | |||
OA | 0.86 | |||||||||||
Kappa | 0.83 |
Confusion Matrix | Forecast Category | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | Total | PA | ||
Category | 1 | 307 | 24 | 3 | 11 | 5 | 4 | 0 | 0 | 0 | 354 | 0.86 |
2 | 19 | 222 | 1 | 24 | 0 | 9 | 0 | 0 | 0 | 275 | 0.80 | |
3 | 1 | 0 | 129 | 5 | 0 | 0 | 0 | 0 | 0 | 135 | 0.95 | |
4 | 6 | 25 | 10 | 439 | 0 | 0 | 34 | 0 | 5 | 519 | 0.84 | |
5 | 11 | 0 | 0 | 0 | 106 | 0 | 0 | 0 | 0 | 117 | 0.90 | |
6 | 0 | 11 | 0 | 0 | 0 | 83 | 0 | 0 | 0 | 94 | 0.88 | |
7 | 2 | 4 | 1 | 64 | 0 | 0 | 104 | 0 | 9 | 184 | 0.56 | |
8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 58 | 0 | 58 | 1 | |
9 | 0 | 0 | 0 | 2 | 0 | 0 | 4 | 0 | 122 | 128 | 0.95 | |
Total | 346 | 286 | 144 | 545 | 111 | 96 | 142 | 58 | 136 | 1864 | ||
UA | 0.88 | 0.77 | 0.89 | 0.80 | 0.95 | 0.86 | 0.73 | 1 | 0.89 | |||
OA | 0.84 | |||||||||||
Kappa | 0.81 |
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Wei, P.; Zhu, W.; Zhao, Y.; Fang, P.; Zhang, X.; Yan, N.; Zhao, H. Extraction of Kenyan Grassland Information Using PROBA-V Based on RFE-RF Algorithm. Remote Sens. 2021, 13, 4762. https://doi.org/10.3390/rs13234762
Wei P, Zhu W, Zhao Y, Fang P, Zhang X, Yan N, Zhao H. Extraction of Kenyan Grassland Information Using PROBA-V Based on RFE-RF Algorithm. Remote Sensing. 2021; 13(23):4762. https://doi.org/10.3390/rs13234762
Chicago/Turabian StyleWei, Panpan, Weiwei Zhu, Yifan Zhao, Peng Fang, Xiwang Zhang, Nana Yan, and Hao Zhao. 2021. "Extraction of Kenyan Grassland Information Using PROBA-V Based on RFE-RF Algorithm" Remote Sensing 13, no. 23: 4762. https://doi.org/10.3390/rs13234762