Landscape Classification System Based on RKM Clustering for Soil Survey UAV Images–Case Study of the Small Hilly Areas in Jurong City
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Collection and Data Pre-Processing
2.3. Selection and Classification of Landscape Factors
2.4. Rough K-Means Clustering
2.5. Kappa Coefficient
2.6. Silhouette Coefficient
2.7. Determination of the Optimal Number of Clusters
3. Results and Analysis
3.1. Classification and Evaluation of Landscape Factors
3.2. Landscape Classification
3.3. Landscape Outcome Assessment
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Serial Number of Spectrum | Name of Spectrum | Central Wavelength/nm | Full Width at Half Peak/nm |
---|---|---|---|
Blue | 450 | 32 | |
Green | 560 | 32 | |
Red | 650 | 32 | |
Red Edge | 730 | 32 | |
Near InfraRed | 840 | 52 |
Feature Category | Feature Parameter | Formula |
---|---|---|
Spectrum feature | Mean | |
Standard Deviation | ||
Shape feature | NDVI | |
NDWI | ||
REDNDVI | ||
Geometric feature | Length/Width | |
Shape Index | ||
Texture features | GLCM |
Topography | Slope | Aspect | ||||
---|---|---|---|---|---|---|
Code | Category | Code | Category | Slope | Code | Category |
1 | Flat | 1 | Flat | ≤2° | 1 | North |
2 | Ridge | 2 | Gentle Slope | 2°–6° | 2 | East |
3 | Spur | 3 | Gentle–Middle Slope | 6°–15° | 3 | South |
4 | Hollow | 4 | Middle Slope | 15°∼25° | 4 | West |
5 | Valley | 5 | Steep Slope | ≥25° | ||
6 | Peak | |||||
7 | Shoulder | |||||
8 | Slope | |||||
9 | Footslope | |||||
10 | Pit |
Elevation | Land use | Object | |||
---|---|---|---|---|---|
Code | Elevation | Code | Category | Code | Category |
1 | 0–30 m | 1 | Paddy field | 1 | Paddy |
2 | 30–40 m | 2 | Watered land | 2 | Vegetables |
3 | 40–50 m | 3 | Dry land | 3 | Wheat |
4 | 50–60 m | 4 | Orchard | 4 | Rape |
5 | 60–70 m | 5 | Tea plantation | 5 | Maize |
6 | 70–80 m | 6 | Nursery | 6 | Sesame |
7 | 80–90 m | 7 | Woodland | 7 | Orchard |
8 | 90–100 m | 8 | Wasteland | 8 | Tea |
9 | 100–130 m | 9 | Greenhouse | 9 | Nursery |
10 | Building | 10 | Evergreen forest | ||
11 | Water | 11 | Mixed forests | ||
12 | Deciduous forest | ||||
13 | Wasteland | ||||
14 | Building | ||||
15 | Water |
Landscape | Area | Area Percentage | Code | Landscape | Area | Area Percentage | Code |
---|---|---|---|---|---|---|---|
t1-s1-a1-d4-l3-o3 | 1426.999 | 1.58% | 1 | t6-s2-a4-d9-l3-o3 | 25.5882 | 0.03% | 50 |
t2-s1-a1-d5-l3-o3 | 324.39 | 0.36% | 2 | t6-s2-a4-d9-l7-o10 | 812.6314 | 0.90% | 51 |
t2-s2-a3-d5-l1-o3 | 71.4506 | 0.08% | 3 | t6-s3-a1-d5-l7-o8 | 2000.857 | 2.22% | 52 |
t3-s1-a1-d3-l1-o1 | 979.5747 | 1.09% | 4 | t6-s3-a1-d5-l9-o2 | 168.0305 | 0.19% | 53 |
t3-s1-a1-d5-l3-o5 | 863.9857 | 0.96% | 5 | t6-s3-a1-d9-l1-o1 | 62.9914 | 0.07% | 54 |
t3-s1-a2-d7-l7-o10 | 129.5776 | 0.14% | 6 | t6-s3-a4-d3-l3-o3 | 820.5404 | 0.91% | 55 |
t3-s1-a3-d5-l3-o3 | 1008.926 | 1.12% | 7 | t6-s4-a1-d2-l1-o1 | 389.4215 | 0.43% | 56 |
t3-s1-a3-d7-l3-o5 | 15.9806 | 0.02% | 8 | t6-s4-a2-d2-l4-o7 | 607.1563 | 0.67% | 57 |
t3-s1-a4-d5-l9-o2 | 49.6719 | 0.06% | 9 | t6-s4-a2-d5-l3-o3 | 648.872 | 0.72% | 58 |
t3-s2-a1-d6-l1-o1 | 192.4146 | 0.21% | 10 | t6-s4-a2-d7-l7-o12 | 563.2972 | 0.62% | 59 |
t3-s2-a4-d5-l1-o1 | 361.8445 | 0.40% | 11 | t6-s4-a4-d4-l7-o13 | 1315.331 | 1.46% | 60 |
t3-s3-a1-d9-l7-o12 | 140.2821 | 0.16% | 12 | t6-s4-a4-d5-l1-o1 | 344.0997 | 0.38% | 61 |
t3-s3-a4-d4-l7-o10 | 972.6522 | 1.08% | 13 | t6-s4-a4-d8-l3-o5 | 162.8713 | 0.18% | 62 |
t3-s3-a4-d9-l3-o5 | 39.3105 | 0.04% | 14 | t7-s1-a4-d3-l3-o3 | 207.3702 | 0.23% | 63 |
t4-s1-a1-d2-l3-o3 | 469.9133 | 0.52% | 15 | t7-s2-a1-d2-l7-o9 | 350.6233 | 0.39% | 64 |
t4-s1-a4-d2-l7-o8 | 471.4972 | 0.52% | 16 | t7-s2-a3-d2-l1-o3 | 43.0202 | 0.05% | 65 |
t4-s1-a4-d6-l7-o10 | 376.4227 | 0.42% | 17 | t7-s2-a3-d9-l1-o1 | 24.0997 | 0.03% | 66 |
t4-s2-a1-d5-l1-o3 | 93.5883 | 0.10% | 18 | t7-s2-a4-d9-l7-o10 | 330.6616 | 0.37% | 67 |
t5-s1-a3-d6-l3-o5 | 119.127 | 0.13% | 19 | t7-s3-a1-d5-l7-o10 | 544.4619 | 0.60% | 68 |
t5-s1-a4-d3-l1-o3 | 63.526 | 0.07% | 20 | t7-s3-a1-d7-l3-o5 | 49.7057 | 0.06% | 69 |
t5-s1-a4-d5-l7-o12 | 277.9403 | 0.31% | 21 | t7-s3-a4-d9-l9-o2 | 50.7333 | 0.06% | 70 |
t5-s2-a1-d3-l3-o5 | 381.3906 | 0.42% | 22 | t7-s4-a1-d2-l1-o1 | 103.57 | 0.11% | 71 |
t5-s2-a1-d5-l1-o3 | 127.9758 | 0.14% | 23 | t7-s4-a1-d4-l3-o3 | 182.2174 | 0.20% | 72 |
t5-s2-a1-d5-l9-o2 | 77.3689 | 0.09% | 24 | t7-s4-a2-d3-l7-o13 | 121.2364 | 0.13% | 73 |
t5-s2-a1-d7-l7-o12 | 198.5637 | 0.22% | 25 | t7-s4-a3-d6-l3-o3 | 168.7981 | 0.19% | 74 |
t5-s2-a4-d2-l3-o5 | 772.3075 | 0.86% | 26 | t7-s4-a3-d6-l7-o10 | 414.9493 | 0.46% | 75 |
t5-s2-a4-d2-l8-o13 | 264.2767 | 0.29% | 27 | t7-s4-a4-d4-l1-o2 | 194.7145 | 0.22% | 76 |
t5-s2-a4-d5-l1-o1 | 107.1413 | 0.12% | 28 | t7-s4-a4-d4-l3-o7 | 61.2594 | 0.07% | 77 |
t5-s2-a4-d9-l1-o1 | 31.4418 | 0.03% | 29 | t8-s1-a1-d4-l7-o8 | 1251.572 | 1.39% | 78 |
t5-s2-a4-d9-l7-o11 | 115.1422 | 0.13% | 30 | t8-s2-a3-d2-l8-o13 | 517.4768 | 0.57% | 79 |
t5-s3-a1-d5-l7-o10 | 577.5642 | 0.64% | 31 | t8-s2-a4-d2-l3-o5 | 567.8578 | 0.63% | 80 |
t5-s3-a1-d9-l3-o3 | 40.225 | 0.04% | 32 | t9-s1-a1-d2-l1-o3 | 1216.079 | 1.35% | 81 |
t5-s3-a4-d8-l3-o5 | 82.1636 | 0.09% | 33 | t9-s1-a1-d6-l7-o13 | 299.9196 | 0.33% | 82 |
t5-s3-a4-d8-l9-o2 | 21.2627 | 0.02% | 34 | t9-s1-a4-d3-l1-o3 | 380.9284 | 0.42% | 83 |
t5-s4-a1-d2-l7-o10 | 289.9934 | 0.32% | 35 | t9-s1-a4-d4-l7-o10 | 1468.644 | 1.63% | 84 |
t5-s4-a2-d3-l1-o1 | 79.8899 | 0.09% | 36 | t9-s1-a4-d9-l3-o5 | 27.4092 | 0.03% | 85 |
t5-s4-a3-d8-l7-o12 | 247.3944 | 0.27% | 37 | t9-s2-a1-d2-l7-o12 | 429.399 | 0.48% | 86 |
t5-s4-a4-d4-l3-o3 | 187.5225 | 0.21% | 38 | t9-s2-a2-d5-l1-o1 | 2456.642 | 2.72% | 87 |
t5-s4-a4-d4-l7-o9 | 783.618 | 0.87% | 39 | t9-s2-a3-d5-l3-o3 | 1827.01 | 2.03% | 88 |
t6-s1-a1-d3-l3-o3 | 377.7436 | 0.42% | 40 | t9-s2-a3-d8-l7-o11 | 139.7857 | 0.15% | 89 |
t6-s1-a1-d9-l7-o10 | 142.0589 | 0.16% | 41 | t9-s2-a4-d2-l1-o1 | 832.891 | 0.92% | 90 |
t6-s1-a4-d3-l1-o1 | 862.9392 | 0.96% | 42 | t9-s3-a1-d7-l3-o5 | 21.2196 | 0.02% | 91 |
t6-s1-a4-d3-l7-o9 | 765.05 | 0.85% | 43 | t9-s3-a2-d3-l3-o5 | 54.4972 | 0.06% | 92 |
t6-s1-a4-d6-l7-o13 | 1480.8 | 1.64% | 44 | t9-s3-a4-d5-l3-o5 | 64.5773 | 0.07% | 93 |
t6-s2-a1-d3-l7-o13 | 1339.79 | 1.49% | 45 | t9-s4-a2-d4-l7-o13 | 107.5602 | 0.12% | 94 |
t6-s2-a1-d5-l1-o1 | 508.4847 | 0.56% | 46 | t10-s1-a1-d4-l7-o9 | 510.7356 | 0.57% | 95 |
t6-s2-a1-d5-l7-o8 | 680.4379 | 0.75% | 47 | Water | 17925.71 | 19.87% | 96 |
t6-s2-a1-d9-l3-o5 | 51.557 | 0.06% | 48 | Building | 5963.977 | 6.61% | 97 |
t6-s2-a4-d5-l3-o3 | 1297.76 | 1.44% | 49 | Buffer Zone | 23035.35 | 25.54% | 98 |
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Fang, Z.; Lu, W.; Zhu, F.; Zhu, C.; Li, Z.; Pan, J. Landscape Classification System Based on RKM Clustering for Soil Survey UAV Images–Case Study of the Small Hilly Areas in Jurong City. Sensors 2022, 22, 9895. https://doi.org/10.3390/s22249895
Fang Z, Lu W, Zhu F, Zhu C, Li Z, Pan J. Landscape Classification System Based on RKM Clustering for Soil Survey UAV Images–Case Study of the Small Hilly Areas in Jurong City. Sensors. 2022; 22(24):9895. https://doi.org/10.3390/s22249895
Chicago/Turabian StyleFang, Zihan, Wenhao Lu, Fubin Zhu, Changda Zhu, Zhaofu Li, and Jianjun Pan. 2022. "Landscape Classification System Based on RKM Clustering for Soil Survey UAV Images–Case Study of the Small Hilly Areas in Jurong City" Sensors 22, no. 24: 9895. https://doi.org/10.3390/s22249895
APA StyleFang, Z., Lu, W., Zhu, F., Zhu, C., Li, Z., & Pan, J. (2022). Landscape Classification System Based on RKM Clustering for Soil Survey UAV Images–Case Study of the Small Hilly Areas in Jurong City. Sensors, 22(24), 9895. https://doi.org/10.3390/s22249895