Testing Accuracy of Land Cover Classification Algorithms in the Qilian Mountains Based on GEE Cloud Platform
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Preparation
2.2.1. Sentinel-2 Image Data
2.2.2. Sentinel-1 Image Data
2.2.3. SRTM Data
2.2.4. Land Cover Datasets
2.3. Methods
2.3.1. Sampling Strategies
2.3.2. Feature Construct
- Spectral indices
- Texture features
- Radar features
- Terrain features
2.3.3. Classification Algorithms
- Support Vector Machine
- Classification and Regression Tree
- Random Forest
2.3.4. Accuracy Assessment
3. Results
3.1. Classification Results and Accuracy of Classification Results
3.2. Influence of the Feature Variables on the Classification Accuracy
3.2.1. Importance Scores of the Variables Used in the RF Classification Algorithm
3.2.2. Influence of the Feature Variables on the OA
3.2.3. Influence of the Feature Variables on the PA of the Different Land Cover Types
3.2.4. Influence of the Feature Variables on the UA of the Different Land Cover Types
3.3. Comparison of Classification Results with Other Land Cover Products
4. Discussion
4.1. Comparison of the Performances of the Different Classification Algorithms
4.2. Influence of Feature Variables on Remote Sensing Classification
4.3. Comparison of the Land Cover Results Obtained in This Study with Existing Land Cover Products
4.4. Limitations and Prospects of Land Cover Classification in QLM
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
Appendix A
Code | Land Cover Types | Number |
---|---|---|
1 | Croplands | 328 |
2 | Forests | 1192 |
3 | Grasslands | 9103 |
4 | Shrublands | 113 |
5 | Wetlands | 199 |
6 | Water bodies | 703 |
7 | Construction lands | 261 |
8 | Bare lands | 8730 |
9 | Permanent snow and ice | 482 |
Total | 21,111 |
Land Cover Types | Feature Variable Combinations | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Spectral Bands | Spectral Bands + Spectral Indices | Spectral Bands + Spectral Indices + Terrain Features | Spectral Bands + Spectral Indices + Terrain Features + Radar Features | Spectral Bands + Spectral Indices + Terrain Features + Radar Features + Texture Features | |||||||||||
SVM | CART | RF | SVM | CART | RF | SVM | CART | RF | SVM | CART | RF | SVM | CART | RF | |
CR | 9.42 | 55.33 | 61.59 | 34.33 | 58.04 | 63.47 | 47.76 | 66.32 | 68.60 | 59.10 | 60.23 | 71.25 | 55.83 | 65.55 | 66.66 |
FO | 79.24 | 90.09 | 90.93 | 84.67 | 87.14 | 91.95 | 83.18 | 89.38 | 93.02 | 86.43 | 90.49 | 94.02 | 80.44 | 89.70 | 94.13 |
GL | 98.61 | 94.61 | 97.89 | 98.45 | 94.85 | 97.99 | 97.53 | 96.01 | 98.16 | 97.63 | 95.44 | 98.19 | 97.61 | 96.34 | 98.44 |
SL | 8.61 | 66.28 | 58.68 | 14.22 | 62.41 | 50.85 | 11.81 | 66.67 | 51.30 | 13.00 | 64.03 | 55.60 | 33.89 | 57.72 | 52.55 |
WL | 6.26 | 24.30 | 22.55 | 16.09 | 24.44 | 21.80 | 17.08 | 42.35 | 41.87 | 19.78 | 39.49 | 49.00 | 32.71 | 40.79 | 44.97 |
WB | 92.49 | 92.44 | 93.35 | 92.58 | 92.19 | 94.76 | 95.04 | 97.03 | 96.67 | 95.25 | 95.46 | 95.86 | 95.58 | 95.77 | 96.16 |
CL | 2.25 | 56.29 | 49.32 | 5.55 | 55.45 | 46.82 | 50.57 | 69.09 | 73.55 | 91.66 | 83.33 | 84.74 | 90.00 | 82.63 | 87.31 |
BL | 99.15 | 96.80 | 99.05 | 99.40 | 97.02 | 99.01 | 99.01 | 97.80 | 99.21 | 99.23 | 98.15 | 99.26 | 99.17 | 98.18 | 99.37 |
PSI | 100 | 99.11 | 99.42 | 99.07 | 97.93 | 99.31 | 97.51 | 99.08 | 99.44 | 94.22 | 99.08 | 99.42 | 94.44 | 97.90 | 99.57 |
Land Cover Types | Feature Variable Combinations | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Spectral Bands | Spectral Bands + Spectral Indices | Spectral Bands + spectral Indices + Terrain Features | Spectral Bands + Spectral Indices + Terrain Features + Radar Features | Spectral Bands + Spectral Indices + Terrain Features + Radar Features + Texture Features | |||||||||||
SVM | CART | RF | SVM | CART | RF | SVM | CART | RF | SVM | CART | RF | SVM | CART | RF | |
CR | 100 | 59.26 | 77.65 | 86.79 | 51.01 | 81.25 | 72.07 | 65.26 | 82.90 | 67.34 | 57.78 | 84.50 | 65.52 | 61.44 | 85.52 |
FO | 87.42 | 86.07 | 92.50 | 86.02 | 86.75 | 91.08 | 89.82 | 88.55 | 93.40 | 88.05 | 89.21 | 93.32 | 83.89 | 90.90 | 94.15 |
GL | 91.79 | 95.54 | 95.55 | 93.19 | 95.37 | 95.79 | 93.44 | 96.14 | 96.49 | 94.73 | 95.99 | 96.97 | 94.18 | 96.14 | 96.75 |
SL | 100 | 61.21 | 100 | 100 | 52.35 | 100 | 100 | 62.54 | 100 | 100 | 57.65 | 99.20 | 93.75 | 54.80 | 100 |
WL | 100 | 20.69 | 63.16 | 83.33 | 20.91 | 72.32 | 83.33 | 43.92 | 71.24 | 92.21 | 39.90 | 69.67 | 64.20 | 38.13 | 65.89 |
WB | 99.21 | 94.56 | 98.09 | 98.75 | 94.06 | 97.69 | 98.38 | 95.97 | 97.87 | 98.52 | 96.68 | 98.52 | 97.66 | 97.34 | 98.49 |
CL | 100 | 51.94 | 94.74 | 100 | 59.59 | 90.65 | 72.20 | 73.75 | 95.74 | 92.06 | 87.23 | 98.09 | 92.16 | 82.02 | 96.38 |
BL | 95.48 | 96.71 | 96.94 | 95.48 | 97.12 | 97.02 | 96.54 | 97.71 | 98.10 | 98.06 | 97.82 | 98.21 | 98.34 | 98.46 | 98.63 |
PSI | 99.29 | 97.89 | 98.50 | 99.29 | 98.83 | 98.51 | 99.79 | 99.76 | 98.45 | 100 | 99.53 | 99.18 | 97.77 | 99.75 | 99.14 |
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Products | Data Source | Time | Spatial Resolution | Classification Algorithms |
---|---|---|---|---|
FROM-GLC30 | Landsat TM/ETM+ | 2010, 2015, 2017 | 30 m | SVM, supervised classification |
GlobeLand30 | Landsat TM/ETM+, HJ-1 | 2000, 2010 | 30 m | Pixel-object-knowledge-based (POK-based) method |
FROM-GLC10 | Sentinel-2 | 2017 | 10 m | RF, supervised classification |
Land Cover Dataset at Qilian Mountain Area from 1985 to 2019 (V2.0) | Landsat 8 TM/ETM+/OLI | 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2016, 2017, 2018, 2019 | 30 m | Supervised classification |
Code | Class | Abbreviation | Description |
---|---|---|---|
1 | Cropland | CO | A land cover type that is greatly affected by intensive human activities. It varies greatly from bare field to seeding to crop growing to harvesting in the course of a year. It includes paddy fields, greenhouse farming, and other arable and tillage land. |
2 | Forest | FO | Areas in which the tree cover percentage is >15% and the tree height is > 3 m, including natural forests, plantations, and fruit trees. |
3 | Grassland | GL | Areas in which the herbaceous cover percentage is >15%, including natural grassland and pastures. |
4 | Shrublands | SL | Area in which the shrublands’ height range is 0.3–5 m, and cover percentage is >15%, have unique texture. |
5 | Wetlands | WL | Usually has obvious high reflectivity in the NIR band; marshland covered with aquatic herbaceous plants; mudflats are also included. |
6 | Water bodies | WB | All inland waterbodies; dominated by natural waterbodies and artificial waterbodies. |
7 | Construction land | CL | Includes urban areas, rural areas, and industrial and mining land greatly affected by human activities. |
8 | Bare land | BL | Areas without vegetation cover, including wasteland, deserts, and the Gobi Desert. |
9 | Permanent snow and ice | PSI | Perennial snow and ice distributed in the high mountains. |
Methods | Land Cover Types | CR | FO | GL | SL | WL | WB | CL | BL | PSI | PA |
---|---|---|---|---|---|---|---|---|---|---|---|
SVM | CR | 67 | 5 | 43 | 0 | 1 | 0 | 0 | 2 | 0 | 0.57 ± 0.09 |
FO | 2 | 319 | 45 | 0 | 0 | 0 | 0 | 0 | 0 | 0.87 ± 0.03 | |
GL | 19 | 15 | 2669 | 0 | 0 | 0 | 0 | 19 | 0 | 0.98 ± 0.01 | |
SL | 2 | 18 | 10 | 5 | 0 | 0 | 0 | 0 | 0 | 0.14 + 0.12 | |
WL | 1 | 0 | 38 | 0 | 12 | 1 | 0 | 11 | 0 | 0.19 ± 0.10 | |
WB | 1 | 0 | 0 | 0 | 1 | 214 | 2 | 11 | 0 | 0.93 ± 0.03 | |
CL | 1 | 0 | 1 | 0 | 0 | 0 | 60 | 6 | 0 | 0.88 ± 0.08 | |
BL | 0 | 1 | 18 | 0 | 0 | 0 | 5 | 2590 | 0 | 0.99 ± 0.01 | |
PSI | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 126 | 0.97 ± 0.03 | |
UA | 0.72 ± 0.09 | 0.89 ± 0.03 | 0.94 ± 0.01 | 1.00 ± 0 | 0.86 ± 0.14 | 0.99 ± 0.01 | 0.90 ± 0.07 | 0.98 ± 0.01 | 1.00 ± 0 | ||
OA | 0.96 ± 0.01 | ||||||||||
Kappa | 0.93 | ||||||||||
CART | CR | 66 | 0 | 35 | 0 | 4 | 1 | 0 | 1 | 0 | 0.62 ± 0.09 |
FO | 0 | 327 | 28 | 12 | 0 | 0 | 0 | 0 | 0 | 0.89 ± 0.03 | |
GL | 43 | 29 | 2617 | 14 | 12 | 0 | 1 | 38 | 0 | 0.95 ± 0.01 | |
SL | 0 | 10 | 5 | 21 | 0 | 0 | 0 | 0 | 0 | 0.58 ± 0.16 | |
WL | 5 | 0 | 15 | 2 | 23 | 2 | 1 | 7 | 0 | 0.42 ± 0.13 | |
WB | 0 | 0 | 0 | 0 | 1 | 190 | 1 | 3 | 0 | 0.97 ± 0.02 | |
CL | 0 | 0 | 1 | 0 | 1 | 0 | 60 | 8 | 0 | 0.86 ± 0.08 | |
BL | 2 | 0 | 30 | 0 | 7 | 6 | 8 | 2585 | 0 | 0.98 ± 0.01 | |
PSI | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 145 | 0.99 ± 0.01 | |
UA | 0.57 ± 0.09 | 0.89 ± 0.03 | 0.96 ± 0.01 | 0.43 ± 0.14 | 0.48 ± 0.14 | 0.95 ± 0.03 | 0.85 ± 0.08 | 0.98 ± 0.01 | 1.00 ± 0 | ||
OA | 0.95 ± 0.02 | ||||||||||
Kappa | 0.93 | ||||||||||
RF | CR | 78 | 0 | 24 | 0 | 8 | 1 | 0 | 2 | 0 | 0.69 ± 0.09 |
FO | 0 | 329 | 24 | 0 | 0 | 0 | 0 | 0 | 0 | 0.93 ± 0.03 | |
GL | 13 | 7 | 2717 | 0 | 1 | 1 | 0 | 13 | 0 | 0.99 ± 0.01 | |
SL | 0 | 7 | 5 | 17 | 0 | 0 | 0 | 0 | 0 | 0.59 ± 0.18 | |
WL | 0 | 0 | 19 | 0 | 28 | 2 | 0 | 4 | 0 | 0.53 ± 0.13 | |
WB | 0 | 0 | 0 | 0 | 1 | 190 | 1 | 1 | 3 | 0.97 ± 0.03 | |
CL | 1 | 0 | 1 | 0 | 0 | 1 | 63 | 9 | 0 | 0.84 ± 0.08 | |
BL | 0 | 0 | 10 | 0 | 0 | 0 | 0 | 2567 | 0 | 0.99 ± 0.01 | |
PSI | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 144 | 0.99 ± 0.01 | |
UA | 0.85 ± 0.07 | 0.96 ± 0.02 | 0.97 ± 0.01 | 1.00 ± 0 | 0.74 ± 0.14 | 0.97 ± 0.02 | 0.98 ± 0.02 | 0.99 ± 0.01 | 0.98 ± 0.02 | ||
OA | 0.97 ± 0.01 | ||||||||||
Kappa | 0.96 |
Land Cover Types | RF | CART | SVM | FROM-GLC30 | FROM-GLC10 | LCD-QLM (V2.0) | GlobaLand30 |
---|---|---|---|---|---|---|---|
CR | 0.97 | 1.06 | 1.02 | 2.81 | 1.73 | 0.29 | 3.53 |
FO | 1.67 | 1.92 | 3.75 | 2.98 | 4.53 | 1.72 | 2.72 |
GL | 37.19 | 37.11 | 34.53 | 43.73 | 42.59 | 50.54 | 55.34 |
SL | 0.02 | 0.43 | 0.02 | 0.14 | 0.06 | 0.003 | 0.63 |
WL | 0.12 | 0.32 | 0.04 | 0.32 | 0.05 | 0.19 | 0.37 |
WB | 3.24 | 3.56 | 3.15 | 3.73 | 3.02 | 3.12 | 3.11 |
CL | 0.16 | 0.43 | 0.16 | 0.59 | 0.03 | 0.03 | 0.31 |
BL | 55.5 | 53.91 | 56.21 | 43.81 | 46.7 | 38.63 | 31.36 |
PSI | 1.14 | 1.24 | 1.15 | 1.87 | 1.28 | 5.21 | 2.63 |
Land Cover Types | This Study | FROM-GLC10 | FROM-GLC30 | GlobeLand30 | LCD-QLM (V2.0) | |||||
---|---|---|---|---|---|---|---|---|---|---|
PA | UA | PA | UA | PA | UA | PA | UA | PA | UA | |
CR | 71.25 | 85.52 | 59.09 | 64.11 | 64.04 | 57.48 | 86.18 | 51.21 | 80.00 | 91.30 |
FO | 94.02 | 94.15 | 87.50 | 58.79 | 89.22 | 65.16 | 52.86 | 53.78 | 46.67 | 47.14 |
GL | 98.19 | 96.75 | 92.63 | 96.74 | 91.92 | 95.20 | 90.71 | 92.49 | 86.50 | 94.28 |
SL | 55.60 | 100 | 36.57 | 33.54 | 66.67 | 71.43 | 32.25 | 26.32 | 21.36 | 19.09 |
WL | 48.96 | 65.89 | 60.61 | 66.67 | 70.15 | 69.27 | 47.50 | 63.33 | 28.28 | 25.00 |
WB | 95.86 | 98.49 | 98.91 | 96.27 | 89.27 | 90.15 | 92.75 | 97.46 | 88.66 | 72.88 |
CL | 84.74 | 96.38 | 33.33 | 85.71 | 66.67 | 25.88 | 63.86 | 68.64 | 41.18 | 35.90 |
BL | 99.26 | 98.63 | 92.17 | 92.54 | 89.76 | 98.03 | 97.50 | 99.38 | 90.96 | 96.79 |
PSI | 99.42 | 99.14 | 90.00 | 93.75 | 92.00 | 95.83 | 94.00 | 79.66 | 96.00 | 50.53 |
OA (%) | 97.18 | 89.67 | 87.77 | 85.18 | 79.81 | |||||
Kappa | 0.95 | 0.73 | 0.70 | 0.65 | 0.51 |
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Yang, Y.; Yang, D.; Wang, X.; Zhang, Z.; Nawaz, Z. Testing Accuracy of Land Cover Classification Algorithms in the Qilian Mountains Based on GEE Cloud Platform. Remote Sens. 2021, 13, 5064. https://doi.org/10.3390/rs13245064
Yang Y, Yang D, Wang X, Zhang Z, Nawaz Z. Testing Accuracy of Land Cover Classification Algorithms in the Qilian Mountains Based on GEE Cloud Platform. Remote Sensing. 2021; 13(24):5064. https://doi.org/10.3390/rs13245064
Chicago/Turabian StyleYang, Yanpeng, Dong Yang, Xufeng Wang, Zhao Zhang, and Zain Nawaz. 2021. "Testing Accuracy of Land Cover Classification Algorithms in the Qilian Mountains Based on GEE Cloud Platform" Remote Sensing 13, no. 24: 5064. https://doi.org/10.3390/rs13245064