A Synergy Cropland of China by Fusing Multiple Existing Maps and Statistics
Abstract
:1. Introduction
2. Data and Method
2.1. Data Sources
2.2. Data Processing
2.3. Methodology
3. Results and Analysis
3.1. Agreement Analysis and Accuracy Assessment of the Five Cropland Datasets
3.2. Cropland Map Developed by HOSA
3.3. Accuracy Assessment
3.4. Comparison with Statistics
4. Discussion
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Dataset | Spatial Resolution | Sensor | Epoch | Classification Method |
---|---|---|---|---|
GlobeLand30 | 30 m | Landsat TM/HJ-1 | 2010 | POK |
CCI-LC | 300 m | MERIS | 2008–2012 | Unsupervised/supervised clustering |
GlobCover 2009 | 300 m | MERIS | 2009 | Unsupervised/supervised clustering |
MODIS C5 | 500 m | MODIS | 2010 | Decision tree classification |
MODIS Cropland | 250 m | MODIS | 2000–2008 | Decision tree classification |
Dataset | Definition of Cropland | Cropland Accuracy Released by Producer | Cropland Percentage |
---|---|---|---|
GlobeLand30 | Cultivated land | 80.33% | 100% |
CCI-LC | Cropland, rainfed | 85% | 100% |
Herbaceous cover | __ | 80% | |
Tree or shrub cover | __ | 80% | |
Cropland, irrigated or post-flooding | 88% | 100% | |
Mosaic cropland (>50%)/natural vegetation (tree, shrub, herbaceous cover) (<50%) | 68% | 60% | |
Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%)/cropland (<50%) | 63% | 40% | |
GlobCover 2009 | Post-flooding or irrigated croplands (or aquatic) | 88% | 100% |
Rainfed croplands | 81% | 100% | |
Mosaic cropland (50–70%)/vegetation (20–50%) | 64% | 60% | |
Mosaic vegetation (50–70%)/cropland (20–50%) | 46% | 40% | |
MODIS C5 | Cropland | 83.3% | 100% |
Cropland/natural vegetation mosaics | 60.5% | 60% | |
MODIS Cropland | Cropland | __ | 100% |
Score | #1 | #2 | #3 | #4 | #5 |
---|---|---|---|---|---|
10 | 1 | 1 | 1 | 0 | 0 |
9 | 1 | 1 | 0 | 1 | 0 |
8 | 1 | 0 | 1 | 1 | 0 |
7 | 0 | 1 | 1 | 1 | 0 |
6 | 1 | 1 | 0 | 0 | 1 |
5 | 1 | 0 | 1 | 0 | 1 |
4 | 0 | 1 | 1 | 0 | 1 |
3 | 1 | 0 | 0 | 1 | 1 |
2 | 0 | 1 | 0 | 1 | 1 |
1 | 0 | 0 | 1 | 1 | 1 |
Validation Samples | |||||
---|---|---|---|---|---|
Cropland | Noncropland | Sum | Commission Error | ||
Synergy map | Cropland | 1111 | 311 | 1422 | 21.87% |
Noncropland | 292 | 1110 | 1402 | 20.83% | |
Sum | 1403 | 1421 | 2824 | ||
Omission error | 20.81% | 21.89% | |||
Overall accuracy = 78.65%, Kappa coefficient = 0.57 |
GlobeLand30 | CCI-LC | GlobCover 2009 | MODIS C5 | MODIS Cropland | Synergy Map | |
---|---|---|---|---|---|---|
AD (ha) | 1585.11 | 8342.77 | 2357.00 | 499.88 | –1895.28 | 12.02 |
AARD | 0.45 | 3.50 | 2.00 | 0.32 | 0.65 | 0.09 |
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Lu, M.; Wu, W.; You, L.; Chen, D.; Zhang, L.; Yang, P.; Tang, H. A Synergy Cropland of China by Fusing Multiple Existing Maps and Statistics. Sensors 2017, 17, 1613. https://doi.org/10.3390/s17071613
Lu M, Wu W, You L, Chen D, Zhang L, Yang P, Tang H. A Synergy Cropland of China by Fusing Multiple Existing Maps and Statistics. Sensors. 2017; 17(7):1613. https://doi.org/10.3390/s17071613
Chicago/Turabian StyleLu, Miao, Wenbin Wu, Liangzhi You, Di Chen, Li Zhang, Peng Yang, and Huajun Tang. 2017. "A Synergy Cropland of China by Fusing Multiple Existing Maps and Statistics" Sensors 17, no. 7: 1613. https://doi.org/10.3390/s17071613
APA StyleLu, M., Wu, W., You, L., Chen, D., Zhang, L., Yang, P., & Tang, H. (2017). A Synergy Cropland of China by Fusing Multiple Existing Maps and Statistics. Sensors, 17(7), 1613. https://doi.org/10.3390/s17071613