Consistency Analysis and Accuracy Assessment of Three Global 30-m Land-Cover Products over the European Union using the LUCAS Dataset
Abstract
:1. Introduction
2. Study Area and Datasets
2.1. Study Area
2.2. Global Land-Cover Products
2.3. LUCAS Validation Dataset
3. Methods
3.1. Harmonization of the Classification Systems Used for Different GLC Products
3.2. Consistency Analysis for GLC Products
3.2.1. Area-Based Consistency Analysis
3.2.2. Pixel-Based Consistency Analysis
- High consistency: the target classes for the three GLC products were exactly the same at a given pixel;
- Moderate consistency: any two products had the same target class at a given pixel;
- Low consistency: the three GLC products all had different target classes at a given pixel.
3.3. Accuracy Assessment Using the Validation Dataset
4. Results
4.1. Evaluation of Areal Consistency
4.1.1. Areal Consistency between the Three 30-m GLC Products
4.1.2. Areal Consistency between the Three 30-m GLC Products
4.2. Evaluation of Spatial Consistency
4.2.1. Spatial Consistency between All Three Products
4.2.2. Spatial Consistency between Pairs of Product
4.3. Accuracy of the Three GLC Products
5. Discussion
5.1. Reasons for the Low Level of Consistency between the Different Products
5.2. Uncertainty Due to Different Classification Systems
5.3. Issues Related to the Comparative Assessment of GLC Products for Specific Applications
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Name | Number of Land Cover Types | Time | Method | O.A. Provided by Data Producer | Production Institution | Satellite | Link and Reference |
---|---|---|---|---|---|---|---|
GlobeLand30 | 10 | 2010 | POK (based on pixels, objects, and knowledge rules) | 0.803 | National Geomatics Center of China | Landsat TM/ETM+, HJ-1 A/B | http://www.globallandcover.com/ |
FROM_GLC30 | 26 | 2015 | Random forest | 0.773 | Tsinghua University | Landsat TM/ETM+/OLI | http://data.ess.tsinghua.edu.cn/ |
GLC_FCS30 | 30 | 2015 | Local random forest | 0.814 | Chinese Academy of Sciences | Landsat TM/ETM+/OLI | https://doi.org/10.5281/zenodo.3986872 |
Target Class 1 | GlobeLand30 | FROM_GLC30 | GLC_FCS30 |
---|---|---|---|
Cropland | Cropland | Rice paddy | Rainfed cropland |
Greenhouse | Herbaceous cover | ||
Other/orchard | Tree or shrub cover (orchard) | ||
Bare farmland | Irrigated cropland | ||
Forest | Forest | Broadleaf, leaf-on | Evergreen broadleaf forest |
Broadleaf, leaf-off | Closed/open deciduous broadleaf forest | ||
Needleleaf, leaf-on | Closed/open evergreen needleleaf forest | ||
Needleleaf, leaf-off | Closed/open deciduous needleleaf forest | ||
Mixed leaf, leaf-on | Mixed-leaf forest | ||
Mixed leaf, leaf-off | Sparse vegetation (fc < 15%) associated with forest | ||
Grassland | Grassland | Pasture | Grassland |
Natural grassland | Sparse herbaceous cover | ||
Grassland, leaf-off | Sparse vegetation (fc < 15%) associated with grassland | ||
Shrubland | Shrubland | Shrubland, leaf-on | Shrubland |
Shrubland, leaf-off | Evergreen/Deciduous shrubland | ||
Sparse shrubland | |||
Sparse vegetation (fc < 15%) associated with shrubland | |||
Wetland | Wetland | Marshland | Wetlands |
Marshland, leaf-off | |||
Mudflat | |||
Water | Water | Water | Water |
Impervious surface | Impervious surface | Impervious surface | Impervious surface |
Bare land | Bare land | Bare land | Bare areas |
Consolidated bare areas | |||
Unconsolidated bare areas | |||
Sparse vegetation (fc < 15%) associated with bare land | |||
Permanent ice/snow | Permanent ice/snow | Snow/Ice | Permanent ice and snow |
GlobeLand30_2010 | GLCFCS30_2015 | FROM_GLC_2015 | |
---|---|---|---|
GlobeLand30_2010 | 1.00 | ||
GLCFCS30_2015 | 0.930 | 1.00 | |
FROM_GLC_2015 | 0.538 | 0.679 | 1.00 |
GlobeLand30-2010 | GLC_FCS30-2015 | FROM_GLC30-2015 | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
P.A. ± c.i. % | U.A. ± c.i. % | P.A. ± c.i. % | U.A. ± c.i. % | P.A. ± c.i. % | U.A. ± c.i. % | |||||||
CRP | 99.01 | 0.01 | 88.79 | 0.01 | 83.64 | 0.01 | 91.53 | 0.01 | 63.46 | 0.01 | 85.93 | 0.01 |
FST | 93.6 | 0.01 | 97.36 | 0.01 | 92.86 | 0.01 | 92.14 | 0.01 | 81.59 | 0.02 | 74.91 | 0.02 |
GRS | 17.12 | 0.02 | 59.05 | 0.09 | 70.95 | 0.05 | 48.11 | 0.04 | 57.96 | 0.09 | 17.09 | 0.02 |
SHR | 79.12 | 0.07 | 56.75 | 0.05 | 53.64 | 0.07 | 39.5 | 0.06 | 2.06 | 0.02 | 35.82 | 0.31 |
WET | 35.81 | 0.02 | 91.88 | 0.08 | 36.15 | 0.04 | 70.19 | 0.11 | 0 | 0 | 28.44 | 11.35 |
WAT | 93.41 | 0.03 | 95.75 | 0.03 | 90.41 | 0.02 | 96.68 | 0.02 | 35.6 | 0.09 | 23.13 | 0.07 |
IMP | 84.88 | 0.06 | 87.39 | 0.07 | 85.46 | 0.08 | 73.96 | 0.08 | 51.68 | 0.05 | 69.21 | 0.09 |
BAL | 71.43 | 0.09 | 78.06 | 0.12 | 34.14 | 0.11 | 36.12 | 0.14 | 8.81 | 0.06 | 35.73 | 0.28 |
PSI | 47.4 | 0.67 | 43.83 | 0.84 | 77.4 | 0.27 | 75.59 | 0.33 | 33.85 | 0.47 | 42.09 | 0.72 |
O.A./% | 88.90 ± 0.68 | 84.33 ± 0.80 | 65.31 ± 1.00 |
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Gao, Y.; Liu, L.; Zhang, X.; Chen, X.; Mi, J.; Xie, S. Consistency Analysis and Accuracy Assessment of Three Global 30-m Land-Cover Products over the European Union using the LUCAS Dataset. Remote Sens. 2020, 12, 3479. https://doi.org/10.3390/rs12213479
Gao Y, Liu L, Zhang X, Chen X, Mi J, Xie S. Consistency Analysis and Accuracy Assessment of Three Global 30-m Land-Cover Products over the European Union using the LUCAS Dataset. Remote Sensing. 2020; 12(21):3479. https://doi.org/10.3390/rs12213479
Chicago/Turabian StyleGao, Yuan, Liangyun Liu, Xiao Zhang, Xidong Chen, Jun Mi, and Shuai Xie. 2020. "Consistency Analysis and Accuracy Assessment of Three Global 30-m Land-Cover Products over the European Union using the LUCAS Dataset" Remote Sensing 12, no. 21: 3479. https://doi.org/10.3390/rs12213479
APA StyleGao, Y., Liu, L., Zhang, X., Chen, X., Mi, J., & Xie, S. (2020). Consistency Analysis and Accuracy Assessment of Three Global 30-m Land-Cover Products over the European Union using the LUCAS Dataset. Remote Sensing, 12(21), 3479. https://doi.org/10.3390/rs12213479