Automated Glacier Extraction Index by Optimization of Red/SWIR and NIR /SWIR Ratio Index for Glacier Mapping Using Landsat Imagery
Abstract
:1. Introduction
2. Study Area and Data
2.1. Study Areas
2.2. Data Sources and Pre-Processing
3. Methods
3.1. Existing Classifiers for Glacier Mapping
3.2. Automated Glacier Extraction Index (AGEI)
3.2.1. Pure-pixel Selection
3.2.2. Formulation of AGEI
3.3. Optimization of Weighted Coefficient and Threshold
3.3.1. The Weighted Coefficient “α” of the AGEI Equation
3.3.2. Threshold Selection and Optimization
3.4. Accuracy Validation Methods
3.4.1. Overall Accuracy Evaluation of Classified Glacier Maps
3.4.2. Mixed Edge Pixels Accuracy Assessment
3.4.3. Challenging Features’ Assessment at Validation Plot Scale
3.5. Comparison Glacier Maps with Different Sensors
4. Results
4.1. Comparison of Glacier Mapping Results
4.2. Accuracy Assessment of Glacier Mapping
4.2.1. Overall Accuracy Evaluation of AGEI with Different Coefficients
4.2.2. Overall accuracy evaluation for the five classifiers with multiple thresholds
4.2.3. Mixed Edge Pixels Evaluation for the Five Classifiers
4.2.4. Evaluation of Validation Plots in Different Land-Cover Backgrounds
4.3. Comparison Glacier Mapping of Landsat and Sentinel Imagery
5. Discussion
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Test Site | Satellite | Sensor | Scene | Reference Data and Sources | |||
---|---|---|---|---|---|---|---|
Place | GLIMS_ID | Experiment data | Google EarthTM image | Landsat data | SCGI | ||
Region I | G084633E29798N | Landsat-8 | OLI | 16 October 2016 | 1 December 2016 | 14 September 2016 | 30 January 2009 |
Sentinel-2 | MSI | 23 October 2016 | |||||
Region II | G088362E43816N | Landsat-5 | TM | 3 October 2011 | 5 October 2011 | 23 July 2011 | 13 August 2010 |
Region III | G090407E30339N | Landsat-5 | TM | 16 January 2008 | 29 October 2007 | 8 July 2007 | 2 November 2009 |
Region IV | G090510E28196N | Landsat-5 | TM | 18 November 2003 | 17 December 2003 | 12 May 2004 | 6 February 2010 |
Name of Classifier | Center Wavelength (μm) | Design Algorithm | Value Used |
---|---|---|---|
Maximum-Likelihood classification | Multispectral combination | Select ROI samples | Spectral reflectance values |
NDSI | Band (Green):0.561 Band (SWIR):1.609 | Spectral reflectance values | |
Red/SWIR | Band (Red):0.655 Band (SWIR):1.609 | Raw digital number values (DN) | |
NIR/SWIR | Band (NIR):0.865 Band (SWIR):1.609 | Raw digital number values (DN) | |
AGEI (this work) | Band (Red):0.655 Band (NIR):0.865 Band (SWIR):1.609 | Raw digital number values (DN) |
Classifier | Threshold | Glacier Total-Error (%) | Non-Glacier Total-Error (%) | Overall Accuracy (%) | Kappa Coefficient | Threshold | Glacier Total-Error (%) | Non-Glacier Total-Error (%) | Overall Accuracy (%) | Kappa Coefficient |
---|---|---|---|---|---|---|---|---|---|---|
Region I | RegionII | |||||||||
ML | -- | 16.68 | 28.79 | 89.122 | 0.761 | -- | 26.21 | 48.03 | 82.336 | 0.614 |
Red/SWIR | 1.70 | 18.29 | 31.49 | 88.400 | 0.745 | 3.00 | 24.00 | 36.60 | 85.483 | 0.695 |
1.90 | 18.52 | 30.91 | 88.710 | 0.747 | 2.90 | 23.67 | 37.00 | 85.600 | 0.697 | |
1.80 | 18.17 | 30.90 | 88.710 | 0.744 | 2.95 | 24.00 | 38.82 | 84.848 | 0.678 | |
1.95 | 18.69 | 30.28 | 89.020 | 0.741 | 2.85 | 25.00 | 40.30 | 84.262 | 0.670 | |
NIR/SWIR | 1.70 | 18.17 | 25.75 | 89.090 | 0.764 | 2.00 | 25.77 | 35.45 | 85.254 | 0.692 |
1.80 | 18.50 | 24.45 | 89.270 | 0.760 | 2.10 | 25.56 | 35.48 | 85.359 | 0.695 | |
1.90 | 17.81 | 24.17 | 89.540 | 0.765 | 1.95 | 25.97 | 36.66 | 84.946 | 0.691 | |
2.00 | 18.60 | 25.55 | 89.200 | 0.760 | 2.20 | 24.07 | 37.42 | 84.799 | 0.687 | |
NDSI | 0.40 | 21.51 | 38.16 | 85.670 | 0.684 | 0.40 | 29.02 | 62.81 | 75.520 | 0.426 |
0.60 | 19.01 | 31.16 | 87.266 | 0.727 | 0.57 | 27.52 | 56.70 | 79.008 | 0.523 | |
0.70 | 18.30 | 28.68 | 88.094 | 0.737 | 0.60 | 27.71 | 55.87 | 79.552 | 0.540 | |
0.80 | 19.59 | 28.40 | 87.763 | 0.735 | 0.65 | 30.04 | 59.30 | 78.592 | 0.529 | |
AGEI | 1.80 | 17.02 | 24.74 | 89.870 | 0.772 | 2.50 | 23.76 | 34.00 | 85.630 | 0.705 |
1.85 | 17.00 | 23.50 | 90.249 | 0.785 | 2.65 | 22.98 | 33.90 | 86.100 | 0.710 | |
2.00 | 17.17 | 26.90 | 89.910 | 0.774 | 2.55 | 23.03 | 34.82 | 85.777 | 0.705 | |
1.90 | 17.78 | 26.11 | 90.020 | 0.775 | 2.70 | 22.45 | 36.00 | 85.532 | 0.696 | |
RegionIII | RegionIV | |||||||||
ML | -- | 26.85 | 24.01 | 88.024 | 0.734 | -- | 31.44 | 35.50 | 82.691 | 0.650 |
Red/SWIR | 1.80 | 24.31 | 25.19 | 88.626 | 0.745 | 3.60 | 25.60 | 36.43 | 85.054 | 0.687 |
1.90 | 24.52 | 23.56 | 88.710 | 0.747 | 3.65 | 25.50 | 35.79 | 85.163 | 0.690 | |
2.00 | 24.58 | 24.21 | 88.760 | 0.748 | 3.70 | 25.44 | 35.50 | 85.218 | 0.692 | |
2.05 | 25.07 | 24.08 | 88.626 | 0.745 | 3.80 | 25.67 | 35.17 | 85.145 | 0.691 | |
NIR/SWIR | 1.30 | 25.47 | 24.59 | 88.409 | 0.741 | 2.50 | 24.29 | 35.85 | 85.545 | 0.696 |
1.40 | 24.64 | 24.94 | 88.593 | 0.744 | 2.60 | 24.77 | 35.10 | 85.636 | 0.698 | |
1.50 | 26.38 | 24.55 | 88.109 | 0.735 | 2.70 | 24.54 | 35.71 | 85.691 | 0.699 | |
1.60 | 26.70 | 23.70 | 88.092 | 0.735 | 2.80 | 25.16 | 34.26 | 85.654 | 0.703 | |
NDSI | 0.30 | 24.05 | 28.89 | 87.592 | 0.724 | 0.4 | 25.29 | 54.49 | 81.673 | 0.582 |
0.35 | 23.37 | 26.20 | 88.560 | 0.743 | 0.5 | 25.77 | 48.75 | 83.164 | 0.626 | |
0.40 | 23.32 | 24.82 | 88.960 | 0.752 | 0.7 | 27.59 | 37.03 | 84.527 | 0.676 | |
0.50 | 25.08 | 23.89 | 88.643 | 0.746 | 0.8 | 33.52 | 35.41 | 81.364 | 0.667 | |
AGEI | 1.40 | 21.65 | 24.14 | 89.526 | 0.763 | 3.00 | 23.17 | 35.44 | 86.509 | 0.709 |
1.50 | 21.72 | 23.12 | 89.794 | 0.769 | 3.10 | 23.12 | 33.28 | 86.673 | 0.716 | |
1.55 | 22.26 | 23.03 | 89.677 | 0.767 | 3.15 | 23.44 | 34.18 | 86.545 | 0.712 | |
1.60 | 22.54 | 22.84 | 89.643 | 0.766 | 3.20 | 23.36 | 33.66 | 86.636 | 0.715 |
Classifiers | L Non-Glacier S Non-Glacier | L Glacier S Glacier | L Non-Glacier S Glacier | L Glacier S Non-Glacier |
---|---|---|---|---|
Red/SWIR | 63.866 | 32.376 | 0.203 | 3.553 |
NIR/SWIR | 66.149 | 30.832 | 0.624 | 2.393 |
AGEI | 64.409 | 32.788 | 0.551 | 2.249 |
ML classification | 59.390 | 37.771 | 0.937 | 1.900 |
NDSI | 62.934 | 34.253 | 1.118 | 1.693 |
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Zhang, M.; Wang, X.; Shi, C.; Yan, D. Automated Glacier Extraction Index by Optimization of Red/SWIR and NIR /SWIR Ratio Index for Glacier Mapping Using Landsat Imagery. Water 2019, 11, 1223. https://doi.org/10.3390/w11061223
Zhang M, Wang X, Shi C, Yan D. Automated Glacier Extraction Index by Optimization of Red/SWIR and NIR /SWIR Ratio Index for Glacier Mapping Using Landsat Imagery. Water. 2019; 11(6):1223. https://doi.org/10.3390/w11061223
Chicago/Turabian StyleZhang, Meng, Xuhong Wang, Chenlie Shi, and Dajiang Yan. 2019. "Automated Glacier Extraction Index by Optimization of Red/SWIR and NIR /SWIR Ratio Index for Glacier Mapping Using Landsat Imagery" Water 11, no. 6: 1223. https://doi.org/10.3390/w11061223