A Hybrid Approach for Extracting Large-Scale and Accurate Built-Up Areas Using SAR and Multispectral Data
Abstract
:1. Introduction
1.1. Motivation of the Study
1.2. Correlation between Input Data
1.3. Random Forest Classifier in Remote Sensing
1.4. Large Scale Data on Impervious Surfaces
2. Materials and Methods
2.1. Workflow for Data Processing
2.2. Study Area
2.3. Input Data and Samples
Used Data | Format | Type | Range |
---|---|---|---|
Landsat 5 TM collection 2 tier 1 calibrated at top-of-atmosphere (TOA) reflectance, Chander et al. [63] | Raster | Multispectral | 19 April 1984 to 2011 |
Landsat 8 collection 1 tier 1 composite, Chander et al. [63] | Raster | Multispectral | 7 April 2013 to 2022 |
Sentinel-1: Dual-polarization, C-band synthetic aperture radar (SAR), Filipponi [64] | Raster | Radar | October 2014 to 2022 |
Global SAR/PALSAR and PALSAR 2 mosaic, Shimada et al. [53] | Raster | Radar | 1 January 2007 to 1 January 2021 |
3. Results and Discussion
3.1. Evaluation Metrics and Comparison
3.2. Visual Interpretation
4. Conclusions and Perspectives
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Location | Built Up | Vegetation | Bare Soil | Water | Total Samples |
---|---|---|---|---|---|
Train_Casa_2000 | 64 | 74 | 169 | 8 | 315 |
Train_Casa_2005 | 75 | 71 | 162 | 7 | 315 |
Train_Casa_2010 | 94 | 67 | 146 | 8 | 315 |
Train_Casa_2015 | 75 | 71 | 162 | 7 | 315 |
Train_Casa_2020 | 112 | 90 | 98 | 6 | 306 |
Train_Kech_2000 | 103 | 54 | 106 | 13 | 276 |
Train_Kech_2005 | 49 | 55 | 167 | 5 | 276 |
Train_Kech_2010 | 87 | 48 | 133 | 8 | 276 |
Train_Kech_2015 | 103 | 54 | 106 | 13 | 276 |
Train_Kech_2020 | 113 | 54 | 96 | 13 | 276 |
Train_Tanger_2000 | 34 | 74 | 142 | 2 | 252 |
Train_Tanger_2005 | 36 | 63 | 151 | 2 | 252 |
Train_Tanger_2010 | 54 | 54 | 142 | 2 | 252 |
Train_Tanger_2015 | 89 | 62 | 100 | 1 | 252 |
Train_Tanger_2020 | 97 | 84 | 70 | 1 | 252 |
Total | 1185 | 975 | 1950 | 96 | 4206 |
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Data | Data Citation | Data Source | Availability in GEE | Resolution in Meters | Range |
---|---|---|---|---|---|
Global Human Settlement Layer, built-up grid | Pesaresi et al. [44] | GHSL [45] | Available in GEE | 30 | 1975–2014 |
GlobeLand30 | Jun et al. [46] | GLOBELAND30 [47] | Not available in GEE | 30 | 2000–2020 |
GAIA | Gong et al. [48] | GAIA [49] | Available in GEE | 30 | 1985–2018 |
GISD30: global 30 m impervious surface dynamic dataset from 1985 to 2020 [50] | Zhang et al. [51] | Zendo [52] | Not available in GEE | 30 | 1985–2020 |
Band | Landsat 8 OLI and TIRS Wavelengths in Micrometers | Landsat 5 TM Wavelength in Micrometers |
---|---|---|
NIR | Also called B5, with wavelengths ranging from 0.85 to 0.88 | Also called B4, with wavelengths ranging from 0.76 to 0.90 |
Red | Also called B4, with wavelengths ranging from 0.64 to 0.67 | Also called B3, with wavelengths ranging from 0.76 to 0.90 |
Green | Also called B3, with wavelengths ranging from 0.53 to 0.59 | Also called B2, with wavelengths ranging from 0.63 to 0.69 |
SWIR | Also called B6, with wavelengths ranging from 1.57 to 1.65 | Also called B5, with wavelengths ranging from 1.55 to 1.75 |
Layer | Importance Factor |
---|---|
Blue | 58.28 |
Green | 48.84 |
Red | 45.65 |
NIR | 46.01 |
SWIR1 | 47.90 |
LST | 39.70 |
Polarization VH | 69.89 |
Polarization VV | 70.17 |
Elevation | 35.85 |
NDBI | 62.88 |
NDVI | 68.54 |
NDWI | 63.78 |
Slope | 33.92 |
City | Date | Overall Accuracy | Kappa Coefficient | Producer Accuracy | User Accuracy |
---|---|---|---|---|---|
Casablanca 0: Built up 1: Bare soil 2: Vegetation 3: Water | 2000 | 0.77 | 0.61 | 0: 0.8 1: 0.57 2: 0.84 3: 0.6 | 0: 0.74 1: 0.75 2: 0.77 3: 1 |
2005 | 0.8 | 0.68 | 0: 0.71 1: 0.84 2: 0.82 3: 1 | 0: 0.78 1: 0.75 2: 0.82 3: 1 | |
2010 | 0.81 | 0.72 | 0: 0.68 1: 0.86 2: 0.92 3: 0.5 | 0: 0.90 1: 0.89 2: 0.72 3: 0 | |
2015 | 0.81 | 0.72 | 0: 0.93 1: 0.70 2: 0.78 3: 1 | 0: 0.85 1: 0.8 2: 0.78 3: 1 | |
2020 | 0.91 | 0.86 | 0: 0.87 1: 0.92 2: 0.95 3: 1 | 0: 0.96 1: 0.92 2: 0.84 3: 1 | |
Marrakech | 2000 | 0.73 | 0.47 | 0: 0.40 1: 0.56 2: 0.90 3: 1 | 0: 0.81 1: 0.64 2: 0.74 3: 1 |
2005 | 0.74 | 0.53 | 0: 0.60 1: 0.68 2: 0.83 3: 0 | 0: 0.70 1: 0.65 2: 0.78 3: 0 | |
2010 | 0.84 | 0.76 | 0: 0.85 1: 0.80 2: 0.86 3: 0.50 | 0: 0.82 1: 0.86 2: 0.84 3: 0 | |
2015 | 0.83 | 0.75 | 0: 0.90 1: 0.78 2: 0.85 3: 0.42 | 0: 0.81 1: 0.78 2: 0.87 3: 1 | |
2020 | 0.83 | 0.75 | 0: 0.83 1: 0.9 2: 0.91 3: 0.14 | 0: 0.85 1: 0.69 2: 0.89 3: 1 | |
Tangier | 2000 | 0.71 | 0.48 | 0: 0.64 1: 0.5 2: 0.85 | 0: 0.81 1: 0.69 2: 0.70 |
2005 | 0.80 | 0.62 | 0: 0.66 1: 0.63 2: 0.93 3: 0 | 0: 0.88 1: 0.79 2: 0.80 3: 0 | |
2010 | 0.75 | 0.59 | 0: 0.66 1: 0.74 2: 0.80 | 0: 0.7 1: 0.83 2: 0.74 | |
2015 | 0.76 | 0.63 | 0: 0.74 1: 0.66 2: 0.86 3: 0 | 0: 0.76 1: 0.92 2: 0.72 3: 0 | |
2020 | 0.79 | 0.68 | 0: 0.71 1: 0.82 2: 0.84 | 0: 0.77 1: 0.88 2: 0.73 |
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Azmi, R.; Chenal, J.; Amar, H.; Tekouabou Koumetio, C.S.; Diop, E.B. A Hybrid Approach for Extracting Large-Scale and Accurate Built-Up Areas Using SAR and Multispectral Data. Atmosphere 2023, 14, 240. https://doi.org/10.3390/atmos14020240
Azmi R, Chenal J, Amar H, Tekouabou Koumetio CS, Diop EB. A Hybrid Approach for Extracting Large-Scale and Accurate Built-Up Areas Using SAR and Multispectral Data. Atmosphere. 2023; 14(2):240. https://doi.org/10.3390/atmos14020240
Chicago/Turabian StyleAzmi, Rida, Jérôme Chenal, Hicham Amar, Cédric Stéphane Tekouabou Koumetio, and El Bachir Diop. 2023. "A Hybrid Approach for Extracting Large-Scale and Accurate Built-Up Areas Using SAR and Multispectral Data" Atmosphere 14, no. 2: 240. https://doi.org/10.3390/atmos14020240
APA StyleAzmi, R., Chenal, J., Amar, H., Tekouabou Koumetio, C. S., & Diop, E. B. (2023). A Hybrid Approach for Extracting Large-Scale and Accurate Built-Up Areas Using SAR and Multispectral Data. Atmosphere, 14(2), 240. https://doi.org/10.3390/atmos14020240