Flood Monitoring in Vegetated Areas Using Multitemporal Sentinel-1 Data: Impact of Time Series Features
<p>Overview map with the location of the study area (red rectangle) in Greece/Turkey (<b>a</b>). Study area in Greece/Turkey (Satellite data: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AeroGRID, IGN, and the GIS Use Community) (<b>b</b>). The red rectangles represent the reference mask extents for northern (<b>c</b>), and southern (<b>d</b>) areas. The reference masks based on World-View 2 data (March 11, 2015) (<b>c</b>) and RapidEye (April 4, 2015) data (<b>d</b>).</p> "> Figure 2
<p>High-resolution false-color (NIR (near infrared), green, blue) WorldView2 image (March 11, 2015) (© European Space Imaging/DigitalGlobe) for northern Greece/Turkey containing temporary flooded vegetation (TFV) (<b>a</b>), temporary open water (TOW) (<b>b</b>), and dry land (DL) (<b>c</b>).</p> "> Figure 3
<p>High-resolution false-color (NIR, green, blue) RapidEye image (April 04, 2015). (© Planet Labs Inc.) for southern Greece/Turkey containing TOW (<b>a</b>), DL (<b>b</b>), and TFV (<b>c</b>).</p> "> Figure 4
<p>Overview map with the location of the study area (red rectangle) in China (<b>a</b>), study area in China (<b>b</b>). The red rectangle represents the reference mask extent (<b>c</b>), which was derived from S-2 data (June 27, 2017).</p> "> Figure 5
<p>High-resolution false-color (NIR, green, blue) Sentinel-2 image (June 27, 2017) for southern Greece/Turkey containing DL (<b>a</b>), TFV (<b>b</b>) and TOW (<b>c</b>).</p> "> Figure 6
<p>Multitemporal behavior of the backscatter intensity for TOW areas for VV (decibel (dB)), VH (dB), VV/VH (linear scale), and Normalized Difference Vegetation Index (NDVI) values in northern Greece/Turkey. The blue bars mark the analyzed date at the flood event.</p> "> Figure 7
<p>Multitemporal behavior of the backscatter intensity for TFV areas for VV (dB), VH (dB), VV/VH (linear scale), and NDVI values in southern Greece/Turkey. The blue bars mark the analyzed date at the flood event.</p> "> Figure 8
<p>Multitemporal behavior of the backscatter intensity for TFV areas for VV (dB), VH (dB), VV/VH (linear scale), and NDVI values in northern Greece/Turkey. The blue bars mark the analyzed date at the flood event.</p> "> Figure 9
<p>Multitemporal behavior of the backscatter intensity for TFV areas for VV (dB), VH (dB), VV/VH (linear scale), and NDVI values in China. The blue bars mark the analyzed date at the flood event.</p> "> Figure 10
<p>Pixel-based classification result (<b>a</b>), object-based classification result (<b>b</b>) for the study area in southern Greece/Turkey.</p> "> Figure 11
<p>Pixel-based classification result (<b>a</b>) and object-based classification result (<b>b</b>) for the study area in northern Greece/Turkey.</p> "> Figure 12
<p>Pixel-based classification result (<b>a</b>), object-based classification result (<b>b</b>) and validation mask (c) for the study area in China.</p> "> Figure 13
<p>Comparison of pixel-based time series features as a function of mean value and coefficient of variation. The statistical values (mean value and coefficient of variance) were calculated on the basis of the PA and UA accuracy values.</p> "> Figure 14
<p>Comparison of object-based time series features as a function of mean value and coefficient of variation. The statistical values (mean value and coefficient of variance) were calculated on the basis of the PA and UA accuracy values.</p> ">
Abstract
:1. Introduction
- to investigate the relevance of polarization and time series features for the derivation of TFV with respect to vegetation types in both study areas;
- to examine if the relevant time series features for the analyzed study areas correspond to the relevant time series features of the previous study area (Namibia) in [8], despite the occurrence of different vegetation types and
- to identify a single time series feature that is relevant for the extraction of different TFV types and for all study areas in order to demonstrate the potential for the transferability and operational use of this time series approach.
2. Materials and Methods
2.1. Study Areas and Available Data Sets: Greece/Turkey and China
2.2. Methodology
3. Results and Discussion
3.1. Level of Contribution of the Time Series Features
3.2. Classification Results
3.3. Time Series Features—Transferability Analysis
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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No | Date | No | Date | No | Date | No | Date |
---|---|---|---|---|---|---|---|
1 | October 19, 2014 | 16 | June 16, 2015 | 31 | January 6, 2016 | 46 | July 16, 2016 |
2 | October 31, 2014 | 17 | June 28, 2015 | 32 | January 18, 2016 | 47 | July 28,2016 |
3 | November 24, 2014 | 18 | July 10, 2015 | 33 | January 30, 2016 | 48 | August 9, 2016 |
4 | December 6, 2014 | 19 | July 22, 2015 | 34 | February 11, 2016 | 49 | August 21, 2016 |
5 | December 18, 2014 | 20 | August 15, 2015 | 35 | February 23, 2016 | 50 | September 2, 2016 |
6 | December 30, 2014 | 21 | August 27, 2015 | 36 | March 6, 2016 | 51 | September 14, 2016 |
7 | January 11, 2015 | 22 | September 8, 2015 | 37 | March 18, 2016 | 52 | September 26, 2016 |
8 | February 4, 2015 | 23 | September 20, 2015 | 38 | March 30, 2016 | 53 | October 2, 2016 |
9 | February 16, 2015 | 24 | October 2, 2015 | 39 | April 11, 2016 | 54 | October 14, 2016 |
10 | March 12, 2015 | 25 | October 14, 2015 | 40 | April 23, 2016 | 55 | October 26, 2016 |
11 | March 24, 2015 | 26 | October 26, 2015 | 41 | May 5, 2016 | 56 | November 7, 2016 |
12 | April 5, 2015 | 27 | November 19, 2015 | 42 | May 17, 2016 | 57 | November 19, 2016 |
13 | April 17, 2015 | 28 | December 1, 2015 | 43 | May 29, 2016 | 58 | December 1, 2016 |
14 | May 11, 2015 | 29 | December 13, 2015 | 44 | June 10,2016 | 59 | December 13, 2016 |
15 | June 4, 2015 | 30 | December 25, 2015 | 45 | July 4, 2016 | 60 | December 25, 2016 |
No | Date | No | Date | No | Date | No | Date |
---|---|---|---|---|---|---|---|
1 | October 19, 2016 | 11 | February 16, 2017 | 21 | July 10, 2017 | 31 | November 19, 2017 |
2 | October 31, 2016 | 12 | February 28, 2017 | 22 | July 22, 2017 | 32 | December 1, 2017 |
3 | November 12, 2016 | 13 | March 12, 2017 | 23 | August 3, 2017 | 33 | December 13, 2017 |
4 | November 24, 2016 | 14 | March 24, 2017 | 24 | August 15, 2017 | 34 | December 25, 2017 |
5 | December 6, 2016 | 15 | April 5, 2017 | 25 | August 27, 2017 | 35 | January 6, 2018 |
6 | December 18, 2016 | 16 | April 17, 2017 | 26 | September 8, 2017 | 36 | January 30, 2018 |
7 | December 30, 2016 | 17 | April 29, 2017 | 27 | October 2, 2017 | 37 | February 11, 2018 |
8 | January 11, 2017 | 18 | June 11, 2017 | 28 | October 14, 2017 | 38 | February 23, 2018 |
9 | January 23, 2017 | 19 | June 4, 2017 | 29 | October 26, 2017 | ||
10 | February 4, 2017 | 20 | June 28, 2017 | 30 | November 7, 2017 |
Sensor Properties | Values |
---|---|
Wavelength Mode | Interferometric Wide Swath (IW) |
Polarization | VV − VH |
Frequency | C-band (GHz) |
Resolution | 20 × 22 m (az. × gr. range) |
Pixel spacing | 10 × 10 m (az. × gr. range) |
Inc. angle | 30.5°–46.3° |
Orbit | Ascending |
Product-level | Level-1 (Ground Range Detected High Resolution (GRDH)) |
Southern Greece/Turkey (%) | Northern Greece/Turkey (%) | China (%) | |
---|---|---|---|
Z-Score VV | 35.64 | 29.26 | 31.35 |
Z-Score VH | 15.51 | 23.07 | 24.61 |
Z-Score VV + VH | 39.44 | 35.3 | 33.37 |
Z-Score VV – VH | 5.91 | 7.33 | 3.87 |
Z-Score VV/VH | 3.51 | 5.08 | 6.81 |
Southern Greece/Turkey (%) | Northern Greece/Turkey (%) | China (%) | |
---|---|---|---|
Z-Score VV | 32.61 | 28.39 | 33.21 |
Z-Score VH | 2.79 | 11.30 | 17.32 |
Z-Score VV + VH | 18.66 | 21.53 | 33.84 |
Z-Score VV – VH | 41.57 | 16.37 | 8.56 |
Z-Score VV/VH | 4.38 | 22.41 | 7.10 |
Southern Greece/Turkey (%) | Northern Greece/Turkey (%) | China (%) | ||||
---|---|---|---|---|---|---|
Pixel-based | Object-based | Pixel-based | Object-based | Pixel-based | Object-based | |
DL—UA | 76.64 | 77.62 | 56.65 | 53.25 | 85.26 | 87.66 |
TOW—UA | 86.53 | 86.87 | 97.58 | 98.23 | 79.73 | 85.65 |
TFV—UA | 90.37 | 92.17 | 76.83 | 73.86 | 78.28 | 83.01 |
DL—PA | 77.86 | 79.61 | 91.67 | 96.60 | 69.89 | 78.95 |
TOW—PA | 85.94 | 86.38 | 91.02 | 91.67 | 90.47 | 91.14 |
TFV—PA | 89.52 | 90.09 | 28.18 | 6.32 | 90.09 | 91.23 |
OA | 84.26 | 85.17 | 81.47 | 79.59 | 81.37 | 85.84 |
Kappa | 0.76 | 0.78 | 0.66 | 0.63 | 0.71 | 0.78 |
Z-Score VV | Z-Score VH | Z-Score VV + VH | Z-Score VV − VH | Z-Score VV/VH | ||||||
---|---|---|---|---|---|---|---|---|---|---|
UA | PA | UA | PA | UA | PA | UA | PA | UA | PA | |
Namibia | 88.72 | 78.43 | 47.11 | 90.52 | 85.64 | 82.38 | 92.62 | 69.96 | 67.90 | 86.32 |
China | 82.26 | 92.65 | 51.26 | 99.30 | 78.28 | 90.09 | 52.37 | 99.24 | 44.46 | 99.50 |
North Greece | 76.83 | 28.18 | 50.34 | 0.81 | 65.85 | 3.26 | 37.10 | 4.71 | 68.58 | 56.72 |
South Greece | 96.10 | 77.17 | 35.25 | 42.22 | 94.89 | 53.17 | 90.37 | 89.52 | 60.85 | 93.48 |
Mean values of UA and PA | 77.54 | 52.10 | 69.18 | 65.74 | 72.23 | |||||
Coefficient of variance | 0.22 | 0.41 | 0.36 | 0.47 | 0.18 |
Z-Score VV | Z-Score VH | Z-Score VV + VH | Z-Score VV − VH | Z-Score VV/VH | ||||||
---|---|---|---|---|---|---|---|---|---|---|
UA | PA | UA | PA | UA | PA | UA | PA | UA | PA | |
Namibia | 84.05 | 80.71 | 49.26 | 17.47 | 84.37 | 5.99 | 54.93 | 89.29 | 76.1 | 91.2 |
China | 82.38 | 92.65 | 62.51 | 92.46 | 83.01 | 91.23 | 66.68 | 97.06 | 42.86 | 97.86 |
North Greece | 73.86 | 6.32 | 50.35 | 0.82 | 65.99 | 3.29 | 48.61 | 2.92 | 68.75 | 57.22 |
South Greece | 97.05 | 87.81 | 26.90 | 31.64 | 98.52 | 75.19 | 92.17 | 90.09 | 65.20 | 92.74 |
Mean values of UA and PA | 75.57 | 41.43 | 63.44 | 67.72 | 73.99 | |||||
Coefficient of variance | 0.31 | 0.62 | 0.52 | 0.40 | 0.19 |
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Tsyganskaya, V.; Martinis, S.; Marzahn, P. Flood Monitoring in Vegetated Areas Using Multitemporal Sentinel-1 Data: Impact of Time Series Features. Water 2019, 11, 1938. https://doi.org/10.3390/w11091938
Tsyganskaya V, Martinis S, Marzahn P. Flood Monitoring in Vegetated Areas Using Multitemporal Sentinel-1 Data: Impact of Time Series Features. Water. 2019; 11(9):1938. https://doi.org/10.3390/w11091938
Chicago/Turabian StyleTsyganskaya, Viktoriya, Sandro Martinis, and Philip Marzahn. 2019. "Flood Monitoring in Vegetated Areas Using Multitemporal Sentinel-1 Data: Impact of Time Series Features" Water 11, no. 9: 1938. https://doi.org/10.3390/w11091938