An Adaptive Thresholding Approach toward Rapid Flood Coverage Extraction from Sentinel-1 SAR Imagery
"> Figure 1
<p>Map of the study sites: locations of (<b>a</b>) the two provinces in China, (<b>b</b>) WeiFang in Shandong Province, (<b>c</b>) Jiangxi Province, and (<b>d</b>) Ji’An City.</p> "> Figure 1 Cont.
<p>Map of the study sites: locations of (<b>a</b>) the two provinces in China, (<b>b</b>) WeiFang in Shandong Province, (<b>c</b>) Jiangxi Province, and (<b>d</b>) Ji’An City.</p> "> Figure 2
<p>Flowchart showing the processes of our algorithm.</p> "> Figure 3
<p>The pre-processing process for Sentinel-1 data. (<b>a</b>) Location of the study area, (<b>b</b>) original image, (<b>c</b>) pre-processed images.</p> "> Figure 4
<p>Two types of histograms for images (Band VV). The red line is the determined threshold of the VV band and the blue line is the grayscale curve of that threshold.</p> "> Figure 5
<p>Locations of 200 random points sampled at each of the two sites for visual inspection of the extraction results of the AT-EWC method (Ji’An (<b>a</b>) and WeiFang (<b>b</b>)).</p> "> Figure 6
<p>Water extraction results at the WeiFang site on 26 August 2018 (<b>a</b>) and Ji’An site on 25 June 2019 (<b>b</b>).</p> "> Figure 7
<p>Water level changes in the Fushan Reservoir during the flood event at the WeiFang site (<b>a</b>), and changes in the river during the entire flood event at the Ji’An site (<b>b</b>). At each date, the left image is the false-color composite image from Sentinel-1 data, and the right image shows the extracted water bodies.</p> "> Figure 8
<p>Inundation range at the WeiFang site during the flood period. The blue portion represents the persistent open water. The green portion indicates the inundation range. The image on the right is a Sentinel-1 image from 21 July 2018.</p> "> Figure 9
<p>Comparison with Otsu method at the WeiFang site. (<b>a</b>) 20 km × 20 km block (high) (<b>b</b>) 20 km × 20 km block (medium) (<b>c</b>) 20 km × 20 km block (low) (<b>d</b>) 4000 by 4000 pixels image.</p> "> Figure A1
<p>Comparison of the old and new versions of cDSWE (v1 (<b>A</b>,<b>B</b>) and v3 (<b>C</b>,<b>D</b>)).</p> "> Figure A2
<p>The location of the selected nine grids. (<b>a</b>) Location of sample block; (<b>b</b>) the WeiFang study site.</p> ">
Abstract
:1. Introduction
- A fully automated thresholding method was developed for extracting flood ranges with higher temporal resolution. Existing global water monitoring products are mostly generated by optical remote sensors. There are also annual composite data because of the influence of cloud cover. The inundation area during the flood period can be ascertained by our method, which makes up for the lack of temporal resolution of optical products.
- The proposed method is tested over reservoir and watershed study sites in China. The area of water and non-water can be extracted quickly and accurately with our fully automated approach. The classification results of the two types of sites also show that our method is better than existing thresholding methods (e.g., Otsu [6]).
2. Study Area and Data
2.1. Study Area
2.2. Remote-Sensing Datasets
2.2.1. Sentinel-1 Data
2.2.2. Landsat Dataset on GEE
2.2.3. GF-3 Data
3. Methodology
3.1. Derivation of Persistent Open-Water Extent from cDSWE
3.2. Pre-Processing of Sentinel-1 SAR Data
3.3. Extraction of Flood Coverage
3.4. Accuracy Assessment
4. Results
4.1. Time-Series Classification of Water and Non-Water
- —
- First, if > −16 dB, mark as water.
- —
- Second, if > −22 dB, mark as water.
- —
- Third, if < 15 dB, mark as water.
4.2. Inundation Range over Flood Event
4.3. Accuracy Assessment at Two Sites
4.3.1. Adaptation of the Proposed Thresholds at the Ji’An Site
4.3.2. Comparison with the Otsu Method at the WeiFang Site
5. Discussion
5.1. Significance of This Study
5.2. Limitations and Potential Improvements
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
- --
- First, determine the latitude and longitude of the study area and the time of using the data; import the used data; set the name of the save folder.
- --
- Second, write a function that calculates the water body index (MNDWI, NDVI, MBSR, AWESH). The arguments of this function are calculated as follows:
- (a)
- Modified Normalized Difference Wetness Index (MNDWI) = (green − SWIR1)/(green + SWIR1)
- (b)
- Multi-band Spectral Relationship Visible (MBSRV) = green + red
- (c)
- Multi-band Spectral Relationship Near-Infrared (MBSRN) = NIR + SWIR1
- (d)
- Automated Water Extent Shadow (AWESH) = blue + (2.5 × green) − (1.5 × MBSRN) − (0.25 × SWIR2)
- (e)
- Normalized Difference Vegetation Index (NDVI) = (NIR − red)/(NIR + red)
- --
- Third, encode the five basic experimental functions of the DSWE algorithm.
- --
- Forth, in the code, write three classification functions corresponding to Water (Pixel Value = 1 and 2), Land (Pixel Value = 0) and Potential (Pixel Value = 3).
- --
- Fifth, output the final classification result to Google Drive.
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Method | Category | Pros and Cons | Example |
---|---|---|---|
Image Thresholding | Otsu Binarization | an exhaustive algorithm for searching the global optimal threshold; easy to be severely corrupted by noise | [6,27,48,49] |
Entropy Threshold | features are easy to select and replace; a large amount of computation | [34,35,40,50] | |
Bimodal Histogram | effectiveness and efficiency; strict requirements are necessary for the shape of the histogram; low applicability | [50,51,52] | |
Machine Learning | Support Vector Machine (SVM) | achieves accurate extraction results and improves the influence of noise; the parameters greatly affect the classification results | [44,45,53] |
K-Nearest Neighbor (KNN) | decrease the overfitting and variance problems in the training dataset; a large amount of calculation and debugging is required | [43,54] | |
Random Forest (RF) | the most accurate method for the classification of water bodies at present; a training dataset is difficult to obtain, and the classifier training is relatively time-consuming | [45,47,55,56,57] |
Data | Overall Accuracy | Kappa Coefficient | Commission Error | Omission Error | ||
---|---|---|---|---|---|---|
Land | Water | Land | Water | |||
1 June 2019 | 95.00% | 0.7715 | 3.41% | 16.67% | 2.30% | 23.08% |
13 June 2019 | 96.00% | 0.8172 | 2.84% | 12.50% | 1.72% | 19.23% |
25 June 2019 | 94.50% | 0.7443 | 3.95% | 17.39% | 2.30% | 26.92% |
Data | Overall Accuracy | Kappa Coefficient | Commission Error | Omission Error | ||
---|---|---|---|---|---|---|
Land | Water | Land | Water | |||
1 June 2019 | 95.00% | 0.7790 | 2.87% | 19.23% | 2.87% | 19.23% |
13 June 2019 | 96.50% | 0.8427 | 2.29% | 12.00% | 1.72% | 15.38% |
25 June 2019 | 95.50% | 0.7978 | 2.86% | 16.00% | 2.30% | 19.23% |
Type | Threshold | Overall Accuracy | Kappa Coefficient | Commission Error | Omission Error | ||
---|---|---|---|---|---|---|---|
Land | Water | Land | Water | ||||
Image | AT-EWC | 95.50% | 0.9080 | 3.57% | 2.65% | 3.57% | 2.65% |
Otsu | 94.50% | 0.8876 | 4.39% | 6.98% | 5.22% | 5.88% | |
(−) | (0.01) | (0.02) | (−0.01) | (−0.04) | (−0.02) | (−0.03) | |
20 km × 20 km Block (High) | AT-EWC | 96.00% | 0.9199 | 2.91% | 5.15% | 4.76% | 3.16% |
Otsu | 95.00% | 0.8997 | 4.76% | 5.26% | 4.76% | 5.26% | |
(−) | (0.01) | (0.02) | (−0.02) | (0.00) | (0.00) | (−0.02) | |
20 km × 20 km Block (Middle) | AT-EWC | 96.00% | 0.8947 | 2.03% | 9.62% | 3.33% | 6.00% |
Otsu | 84.00% | 0.6364 | 2.42% | 38.16% | 19.33% | 6.00% | |
(−) | (0.12) | (0.26) | (0.00) | (−0.29) | (−0.16) | (0.00) | |
20 km × 20 km Block (Low) | AT-EWC | 97.50% | 0.8697 | 1.12% | 13.64% | 1.68% | 9.52% |
Otsu | 81.00% | 0.4136 | 0.69% | 66.07% | 20.56% | 5.00% | |
(−) | (0.17) | (0.46) | (0.00) | (−0.52) | (−0.19) | (0.05) |
Time(s) | #1 * | #2 | #3 | #4 | #5 | #6 | #7 | #8 | #9 | Average |
---|---|---|---|---|---|---|---|---|---|---|
AT-EWC | 0.09 | 0.09 | 0.11 | 0.09 | 0.09 | 0.09 | 0.09 | 0.09 | 0.19 | 0.10 |
Otsu | 0.29 | 0.26 | 0.24 | 0.23 | 0.23 | 0.23 | 0.25 | 0.29 | 0.24 | 0.25 |
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Chen, S.; Huang, W.; Chen, Y.; Feng, M. An Adaptive Thresholding Approach toward Rapid Flood Coverage Extraction from Sentinel-1 SAR Imagery. Remote Sens. 2021, 13, 4899. https://doi.org/10.3390/rs13234899
Chen S, Huang W, Chen Y, Feng M. An Adaptive Thresholding Approach toward Rapid Flood Coverage Extraction from Sentinel-1 SAR Imagery. Remote Sensing. 2021; 13(23):4899. https://doi.org/10.3390/rs13234899
Chicago/Turabian StyleChen, Shujie, Wenli Huang, Yumin Chen, and Mei Feng. 2021. "An Adaptive Thresholding Approach toward Rapid Flood Coverage Extraction from Sentinel-1 SAR Imagery" Remote Sensing 13, no. 23: 4899. https://doi.org/10.3390/rs13234899