Rapid Response to a Typhoon-Induced Flood with an SAR-Derived Map of Inundated Areas: Case Study and Validation
"> Figure 1
<p>Information about Typhoon Soulik and its moving tracks forecasted at 20:00, 11 July. Two COSMO-SkyMed 1 radar satellite images with a spatial resolution of 30 m and a 100 × 100 km<sup>2</sup> coverage area were scheduled at 5:46:59, 13 July [<a href="#B26-remotesensing-07-11954" class="html-bibr">26</a>]. The region of the image taken is shown by the green box. The different colors of the path lines show the forecasted typhoon moving tracks provided by different forecasting models, such as the Weather Research and Forecasting Model (WRF) and the Fifth-Generation Penn State/ National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5).</p> "> Figure 2
<p>(<b>a</b>) Formosat-2 true color satellite image of I-Lan County, which is located in the northwestern part of Taiwan. The region in the green triangle is Lanyang Plain. (<b>b</b>) The region denoted as the purple square is covered by the SAR image. The region denoted as the yellow square is the same geographic position as the areas shown in <a href="#remotesensing-07-11954-f003" class="html-fig">Figure 3</a>.</p> "> Figure 3
<p>The positions of water level stations in the study area, with the geographic position being the same as that indicated by the yellow square in <a href="#remotesensing-07-11954-f002" class="html-fig">Figure 2</a>.</p> "> Figure 4
<p>The flowchart of flood extent detection using the Expert Synthetic Aperture Radar Imagery Waterbody Delineation System (ESARIWDS).</p> "> Figure 5
<p>An example of the low ratio of the dark region in one scene. (<b>a</b>) Dark regions determined by ESARIWDS (green polygons). (<b>b</b>) Histogram of the current scene, which exhibits a pattern of a single peak.</p> "> Figure 6
<p>An example of the high ratio of the dark region in one scene. (<b>a</b>) Dark regions determined by ESARIWDS (green polygons). (<b>b</b>) Histogram of the current scene, which exhibits a bimodal pattern.</p> "> Figure 7
<p>An example of flood extent detection using ESARIWDS. (<b>a</b>) The most recent optical image of the same area taken by Formosat-2; (<b>b</b>) like-polarization histogram; (<b>c</b>) cross-polarization histogram; (<b>d</b>) dark regions in the SAR image; (<b>e</b>) overlaying the SAR image and the boundary of dark regions onto the corresponding DEM to visually examine the topographical relationship of each dark region.</p> "> Figure 8
<p>The flowchart of inferring the flood depths by combining flooding patterns with the inundation potential maps produced by WRA from 2007 to 2010.</p> "> Figure 9
<p>The close-up photo of the water level gauge and the inundated areas (red shaded polygons) interpreted from the SAR imagery of twelve stations: (<b>a</b>) GJL12, (<b>b</b>) LML2, (<b>c</b>) MFL1, (<b>d</b>)MFL2, (<b>e</b>) ISR1, (<b>f</b>) ISR3, (<b>g</b>) ISR4, (<b>h</b>) ISR6, (<b>i</b>) ISR7, (<b>j</b>) ISR8, (<b>k</b>) ISR9, (<b>l</b>) ISR10, the name and location of each station is defined and illustrated in <a href="#remotesensing-07-11954-f003" class="html-fig">Figure 3</a> that were fully in accordance with the interpretation results.</p> "> Figure 10
<p>The close-up photo of water level gauge and the inundated areas (red shaded polygons) interpreted from the SAR imagery of five stations: (<b>a</b>) GJL1, (<b>b</b>) GJL3, (<b>c</b>) KXL1, (<b>d</b>)ISR2, (<b>e</b>) ISR5, the name and location of each station is defined and illustrated in <a href="#remotesensing-07-11954-f003" class="html-fig">Figure 3</a> that were not consist with the interpretation results. These five stations are all located in a mixed pixel near the boundary of the inundated area.</p> "> Figure 11
<p>The flooded regions in I-Lan County, with a focus on farmland areas.</p> "> Figure 12
<p>The flood extent map compared to different inundation potential maps. Yellow regions show the common areas between the flood extent map and the inundation potential map; green regions show the areas overestimated by the inundation potential map; and red regions show the areas underestimated by the inundation potential map. (<b>a</b>) Comparison of the flood extent map to 200 mm rainfall map in one day. (<b>b</b>) Comparison of the flood extent map to the one-year recurrence period inundation potential map. (<b>c</b>) Comparison of the flood extent map to the two-year recurrence period inundation potential map.</p> "> Figure 13
<p>The flood depth map derived in this research.</p> ">
Abstract
:1. Introduction
2. Flash Flood and Study Area
2.1. Typhoon Soulik
2.2. I-Lan County
3. Data
3.1. SAR Image Request
Steps | Time (Local Time in Taiwan) |
---|---|
Assessment of acquired image | 8:30 a.m. 11 July 2013 (after a sea alert for the typhoon) |
Place an urgent order | 9:00 p.m. 11 July 2013 (after a land warning for typhoon) |
Acquired SAR image | 5:47 a.m. 13 July 2013 (three hours after making landfall) |
SAR image download | 3:00 p.m. 13 July 2013 (begin to download the image) |
Image processing with SARscape | 6:00 p.m. 13 July 2013 (two hours after downloading the image) |
Derive the flood region | 8:00 p.m. 13 July 2013 (five hours after downloading the image) |
3.2. Synthetic Aperture Radar Imagery
3.3. Water Level Station
4. Methods
4.1. Expert Synthetic Aperture Radar Imagery Waterbody Delineation System
4.2. Flood Depth Deriving
5. Results and Discussion
5.1. Inundation Extent
5.2. Inundation Depth
Area of Inundation Potential Map (m2) | Area of Flood Extent Map (m2) | Fit Area (m2) | Overestimated Area of Inundation Potential Map (m2) | Overestimated Area of Flood Extent Map (m2) | Consistent Rate | Overestimation Rate of Flood Extent Map | Underestimation Rate of Flood Extent Map | Accuracy | |
---|---|---|---|---|---|---|---|---|---|
Symbol | A | B | C | A – C = D | B – C = E | C/B = F | D/B = G | E/B = H | F/G = I |
I1 | 80,203,200 | 84,444,893 | 32,336,135 | 47,867,065 | 52,108,758 | 0.3829 | 0.5668 | 0.6171 | 0.68 |
I10 | 135,708,800 | 84,444,893 | 52,866,069 | 82,842,732 | 31,578,825 | 0.626 | 0.981 | 0.374 | 0.64 |
I100 | 156,982,400 | 84,444,893 | 59,381,731 | 97,600,669 | 25,063,163 | 0.7032 | 1.1558 | 0.2968 | 0.61 |
I2 | 110,441,600 | 84,444,893 | 44,185,098 | 66,256,502 | 40,259,795 | 0.5232 | 0.7846 | 0.4768 | 0.67 |
I20 | 143,035,200 | 84,444,893 | 55,200,726 | 87,834,474 | 29,244,167 | 0.6537 | 1.0401 | 0.3463 | 0.63 |
I200 | 161,609,600 | 84,444,893 | 60,716,539 | 100,893,061 | 23,728,354 | 0.719 | 1.1948 | 0.281 | 0.60 |
I25 | 145,214,400 | 84,444,893 | 55,817,560 | 89,396,840 | 28,627,333 | 0.661 | 1.0586 | 0.339 | 0.62 |
I5 | 126,550,400 | 84,444,893 | 49,885,001 | 76,665,399 | 34,559,892 | 0.5907 | 0.9079 | 0.4093 | 0.65 |
I50 | 151,299,200 | 84,444,893 | 57,693,123 | 93,606,077 | 26,751,771 | 0.6832 | 1.1085 | 0.3168 | 0.62 |
I500 | 167,080,000 | 84,444,893 | 62,111,361 | 104,968,639 | 22,333,532 | 0.7355 | 1.243 | 0.2645 | 0.59 |
R200 | 93,379,200 | 84,444,893 | 38,036,356 | 55,342,844 | 46,408,537 | 0.4504 | 0.6554 | 0.5496 | 0.69 |
R350 | 120,953,600 | 84,444,893 | 48,213,636 | 72,739,964 | 36,231,258 | 0.5709 | 0.8614 | 0.4291 | 0.66 |
R450 | 133,155,200 | 84,444,893 | 52,209,337 | 80,945,863 | 32,235,556 | 0.6183 | 0.9586 | 0.3817 | 0.64 |
R600 | 147,368,000 | 84,444,893 | 56,693,289 | 90,674,711 | 27,751,605 | 0.6714 | 1.0738 | 0.3286 | 0.63 |
Name | Derived Flood Depth (cm) | Water Level Station (cm) | Flood | Annotations |
---|---|---|---|---|
GJL1 | 61.9 | 30.7 | ○ | Flood depth overestimated. |
GJL2 | 66.0 | 93.9 | ○ | The water level station is located at the edge of a water body. |
GJL3 | 62.5 | 0.1 | ○ | Flood depth underestimated. |
KXL1 | 70.9 | 48.1 | ╳ | Flood depth overestimated. |
LML2 | 0.0 | 1.7 | ╳ | The water level station is located at the edge of a water body. |
MFL1 | 69.6 | 1.6 | ╳ | Flood depth overestimated. |
MFL2 | 64 | 0.8 | ╳ | The water level station is located at the edge a water body. |
ISR1 | 0.0 | 0.4 | ╳ | The results are consistent with the water level station data. |
ISR2 | 37.7 | 0.3 | ○ | Flood depth overestimated. |
ISR3 | 113.5 | 0.6 | ╳ | Flood depth overestimated. |
ISR4 | 0.0 | 0.4 | ╳ | The results are consistent with the water level station data. |
ISR5 | 66.5 | 21.2 | ╳ | Flood depth overestimated. |
ISR6 | 0.0 | 0.3 | ╳ | The water level station is located at the edge a water body. |
ISR7 | 62.8 | 10.2 | ○ | Flood depth overestimated. |
ISR8 | 0.0 | 30.3 | ○ | The results are consistent with water level station data. |
ISR9 | 36.6 | 5.4 | ○ | Flood depth overestimated. |
ISR10 | 38.4 | 0.5 | ╳ | The water level station is located at the edge a water body. |
6. Concluding Remarks
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Chung, H.-W.; Liu, C.-C.; Cheng, I.-F.; Lee, Y.-R.; Shieh, M.-C. Rapid Response to a Typhoon-Induced Flood with an SAR-Derived Map of Inundated Areas: Case Study and Validation. Remote Sens. 2015, 7, 11954-11973. https://doi.org/10.3390/rs70911954
Chung H-W, Liu C-C, Cheng I-F, Lee Y-R, Shieh M-C. Rapid Response to a Typhoon-Induced Flood with an SAR-Derived Map of Inundated Areas: Case Study and Validation. Remote Sensing. 2015; 7(9):11954-11973. https://doi.org/10.3390/rs70911954
Chicago/Turabian StyleChung, Hsiao-Wei, Cheng-Chien Liu, I-Fan Cheng, Yun-Ruei Lee, and Ming-Chang Shieh. 2015. "Rapid Response to a Typhoon-Induced Flood with an SAR-Derived Map of Inundated Areas: Case Study and Validation" Remote Sensing 7, no. 9: 11954-11973. https://doi.org/10.3390/rs70911954