Rapid Mapping of Natural Disasters using SAR Imagery from Sentinel-1 Satellites

Shannan Jones

25th November 2019

Advisor: Dr. Mong-Han Huang

GEOL 394

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ABSTRACT

Natural disasters can cause catastrophic effects ranging from damage of infrastructure and landscapes to loss of life. With climate change, the consequences and costs of natural disasters effect more people each year (Holmes et al. 2013). The purpose of this research was to evaluate the effectiveness of the technique SAIV (Synthetic Aperture Radar Amplitude Intensity Variation) in mapping potential surface damage caused by various types of natural hazards, such as floods and landslides. The expectation was that SAIV can detect changes in surface features caused by hazards and depict those changes by producing damage proxy maps (DPMs). Synthetic Aperture Radar (SAR) data from Copernicus Sentinel-1A/B satellites were downloaded from Vertex, the Alaska Satellite Facility’s data portal. The images were then uploaded into QGIS and a map was generated. A DPM of the affected area was created by comparing the difference in amplitude of the radar backscatter intensity before and after the disaster event. The technique was first tested by evaluating flooding along the Pungwe River in Mozambique. A series of flood maps or DPMs was produced using the SAIV method, which provided a view of standing water at each time of image acquisition. SAIV was then tested by evaluating a landslide in Minas Gerais, Brazil. The DPM created using SAIV shows the extent of the flow of the landslide and provides a distinction between eroded and deposited sediments. Several SAR images of the landslide were downloaded from Copernicus and stacked in order to filter background noise and smooth the resulting DPMs. A profile of the single result DPM and the stacked result DPM was plotted and showed a correlation of stacking with a decrease in background noise. Finally, the technique was tested by evaluating the Camp Fire in Paradise, California. The DPM created using SAIV shows the extent of the potential surface damage caused by the wildfire. Given these results, it was concluded that the SAIV method can detect and depict changes in ground surface features due to flooding, landslides, and wildfire. This work highlights the advantages of incorporating SAR amplitude intensity into rapid disaster mapping strategies.

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TABLE OF CONTENTS

1. Introduction……………………………………………………………………………………………………………………3 2. Background

2.1 Electromagnetic Spectrum………………………………………………………………………………………3 2.2 Optical vs Radar Sensors………………………………………………………………………………………...4 2.3 Radar Backscatter……………………………………………………………………………………………………4 2.4 Complications…………………………………………………………………………………………………………5

3. Hypotheses…………………………………………………………………………………………………………………….6 4. Methods

4.1 Download SAR Data……………………………………………………………….……………………………….6 4.2 Determine Affected Area……………………………………………………………………………………..…7 4.3 Processing Raw Images………………………………………………………………………………………..…7 4.4 Stacking…………………………………………………………………………………………………………….……8

5. Results 5.1 Tropical Cyclone Idai………………………………………………………………………………………….……8 5.2 Brumadinho Dam Collapse……………………………………………………………………………………11 5.3 Camp Fire……………………………………………………………………………………………………..………16 5.4 Discussion of Results……………………………………………………………………………..……………..18

6. Uncertainty………………………………………………………………………………………………..…………………19 7. Future Work……………………………………………………………………………………………………..………….19 8. Conclusions/Broader Impacts……………………………………………………………………………………….20 9. Acknowledgements………………………………………………………………………………………………………20 10. Bibliography…………………………………………………………………………………………………………………20 11. Appendix……………………………………………………………………………………………………………………..22

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1 INTRODUCTION

Climate change is a natural occurrence on earth which is seen throughout geological history. However, it is the accelerated rate and magnitude of climate change happening at present that is the cause of worldwide concern. One effect observed in recent years is the increase in natural disasters. Coastal communities are becoming exposed to erosive forces due to rising sea levels. More tropical storms are developing due to the increase of water vapor that is evaporated into the atmosphere. Furthermore, the intensity of storms is becoming more severe due to warmer temperatures on the ocean surface and in the atmosphere (U.S. Geological Survey, 2019). As global surface temperatures rise the probability and duration of droughts is increasing. The increase in droughts causes an increased probability of fire. These consequences of natural disasters are sometimes extreme and long lasting. While hurricanes may whirl for days and wildfires may burn for weeks, the possible alterations to landscapes can last for years (Young, 2017).

The National Centers for Environmental Information (NCEI) evaluates climate events across the globe that have great economic and societal impacts. It was seen that 2019 is the fifth consecutive year in which 10 or more climate disaster events, with losses greater than $1 billion each, have affected the United States. These events included 3 floods, 5 severe storms, and 2 tropical cyclones as of October 8. After adjusting consumer price index to 2019, the annual average for 1980 to 2018 is 6.3 disaster events; the annual average for 2014 to 2018 is 12.6 disaster events (NOAA, 2019). This increase in natural disasters warrants an evaluation of viable methods of hazard response.

The Jet Propulsion Laboratory (JPL) and the California Institute of Technology collaborated to form the Advanced Rapid Imaging and Analysis (ARIA) project. The ARIA team is developing automated systems, such as damage proxy maps (DPMs), to use radar and optical remote sensing data, GPS, and seismic observations for rapid hazard response. The DPM algorithm is an enhanced method to compare Interferometric SAR (InSAR) coherence images from before and after a disaster event (Yun et al., 2015). Interferometry uses SAR phase data. A similar concept utilizing SAR amplitude data was used to create a flood map (Clement et al., 2018), but the investigation of other hazard events is extremely limited. This research evaluated the effectiveness of the SAR Amplitude Intensity Variation (SAIV) method. SAIV produces DPMs for various types of hazards, such as floods and landslides, by comparing SAR amplitude data from before and after a disaster event.

2 BACKGROUND

2.1 Electromagnetic Spectrum

The electromagnetic (EM) spectrum is the range of all types of EM radiation. EM radiation can be thought of as a stream of photons travelling in a wave-like pattern at the speed of light. Each photon contains a certain amount of energy. Remote sensors are designed to operate at specific regions of the EM spectrum, depending on their intended applications. SAR is a sensor that operates in the wavelength range of microwaves. Microwave sensors operate in a much lower

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frequency range than optical or infrared sensors. The wavelength of light ranges from 300 to 700 nm whereas the wavelength of microwaves ranges from 0.3 to 40 cm. This difference in wavelength causes features to appear differently in images. This study utilized data from Sentinel-1A/B Satellites which operate C-Band radars with a wavelength of approximately 5.6 cm.

2.2 Optical vs Radar Sensors

Optical sensors have no source of illumination and are therefore limited to operating during the daytime. Both optical and infrared sensors can be hindered by clouds or bad weather conditions (Streicher et al. 2012). Because SAR is a microwave sensor, it can operate during the day or night and in most weather conditions (Schlaffer et al. 2015) (Figure 1). This is very useful for areas such as those in subtropical regions that experience constant cloud coverage.

2.3 Radar Backscatter

The SAR antenna emits a pulse of radiation towards the surface of the earth and then detects the echo reflected from the ground. The strength or intensity of the reflected echo is called the backscatter. The SAR antenna can only detect the portion of the signal that is echoed towards the satellite (Figure 2). Backscatter for a target area will vary based on the type of surfaces present on the ground (Figure 3). Darker areas represent regions of low backscatter, implying that very little energy echoed towards the antenna. Brighter areas represent regions of high backscatter, implying a large portion of the energy was echoed towards the antenna. Thus, when a city becomes flooded, the backscatter changes from a high backscatter region to a low backscatter region. Radar images are composed of pixels which represent the radar backscatter for that location. The SAR antenna can measure both amplitude and phase. This research uses the amplitude data.

Figure 1. Optical image obstructed by cloud cover. SAR image in center box penetrates clouds. Streicher et al. (2012)

Figure 2. Incident radar pulse emitted from SAR antenna. Resulting backscatter or

echo of radar pulse after contacting ground targets. (from ESA Earthnet Online)

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2.4 Complications

There are several complications associated with this technique. First, it can be difficult to distinguish what the SAR image is showing and the cause of the change in intensity. It may be necessary to refer to other resources such as ground observations or optical images. Next, SAR images must be calibrated and corrected for speckle effects. Speckle effect is the typical grainy “salt and pepper” appearance of most radar images. A single type of surface can reflect various backscatter intensities between adjacent resolution cells. This occurs because each resolution cell “contains several scattering centers whose elementary returns, by positive or negative interference, originate light or dark image brightness” (European Space Agency, 2019). Performing speckle filtering causes backscatter boundaries to become better defined (Figure 4). Filtering is a technique to improve spatial resolution. This will be discussed further in the methods.

There are also distortions such as foreshortening and shadowing. Foreshortening (Figure 5) is most common in mountainous regions. Because the radar is side viewing, the echo from the base and peak of the mountain can return to the radar at the same time. This causes the image to appear as if the base and peak are at the same distance. Shadowing (Figure 6) can occur in mountainous regions or areas with densely vegetated canopies. The area at the base of the canopy will not be illuminated by the radar, and no backscatter will be detected for that location (Bouvet et al., 2018). Again, this is due to the radar being positioned as side viewing. These effects and distortions can be addressed by processing in Sentinel Applications Program (SNAP). SNAP is an open source common architecture for all Sentinel Toolboxes used for Earth Observation processing and analysis. It is available on the ESA website and can operate on 32- and 64-bit Windows, Mac OS X and Linux (European Space Agency, 2019).

Figure 2. Before and after of SAR image after speckle filtering. (from ESA Earthnet Online)

Figure 3. Expected resulting backscatter intensity based on different ground surfaces.

(from ESA Earthnet Online)

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HYPOTHESES

Interferometric coherence can be used to map damage caused by earthquakes and other types of hazards (Hoffman, 2007). This procedure compares SAR phase data acquired before and after disaster events. Given that SAR sensors acquire both phase and amplitude data, the following was hypothesized:

1. The difference in SAR amplitude acquired before and after hazard events, such as floods and landslides, can detect changes of surface features.

2. The change in SAR amplitude can differentiate the type of hazard.

Sentinel-1 SAR images have a pixel resolution of 30 x 30 meters. Bearing this in mind, the expectation is that the SAIV method performs best for large scale events and will not be effective for disaster events that span less than 30 m.

METHODS

4.1 Download SAR Data

Assessment of damaged areas was performed using radar data from Copernicus Sentinel-1A/B satellites operated by the European Space Agency (ESA). The Sentinel-1 mission operates two polar orbiting satellites, with a 12-day orbit cycle, developed for C-Band (central frequency 5.404 GHz) SAR imaging. This research uses Level 1 Ground Range Detected High Resolution Dual Polarized (GRD-HD) data. Sentinel data are available through Vertex, the Alaska Satellite Facility’s data portal. Access to the data is free after registration for a free account. Once logged-in on ASF, navigate to the area of interest on the map, zoom in, and highlight the area using the polygon tool. The Advanced Search menu was used to create search parameters. The sensing period was limited to the appropriate range of time. Choose “Mission: Sentinel-1”. Under Product Type select “GRD-HD” and under Sensor Mode select “IW”, then click the Search button when done. Search results are listed on the right and highlighted on the map. The images with preferable

Figure 5. Foreshortening. Side viewing radar distorts

distances of mountain ranges. Bouvet et al. (2018)

Figure 6. Shadowing. Side viewing radar does not

illuminate base of edges of canopy or mountains. No

backscatter detected for those areas. Bouvet et al. (2018)

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placement and date of acquisition were chosen to be downloaded. It is important to use images that have the same flight direction, either ascending or descending (Figure 7).

4.2 Determine Affected Area

The SAR images downloaded from ASF are processed and geocoded. The SAR images were uploaded into QGIS and a map was generated. In the left window of QGIS you can select each image to view and edit its properties. In order to create a threshold, each image was edited to have the same minimum and maximum intensity. Numerically, the most effective threshold will vary for each case study. The Raster Calculator was used to subtract the amplitude of the pre-disaster image from the amplitude of the post-disaster image. Repeating this method for each image spanning the disaster allows the creation of a series of DPMs over time.

4.3 Processing Raw Images (as necessary)

In the event that processed images are not available there are procedures to process raw SAR images. This research began by processing raw images on the Sentinel Application Platform, or SNAP. SNAP can open the zipped file downloaded from ASF without unzipping it first (Saephan, 2018). Then, continue with the following procedures:

a) Create Subset b) Pre-processing

Calibration Speckle Filtering

c) Geometric Correction d) Export Image

4.4 Stacking

Figure 7. Flight direction or viewing direction of an ascending and a

descending SAR satellite. Bouvet et al. (2018)

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Stacking is a method to filter or smooth the speckle in SAR images. After determining the affected area, the expectation is that any highlighted pixels represent a change in amplitude for that location. However, it was commonly observed that the resulting images contained considerable background noise. This noise was caused by speckle in the original images. In order to reduce noise multiple SAR images within a chosen range of time were averaged to create a single stacked image. This method can also be incorporated in order to consider seasonal changes of the region (Miller and Shirzaei, 2019).

RESULTS

5.1 Tropical Cyclone Idai

On March 15, 2019 Tropical Cyclone Idai made landfall in Southeast Africa, causing severe damage in Mozambique, Zimbabwe, and Malawi. The storm was reported to be one of the worst cyclones on record to affect the southern hemisphere. At least 1,300 fatalities were reported, and the damage was estimated to cost more than $2 billion (Devi, 2019). Four days after Cyclone Idai made landfall, the first post-cyclone SAR image was acquired over the Mozambique area. The SAR image acquired directly before the cyclone (Figure 8) was compared to the first SAR image acquired after the cyclone made landfall (Figure 9).

Figure 8. Flood free reference SAR amplitude image of Mozambique before Cyclone Idai makes landfall.

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The backscatter variation represents where a change in amplitude was detected between the pre cyclone flood free reference SAR image and the post cyclone SAR image. The DPM produced that shows the largest extent of flooding was qualitatively validated through comparison to independent analysis performed by the European Space Agency (ESA). Both maps display the widest range of flooding to be an estimated 20 km across from the eastern coast where the Buzi and Pungwe Rivers empty into the Mozambique Channel and the Indian Ocean. The ESA created a false color image (Figure 10) using the same images downloaded for this study. The results obtained using the SAIV method (Figure 11) are consistent with the ESA’s analysis.

Figure 9. Post cyclone SAR amplitude image on March 19, four days after Idai makes landfall.

Figure 10. False color image depicting Flooding in Mozambique due to Cyclone Idai. Created by ESA

using Sentinel-1 data. Red represents standing water at time of acquisition.

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SAR data were updated in six-day increments. A series of DPMs were produced using the SAIV method with the updated data. The DPMs show the areas of potential inundation spanning the cyclone event and the water retreating after the storm dissipated (Figures 12a and 12b). One of the advantages of using SAIV is that updated DPMs can be generated as new data is acquired.

Figure 11. Flood map of Mozambique after Cyclone Idai. Results created by using SAIV method.

Blue indicates standing water at time of acquisition.

Figure 12b. Second retreat of standing water after Cyclone Idai in Mozambique.

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The acquisition of SAR imagery was vital in mapping the flooding caused by Cyclone Idai. An optical image provided by NASA (Figure 13) displays the cloud cover over Southeast Africa during the storm. SAR microwaves were able to penetrate the clouds during the storms.

5.2 Brumadinho Dam Collapse

On January 25, 2019, an earth-fill embankment dam at the Córrego do Feijão iron ore mine suffered a fatal failure. The dam collapse released a mudflow of tailings through the mine and the town of Brumadinho. Two hundred and forty-eight people were reported dead, the mine and nearby town were buried, and the local river was polluted with mining waste. The same company that owns the mine was responsible for a similar event in Brazil a year earlier. The SAR image acquired directly before the dam collapse (Figure 14) was compared to the first SAR image acquired after the dam collapse (Figure 15).

Figure 13. Optical image over Southeast Africa

during Cyclone Idai. Arrow points to area of

flooding in Mozambique.

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Figure 14. Pre-event SAR amplitude image. Acquired on January 17, 2019.

Figure 15. Post-event SAR amplitude image. Acquired on January 29, 2019.

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The resulting DPM shows the extent of the landslide and provides a distinction between eroded and deposited sediments (Figure 16). It is proposed that blue pixels represent erosion while the red pixels represent deposition. Although it is not certain if this pattern of erosion and deposition is meaningful, further investigation could possibly give us understanding on other landslides. The comparison of single images contained a considerable amount of background noise. Stacking was applied to create a smoother DPM (Figure 17). This stacking process increased the clarity of the change in backscatter intensity and made the effects of the landslide more distinguishable. It was essential to individually examine each of the SAR images used for stacked. When it was attempted to stack 10 images post dam collapse, the path of the landslide became visibly brighter. I believe this was due to evapotranspiration. The mine tailings contained a substantial amount of moisture. Over time the water content evaporated, causing the detected backscatter intensity to increase. Therefore, I was limited to only stacking 5 images post dam collapse. The additional stacking was performed on the pre-event images to circumvent this issue.

Figure 16. Comparison of single SAR amplitude images.

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A noise profile of the single result DPM and the stacked result DPMs was plotted and showed a correlation of stacking with a decrease in background noise (Figure 18). This change in amplitude may help to differentiate the type of hazard detected by the radar. Based on the stacked result DPM, the landslide was mapped by sight in QGIS using polygons (Figure 19). The polygon map was qualitatively validated through comparison to an optical image of the same location. The polygon map was a good fit to the optical image (Figures 20a and 20b).

Figure 17. Comparison of stacked SAR amplitude images.

Figure 18. Noise profile of DPMs with successive amounts of stacking.

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Figure 19. Polygon created by sight based on stacked SAIV results.

Figure 20a. Google Satellite image of the area affected by the dam collapse.

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5.3 Camp Fire

The Camp Fire began at 6:30 am Pacific Standard Time on November 8, 2018 northeast of Paradise, California. By 10:00 am the following day the fire had burned 70,000 acres of land (California Department of Forestry and Fire Protection, 2019). A DPM was created using two single SAR amplitude images (Figures 21a and 21b). Varying levels of stacking were then incorporated to improve the data to noise ratio. In order to maintain the integrity of the damage depicted in the post-event stacked image, only SAR images acquired after containment of the fire were stacked. The resulting stacked DPM (Figure 22) increased the clarity of the effects of the wildfire. It became clear that the single result DPM overestimated the damage caused in the center of the city.

Figure 20b. Polygon layered on top of the Google Satellite Image.

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Figure 21a. Pre-Event single SAR amplitude Image of Paradise, CA.

Figure 21b. Post-Event single SAR amplitude Image of Paradise, CA.

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Analyzing the effects of the wildfire proved to be a more challenging task compared to analyzing those of the flood and the landslide. It was found that fire, in comparison to the other hazards, can create a more complex system of scattering surfaces. It is believed that both red and blue pixels of the DPM indicate possible damage. City environments typically produce a high backscatter intensity. Thus, it was assumed that the fire would cause a decrease in amplitude due to buildings being burned. However, it was observed that the city center produced an increase in amplitude after the fire. The blue pixels represent areas with an increase in amplitude intensity. This is possibly due to more buildings and roads being exposed after the covering vegetation was burned. The red pixels represent areas with a decrease in amplitude intensity. It is believed this is also due to the burning of vegetation. Soil or low vegetation on the ground reflects less backscatter than vegetated canopies.

5.4 Discussion of Results

The results of this research support the first hypothesis. The difference in SAR amplitude acquired before and after hazard events, including floods, landslides, and wildfires, can detect changes of surface features. More investigation would be necessary to examine the effectiveness of detecting other types of hazards. However, it was determined that the SAIV method is an effective technique to rapidly generate DPMs.

The results of this research also support the second hypothesis. The change in SAR amplitude can differentiate the type of hazard. This is true with consideration of the distribution of that change. It was seen that flooding results in a decrease in amplitude intensity with a wide

Figure 22. Stacked result image of Paradise, CA.

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distribution of change. The landslide resulted in mostly a decrease of amplitude intensity, but also some extent of increased amplitude. The distribution of the change in amplitude was contained along a path. The wildfire results show both an increase and a decrease in the change of amplitude intensity. The distribution of that change was scattered across a wide extent of surface. However, results for a wildfire in a densely vegetated non-urban environment would likely show a different outcome.

It is essential to keep in mind that SAR can only detect the type of surfaces present on the ground. Hence, SAIV detects the change of surface type. SAR does not generate data indicating depth or volume of material. However, that information could possibly be determined by integrating the results generated through SAIV with the previous topographic maps.

6 UNCERTAINTY

In order to quantify the possible uncertainty of the SAIV method, the results of the Brumadinho Dam Collapse were further analyzed. The extent of the landslide based on the optical data was compared to the polygon created based on SAIV results. At ten locations around the landslide, the difference in position of the optical data and the SAIV results were measured. An average of these ten measurements was calculated to be 3.7 meters.

The noise profile of the Brumadinho dam collapse was also analyzed to calculate a data to noise ratio. The amplitude of the true data measured approximately 60 units. The amplitude of the noise measured approximately 30 units. This suggests a data to noise ratio of 2 units.

7 FUTURE WORK

To further develop this method, the effects of various parameters, including but not limited to radar polarization and frequency could be investigated. SAR polarization and frequency are considered two of the most important factors in SAR design (Lee et al. 2001). SAR sensors are designed to emit and receive microwaves with varying polarizations. Polarization is the orientation of the electric field vector in the plane perpendicular to the direction of wave propagation. Most sensors are linearly polarized and depending on the type of system the sensor emits and receives vertically and/or horizontally polarized waveforms. The sensor can be designed to operate as a single-pol, dual-pol, or quad-pol system. Each of the polarized waveforms interact with various types of surfaces and structures differently, resulting in varied backscatter patterns (Manavalan et al., 2017). For example, cross polarization can be useful to differentiate between soil and vegetation. Investigating the SAIV method would help to establish the relationships between the ground surfaces and structures and the backscatter detected in various polarizations.

Most sensors operate at a single frequency. This determines the wavelength of the EM pulse that is transmitted. Different wavelengths vary in their interactions with ground targets. In this research data was used from Sentinel-1 Satellites which operate C-Band (5.6 cm wavelength) sensors. This was sufficient to analyze hazards affecting areas on sparsely vegetated landscapes; however, it proved to be a challenge when examining forested areas due to its shallower penetration depth. Sensors that transmit longer wavelengths of EM energy, like L-Band (22.5 cm wavelength), are capable of penetrating densely vegetated canopies. The differences in utilizing

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these various wavelengths and how it affects the resulting DPMs could be studied. The data obtained using the SAIV method would provide a critical test of our current understanding of SAR polarimetry and frequency.

8 CONCLUSIONS/BROADER IMPACTS

The SAIV method was effectively used to generate maps of the flooded areas of Mozambique, a landslide in Brazil, and the Camp Fire in California. The results generated were consistent with maps created by other methods. The results were filtered to better differentiate the type of hazard that occurred. With time and more studies, a better understanding of future events may be established. Maps based on ground observations can take months to develop, and optical sensors have limited operation and are often obstructed by cloud coverage. Once SAR data is acquired and processed, which is typically within 3 days of a hazard event, the SAIV method can create DPMs in a matter of minutes. When new data is available an updated DPM can be produced if conditions change. The rapid production of maps such as these is essential for providing accurate and up to date information to aid in emergency response efforts and hazard mitigation. This method has been analyzed in regard to flood mapping. However, greater research is needed to determine the efficiency of this technique in analyzing and differentiating various types of natural disasters such as landslides and wildfires. These findings could possibly serve as useful methods for organizations such as FEMA, NOAA, or OFDA to prepare communities and save lives.

9 ACKNOWLEDGEMENTS

I would like to thank Dr. Mong-Han Huang for advising me through this thesis project and being a constant source of knowledge and support. I want to thank Rebecca Butcher and Jeng Hann Chong for all their help and inspiration. I would also like to thank Dr. Phillip Piccoli for his guidance and support. Finally, I would like to thank the UMD Department of Geology for encouraging my growth as a researcher.

10 BIBLIOGRAPHY

Bouvet, A., Mermoz, S., Ballere, M., Koleck, T., and Toan, T. L. (2018). Use of the SAR Shadowing Effect for Deforestation Detection with Sentinel-1 Time Series. remote sensing, 10(8), 2-19. doi:10.3390/rs10081250

California Department of Forestry and Fire Protection (accessed November 2019).

Clement, M., Kilsby, C., and Moore, P. (2018). Multi-temporal synthetic aperture radar flood mapping using change detection. Jornal of Flood Risk Management, 11(2), 152-168. doi:10.1111/jfr3.12303

Devi, S. (2019). Cyclone Idai: 1 month later, devastation persists. World Report, 393(10181), 1585. doi:10.1016/S0140-6736(19)30892-X

European Space Agency. Earth Online. Retrieved from Radar Course: https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/ers/instruments/sar/applications/radar-courses/content-3/-

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/asset_publisher/mQ9R7ZVkKg5P/content/radar-course-3-image-interpretation-tone (accessed April 2019).

Hoffman, J. (2007). Mapping damage during the Bam (Iran) earthquake using interferometric coherence. International Journal of Remote Sensing, 28(6), 1199-1216. doi:10.1080/01431160600928567

Holmes, R.R., Jr., Jones, L.M., Eidenshink, J.C., Godt, J.W., Kirby, S.H., Love, J.J., Neal, C.A., Plant, N.G., Plunkett, M.L., Weaver, C.S., Wein, Anne, and Perry, S.C., 2013, U.S. Geological Survey natural hazards science strategy— Promoting the safety, security, and economic well-being of the Nation: U.S. Geological Survey Circular 1383–F, p 79.

Lee, J.S., Grunes, M. R., and Pottier, E., Quantitative comparison of classification capability: fully polarimetric versus dual and single-polarization SAR, in IEEE Transactions on Geoscience and Remote Sensing, vol. 39, no. 11, pp. 2343-2351, Nov. 2001. doi: 10.1109/36.964970

Manavalan, R., Rao, Y.S., and Krishna, B.M. (2017) Comparative flood area analysis of C-band VH, VV, and L-band HH polarizations SAR data, International Journal of Remote Sensing, 38:16, 4645-4654, doi:10.1080/01431161.2017.1325534.

Miller, M. M., and Shirzaei, M. (2019). Land subsidence in Houston correlated with flooding from Hurricane Harvey. Remote Sensing of Environment,225, 368-378. doi:10.1016/j.rse.2019.03.022

NOAA National Centers for Environmental Information (NCEI) U.S. Billion-Dollar Weather and Climate Disasters (2019). https://www.ncdc.noaa.gov/billions/ (accessed November 2019)

Saephan, S. (2018, May 28). Flood Mapping With Sentinel-1 Data Using SNAP And QGIS. Retrieved from open.gis.lab: https://opengislab.com/blog/2018/5/14/flood-mapping-with-sentinel-1-data-using-snap-and-qgis

Schlaffer, S., Matgen, P., Hollaus, M., Wagner, W. (2015). Flood detection from multi-temporal SAR data using harmonic analysis and change detection, International Journal of Applied Earth Observation and Geoinformation,Volume 38, pp. 15-24,ISSN 0303-2434. doi:10.1016/j.jag.2014.12.001.

Streicher, A., Dambier, N., and Roller, W. (2012). Task-Centered Selection of Learning Material. doi:10.13140/2.1.3629.7767.

U.S. Geological Survey. Science for a Changing World: https://www.usgs.gov/faqs/how-can-climate-change-affect-natural-disasters-1?qt-news_science_products=0#qt-news_science_products (accessed November 2019).

Young, S.M., 2017, Land Change Monitoring, Assessment, and Projection (LCMAP) revolutionizes land cover and land change research: U.S. Geological Survey General Information Product 172, p. 4, https://doi.org/10.3133/gip172.

Yun, S.-H., Hudnut, K., Owen, S., Webb, F., Simons, M., and Sacco, P. (2015). Rapid Damage Mapping for the 2015 Mw 7.8 Gorkha Earthquake Using Synthetic Aperture Radar Data from

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COSMO–SkyMed and ALOS-2 Satellites. Seismological Research Letters, 86(6), 1549-1556. doi:10.1785/0220150152

11 APPENDIX

Table A1. Dates of Sentinel 1A/B, C-Band ascending SAR amplitude images used for flood mapping via a flood-free reference image (indicated with *) and post-cyclone images.

Acquisition Date Path Frame

13-Mar-2019* 174 1109

19-Mar-2019 174 1112

19-Mar-2019 174 1117

25-Mar-2019 174 1109

25-Mar-2019 174 1115

31-Mar-2019 174 1112

"I pledge on my honor that I have not given nor received any unauthorized assistance on this assignment/examination."