Detection of Flooded Areas using AMSR-E Observations Alexander R. Davies 123 and Xiwu Zhan 1 1 NOAA, NESDIS, STAR, SMC—Camp Springs, MD 2 Ernest Hollings Scholarship Program, ORISE/NOAA Office of Education—Oak Ridge, TN 3 Department of Earth Sciences, Millersville University of Pennsylvania—Millersville, PA ABSTRACT The purpose of this study is to successfully identify flooded areas using satellite observations. As a means of doing this, Advanced Microwave Scanning Radiometer data from NASA’s Earth Observing System (AMSR-E) is used for identification. Daily AMSR-E data has been complied for the period of time ranging from January 2008 through most of July The data is downloaded in a level 3 format, after which a series of manipulations must be preformed to covert the data format, apply the proper grid system, and build 3-day data composite in order to prepare the data for experimentation. Seven different wavelength-signals are used in the study to extract a flood index—the vertical and horizontal 6.9 GHz, 10.7 GHz, and 18GHz signals, along with the 36.5 GHz vertical signal. Using the Change Vector Analysis (change detection), along with various arrangements of the signals and their sequential structure, the hypothesis is that it is possible for flooded areas to be identified. Experiments using a band ratio of the 6.9 GHz horizontal frequency to the 36.5 GHz vertical frequency have confirmed that flood detection is possible using these techniques. In future research a statistical analysis will be developed to quantitatively verify these results with a ground truth. Additionally flood detection using a band ratio of the 6.9 GHz horizontal frequency to the 6.9 GHz vertical frequency will be analyzed. BACKGROUND In 2002 the National Aeronautics and Space Administration (NASA) launched Aqua, the second of three multi-national Earth Observing System (EOS) satellites, into space. The platform carries six instruments on board to remotely sense the earth’s system. One of the sensors is the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMRS-E). The AMSR-E is a passive microwave radiometer sensing microwave radiation (brightness temperatures) through twelve channels and six frequencies. The twelve channels consist of the horizontally and vertically polarized 6.9, 10.6,18.7, 23.8, 36.5, and 89.0 GHz frequencies with mean spatial resolution increasing with ascending order. This study will specifically use the 6.9, 10.6,18.7, and 23.8 GHz horizontally and vertically polarized channels, along with the 36.5 GHz vertically polarized channel. The hypothesis for the project is that flood detection is possible using AMSR-E data and applying the change vector analysis, along with other methodology. Being able to quickly identify regions of flood is critical for agriculture and when assessing the amount of aid required in the flooded region. With a spatial resolution of this product only being 25 km 2, this detection can only be used to identify vague areas of flood whereas MODIS derived products are able to supply a greater spatial resolution. With that said, flood detection using microwaves is valuable because of the ability for microwave sensors to make observations through clouds. METHODOLOGY CHANGE VECTOR ANALYSIS FUTURE WORK Work Cited Njoku, E.G. (1999). AMSR Land Surface Parameters: Algorithm Theoretical Basis Document, version 3.0. Jet Propulsion Laboratory, California Institute of Technology. Zhan, X., R.A. Sohlberg, J.R.G. Townshend, C. DiMiceli, M.L. Carrol, J.C. Eastman, M.C. Hansen, and R.S. DeFries (2002). Detection of land cover changes using MODIS 250 m data. Remote Sensing of Environment 83, 336 – 350. Zhan, X., R. DeFries, J.R.G. Townshend, C. DiMiceli, C. Huang, and R. Sohlberg (2000). The 250 m global land cover change product from the Moderate Resolution Imaging Spectroradiometer of NASA’s Earth Observing System. International Journal of Remote Sensing vol 21, 6 & 7, 1433 – Acknowledgments : - Jicheng Liu - National Environmental Satellite, Data, and Information Service, Center for Satellite Application and Research, Satellite Meteorology and Climate Division. - Ajoy Kumar, Sepi Yalda, Andrew Muller, and Richard Clark - This research was supported by an appointment to the National Oceanic and Atmospheric Administration Participation Program through a grant awarded to Oak Ridge Institute for Science and Education Variable A Variable B Time 1 Time 2 Ames, Iowa RESULTS United States Flooding: June 8 th, 2008 The results confirm the hypothesis that flood detection is possible using these methods with the band ratio Tbr07. Figure 3 is a great example of the Vector Change Analysis working because it becomes obvious where the major flooding event occurred in June. However as seen in Figures 4 and 5, spatial resolution and threshold issues have resulted in some problems with the results. Resolution issues are more than likely the problem with Figure 4 which depicts the flooding caused by Cyclone Nagris in Myanmar on May 5 th, Threshold issues could be the problem in Figure 5 where flood detection is shown for the Mid-west event on June 8 th, Additionally resolution issues also remain in the case of the mid-west flood event because of the non-linearity of rivers and streams and more localized spatial scales of the flooding. In all the results are encouraging because they indicate that flood detection would be possible under the correct methodology and with proper resolution. The primary method used in calculating the flood index is the Vector Change Analysis which is a form of change detection. In general terms, the Change Vector Analysis is used to temporally evaluate the change between the ratio of two variables. Plotting the ratio in two dimensional space, with a variable on each axis, allows for a vector to be draw between the ratio and an initial time and some time in the future (Figure 2). This the magnitude and direction of the vector can be used to determine the type of change that has taken place using pre-constructed look-up tables (LUTs). When specifically considering this specific project, band ratioing acts in the same manner as the variable ratioing when applied to the general description above. For this analysis, the band ratio is the 6.9 GHz vertical channel over the 36.0 GHz vertical channel—given the variable abbreviation ‘Tbr07.’ Under usual or non-flood times, the ratio remains generally constant throughout the year and thus a running mean can be used as the initial time. When a potential flood event occurs, the value of the band ratio must surpass a certain threshold (or departure from the mean) for the event to be considered a flood event within the flood index. A time series of the band ratio’s with time can be seen for Ames, Iowa in Figure 3. The mean is the generally flat, or established trend and the flood event is obvious with the sudden change in the values of the band ratios. While results confirm that flood detection is possible—more work certainly needs to be done. Dr. Xiwu Zhan is dedicated to the AMSR-E products and working soil moisture observations into model initializations. Along with the assistance and support from X. Zhan and Dr. Ajoy Kumar, Alex Davies will continue this project at Millersville University’s Nationally Science Foundation funded remote sensing laboratory. For further success of the project, a statistical method must be used to correlate the success of the flood detection to ground truth—more than likely MODIS derived flood detection products. Additionally the band ratio of 6.9 GHz horizontal channel over the 6.9 GHz vertical channel will be analysis (Tb07r – black line in Figure 3). Step 1: Download Level 3 Soil Moisture Data (NASA Aqua image courtesy of NASA, Google images) Step 2:Convert hdf files to binary files (image courtesy of google images) Step 3: EASE-Grid to Latitude & Longitude (image courtesy of the University of Hawaii) Step 4: Construct a three day composite of the data (image courtesy of NASA) Step 5: Compute the Flood Index Calculating Variables Change Vector Analysis Masking “Bad” Pixels Step 6: Mapping Results on GrADS The initial step is to download the data which comes as a hdf formatted level 3 soil moisture product. The soil moisture variable is derived from the observed brightness temperature at that given location. Additionally, because the data is level 3, it comes pre-gridded. The grid system is the Equal-Area Scalable Earth Grid (EASE-Grid) which is a cylindrical and equal-area projection. After the data has been downloaded, an IDL program is applied to convert the data from hdf files to binary files. Once converted to binary files, a Fortran program is used to covert the data from EASE- Grad to a Longitude and Latitude projection. The next obstacle that needed to be overcome is to use a Fortran program to build a three-day composite of the data to ensure complete observational coverage of the earth. From there the data ready to compute the flood index. The first step in this process is to create a number of band ratio’s. The band ratio used in this study is a ratio between the 6.9 GHz vertical channel and the 36.0 GHz vertical channel given the variable abbreviation Tbr07. The Change Vector Analysis is used to determine floods by calculating the change in soil moisture of a given pixel, on a given day by assessing a change threshold versus a set mean. More details can be found in the Change Vector Analysis section. Once a flood has been identified and bad pixels (coast, desert, ocean, and ice) removed, the flooded areas can be mapped on GrADS. These methods are shown in Figure 1. O R I S E Figure 1. is a flow chart of the methods used in the project as described in the section titled, “Methodology.” Myanmar Flooding: May 5 th, 2008 Figure 2. shows the general case of the Change Vector Analysis(CVA) as described in that section. CVA can be is a form of change detection used to compute a flood index. Figure 3. is the time progression through 2008 of all the band ratio’s calculated in this study for Ames, Iowa. The only band ratio used to compute the flood index is Tbr07 (Red). Figure 5. The left hand image is the flood detection using the Tbr07 variable graphed using GrADS. The image on the right is the NOAA/USGS flood gage data for the day with black representing a river at /above major flood stage. Figure 4. The left hand image is the flood detection using the Tbr07 variable graphed using GrADS. The image on the right is the UNOSAT MODIS derived flood detection used to qualitatively verify results