GOES-R AWG FSW Team: ABI Flood/Standing Water ( FSW ) Algorithm (AFSWA) June 15, 2010

1. GOES-R AWG FSW Team: ABI Flood/Standing Water (FSW) Algorithm (AFSWA)June 15, 2010 Presented by: Donglian Sun1 With contributions from FSW team: Rui Zhang1, Sanmei Li1, and Bob Yu2 1George Mason University 2NOAA/NESDIS/STAR

2. Outline • Executive Summary (1 slide) • Algorithm Description (2-4 slides) • ADEB and IV&V Response Summary (1-2 slides) • Requirements Specification Evolution (2 slides) • Validation Strategy (3-5 slides) • Validation Results (3-5 slides) • Summary (1-2 slides)

3. Executive Summary • This ABI Flood/Standing Water Algorithm (AFSWA) generates the Option 2 product of flood/standing water. • Version 5 will be delivered in June. ATBD (100%) is on track for a July delivery • A unique decision tree approach has been developed that utilizes improved GOES-R ABI spectral capabilities over GOES-IM and GOES-NOP. • Validation tools have been developed and applied to MODIS data. Map of river flooding from the USGS,River Flood Outlook and River Forecasting from the National weather Services of NOAA and analysis of flood/water spatial distribution and temporal change • MODIS and SEVIRI analyses indicate spec compliance for all products.

4. Decision tree (DT) method: a supervised machine learning approach to find hidden relationships among multiple attributes/parameters. DT algorithms tested: • the J48 (C4.5, originally proposed by Quinlan (1986)), • NBTree, which is a Naïve Bayes/Decision Tree hybrid (Kohavi, 1996), • Random Tree, • Random Forest (Breiman, 2001), • REP Tree, • BFTree, • Decision stump, • and CART (Classification and Regression Trees) and FT (Final Tree) tree classifiers. The basic strategy is to select an attribute that will best separate the samples into individual classes by a measurement ‘Information Gain Ratio’. Algorithm Description

5. Algorithm DescriptionPredictors Selected Predictors selected: CH2, CH3, CH5, CH3/CH2 , CH3-CH2 , NDVI, NDWI. According to the spectral characteristics, water has lower reflectance in near-infrared (GOES-R ABI Channel 3, 0.88 µm ) than vegetation and other land cover types. On the contrary, water has slightly higher values than land features in ABI channel 2 (0.64 µm). Therefore, the ratio image and the difference image between CH3 and CH2 can be used to enhance the difference between water and land (Sheng and Xiao, 1994). In the ratio image, water has extremely low value, while land has relatively high value. MODIS/SEVIRI data were used as the proxy data for the GOES-R ABI data. The MODIS CH2 corresponds to the ABI CH3, and MODIS CH1 is similar to ABI CH2.

6. Algorithm Summary • For binary yes/no detection of standing water, we select decision tree algorithm. This algorithm can integrate all useful predictors, and gives accuracy estimates. • Input Datasets: Proxy ABI data are used to test the algorithm: 1) 1 km MODIS, 2) 3 km MSG SEVIRI. • Water/Land are firstly identified. • Flood detections can then be made based on the difference in water/land classification map after and before flooding • A series of tests are performed to ensure that the algorithm can meet the accuracy requirements

7. Motivation for Algorithm Channel Selection • The AFSWA represents a new approach for flood remote sensing on GOES. • The GOES Imagers before GOES-R don’t provide near infrared measures (0.86 um). This NIR channel can provide the most useful information for flood/standing water identification. • The GOES-R ABIprovides near IR (NIR) and shortwave IR (SWIR) channels that provide sensitivity to flood/water identification.

8. Expected ABI Performance Relative to Other Sensors • Like the ABI, the VIIRS (like MODIS) sensor onboard the NPP/JPSS offers similar visible (VIS: 0.672 um) and near IR (NIR: 0.865 um), which are critical for water identification, and shortwave IR (SWIR: 1.61 um) channels. This makes VIIRS a very attractive and good candidate sensor for flood detection. ABI is skilled in identifying flood/standing water as well as SEVIRI, MODIS and VIIRS. Similar MODIS/VIIRS FSW channels ABI FSW channels

9. AFSWA Processing Schematic

10. Example FSW Output(Japan Tsunami/Flood)

11. Algorithm Changes from 80% to 100% • Directly work with simulated ABI data. At the 80% version, the proxy MODIS and SEVIRI were used to test the algorithm performance. • Metadata output added. • Quality control flags standardized.

12. ADEB and IV&V Response Summary Recommendation 1: Insufficient Validation • For the validation of the FSW product of the GOES-R in the development phase, two validation methods will be included. The first validation method is gauging station based spot validation. Flood locations occurred on rivers or lakes over the CONUS, which can be obtained from the USGS waterwatch service or NOAA/NWS significant river flood outlook, will be matched to the satellite image derived flood maps according to the geolocation, and streamdage-based flood information will be used as the true flood spot to validate the effectiveness and accuracy of the FSW algorithm output derived from the proxy remote sensing images. • The second validation method is using high spatial resolution satellite data as the reference flood map. When flood events occur, high spatial resolution remote sensing data covering the same flood event will be collected simultaneously, and flood detection will be performed in the high-resolution data. After downscaling the high resolution reference data to GOES-R FSW product resolution, it will then be compared with the FSW detection map, and correct classification rate or accuracy will be reported. Because the high spatial resolution remote sensing data is rarely available during flood events, the second validation method will only be performed for some specific flood cases.

13. ADEB and IV&V Response Summary • All ATBD errors and clarification requests have been addressed. • No feedback required substantive modifications to the approach. 13

14. Requirements Request to change the FSW product resolution from 10 km to 1km is still pending!

15. Qualifiers Theses are made with the following qualifiers. • Daytime • Sensor zenith angle < 67 degrees • Local zenith angle < 67 degrees • Cloud mask available

16. Validation Approach

17. Validation Approach • Validate GOES-R FSW Algorithm against the Streamgage-Based Flood Observations from the USGS and NOAA • Water Watch (http://waterwatch.usgs.gov) at the U.S. Geological Survey (USGS) provides streamgage-based flood maps that show the locations of more than 3,000 long-term (30 years or more) USGS streamgages; displays maps, graphs, and tables describing real-time, recent, and past stream flow conditions for the United States; and highlights locations where floods are occurring. • The flood map detected from the USGS streamgage observations are valuable to evaluate GOES-R FSW algorithm accuracy.

18. Validation Approach • USGS WaterWatch also provides tables of flooding locations above National Weather Service (NWS) defined flood stage (http://waterwatch.usgs.gov/new/index.php?id=flood-table). • These products are automated applications of a calibrated stream hydraulic model which mines USGS streamgages and NWS river flood forecast data. • The latitude and longitude of flooding from the USGS streamgages will be extracted and saved in a txt file. • Software is developed to read this file and search the same locations in the flood images generated from proxy MODIS data and GOES-R FSW algorithm. • Summary statistics is performed to calculate the accuracy as the percentage of accurate detection (USGS flood locations are correctly detected by GOES-R FSW algorithm); omission error as the percentage of omitted floods which are observed by the streamgages but not detected by GOES-R FSW algorithm; and commission error as the percentage of non-flood cases in USGS flood map but misclassified as flood by FSW algorithm.

19. Validation Results

20. Validation Strategy Late on May 2, 2011, in an effort to save the city of Cairo, Illinois, the U.S. Army Corps of Engineers used explosives to breach a protective levee near the confluence of the Ohio and Mississippi Rivers. As predicted, the two-mile hole in the levee flooded 130,000 acres of nearby farmland in what is known as the Birds Point-New Madrid Floodway.

21. Flood Detection from FSW Algorithm 21 April 29, 20011 May 4, 20011 May 4, 20011

22. FSW Validation

23. FSW Validation Summary of Recent Flood and High Flow Conditions(2011-05-01 -- 2011-05-15) ……. All similar tables are merged and saved into a file

24. Validation Results Summary The proposed algorithm has ability of detecting floods on major rivers and lakes. If the reflectance values of the water bodies are similar to surrounded land pixels, these water pixels are difficult to be classified correctly, and thus these flood pixels are difficult to detect. If flood occurred on minor stream and river, and the width of river is smaller than the spatial resolution of the sensor, the water pixels cannot be identified from the image. For these kind of situations, floods cannot be detected.

25. Validation Results Summary Overall Accuracy = (8977/10127) 88.64% Flood commission error = (807/9784) 8.25% Flood omission error = (1150/10127) 11.36%

26. Summary • The ABI Flood/Standing Water Algorithm provides a unique solution that utilizes the new capabilities offered by the ABI • Version 5 will be delivered in June and the 100% ATBD is coming. • These products meet the specifications and are proving useful to downstream applications