A Primer on Semi-Automated Flood Mapping

1. A Primer on Semi-Automated Flood Mapping Prof. G. Robert Brakenridge March 12, 2011 Director, Dartmouth Flood Observatory http://floodobservatory.colorado.edu/ CSDMS, INSTAAR, University of Colorado Campus Box 450 Boulder, CO 80309-0450 USA Office: 303-735-5485 Cell: 603-252-0659 Email: Robert.Brakenridge@Colorado.edu

2. Basic Tasks Image Data Acquisition Identification of water pixels Create vector GIS (water boundary) polygon around water pixels Import GIS file into Surface Water Record Sort GIS files, within workspace; create map Publish map

3. Software used at DFO Web browsers: the usual suspects (final product should be viewable by all) Remote sensing: Envi GIS: MapInfo Web publisher: Dreamweaver

4. Task 1: Image Acquisition These and other subsets are available from this website in Geotiff format; choose 250 m images and the 721 band combination http://rapidfire.sci.gsfc.nasa.gov/subsets/

5. Data Considerations Geotiffs are in byte format (range of 0-255 shades of gray or numbers, per band). Thus, original radiometric resolution of the MODIS sensors is reduced in this format. The files are geocoded at full spatial resolution (bands 1 and 2 at 250 m). Band 7, originally at 500 m resolution, has been resampled to 250 m). Band 2 provides the most information for water/land discrimination. Simple thresholding of the band 2 images can separate water (dark) from land (relatively light) pixels, however cloud shadows will be misclassified as water if simple thresholding is employed.

6. Task 2. Identification of water pixels To solve the cloud shadow problem: acquire six images (preferably, Terra and Aqua, today and two days prior). The next four slides, as examples, show dark water, and (also dark) shifting cloud shadows. Images from May 18 and 17, 2009, G (green band, MODIS band 2) from the RGB tiff.

11. Task 2, continued: water classification DFO finds the following approach works well: 1. Use ENVI band math tool. After reading in all four geotiffs, follow (two) steps below. a. Bandmath step 1: (float(b2) gt 150 ) or (((float(b1)+1)/(float(b2)+80)) gt .7) or float (b3) >50 where b1 is green band of the 721 (red,green,blue) geotiff, b2 is the blue band, and b3 is the red band. (this is calculating an adjusted ratio of MODIS band 2/band1, after removing clouds and cloud shadow on cloud: failed thresholds assign a “1” value)

12. Task 2, continued b.Band math step 2: float(b1)+float(b2)+float(b3)+float(b4)+float(b5)+float(b6) where b1 through b6 are the results of the step 1 math, applied to six images (commonly, three consecutive days, Terra and Aqua) (“float” sets the band math operations into floating point notation, so values may range from +/– 1e38) 2. Apply threshold to the result. Pixels < 4 are water, so all pixels with values of 0, 1,2, or 3.

13. How does this work? For each single image, clouds are masked: they receive a value of 1. Where corrected ratio of the two bands is > 0.7, a 1 also assigned. Ratio is corrected in order to avoid dividing by 0, and, empirically, to provide best threshold for water/land in most scenes. The band ratio approach also improves scene-scene calibration The result for each scene is binary: all pixels are assigned 1 (cloud or land) or 0 (water or cloud shadow) Adding the values for four such scenes and thresholding water at <4, segregates water pixels (at least three scenes with 0 values) from land, and from cloud, or cloud shadow pixels.

14. Example The following four slides show the result of the first band math equation. Then a fifth slide shows the addition of the four bands (second equation), with red threshold indicating pixels < 3 (water pixels) Experience indicates that using 6 images, with threshold set at <4, more effectively removes cloud shadows than using 4.

20. Advantages of this technique Method is fast: requires only several minutes on older PCs. With 6 images, nearly all cloud shadows are eliminated. Cloud-obscured or missing data areas, if in only three of six images, are filled in. Thesholds and overall approach may be kept constant for most global mapping: raising the opportunity for full automation. Reference water layers can be obtained during times of normal water for the same subsets and using the identical processing technique and data source.

21. Task 3. Create GIS polygon around water pixels ENVI 4.5 and higher allows for immediate enclosure of the red pixels, from the threshold window, into ENVI .evf vector format. Then, export .evf to .shp format. Then, Mapinfo “universal translator” imports .shp to MapInfo native format. Specify projection: latitude and longitude, WGS 84. Sample result follows. .

22. GIS outlines (vectors) of entire scene, blue-filled polygons GIS outlines (vectors) of detail, blue-filled polygons GIS outlines (vectors) of detail, unfilled polygons

23. Task 4. Import GIS file into Surface Water Record In this example, solid blue layer is comparison water, March 15, 2009 and is superimposed above solid red layer, which is current surface water, May 25, 2010. Much “red” is hidden below blue, visible red is flooding

24. Task 4, continued Identification of flood water is dependent on comparison data, obtained by mapping of normal surface water extent . GIS workspaces have capability to store and display multiple water map layers. If normal range of annual variability is imaged, and mapped, then flood layer can be evaluated in comparison to normal water variability. Flood Inundation map should indicate where unusual high water is inundating land. Following is approach used at DFO. .

25. First, Establish Region of Interest and Create GIS Workspace Assemble map data layers: shaded relief (raster; all others are vector); drainage, latitude and longitude (graticule), cities and towns, national boundaries, watersheds, possibly roads, other features. DFO uses drainage features and towns from the Digital Chart of the World (DCW) for many of its maps;. Access to more recent national GIS data can be of much benefit. Publically available topography includes GTOPO (1 km) and SRTM (80 m); both available online. Commence mapping water and building water layers using the same sensor to be used for mapping floods…in this case, MODIS aboard Terra and Aqua. Create workspace and display. DFO calls these GIS workspaces, and resulting displays, a “Surface Water Record”. Mapping of floods result in a permanent, and constantly growing record of lands observed at various times to be subject to inundation.

26. Strategy for GIS Data Display Large format JPG or Geotiff output can be ingested into other GIS if coordinates and projection are provided. Vector GIS data export (flood inundation polygons), in contrast, can be cumbersome and require additional processing to be useful to end users. Still, many end users request the GIS vector data. It is essential display normal surface water. Arrange any new layer, showing currently flooded land, beneath such reference water layers. Using our fixed-area Surface Water Records, DFO rapidly ingests newly mapped water, and locates such above previously mapped floods but below normal water areas. Dark blue = current flooding; light red = previously flooded land; light blue is normal surface water. Map conveys at a glance where flooding is now underway and where it has occurred in the past. When new data are acquired and ingested, these water areas are now coded in dark blue and previous date water areas are recoded to the appropriate light red colors for previous flooding.

27. Examples of Surface Water Record Maps Current and Past Flooding, E30N10 (next slide) then 140ES30 Dark Blue is current flooding Light Blue is permanent surface water. Medium red is land flooded earlier this year. Light red is mapped flooding, 2001 to January 1 of this year.. Clickable white dots provide AMSR-E estimated river discharge, 2002-present.