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5.2 Development of a methodology for identification and extraction of flooded area from LANDSAT - TM and 
ERS -1 SAR images 
The distinction between flooded zones and the hydrological network is done with the use of auxiliary information 
(topographic maps) and / or change detection approach between multitemporal ERS SAR scenes. Change detection 
techniques of multi - temporal ERS SAR images which are generally used are photo - interpretation (Matthews et al., 
1994), colour composition (Blyth et al., 1993), ratioing (Rignot et al., 1993) and differencing (Badji et al., 1994). 
The basic need of such a multitemporal approach is analysis of each temporal data set that permits accurate 
classification of all cover types of interest and the identification of changes of these cover types within a certain time 
frame. In the next step a water mask was created by image enhancement techniques from each available acquisition 
date. In this context, it can be stated that multispectral information concerning the extension of flooded areas can be 
suitable accessed and extracted by using black - and - white images of band ratios 5/1 (TM), and 1/3 (ERS-1 SAR), 
H.Mehl & K. Hiller. A density slice of the 5/1 image enabled a separation between permanent water and dry land 
surfaces. To determine the density slice ranges for these two classes, a histogram for this image was generated which 
showed two distinct clusters. The density slice ranges were 1-15 (permanent water), 16-255 (flooded areas), and 26-255 
(dry land). Knowing the number of pixels associated with each class and the size of a pixel (30x30 m), it was possible 
to calculate the amount of area covered under each class. 
Single band enhancement techniques don't produce satisfactory results to clearly separate water from non-water areas. 
Inorganic and organic suspended water as well as swampy areas show the same or similar reflectance behaviour than 
natural grassland or freshly ploughed fields. In a further step all the extracted water masks had to be generalised in 
relation to the required scale of 1: 10000. 
As previously mentioned, the goal of image enhancement is to improve the visual interpretability of an image by 
increasing the apparent distinction between the features in the scene. 
A density slice images was created from ERS -1 SAR image on 3 June 1992 and 25 November 1992 (the day with 
flooded areas). To determine the density slice ranges for three classes (permanent water, flooded areas and dry land) 
was generate a histogram, which showed three distinct clusters. The results look something like a contour map, except 
that the areas between boundaries are occupied by pixels displayed at the same DN. Each level can also be shown as a 
single colour. Results a mask, which used to segment an image into two classes (binary mask). Figure 5 a, b illustrates 
the application of level slicing to the " water" portion of the scene illustrated in figure 4 and the binary mask associated 
of each images. 
  
Figure 5 a, b. Density slices + binary mask operations applied to ERS - 1 SAR image data, a - mask1 image from 3 June 
1992 (the day without flooded areas) and b - mask 2 image from 25 November 1992 (the day with flooded areas) 
Through the subtraction operation between image mask 2 from 25 November 1992 (the day with flooded areas) and 
image mask1 from 3 June 1992 (the day without flooded areas) results a binary image where to can identify the flood 
extent areas (figure 6). 
  
Figure 6. The flooded areas 
  
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B7. Amsterdam 2000. 1189