Digital image processing for large scale irrigation management and monitoring
In 1976 Jakara dam was completed, approximately 20 kilometers downstream from Kano City, and the valley was first flooded during the 1977 wet season. This resulted in the loss of well over 1000 hectares of farmland due to indunation. (The water Resources and Engineering Construction Authority gives 1959ha. projected water surface area estimate (water resources and Engineering and Construction Authority , 19800, much of it fertile fadama (floodplain) as well as much fadama land further downstream due to the controlled water regime (Nichol. Op. cit). The scheme, costing well over US$ 3 million at construction, was originally conceived mainly for the purposes of irrigation, with the aim of irrigating 2150 hectors, though no irrigation works have been commenced to date.
Data collection
The two dates of imagery for which CCTs were available encompassed three wet seasons and three dry seasons, but only two wet seasons and one dry season following dam construction. A further disadvantage was the seven week difference in state of dry season between the two images. Thus the November 1978 image in early dry season would expectedly be more vegetated that that of January 1986.
However, since one aim of the study is to demonstrate a methodology, for detecting change, identification of the expected seasonal changes by the techniques used confirms the potential of the method. Image processing was carried out on the Iconoclast Image Processing System at Aston University. U.K.
Image processing techniques
- Vegetation Index
The Normalized Difference Vegetation Index (NDVI)effectively exploits the large difference in radiance values of green vegetation between the red and infra-red bands (MSS bands 5 and 7) by rationing, thus : -
NDVI - MSS 7-5 / MSS 7+5
This effectively creates a new band which can be displayed as a vegetation index image with high values representing vigorous vegetation areas as light tone, while less vegetated areas appear darker.
- Change detection
Digital change detection is difficult to carry out accurately. The results are not as accurate as those produced from the visual interpretation of large scale air photos of different dates and transfer of boundaries to a map. However, it is many times quicker and cheaper. Accuracy depends on the ability to acquire comparable imagery or different dates, and to geometrically register the images to the same geographical reference system.
Two techniques are described here :
- Image differencing and
- Image overlay
- Image differencing
This involves the subtracting of one band of the imagery of one date from that of another. The subtraction results in positive and negative values in areas of radiance change and zero value in areas of no change. This yields a difference distribution which is Gaussian, where pixels of 0change value are distributed around the mean and change pixels are in the tails of the distribution. A critical factor in the method is in deciding where to put the threshold boundary between change and no pixels. Often, one standard deviation from the mean is selected then tested empirically.
- Image overlay
This method, described by Howarth and Boasson (1983) involves displaying the MSS band 5 image of one date in blue on the monitor and the same band of a different date in red, areas of no change appear grey, and areas of change appear with differing intensities of blue or depending on the direction and degree of change.
