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Accueil du site → Doctorat → Royaume-Uni → 1989 → Utility of spatial filtering techniques in the remote sensing of soil erosion in the Sefin-Rud reservoir catchment in Iran

University of Glasgow (1989)

Utility of spatial filtering techniques in the remote sensing of soil erosion in the Sefin-Rud reservoir catchment in Iran

Disfani, M.N

Titre : Utility of spatial filtering techniques in the remote sensing of soil erosion in the Sefin-Rud reservoir catchment in Iran

Auteur : Disfani, M.N

Université de soutenance  : University of Glasgow

Grade : Doctor of Philosophy (PhD) 1989

The objective of this study is to investigate the applicability of Landsat Thematic Mapper digital images assisted by computer analysis to the study of soil erosion. The study aims to identify the sources of sediment and areas of dissected land in the catchment basin of the Sefin Rud reservoir in northern Iran. This catchment basin includes mountaineous areas in both the Zagros and the Albors mountains of Iran. The dam was constructed to generate hydroelectricity and supply water for irrigation, but it has gradually failed owing to the high rate of siltation. Active gullies not only dissect the land and split it into different segments by headward erosion, but they also expand the gully width by pushing back the flanks of the gully into adjacent flat lands. The other problem of gully erosion is that it removes the soil down to alluvial fans, flood plains and finally reservoirs. For image processing the IAX general-purpose image processsing software on the Glasgow University IBM mainframe computer, and also the DIAD image processor in the Geography department were used. Aerial photographs (1:20,000 and 1:50,000) of sample areas were interpreted and the interpretation verified in the field. Both supervised and unsupervised classification methods were applied on the Thematic Mapper images of the study area. It was found that neither of them is successful for identifying badlands and gullied areas and their severity. Filtering techniques were used as an alternative method to the unsuccessful classification method, for identifying the erosion features. Histogram equalization, enhancing, edge and line detection, thresholding, masking, smoothing and finally density slicing are the different stages of the new method, the Dissected Land Detection Technique (DLDT), which was invented and used successfully for identifying soil erosion features. Despite the presence of much literature on line and edge detecting techniques, no published work applying this to soil erosion on Landsat data could be found, so this is believed to be the first such attempt. First, histogram equalization is deliberately applied to the original band 3 to reduce the noise and unwanted edges and lines in the dark tail of the histogram, mainly vegetation, and the light tail, the non-eroded areas, and also to improve the visual appearance of edges and lines on the processed image. The next step is high pass filtering, unlike the conventional edge detection technique in which the first step is low pass filtering. In this instance, the result of low pass filtering was that faint edges, evidence of the gullies, were removed and highly eroded areas appeared as non eroded areas. Therefore low pass filtering was replaced with high pass filtering, which highlighted faint edges and lines. The next step is detecting the edges and lines. When using the edge and line detecting for detecting dissected lands one needs to take into account that a gully might appear as two or three edges if its width is more than one pixel or as one line if it is just one pixel of less than one pixel in width on the Thematic Mapper image. Therefore an algorithm should be chosen which has the ability to detect both edges and lines. The existing edge and line detecting filters such as the Sobel, the Robert, compass, the Laplacian convolution masks and the directional line detecting technique were evaluated. The Sobel and the Robert operators were found to be powerful edge detecting techniques, but the Laplacian convolution mask was found to be the best for detecting the badland and gullied areas because it has the ability to detect faint edges as well as coarse edges. Not only does it detect both edges and lines, but it also gives stronger weight to the lines than the edges. Only edges and lines in gullied areas were of interest for detecting the dissected lands, but all other artifical and natural lines and edges were also detected. The result of applying the Laplacian function

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