The heavy floods in the Terengganu have showed an increasing trend in recent years. Terrain characteristics of land and meteorological properties of the region are main natural factors for this disaster. In this paper, Terengganu was selected as the case study for flood risk analysis. Geographical Information System (GIS) is integrated with Multicriteria Decision Analysis (MCDA) to evaluate the potential flood risk areas. Some of the causative factors for flooding in watershed are taken into account as annual rainfall, basin slope, drainage network and the type of soil. The spatial multi-criteria analysis was used to rank and display potential locations, while the analytical hierarchy process method was used to compute the priority weights of each criterion. Using AHP, the percentages derived from the factors were Rainfall 38.7%, Drainage network 27.5%, Slope of the river basin 19.8% and Soil type 14%. At the end of the study, a map of flood risk areas was generated and validated with a view to assisting decision makers on the menace posed by the disaster.
This paper presents a lineament detection method using multi-band remote sensing images. The main objective of this work is to design an automatic image processing tool for lineament mapping from Landsat-7 ETM + satellite data. Five procedures were involved: 1) The Principal Component Analysis; 2) image enhancement using histogram equalization technique 3) directional Sobel filters of the original data; 4) histogram segmentation and 5) binary image generation. The applied methodology was contributed in identifying several known large-scale faults in the Northeast of Tunisia. The statistical and spatial analyses of lineament map indicate a difference of morphological appearance of lineaments in the satellite image. Indeed, all the lineaments present a specific organization. Five groups were classified based on three orientations: NE-SW, E-W and NW-SE. The overlapping of lineament map with the geologic map confirms that these lineaments of diverse directions can be identified and recognized on the field as a fault. The identified lineaments were linked to a deep faults caused by tectonic movements in Tunisia. This study shows the performance of the satellite image processing in the analysis and mapping of the accidents in the northern Atlas.
Digital Elevation Models (DEMs) provide one of the most useful digital datasets for a wide range of users. Both the Shuttle Radar Topographic Mission (STRM V.4.1) topography and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER-GDEM V.2) have been widely used in geomorphology, hydrology, tectonic, and others since they were made access to the public. The magnitude of vertical errors of two near-global DEMs-SRTM and ASTER-GDEM is compared and validated against a reference DEM which has a relatively high precision of 1:25,000 scale constructed from topographical map. Moreover, the reference DEM, ASTER-GDEM and SRTM were used as basic topographic data to extract some Morphometric index. The parameters like slope and shaded reflectance maps, were derived from the elevation distribution to provide a more sensitive indication of DEM quality. A square area in the North East of Tunisia was selected as a case study to test and evaluate the elevation accuracy of ASTER-GDEM and SRTM. The relative accuracy approach and absolute accuracy were adopted to evaluate global DEMs. The comparisons show that SRTM overestimates and ASTER-GDEM underestimates elevations, both DEMs can be used to extract the elevations of required geometric data, i.e. sub watershed boundaries, drainage information and cross sections. However, small errors still exist in. The lower root mean square errors values indicate that SRTM is comparatively more accurate than ASTER-GDEM.
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