The Pozm fishery port is located in the south-east part of Pozm Bay in the Oman Sea. The area adjacent to this port especially in the lee of its breakwater is known to be highly affected by deposition of sediments since 1988. Intensity of deposition in this area has been so high that it caused the port to lose its functionality and be out of operation in only few years. No doubt, deposition in this area is not only affected by construction of breakwater and other engineering activities performed in this area, but is also related to and is in equilibrium with coastal processes in the whole Bay. To understand better these processes and determine shoreline changes in the Pozm bay, an analysis of available Landsat satellite images was performed over a period of 13 years ending in 2001. Technical problems have made newer images of TM sensor of Landsat unavailable since 2002. Besides, information about the shoreline situation in the future is necessary to assist in coastal management and regulatory programs.
In this paper, the shoreline position in Pozm Bay is predicted for years 2005 and 2010, using the images provided by the TM sensor of Landsat satellite from the study area in 1988, 1998 and 2001, and through application of 4 different predicting methods. Moreover, the results are analyzed and compared to determine the best predicting method for the study area.
The selected images have been recorded in cloud free and calm conditions and with 30 m by 30 m resolution. The first step in this research is to plot the data so as to be able to view the patterns of shoreline change both spatially and temporally. Then, different methods have been used to analyze this information, resolve the pattern of shoreline change and predict the shoreline position in the future. There are several possible methods for calculating an average rate of change within a selected time segment having more than two measurement points. One strategy is to compare the results of three or more different rate calculations, to observe the reasons for any significant differences in the results, and to recommend what is determined to be the best estimate of the rate. The calculation methods used in this study are the following:
1) The least-squares method which uses the slope of the least-squares fit (Y on X) to the distance/time data points within the time segment.
2) The "end-point" rate method which defines the first to last net difference in distance divided by the net time for the segment.
3) The Rate Averaging method which calculates the arithmetic average of all "long-term" rates within a time segment.
4) The compound method which is the average of the results obtained from other methods.
Finally, the results obtained from different calculation methods are compared to determine the sensitivity of the results to the method of calculation.
The results reveal that the spacing and accuracy of the data can have important effects on the rate calculation. The uneven clustering of data points resulted in significant differences between the outputs of the least square method and others. In the case where the time segment is short and the number of points are small (e.g. three), it is not recommended to use the least squares rate. An interesting end-point calculation is the first to last net effective change over the total time record, regardless of the path in between. This may or may not be meaningful, and can be very misleading, depending on that path. Therefore, the simple end-point rate can be used for comparison with other rate calculations as a checking device, and in those situations where any other rate is meaningless. The results of the compound method are the most accurate and reliable.