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Public awareness and knowledge on climate change constitute essential background to deal with climate change and related problems. Alongside this background, this study assesses the awareness and quality of knowledge regarding climate change in Muscat governorate, Oman. A survey of 350 randomly sampled respondents was conducted using a standard questionnaire. A structured questionnaire was used for data collection. This questionnaire was administered to respondents who were evenly distributed among the six divisions (Wilayat) of Muscat governorate in Oman while descriptive statistics were the main analysis techniques. Results have revealed that public awareness is fairly high despite some limitations on the knowledge on the causes and prevention of climate change. Regression analysis finds that gender, years of education, and income are significant factors that determine the level of awareness. Climate change is an area that is in need of publicity to help the public make informed decisions in its adaptation and mitigation. Results further indicate that most of the respondents have fair general knowledge about the subject.

Some techniques and methods for deriving water information from SPOT-4 (XI) image were investigated and discussed in this paper. An algorithm of decision-tree (DT) classification which includes several classifiers based on the spectral responding characteristics of water bodies and other objects, was developed and put forward to delineate water bodies. Another algorithm of decision-tree classification based on both spectral characteristics and auxiliary information of DEM and slope (DTDS) was also designed for water bodies extraction. In addition, supervised classification method of maximum-likelyhood classification (MLC), and unsupervised method of interactive self-organizing dada analysis technique (ISODATA) were used to extract waterbodies for comparison purpose. An index was designed and used to assess the accuracy of different methods adopted in the research. Results have shown that water extraction accuracy was variable with respect to the various techniques applied. It was low using ISODATA, very high using DT algorithm and much higher using both DTDS and MLC.Extraction of water information from digital satellite images has been studied broadly in recent twenty years. The methods of identifying waterbodies such as thresholding, segment, Landsat chromaticity coordinates, proportion estimation, and descriptive algorithm based on knowledge of water spectrum feature, have been put forward and applied to a variety of satellite data for management of water resources and monitoring of floods. SHIH (1985) used Landsat MSS data to delineate the water surface area from the surrounding land. tn his research he indicated that both techniques of density slicing from band7(near infrared) and ELAS classification with combination of bands 5 and 7 could successfully assess the water-surface area. The deviation of the surface area assessment between two techniques was within 3%. LU Jia-ju(1992) used techniques of thresholding, Landsat chromaticity coordinates and "proportion estimation" to extract water bodies based on Landsat CCT data, and found that the proportion estimation approach can distinguish smaller water bodies effectively. SHENG Yong-wei et al. (1994) tried to discriminate water bodies using FY--1B VHRSR data, they indicated that water bodies could be identified if the ratio of CH2 and CH1 be used. ZHOU Cheng-hu et al. (1996) and Du Yun-yan et al. (1998) developed a descriptive model for automatically extracting and recognizing water bodies based on the knowledge of water spectrum feature using NOAA/ AVHRR data. YANG Chun-jian et al. (1998) designed a algorithm to extract water bodies from Landsat TM. Based on their analyzed results that the sum of TM2 and TM3 were larger than that of TM4 and TM5 for water bodies, they used the algorithm to distinguish water bodies from shadows effectively in mountainous areas. Other methods were used to extract water information from different satellite data By BARTON I Jet al. (1989), LIU Jian-bo et al. (1996), XIAO Qian-guang et al. (1987). Due to the different spatial re...

Urban growth represents specific response to economic, demographic and environmental conditions. Rapid urbanization and industrializations have resulted in sharp land cover changes. The present investigation was carried out from Shaoxing City to quantify satellite-derived estimates of urban growth using a three-epoch time series Landsat TM data for the years 1984, 1997 and ETM 2000. The methodology used was based on post classification comparison. The use of GIS allowed spatial analysis of the data derived from remotely sensed images. Results showed that the built-up area surrounding Shaoxing City has expanded at an annual average of 7 km2. Analysis of the classified map showed that the physical growth of urban area is upsetting the other land cover classes such as farming, water resources, etc. The study conclusion mainly emphasized the need for sustainable urban capacity.

The Sultanate of Oman has a long coastline extending for about 3165 km including a number of bays and islands. Oman's coastline borders the Arabian Gulf, the Sea of Oman and the Arabian Sea. Most of this coastline is soft and low laying shore subject to the dynamics of sediment transport and the landward retreat of the shoreline, caused by anthropogenic factors and sea level rise associated with climate change. This paper aims to assess the vulnerability of the entire Omani coastal zone to the expected sea level rise and storm surge. Methodology is based on applying Coastal Vulnerability Index (CVI) to identify clusters of high vulnerability areas according to their sensitivity and dynamic nature and increased risk resulted from seal level rise, erosion and extreme weather events. The coastal line of the governorates of Al Batinah, Muscat and Al-Wusta has scored highly due to possessing similar physical attributes. Based on that assessment a coastal vulnerability database utilizing GIS was created to help stakeholders involved in the coastal management to make better decisions.