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In the global pursuit of sustainability, higher education institutions serve as powerful actors, leveraging their multifaceted contributions to advance the implementation of the United Nations' Sustainable Development Goals (SDGs). This study employed a multi-dimensional analysis to evaluate the prioritization of these goals within the institutional framework of Arab universities and identify the key factors that drive their involvement with these goals. The methodology used involved a quantitative approach, utilizing a survey questionnaire to collect data from faculty members representing 30 public universities across the Arab region. The results of the study regarding prioritization analysis reveal that SDG4 (Quality Education) and SDG 8 (Decent Work and Economic Growth) are top priorities across all universities, with prioritization scores exceeding 65.4. Furthermore, the results of the ordinal logistic regression analysis demonstrate that institutional governance and research productivity are significant factors that influence Arab universities' engagement with the SDGs. The findings of this study have important implications for higher education policies, practices, and interventions, aimed at fostering university engagement with the Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

Sewer Treatment Plants (STPs) are essential pieces of infrastructure given the growing scarcity of water sources due to the challenges of urbanization. The positioning of STPs is a complex multidimensional process that involves integrative decision-making approaches that consider multiple sustainability criteria to ensure their optimal placement. The Multi-Criteria Decision Method (MCDM) is a suite of approaches available to decision-makers when making systematic and scientifically informed decisions on siting wastewater treatment plants. Although MCDM methods have manifold applications in different geographic contexts, there is a paucity of studies employing MCDM models for the siting of STPs within the context of Oman. In this study, we assessed the locations of existing STPs and identified suitable locations for future STPs within the Muscat Governorate of Oman using a Multi-Criteria Decision-Making Analytic Hierarchy Process (MCDM-AHP) model in a Geographic Information System (GIS) environment. Eight factors were considered in the MCDM-AHP model: slope, elevation, proximity to built-up areas, airports, valleys, road networks, the sea, parks, and golf courses. Each factor was assigned priority weights based on its importance using the AHP method. Thematic maps were generated to categorize the potential sites into different suitability levels. The results showed that the coastal areas of A'Seeb and Bowsher were the most suitable locations for STPs, representing only 1.19% of the total study area. The novelty of this study stems from the perspective of an original application within the context of Oman, which has generated novel results and interpretations. This has significant implications for urban policy and planning with respect to better informing decision-makers with a systematic framework for efficient wastewater treatment. [ABSTRACT FROM AUTHOR]

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Land surface temperature (LST) is essentially considered to be one of the most important indicators used for assessment of the urban thermal environment. It is quite evident that land-use/land-cover (LULC) and landscape patterns have ecological implications at varying spatial scales, which in turn influence the distribution of habitat and material/energy fluxes in the landscape. This article attempts to quantitatively analyse the complex interrelationships between urban LST and LULC landscape patterns with the purpose of elucidating their relation to landscape processes. The study employed an integrated approach involving remote-sensing, geographic information system (GIS), and landscape ecology techniques on bi-temporal Landsat Thematic Mapper images of Southwestern Sydney metropolitan region and the surrounding fringe, taken at approximately the same time of the year in July 1993 and July 2006. First, the LULC categories and LST were extracted from the bi-temporal images. The LST distribution and changes and LST of the LULC categories were then quantitatively analysed using landscape metrics and LST zones. The results show that large differences in temperature existed in even a single LULC category, except for variations between different LULC categories. In each LST zone, the regressive function of LST with fractional vegetation cover (FVC) indicated a significant relationship between LST and FVC. Landscape metrics of LULC categories in each zone in relation to the other zones showed changing patterns between 1993 and 2006. This study also illustrates that a method integrating retrieval of LST and FVC from remote-sensing images combined with landscape metrics provides a novel and feasible way to describe the spatial distribution and temporal variation in urban thermal patterns and associated LULC conditions in a quantitative manner. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Remote Sensing image fusion is an effective way to use the large volume of data from multi-source images. This paper introduces a new method of remote sensing image fusion based on support vector machine (SVM), using high spatial resolution data SPIN-2 and multi-spectral remote sensing data SPOT-4. Firstly, the new method is established by building a model of remote sensing image fusion based on SVM. Then by using SPIN-2 data and SPOT-4 data, image classification fusion is tested. Finally, an evaluation of the fusion result is made in two ways. 1) From subjectivity assessment, the spatial resolution of the fused image is improved compared to the SPOT-4. And it is clearly that the texture of the fused image is distinctive. 2) From quantitative analysis, the effect of classification fusion is better. As a whole, the result shows that the accuracy of image fusion based on SVM is high and the SVM algorithm can be recommended for application in remote sensing image fusion processes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]