s. hamzeh
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Desert, Volume:28 Issue: 2, Summer -Autumn 2023, PP 329 -351
Knowing the temporal and spatial changes of land use and the formation of heat islands over time is one of the most important issues in metropolitan planning and policy making. Thus, in this study heat islands and temperature changes and its relationship with land use changes have been monitored over a period of 35 years in two study areas, i.e. the cities of Mashhad and Sari, using the Google Earth Engine platform. For this purpose, the LST was computed and the land use maps of the studied periods were extracted during 8 time steps of 5 years from 1985 to 2020. The aim of this study is to investigate the spatial autocorrelation of heat islands and its relationship with land use in two studied regions with different climatic conditions. The results of temperature monitoring showed an increase in temperature between 1 to 2 °C in all types of land uses during 35 years. This increasing trend of temperature is proportional to the type of land use changes, so that the temperature increase in built-up lands was estimated to be 2 and 1.75 degrees Celsius in the cities of Sari and Mashhad, respectively. The average temperature of the three months of summer in Mashhad city in built-up areas has increased from 34.5°C to 36.25°C and in Sari city from 29.51°C to 31.51°C. while the minimum increase in temperature has occurred in the lands with forest coverage, which is 1.02 °C and 1.19 °C, respectively in the cities of Sari and Mashhad. Conclusively, in both climatic regions, the areas where the changes are in the direction of reducing or removing vegetation and creating residential areas, the temperature increase is the maximum, and the areas where the changes are in the direction of increasing forests and agricultural lands, the temperature increase is the minimum.
Keywords: Google Earth Engine, Hot Spots, Temperature changes, land use changes -
Desert, Volume:27 Issue: 2, Summer -Autumn 2022, PP 291 -305Climate change is one of the most pressing problems among scientists worldwide, with experts warning about it and even referring to it as unfathomable human agony. In this study, we reviewed previous studies and examined two gaps in the existing approach to climate change studies. First, look at the "side effects" of global warming that have been overlooked in the process and then look at the leading "cause" of global warming, namely "humans" and not its "effects". The findings revealed that a 1.4 °C temperature increase (as predicted by United National (UN) projections) would not only raise this amount but also trigger further global warming. As a result, the premise that global warming produces additional global warming was proven. In the Water Area (WA) class, radiant energy increased by 1194.8%, compared to 1205.8%, 1154.9%, 1115.6% and 1229% in the Vegetation Area Class (VAC), Agricultural Area Class (AAC), Bare Area Class (BAC) and Salt Lake Class (SLC), respectively. Although the Land Surface Temperature (LST) of all classes has only increased by about 0.4 °C, these changes in radiant energy are much more pronounced. The current study also revealed that most legitimate research on this subject has focused on the effects of global warming on environmental variations. These studies, which see these changes as "results" of climate change and global warming, have overlooked the primary cause, "human demands", which has prompted humans to alter or exploit their surroundings actively. This study found that concentrating on humans and encouraging them to focus on happiness rather than pleasure is more helpful in addressing global warming issues than focusing on its impacts, such as rising sea level, storms, drought, etc. The results of this study are helpful for a deeper understanding of global warming and a careful study of the cause and dimensions of this phenomenon.Keywords: Heat entropy, thermal remote sensing, Human, Warming effects, Land surface temperature
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Desert, Volume:27 Issue: 2, Summer -Autumn 2022, PP 343 -358Due to climate change, drought events will probably occur more frequently and be more intense. Therefore, effective drought monitoring and assessment is vital in developing knowledge of drought, drought adaptation, and mitigatory actions. Remote sensing has been widely used for monitoring drought in recent years. In the current research, three groups of remote sensing indices, i.e. vegetation, thermal and moisture indices, were applied to determine the correlation between them and the standardized precipitation index (SPI) as drought index for the growing season (April to September) from 1999 to 2005 for rangeland areas in the Alborz province of Iran. The results indicated that normalized difference vegetation index (NDVI) (with a correlation coefficient of 0.74) and land surface temperature (LST) (with a correlation coefficient of 0.67) had the highest correlations with rainfall. Therefore, it concluded that the assumed indices are suitable for drought monitoring for this land use. Temporal analysis of the results showed that the best correlations of remote sensing indices belonged to the 6- and 9-month SPI and indicated the effect of long-term rainfall on plant growth.Keywords: Correlation analysis, SPI, NDVI, LST
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Nowadays, with the population growth, renewable energies- especially solar energy- have grown effectively among governments. These types of energies are considered to be unlimited sources of energy and have the least harmful effects on the environment. In recent years, investment in the solar energy has been rising rapidly in Iran. One of the important challenges in this field is the site selection of solar power plant which is encountered with many spatial and environmental considerations. The contribution of this research is to determine the suitable site for the creation of a solar power plant in Jiroft and Bam cities of Kerman province, using GIS and fuzzy DEMATEL. In this study, six main criteria including solar radiation potential, surface temperature, access to urban areas, access to transportation lines, slope and aspect, were used. Finally, the study area is classified into four categories as “low suitable”, “moderate”, “best suitable” and “not suitable” with an equal interval classification method. The results showed that 8% (1218.282km2) of the study area has low suitable, 14.23% (2406.903 km2) has moderate suitable and 12% (1996.311 km2) has best suitable for solar power plant area. 66% (11291.03 km2) of the study area is not suitable for solar power plant.
Keywords: Multi Criteria Decision Making, Solar Power Plant, DEMATEL, GIS
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