محمود عبیات

Land surface temperature (LST) is one of the important parameters in the study of climate change and physical processes of the earth's surface. Rising land temperature causes the phenomenon of heat islands, which is caused by changes in land-use and land cover in urban areas and today has become a major environmental concern. Ahvaz metropolis, as the capital of Khuzestan province in southwestern Iran, has undergone many changes in land-use and land cover in recent years and has enjoyed significant population growth. In recent years, the pattern of unbalanced urban development in Ahvaz has led to the destruction of agricultural lands, vegetation and gardens around the city as one of the most important factors balancing the land surface temperature. This has led to an increase in land surface temperature and the formation of heat islands. The purpose of this study is to investigate land-use changes in Ahvaz and its effects on spatio-temporal patterns of land surface temperature and heat islands in the years 2002-2021.

First, the land-use changes were studied and then, in order to evaluate the relationship between the changes in each Land-use and the land surface temperature, the maximum temperature in each land-use was determined and then the amount of changes was investigated. Landsat 7 ETM+ (2002) and Landsat 8 OLI/TIRS (2013/2020) images downloaded from the USGS. Object-oriented method was used for classification. Educational samples were implemented on the image surface and their corresponding objects were selected as educational samples for each class. The classification was done by SVM algorithm and the user maps were classified into four classes including vegetation, barren areas, constructed areas and water areas. In this study, estimating the LST was performed base on separate window Algorithm and with Landsat 7 ETM+ band 6 and Landsat 8 OLI/TIRS band 11 data. In this Study, LST was estimated using Split-Window (SW) algorithm on Landsat-7 ETM+ and Landsat-8 OLI/TIRS Thermal Infrared (TIR) bands. Then the NDVI values, Vegetation Proportion and Radiated Power were obtained. Finally, The LST value was extracted based on degrees Celsius. In this study, Pearson correlation coefficient was used to investigate the relationship between LST and air temperature. RMSE values was used to compare the estimated temperature by SW algorithm and the measured temperature by Field research. Also, two indices UHII and UHIII were used to calculate the intensity of heat islands. These indices evaluate the LST using the values of vegetation in one area.

According to the results of this study, Kappa coefficients and overall accuracy were 74% and 76% for year 2002 map, 78% and 85% for year 2013 map and 88% and 93% for year 2020 map, respectively. The area of built-up and barren areas has increased and the area of vegetation and water areas has decreased. The rate of increase in land-use of the constructed areas from 2002 to 2020 was about 579.69 hectares and the decrease in vegetation cover was 3200.15 hectares. The barren areas have increased by 2521.66 hectares from 2002 to 2020. Pearson correlation coefficient between air temperature and LST values of 0.65% is obtained, which shows a positive correlation. The RMSe values in comparison with LST and measured temperature by Field research for the images of 2002, 2013 and 2020 was 1.79, 1.66 and 0.98 degrees, respectively. According to the results, the LST values in year 2002 fluctuated about 24.48-42.55, in year 2013 about 26.32-44.47 and in year 2020 about 28.13-46.65 degrees Celsius. The results of LST show an increasing trend of temperature during the period 2020-2020. After estimating the LST, the maximum temperature of each user was determined, which showed the increasing trend of temperature in all applications. The decreasing trend of vegetation has a direct effect on increasing LST in this land-use. The temperature of this land-use has increased by 6.32 degrees in 2002-2020. Over a period of 18 years, the LST in built-up areas and barren areas has increased by 4.10 and 5.26 degrees, respectively. In order to study the spatio-temporal patterns of LST and heat islands, first with NDVI index, vegetation status in each year was divided into three classes of low, medium and high vegetation and then LST values in each category were determined. According to the results, the highest temperature occurred in the floor with low vegetation. The correlation between the two variables LST and NDVI was significant and regression between them showed an inverse correlation that indicated a negative relationship between LST and vegetation. Therefore, vegetation plays an important role in reducing the intensity of the heat island. UHIII index values in the low vegetation class from 0.63 to 0.67, in the medium vegetation class from 0.57 to 0.61 and in the high vegetation class from 0.51 to 0.54 it is arrived. The highest intensity of heat islands in 2002, 2013 and 2020 were in the southern, eastern and northwestern parts of the city, respectively.

According to the research results, land-use changes in the study period have caused an increase in land surface temperature. The highest temperatures have occurred in built-up areas and barren areas; this is due to the increase in the area of built-up and barren areas. Decreased vegetation has a direct effect on increasing LST in this land-use. The NDVI, UHII, UHIII indices and LST values were used to study the spatio-temporal patterns of land surface temperature and heat islands. The results indicate high temperatures in the vegetation with low vegetation. Due to the correlation of NDVI values with land surface temperature and the intensity of heat islands, the necessity and importance of vegetation protection as a very important variable to regulate the air temperature in the city is essential.

Satellite images have a high capability for estimating the area under agricultural crops. The aim of this study was to identify the area under dominant crops such as in Shushtar Province using Landsat 8 satellite images during the growing season during 2019. With Maximum Probability technique and Support Vector Machine in the first approach and using NDVI index in the second approach, crops in different growing seasons and according to their calendar, a cropping pattern map was drawn. In order to evaluate the accuracy of the results, the generated maps with reference data were examined. Agricultural Jihad statistics of Khuzestan were also used. The results showed that Kappa coefficient and overall accuracy were calculated as 90% and 80% in the Maximum Probability technique, 92% and 90% in the Support Vector Machine and 95% and 93% in the NDVI, respectively. Based on the results, the cultivation area of wheat, barley, rice, and corn, in the Maximum Probability technique, in comparison with the statistics of Agricultural Jihad, had an error of 12.6, 16.4, 8.7 and 6.6%, respectively and in the Support Vector Machine had an error of 10.1, 8.3, 5.1 and 7.2%, respectively. However, using the NDVI index as the best approach for estimating the cultivation area in this region, in comparison with the statistics of Agricultural Jihad, has an error of 2.4, 1.5, 4.3 and 4.6%, respectively, which indicates the high capability of vegetation indices to estimate the Cultivation Area, According to their phenological stage.

Existence of important sources of pollutants and points of human activities on the ground and the penetration of these pollutants into aquifers, reduces the quality of groundwater, so in the management of groundwater resources, pollution prevention these waters are essential. The groundwater of Karun Township in Khuzestan province is always exposed to pollution due to the prosperity of agricultural and industrial activities in that area. Therefore, the aim of this study is to evaluate and zoning the aquifer vulnerability of the region using DRASTIC and SINTACS models. By mapping and combining hydrogeological parameters effective in the transfer of contamination to the aquifer, vulnerability maps of the two models were prepared in GIS software environment. To validate the models used, the measured amounts of nitrate in the wells in the area were used and the Pearson correlation coefficient between the models and the nitrate layer was calculated and determined. The results showed that the vulnerability index of the DRASTIC model varies Between 68 to 215 and the vulnerability index of the SINTACS model varies between 52 and 195. In the DRASTIC method, 485.2 hectares of the area without risk of pollution, 751.6 hectares with very low vulnerability, 3305.5 hectares with low vulnerability, 12411.7 hectares with moderate vulnerability, 11191.2 hectares with Moderate to high vulnerability, 9427.3 hectares with high vulnerability, 58861 hectares with very high vulnerability and 478.5 hectares are completely susceptible to infection. In the SINTACS method, 455.4 hectares of the area without risk of pollution, 789.1 hectares with very low vulnerability, 32281 hectares with low vulnerability, 12426.7 hectares with medium vulnerability, 11169.4 hectares with Moderate to high vulnerability, 9449.3 hectares with high vulnerability, 5844.2 hectares with very high vulnerability and 495.7 hectares are completely susceptible to infection. Also, the correlation of groundwater nitrate map with DRASTIC and SINTACS models was 0.68 and 0.51, respectively, which indicates the higher capability of the DRASTIC model than the SINTACS model for the aquifer in the study area.

Soil erosion is a global problem that threatens water and soil resources and land use change is one of the important factors in soil erosion. The purpose of this study is to evaluate the effect of land use change on soil erosion and sediment production in Ramhormoz basin in Khuzestan province. Landsat ETM+ (2002) and OLI (2019) images were used for this purpose. First, satellite images were classified using the Object Oriented Method (SVM) algorithm and the land use changes were studied during the years 2019-2002. Then the amount of soil erosion was calculated using the RUSLE model and the amount of sediment load in the area was estimated. The results showed that the residential areas, barren and rainfed cultivated lands increased by 3520.86, 7041.72 and 5281.29 hectares, respectively, and water bodies, pastures and irrigated lands have lost 1760, 43.02, 02.02. 12323 and 1760/43 hectars of their lands, respectively. The result of these changes in areas has been a decrease in natural soil cover and an increase in erosion in the region. Considering the landing limit of about one ton per hectare per year, it was found that the amount of erosion have been more than the acceptable level in 43.24 and 64.99% of the area in years 2002 and 2019, respectively. The results of calculations of sediment delivery ratio methods also showed that the rate of sediment delivery ratio varies from 0.07 to 0.28 and the maximum sediment load varies from 0.18 to 0.63 tons per hectare per year. Therefore, the results of this study clarify the need to address the issue of soil erosion in the region and to provide management solutions.

Monitoring land use and vegetation changes plays an important role in urban management. The purpose of this study was to investigate the changes in vegetation cover of Ahvaz metropolis pertaining to land use changes. First, satellite imagery was prepared through object-oriented vector machine-based algorithm, classified method, and land use maps. To increase the accuracy of the maps, three normalized indices of vegetation difference, soil effect modifier and relative vegetation were used separately in the classification. The results showed that soil effect modifier index had higher capability and its maps with the highest kappa coefficients and overall accuracy were entered into land change modeling to detect changes. Changes in the next 10 years were also predicted using the Markov chain automated cell model. The results of the changes showed that the vegetation has a decreasing trend, so that 1339.65 hectares the vegetation in the period 1989-1989 and 1860.50 hectares in the period 2019-2002, have reduced. The most changes are related to the conversion of vegetation into man-made areas with 686.44 hectares in the period 1989-1989 and 1032.51 hectares in the period 2019-2002. The least changes are related to the conversion of vegetation into water areas with 7.18 hectares in the period 1989-2009 and 9.33 hectares in the period 2019-2002. The results of forecasting changes by 2029 also confirmed the reduction of vegetation. In 10 years, 785.77 hectares of vegetation will be reduced and the area will reach 2923.24 hectares.

The increasing population growth along with human activities has led to the booming of waste production.  The selection of inappropriate landfill sites contributes to the increased potential of underground pollutions. The purpose of this study was to zone the underground water vulnerabilities of Karun County in Khuzestan Province to select the optimal landfill site, so that a site with the least vulnerability of aquifer would be selected for waste disposal. Two models were used to zone the aquifer pollution potential, namely GODS and SINTACS. After preparing the vulnerability maps of the two models in GIS software, the appropriateness levels of the sites for waste disposal were prioritized. The results of SINTACS model demonstrated the following vulnerability statistics: 455.22 hectares have no vulnerability potential, 780.84 hectares very low vulnerability, 3281.31 hectares low vulnerability, 12582.4 hectares low to medium vulnerability, 11169.9 hectares medium to high vulnerability, 9449.6 hecaters high vulnerability, 5844.15 hectares very high vulnerability, and 495.72 hectares totally at vulnerability risk. Moreover, in GODS model, nearly 215.576 hectares have negligible vulnerability, 560.544 hectares low vulnerability, 5169.08 hectares medium vulnerability, 37471.9 hectares high vulnerability, and 641.9 hectares very high vulnerability. In both models, the least vulnerable zones comprise a small part of the area (1.03 percent in SINTACS and 0.49 percent in GODS model). This level can be selected as the optimal landfill site (rank 1), while other zones are ranked lower due to their higher aquifer vulnerability levels.

Today the environmental hazards caused by mismanagement of waste to be one of the major problems the country and this problem is higher in rural areas than cities. In rural areas, despite the differences between the regions in terms of the lifestyle with urban environments, Due to changes in the lifestyle of people, Waste Management is considered as a very important. Environmental pollution and thereby endanger the health of villagers require of appropriate waste management based on existing conditions. The main purpose of this research is Assessment and evaluation of the optimal waste management in rural areas Abadan city this research done by the descriptive - analytical Method and in processing is used from the Topsis Fuzzy AHP model. And is used from the Excel software to perform analyzes in this paper. The results of fuzzy TOPSIS model based on the calculated weights shows That in the index studied, the index of the waste master plan studies (With similarity index0/755 The most important thing for waste management in rural areas is the city of Abadan Also the index of the culture and education programs(With similarity index0/643 gathering and transportation(With similarity index0/626), final disposal(With similarity index0/612), public participation and private sector(With similarity index0/607), breakdown by origin of production(With similarity index0/592), skilled manpower (With similarity index0/586) and creating data bases are located(With similarity index0/561), respectively, in the next rankings.

One of the things that is important in waste management. Topic, Site Selection landfill and leachate from this landfill in the rural areas. So far in relation to aquifer pollution vulnerability assessment by using these models or similar models (like Avi and Drastic, Si, etc.) Many studies have been done. But According To The importance of waste and its impact on the surrounding environment as well as pollution of groundwater by leachate waste, this model is described as a powerful instrument for the protection of these areas that are affected by contaminants.The aims of this research the application of the GODS model in selection of appropriate area of the rural landfill. In fact should be select landfill sites that have the least impact on the aquifer pollution. Methods of collecting this research uses attritional, case study and study method are descriptive-analytical methods .In this Study, Gods model was used to landfill site selection. Each of these models are made from combining the hydrogeological parameters affecting the transport of contaminants to the aquifer. These parameters appear in the GIS software for seven layer that the necessary checks on them. Results show By using Gods model in the current situation, about 1.6 % of the study area in total In a state of vulnerability has been very low and be negligible that Can be determined by taking these areas are great place to landfill.

1- One of the problems that is important in the waste management is the topic of waste rural landfill site selection and Latex resulting from that in rural areas. The Latex can be because groundwater pollution the contamination vulnerability and groundwater protection assessment has proved to be an effective tool for the delineation of protection zones in area affected by groundwater contamination. So the main objective of this study is waste rural landfill site selection, this study also deals with examining the application of DRASTIC model to determine Potential aquifer pollution in rural areas and zoning maps of vulnerable areas.
2-THEORETICAL FRAMEWORK: During everyday lives to resolve their variety of needs, people use materials found in nature in various ways. Always a part or most of the materials cannot be used which recalled unusable waste. The villagers produced the materials in a state of a variety of material, including liquid, solid or gas that is said the waste.
If the solid materials are more than the other types of materials, they are called solid waste or garbage. All solid materials that are useless to their owners or the general public, are useless waste, waste and non-value of maintaining is called solid waste. In the context the aquifer vulnerability concept, the definition by the National Committee of America in 1993can be noted. This committee knows the groundwater vulnerability to pollution, the desire or possibility of reaching groundwater pollutants to a specified location on the system after they came into being in some places above the aquifer level. The term of the vulnerability in terms of conceptual in the hydrogeology are divided to two forms of intrinsic vulnerability and specific vulnerability. Inherent vulnerability Refers to the possibility of contamination in an area without considering specific pollutants. This type of vulnerability depends on the geological features, a region hydrology and hydrogeology and human activities and is independent of the nature of the pollutant. Methods such as Drastic and Sintacs are used to evaluate this type of vulnerability. The particularly vulnerable also refers to the vulnerability of groundwater to pollutants or a particular group of pollutants. That depends on the characteristics of the contaminant and its relation to various factors inherent vulnerability. DRASTIC model is an empirical model that the first time was raised in 1987 by the United States Environmental Protection Agency to assess the vulnerability of groundwater of the United States and is based on the concept of hydro geological situation. This model has been formed from the combination of seven hydro geological parameters affecting the groundwater contamination that includes water table depth, aquifer net recharge, aquifer media, soil media, topography, unsaturated region and hydraulic. Drastic word also refers to the initials of the seven effective and main parameters in this method.
3- The method of this study is the documentation (library), the field and analytic method. In this study, the DRASTIC model is used for aquifer vulnerability zoning. This model is formed from combining seven hydro geologic parameters. These parameters appear as seven layers in the GIS software that required analyses are performed on. Through preparation of these maps in GIS, we can combine different layers and prepare vulnerability zoning map.
4- In order to create a healthy and clean environment for villagers, comprehensive management of the collection and disposal of waste is required. The Basis of health and hygiene in villages is their cleanliness, and collecting and wasting landfills is considered the first of its interest in the cleanliness that a proper system of waste management will pay to this issue. A correct cycle waste management includes elements responsible for reducing the production, gathering, transportation, processing, recycling and disposal of the production to burial place, that implementation of each of the above steps requires careful planning and design.
One of the important points which should be considered in environmental design of landfill site is the management of leachate and preventing the underground water from being polluted. In this article, given that the drastic method is considered as one of most practical methods of rating in comparison with other methods to determine the potential vulnerability of aquifers to pollution, and uses more parameters in the preparation of model, it is used to assess the potential contamination of underground aquifers to leachate of the waste by the DRASTIC model.
5– Aquifer vulnerability zoning study area map which is obtained from the combination of raster map of seven DRASTIC parameters with respect to the weight of each parameter in GIS, shows that, that the parameters of the water table depth (D), and Nutrition Network (R) are the most effective ways in determining vulnerability. In this map, it has been found that about 88 percent of the desired area vulnerabilities are low and only 2% of the land (total), has had high and very high vulnerability. And 5% have moderate vulnerability and 6% of vulnerabilities are very low. Therefore, it can beside that most of leachate contamination of groundwater in the studied area is in the North and North West. Due to unfavorable disposal sites and how to landfill and by taking the final map (aquifer vulnerability) it is expected that with the support of managers and cooperative people of the area, collection and disposal practices are improved to prevent environmental pollution. Also, this method is useful as a tool for planners and designers for landfill site selection and evaluation of the vulnerability of groundwater aquifer by infiltration leachate from the waste.