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In recent decades, severe climate change, decreased precipitation, temperature rise, and increased evapotranspiration (ET) have significantly reduced waterbodies. Furthermore, governments have prioritized the study of water level fluctuations of lakes to protect them from degradation nationally and regionally. The present study investigated the physical changes in lakes Bakhtegan and Tashk due to climatic parameters. To this end, Landsat satellite imagery and the NDWI were employed to calculate the area of the waterbodies from 1986 to 2018. The results showed that the area had decreased during the study period-- since 2009, Lake Bakhtegan had dried up completely. In 2008 and 2010, the lowest precipitation was 127.82 and 107.7 mm, respectively. During the study period (1986 to 2018), the average temperature was 19.44 °C, with an increase of 0.6 °C. Among the climatic parameters, precipitation, with a correlation coefficient of 0.55, and potential evapotranspiration (PET), with a correlation coefficient of about −0.68, were more strongly correlated with changes in the area of the waterbodies. To predict temperature and precipitation in the study area in the coming decades (2020–2050), the HadCM2 model of the CORDEX Project -WAS (South Asia) was used under two scenarios: RCP4.5 and RCP8.5. These scenarios revealed the decrease in precipitation and increase in temperature trends. As a result, the waterbodies’ areas were estimated using the projected precipitation and PET for the period 2050–2020, indicating a decrease in the areas of the waterbodies.

Salt domes are one of the unique geomorphological phenomena that are important in terms of economic development, environment and tourism.. It plays a significant role in earthquakes and land subsidence as an effective aggravating factor in tectonic activity. In this study, after identification and morphometry of salt domes in the southeast Fars region using eight tectonic indices, i.e., the activity status of the domes was evaluated. Then using differential radar interferometry, the amount of vertical displacement of the area was calculated. Finally, Geographically Weighted Regression was employed to determine the relationship between subsidence and collapse at salt domes. The results indicate that in addition to the movement of salt domes, tectonic subsidence factors have also been effective in the rate of vertical displacement in the region. The calculation of indices indicates that some salt domes are active, and the results of differential radar interferometry also indicate the region’s vertical displacement and tectonic activity. However, some salt domes, including salt dome S2 in the western part of Fasa township, with a high score in calculating the indices, were also in the range of uplift due to interferometry. Although other salt domes had a degree of uplift due to their unique characteristics, groundwater loss had caused the highest subsidence rate in salt domes S10, S4, S5, and S6. Therefore, besides the unique characteristics of salt domes, other factors such as subsidence due to groundwater overdraft which are about 70 meters, Comparing the two models, it shows that the GWR model with a coefficient of R2 of about 80% -99% and the lowest error of / 001. And OLS model with R2 coefficient shows about 0.23 and an error 0.07 and GWR model with higher accuracy confirmed the agreement between the two parameters

Extended abstract:Urban Heat Island (UHI) is an important factor for heat change and balance in global studies and is considered as a proxy for climate change. Studying this phenomenon and investigating its mechanism are very important in urban planning. During the last two decades, the great need for earth surface temperature information for environmental studies and land resource management activities has turned earth surface temperature remote sensing into one of the most important scientific issues (Sobrino et al., 2004). In a research, Hay et al. (2018) studied the effect of the topographical factor on LST in mountainous areas. The results of their research showed that there was a high correlation between topography parameters and surface temperature. For the area studied in this research, the relationship between LST and altitude was inversely linear. Tran et al. (2017) used Landsat 5,7,8 in order to determine the relationship between land cover change and land surface temperature of the inner city of Hanoi (plain and flat area). The results revealed that: a) LST depends on the non-linear method of LULC types; b) Foci analysis by using the Getis Ord Gi* statistic allows analysis of LST pattern change over time; c) UHI is affected from both the urban perspective and type of urban development; d) investigation of LST pattern prediction and UHI effect can be done by the proposed model by using nonlinear regression and simulated LULC change scenarios. In a research in a city of China, the harmful effects of land cover and land use changes on the Earth's surface temperature were investigated through vegetation indicators based on three image sensors of TM and ETM+. For this purpose, the researchers obtained the Temperature Vegetation Index (TVX) from the images. Their results showed that land use change is an important factor for increasing the Earth's surface temperature. It also showed high temperature in areas with scattered vegetation and low temperature in areas with dense vegetation. It has been used in the statistical period of 2013-2015 in the city of Isfahan as well. The results of this research showed that there was a sharp thermal gradient due to the presence of Urban Cold Islands (UCI) between the center and suburbs. The largest UCI was identified in Region 6 (Ahmadi et al., 2016).Keywords: Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), spatial statistics, Isfahan City AssumptionsIt seemed that the growth and development of the city had increased the number of heat islands.It seemed that the heat islands had spread from the surroundings towards the city center in the investigated time period. General purposeMeasuring the degree of spatial autocorrelation between land surface temperature and land useSub-goals:Survey of land use in the summer of 2019Spatial survey of thermal penalty in the summer of 2019 Research method:In this research, 6 images obtained from Landsat 8 satellite were used Isfahan city in the summer of 2019. Landsat has two sensors; one is called Operational Land Imager (OLI) for Earth observation and the other is Thermal Infra-Red Sensor (TIRS) thermal observation. These two sensors form 11 bands together. The selected images had the wavelengths of 10.30-11.30 µm, spatial resolution of 100 m, and thermal band of 10. The pictures were taken in the warm season when we had the least amount of cloud cover. To prepare the data, atmospheric and geometric corrections on satellite data were used from the digital height data. Then, the Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) were calculated by using Google Earth Engine and radiometric corrections were implemented on the data. Results and conclusion:This research paid attention to changes in the spatial autocorrelation pattern of surface temperature and its relationship with land use. For this purpose, hot spot index and nearest neighbor average were used. ArcGIS was used to apply the methods. The results of the nearest neighborhood average index showed that Isfahan had some changes in the spatial correlation of ground surface temperature with a high confidence level and the dispersion was clustered at the 99% confidence level. Based on the patterns of hot spots in Regions 5 and 6, which were located in the south of the region, it played a significant role in the formation and creation of thermal islands with 99% hot clusters. There were areas of negative spatial autocorrelation (99% cold clustering areas) in the center of Isfahan city where Regions 1 and 3 existed due to the presence of water use (33 bridges) and thus, a significant part of Isfahan city lacked a statistically significant pattern. The results showed that the areas with vegetation and water use weakened the heat island effect and the areas, in which the built regions were expanded accelerated the heat island effect. In the planning and development of the city, more attention should be paid to greening the city of Amar. Spatial statistics studies can lead to a suitable and new model for the officials, politicians, and urban planners to use in the future. References- Clark, w.a.v & Hosking, p.l (1986). Statistical Methods for geographers, John Wiley, (32)2, 65-68.- Guo, A.Yang J,Sun W,Xiao X (2020). Impact of urban morphology and landscape characteristics on spatiotemporal heterogeneity of land surface temperature. 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Estimation of land surface temperature of high range mountain landscape of Devikulan Taluk using Landsat 8 data. International Journal of Research in Engineering and Technology. 5(1), 321-338.- Tan, J. De Yu, Qiang Li, Xuelan Tan & Weijun Zhou. (2020). Spatial relationship between land-use/land-cover change and land surface temperature in the Dongting Lake area, China. Scientific Reports, 10(1), 1-9.- Thomas,R.W and Hug get,R. J(1980). Modeling in Geography, a mathematical approach, Harper and Row, Publisher, London.- Tran, D. X. Pla, F.Latorre-Carmona, PedroMyint, S, M .Kieu, H.(2017). Characterizing the relationship between land use land cover change and land surface temperature. ISPRS Journal of Photogrammetry and Remote Sensing, 124, 119-132.‏- Wan, Z. (1999). MODIS land-surface temperature algorithm theoretical basis document (LST ATBD). Institute for Computational Earth System Science, Santa Barbara, 75, 18.‏- Wan, Z. Zhanga Y , Q Zhanga Q , Zhao-liang Li a. (2002), Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spector radiometer data. Remote Sensing of Environment, 83(1), 163-180.- Zhao, He. Li A, Wen F & Yu D. (2018). The impact of the terrain effect on land surface temperature variation based on Landsat-8 observations in mountainous areas, International Journal of Remote Sensing, (13)3, 1-20.- Zhao,Li, Z. et. al. (2010). Investigating spatial non-stationary and scale-dependent relationships between urban surface temperature and environmental factors using geographically weighted regression. Environmental Modelling & Software, (25)3, 1789- 1800.- Yuan, F., and Bauer, M. E. (2011). Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote sensing of Environment, 106(3), 375-386.‏- Yue, W., and Xu, L. (2013). Thermal environment effect of urban water landscape. Shengtai Xuebao/Acta Ecologica Sinica, 33(6), 1852-1859.‏- Zhou, G. Huang, and M. L. Cadenasso,.(2011). Does spatial configuration matter? Understanding the effects of land cover pattern on land surface temperature in urban landscapes, Landscape and Urban Planning.(102), 54-63.- Zhu, CIdemudia C. Fengb W.(2019). Improved logistic regression model for diabetes prediction by integrating PCA and K-means techniques. Informatics in Medicine Unlocked, 17, 100179.‏- Zullo, F. Fazio, G. ; Romano, B. ; Marucci, A. ; Fiorini, L..(2019). Effects of urban growth spatial pattern (UGSP) on the land surface temperature (LST): A study in the Po Valley (Italy). Science of the Total Environment, 650, 1740-1751.‏ Figures and Tables- Fig. 1: Map of the study area (Source: authors, 1400)- Fig. 2: Steps to examine the changes through Landsat satellite imagery- Fig. 4: Land use map of Isfahan City in 2019 (Source: writers, 2021)- Table 2: Areas of landuse per square kilometer in 2019 (Source: authors, 2021)- Table 3: Minimum, average, and maximum changes of ground surface temperature in land use (Source: authors, 2021)- Fig. 5: Spatial autocorrelation of surface temperature (Source: authors, 2021)- Fig. 6: Clustering of hot and cold spots (Source: authors, 2021)- Fig. 7: Charts of land use share in hot and cold spots (Source: authors, 2021)

Aim: This research aims to investigate the spatial distribution of public services and measure the level of satisfaction of residents with the quality of access to them in Torbat Heydarieh. Method and data: This research was done using a descriptive-analytical method. The statistical population of the legal area of Tarbiat Heydarieh and the samples were selected using stratified probability sampling. The required data was collected using library and field methods. The collected data was analyzed using descriptive and inferential statistical methods. Findings: The spatial analysis of the distribution and dispersion of public services based on Moran's index shows that educational services and green spaces are irregular and clustered, health and treatment services are regular, and administrative and commercial uses are scattered. The results of the mean comparison test showed that the overall level of satisfaction of the residents with the quality of public services is greater than the average. The correlation coefficient results between the variable of income level and satisfaction with access to services show a significant positive correlation between the two variables. Conclusion: The findings of this research underscore the importance of fair distribution of public services in enhancing the quality of life and residential satisfaction of the residents. Planning for equitable distribution, proportional to the number and density of the population in different areas of the city, with a special focus on the underserved areas, is of paramount importance for urban planners and policymakers. Innovation: This research stands out from similar studies due to its comprehensive approach. In addition to measuring the level of satisfaction of residents by different neighborhoods and the type of services, it also measures and analyzes satisfaction based on income groups. This unique aspect provides a more nuanced understanding of the relationship between public service access and satisfaction, contributing to a more holistic approach to urban planning.

IntroductionSnow, as one of the important climatic-hydrological parameters, has a significant role in providing the world's water resources for industrial, agricultural and drinking purposes. At the same time, the dangerous consequences of heavy snowfall, avalanches, destruction of rural housing, disruption of road transport and communication and numerous other consequences that it has on the natural and human environment are significant for environmental scientists (Shakiba et al, 2015: 88). However, heavy snowfall, especially in the lowlands and lowlands of the middle latitudes, is unexpected and somewhat surprising. So that its continuation for a few days in these areas will have negative effects on practically all living standards of the residents of these areas (Hosseini, 2014: 101). In recent years, the use of satellite data in natural, hydrological and water resource management has grown significantly, and in this regard, MODIS sensor images due to acceptable spatial resolution and fast temporal retrieval power with a variety of bands. Spectral has put it in a good position. Also, due to the very high albedo of snow, it is possible to measure the level of snow cover using satellite data. MethodologyIn the present study, the environmental approach to circulation was used to investigate the relationship between circulation patterns and heavy snowfall. Thus; first, the days of heavy snowfall in the studied stations were identified and then the synoptic patterns and atmosphere of the representative days were analyzed. In this regard, after receiving snow altitude data from the Meteorological Organization, heavy and widespread rainfall events were identified in three western provinces of the country, including Hamadan, Kurdistan and Kermanshah in the form of 16 synoptic stations, during the years 2000 to 2019. In order to study and analyze the synoptic patterns of days with heavy snowfall, by referring to the website of the National Center for Environmental Forecasting / Atmospheric Sciences (NCEP / NCAR), daily data on Sea Level Pressure (SLP), High Geopotential (HGT), zonal wind (UWND) and meridianal wind (VWND), air temperature (Air) and instability index (Omega) were extracted at the intersection of 2.5 * 2.5 and the relevant maps were drawn using GRADS software. Also, the area covered by snow was obtained from MODIS satellite images. MODIS data are of level1b type, which was calculated based on the parameters in the header, radiance and reflectivity. Reflective and thermal parameters for bands 4 and 6 were also used to apply the NDSI (Normalized Difference Snow Index). Results and DiscussionAfter 20 years of study, 8 days were identified that heavy and heavy snow had fallen in the area. On February 4, 2011, in the middle of the atmosphere, a deep trough formed in the western Mediterranean and North Africa, with a strong positive vorticity. This situation has affected the study area.The location of this trough in the Mediterranean provides the moisture needed for snowfall from the Mediterranean Sea. ConclusionsThe results showed in the ground formed a powerful cyclone on Iraq and turbulent weather caused chaos for the region. This condition causes the air to cause accelerated the rise of the package and water vapor in the atmosphere with his quick ascent to the seed quickly convert hexagonal snow. Creates a pressure gradient that causes more than 12 HPa in the region was to create a strong front will be formed in the region. In the high latitudes of cold air and warm air in front of it is the lower latitude. Has caused more than 60 to 70 percent of the study area are covered by snow. A deep trough of cold air loss in middle levels at depths greater than 25 degrees latitude has been. With extreme vorticity and air along rapid ascent has been closed. NDSI index showed the results of actions by deploying the most weather systems has gone down snow-covered forests of western Iran.

In recent years, climate change has caused an increase in the air temperature and consequently the temperature of the earth's surface. The temperature of the earth's surface has had various effects on urban life and has caused the formation of thermal islands in cities. Thermal islands are one of the important criteria in regional planning. The city of Shiraz is considered to be a large population center in the southwest of Iran on the one hand and one of the important tourism centers of Iran on the other hand, so the purpose of this research is to monitor the spatial form of thermal islands in this city. The satellite images of Shiraz city were first collected and extracted in the hot periods of the year from 1985-to 2020 using the data of Landsat’s 4 and 5 (TM), 7 (ETM+), and 8 (OLI/TIRS). After required preprocessing, the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Normalized Difference Built-up Index (NDBI), and the urban heat dispersion index were determined by the Urban Thermal Field Variance Index (UTFVI). The regions with UHI, the variation of the temperature in the city, and the relationship between the LST and surface vegetation were evaluated based on the results of image processing to determine the UHIs of Shiraz. The temporal variations of the temperature pattern of Shiraz city indicated that about 12.76 km2 was added to the area of the fourth temperature class from 1985 to 2020. Results of NDVI evaluation in the studied period showed a decline in the vegetation from 22.28 km2 in 1985 to 17.54 km2 in 2020 due to alteration of the land use which can explain the formation and increase of the UHIs in the mentioned regions. At the beginning of the investigated period (1985), the temperature of the earth's surface was 17°Celsius and reached 21°Celsius at the end of the period (2020). The monitoring of the spatial form of the surface temperature of the earth shows that heat islands are moving from the outskirts of the city towards the city center. UTFVI also revealed that the awful regions (very hot temperatures) are mainly concentrated in the west and north-west to the south west of Shiraz (including some parts of districts 9 and 10), south east of district 7, and north of district 1.

Introduction Today, the phenomenon of land subsidence is one of the most important geomorphological hazards on a global scale, which causes great damage to urban and rural structures, development facilities. Most of the reports from around the world related to the phenomenon of land subsidence have been related to arid and low rainfall areas. According to the US Geological Survey, a subsidence phenomenon involves a collapse or subsidence that can have a small displacement vector. Land subsidence is a geological phenomenon that causes the earth's surface to descend slowly and horizontally. In recent decades, most joints and fissures in agricultural areas have been reported due to over-abstraction of groundwater. Recently, urban areas have also been affected by this phenomenon. These seams and cracks cause a lot of annual damage to ground-level installations such as roads and bridges, buildings, power transmission lines, oil and gas transmission pipes, water pipes and sewage systems, wells wall pipes of exploitation wells. In addition, they provide a path for surface pollutants to move to groundwater sources and contaminate groundwater by infiltrating aquifers. Among the innovations of this study, for the first time, radar interferometry technique was used to investigate the risk of subsidence of Nurabad plain aquifer subsidence. , Identification of vulnerable areas in the aquifer area. Methodology The method used in this research is applied analysis. Groundwater data including piezometer and exploitation wells were used to investigate groundwater level changes and Sentinel 1 images were used to calculate radar interferometry. Geographic weighted regression model (GWR) was used to investigate the relationship between subsidence and groundwater loss parameters. By examining the changes in groundwater level of piezometer wells in the aquifer during a period of 17 years (1380-1397) showed that an average of 15 meters of groundwater drop has occurred in the plain. This decrease in agricultural and residential areas is a priority compared to other uses. By preparing the output of radar images during 4 statistical years, the results show that in 2015 the maximum amount of leakage was 7 cm and in 2016 it increased to 8 cm. In 2017 it is equal to 9 cm and in 2018 it is equal to 10 cm. In the end, by calculating the average of these four years, the amount of subsidence at the aquifer level increases significantly, with the regions in the central and eastern parts of the aquifer having the highest subsidence at the aquifer level. Which has been developed in residential and agricultural areas of the region so that the effects and evidence of these meetings are visible in residential houses and agricultural lands according to Table (2) in the analysis section to examine the status of subsidence rates in the study area using Radar images were taken during the mentioned years and show that in 2015 it is equal to 8 square kilometers, which is compared to 2014, in 2016 it is equal to 34 square kilometers, and in 2017 and 2018 it is equal to 40 and 86 square kilometers. According to the maps, we see in these 4 consecutive years that every year b The amount of subsidence at the plain level has increased and thus subsidence at the plain level has become a kind of hazard. Results and discussion High values ​​of R2 have occurred in the central parts of the model aquifer, which shows a good estimate of the model in estimating the dependent variable and the predictive explanatory variable. Also, relatively lower values ​​are located in the southern parts of the aquifer by examining the subsidence status in the plain. By creating a relationship with the groundwater drop layer, it showed that the observed values ​​with the predicted values ​​indicate a strong relationship. The layer shows the coefficient of determination R2, which is the highest coefficient of R2 in the central part, which is about -0.79 Is 0.53 Figure (7). The distribution of the remaining space of the GWR model errors shows that the model outputs are closer to the actual values. Second, the resulting map shows the low error values ​​in the range. Occlusion The average subsidence rate is about 4 cm per year and the average 4-year average is 16 cm. The average area of ​​the aquifer where the subsidence has taken place during 4 years is about 36 square kilometers, which includes about 5% of its area. The high concentration of wells and the greatest drop in groundwater and the concentration of existing cities and villages. In these areas, due to the water needs of the residential community, including the city of Nurabad and surrounding areas, as well as the water needs of the industrial sector, including factories and agricultural activities, access to water resources is more and groundwater extraction is very high. There has been a high decline that these factors with the intervention of the arid climate of this region has caused the high talent of these areas in the discussion of subsidence and has been identified as one of the critical areas of the city in the subsidence map. The results of radar interferometry show that uncontrolled extraction from the surface of underground reserves has caused the subsidence of the central part of the aquifer and also the presence of several aqueducts at the aquifer has caused damage to residential centers. One of the areas with the highest risk of subsidence in the plain, in the eastern, central and southern parts, the rate of groundwater loss in the aquifer within 14 statistical years is about 22 meters in the same well and the results of GWR method show local R2 in the central parts of the aquifer which is about 79% maximum and shows a high correlation between subsidence and groundwater loss Residual rate in the central part which is equal to 0.007 / 0007 which is a high coefficient of significance.

The present descriptive-analytical study employs a survey research method, documentary technique, and applied-developmental research design to zone the 18 neighborhoods of Sabzevar City in terms of urban poverty indicators. Data collection was done through a questionnaire distributed among a sample with the size of 384 participants selected from for citizens of 18 neighborhoods of Sabzevar City. A total of 17 urban poverty indicators were surveyed in the form of three sociocultural, economic, and access to urban services indicators. For data analysis, the analytic network process (ANP), Grey Relational Analysis (GRA), and spatial statistics tests were used. The results of the integration of the three economic, sociocultural, and access to urban services indicators depict that the highest urban poverty is in neighborhoods 17 and 18, 6, 14, 15, 13, 12, 11, and 9 respectively. All these neighborhoods are among the marginal neighborhoods of the city. The lowest urban poverty levels are in neighborhoods 1, 2, 3, 4, 5 and partly in neighborhoods 13, 14 and 16, which are part of the city's central neighborhoods, mostly including government employees, the salaried, and those with high-paying jobs. Comparing different types of urban textures via the Integrated Zoning Map of Poverty in Sabzevar City shows that urban poverty zones correspond to the areas of unofficial settlements and extension villages, and the economic poverty in the southern regions of the city is higher than other urban areas. According to the principles of Grey Relational Analysis (GRA), neighborhoods 1 and 2, which includes Southern Kashifi St., Northern Asrar St., and Imam Khomeini Blvd., is considered to be at a higher level than other areas in terms of poor urban poverty. The results of spatial statistics tests (spatial autocorrelation test and G-test) indicate the correlation and clustering of the data model or urban poverty indicators of the study area. Keywords: Urban poverty, Spatial structure analysis, ANP model, Grey relational analysis (GRA), Spatial statistics, Sabzevar city