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Widespread and frequent droughts in recent decades in Khuzestan province have become one of the most important challenges of this province. The use of remote sensing products in temporal and spatial monitoring of drought can play a key role in managing this risk and reducing and adjusting its destructive effects. The main goal of this research is to provide a remote sensing index for temporal and spatial monitoring of drought in Khuzestan province and its validation using station meteorological drought indices. In this research, by using the products of vegetation (MOD13C2) and land surface temperature (MOD11C3) of MODIS sensor, a drought index based on vegetation called VHI plant health index was produced. SPI Meteorological Drought Index, which was based on station rainfall data during the statistical period of 2000-2012, was used to evaluate and quantify this index. The comparison of VHI drought index with three-month SPI meteorological drought index values showed a significant correlation between 0.68 and 0.75. By identifying 4 years with widespread and relatively severe drought in Khuzestan province (based on both VHI and SPI indices), which included the years 2000, 2005, 2012, 2015, the spatial distribution pattern of meteorological drought and VHI plant drought to In general, it indicated that the northern parts of the province were generally involved in mild to moderate droughts and the southern parts were generally involved in moderate to severe droughts. The spatial correlation matrix based on the number of 2500 pixels with dimensions of 5x5 km, which included VHI and SPI values of selected drought years, indicated the existence of a significant spatial correlation between the two mentioned indicators. In the widespread drought of 2000, at the level of Khuzestan province, two drought indices VHI and SPI, the correlation was equal to 0.47, and in 2005, equal to 0.35, and

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In vast areas, the possibility of simultaneous access to satellite images with appropriate spatial resolution, such as Landsat images, is always a challenge. In addition, the temporal resolution of the Landsat satellite does not provide the possibility of examining short-term changes in phenomena such as vegetation. The aim of this research is to use the temporal and spatial fusion techniques of Landsat-8 and MODIS satellite images in preparing the Normalized Vegetation Detection Index (NDVI) map. For this purpose, six image fusion algorithms, including NNDiffuse (Nearest Neighbor Diffusion), PC (Principal Component), Brovey, CN (Color Normalized), Gram-Schmidt, and SFIM, have been used in an experimental area in Khuzestan province. After evaluating the results of the algorithms and choosing the most appropriate fusion algorithm, based on the statistical indicators of the spectral (correlation coefficient) and spatial (Laplacen filter) criteria of each of the algorithms, the spectral and spatial information of the reflection of red and near-infrared of 8 mosaicked Landsat-8 images (30 m) were combined with the red and near-infrared bands of one MODIS image (250 m). In order to investigate the vegetation cover, the NDVI was prepared with the fused satellite image in the Khuzestan province. The results of the research have shown that the NNDiffuse integration fusion algorithm has a very good accuracy among other algorithms in terms of the spatial evaluation index and spectral quality criteria. Therefore, this algorithm was recruited to combine the red and near-infrared bands of Landsat-8 and MODIS images. Compared to the original Landsat-8 image, the NDVI map prepared by this algorithm has the lowest statistical error of RMSE (0.1234) and MAE (0.081), respectively.
 

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According the increasing population, especially the urban population in the world and increasing environmental pollution caused by it, The need for urban planning and management approaches based on indicators such as Healthy Cities approach seems inevitable. The purpose of this paper is to analyze the efficiency and ranking of healthy city indicators through 36 indicators (socioeconomic, health services, environmental and health care). research method applied research is descriptive, analytic and development. To analyze the data from the non-parametric linear programming technique of data envelopment analysis, cross ineffective, models and software Dea slover Shannon entropy is used. The geographic area of this study is Khuzestan province and its statistical population is 22 cities according to the census of 2016. The results of this research show that in terms of relative efficiency of Ahwaz city due to the centrality of the province and the availability of infrastructure and sanitary services with a relatively high distance with the highest performance and high level of performance was in the first rank. And the cities of Dezful, Shosh, Khorramshahr, Shoshtar, Abadan, Masjed Soleyman and Behbahan were selected as semi-efficient cities. Finally, it can be concluded that in terms of having the indicators of the healthy city, most of the cities of the province are Inefficient (64%).

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In this Research, the maximum temperature of selected stations in Khuzestan province and the numerical values of 8 extreme climatic indicators belonging to the Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDMI) were used in the statistical period of 1987-2017. To analyze the trend of extreme climatic indices, the Man-Kendall test was used and to estimate the slope of the trend line, the Sen’s estimator was used. In this study, given the importance of global warming that severely affected all aspects of life, the authors explore the relationship between climatic factors and maximum temperature in Khuzestan province until to rely on it, and ones can predict and forecast air temperature at this region. For this purpose, the temperature of selected stations in Khuzestan province and numerical values of  8 climate indicators in the period 1987-2014 have been used. To understand the relationship between climate indicators and maximum temperature at 1 to 12 months of delays, Pearson’s correlation coefficient was used. The results showed that most of the extreme climatic indicators in the study period had a significant trend. The TX10 and TN10 indices have had negative trend in most stations and the TX90, TN90, TXx, TXn, TNx and TNn indices have had positive trend. According to the results of correlation coefficients can be concluded that all studied signals have a significant effect on the province's maximum temperature. The correlation between maximum temperature and indices PNA, TSA, WHWP, WP and NAO, was more than the other climate indicators. Results also showed that the entire indices except NAO have significant positive correlation with maximum temperature of the province. PNA index with a delay of 10 months has the highest positive correlation with maximum temperature of study area.

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Abstract
Iran is one of the most earthquake-prone countries in the world and its cities have suffered a lot due to this natural phenomenon. The purpose of this study was the spatial analysis of earthquake crisis management. The research method has been applied-developmental. The research area of ​​Khuzestan province and the statistical population included elites in the field of urban planning in Khuzestan province who were selected by targeted sampling method. There have also been two statistical tests. The results of statistical analysis showed that from the perspective of statistical individuals, proper crisis management (organizational structure, proper distribution of emergency services, manpower, equipment and information system) has a positive and significant effect on reducing mortality and financial vulnerability. Also, the results of Hot spot analysis showed that hot and earthquake-prone hotspots in Khuzestan province were mostly located in Behbahan, Masjed Soleiman and Andimeshk counties
 

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In this research, the effect of two indicators OMI and RMM of Maden Julian fluctuation on the frequency of dust storms in Abadan, Ahvaz, Bostan, Bandar Mahshahr, Dezful, Ramhormoz and Masjed Soleyman located in Khuzestan province during six months (April to September) of the statistical period (1987 - 2021) was reviewed. Pearson's correlation coefficients between dust data and indicators were investigated and its results were calculated in the form of income zoning maps and the frequency percentage of each indicator for positive and negative phases. The results of the research findings indicate that there is a direct and significant relationship between the positive and negative phases of both indicators with dust, except for Dezful station in the positive phase of OMI and the negative phase of RMM and the highest correlation coefficient for Bandar Mahshahr and Dezful station is between -0.7-20.77 is in the positive phase of the RMM index. The relationship between the Madden Julian Oscillation and dust showed that between 51 and 59 percent of dust storms occurred in the negative phase of the OMI index and 40 to 49 percent in its positive phase. In the RMM index, 56 to 63 percent of dust storms occur in its negative phase and 37 to 50 percent in its positive phase. In fact, the negative phase of the RMM index has a higher percentage of dust storms than the negative phase of the OMI index. According to the results of the Monte Carlo test, the displacement of the positive and negative phases of the RMM index significantly leads to the occurrence of dust storms for most of the stations in Khuzestan province. Tracking the paths of dust entering Khuzestan province with the HYSPLIT model shows the movement of particles from Iraq, Arabia and the eastern parts of Syria towards the studied area.