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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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The increased use of thermal power plants has led to the spread of greenhouse gases in the air and has caused psychological problems for humans. Accordingly, the present study was conducted to measure the pollutants released by Sahand Bonab thermal power plant and to investigate the effects of this pollution on the psychological and psychological pressure of rural residents. The GWP100 method was used to measure the pollutants of the power plant and to measure the mental and emotional pressures of the citizens, a questionnaire was used to assess the psychological stress of Markham. The statistical population of this study is 10254 people over 15 years of age in 7 villages located in the greenhouse of the power plant. Using formulas and simple random sampling, 375 subjects were selected as sample size. The results showed that the most pollutants released are CO2 and NOx, which is 4.17 times the warm seasons in the seasons. Also, analysis of the results by using a Pearson test showed that six variables including neurological and disturbing variables p= 0.272, stress and psychological stress p= 0.325, feeling of energy decrease, p= 0.287, feeling of despair and disappointment in life p = 0.142, feeling Depression in life of p= 0.211 and change in behavior patterns in everyday life p= 0.269 had the most effect on air pollution. Also, mental and psychological stress in nearby villages was higher than in remote villages, more women than young men than older men and elderly people. The results of multivariate regression and path analysis showed that in general, the air pollution caused by the power plant has the ability to explain R² = 37.42 percent of the changes related to the psychological and psychological pressure of the villagers. Finally, it can be said that thermal power plants have negative mental and psychological effects according to type of activity, type of age and gender of the villagers, which should be considered in the studies of the construction of power plants.