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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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There are various indicators to monitor and management of agricultural water resources in arid and semi-arid countries including Iran, some of which can be extracted directly in situ, and some can be retrieved using remote sensing technology and satellite images. Aim of this study is to propose the most appropriate and efficient indicators of agricultural water resource management for achieving maximum production and maximum water efficiency using remote sensing technology, therefore, Crop Water Stress Index (CWSI) and Surface Energy Balance Algorithm (SEBAL) were used to estimate Evapotranspiration (ET). In the first step, ET rate was calculated using SEBAL algorithm for six Landsat 8 satellite images related to the wheat growth period. Then, zoning of this index was done in the range of zero to one, in four categories of very low, low, medium and high, which respectively indicate the lowest to the highest amount of ET. In next step, CWSI was calculated based on Idso equation, and its results show different changes both in cold season and in warm months. Comparison of ET and CWSI shows a significant relationship between these two indices in warm months, while in cold months, no significant relationship can be seen. These findings along with the established relationship between ET and CWSI can inform water management strategies in arid environments for sustainable crop production.

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The tourism industry has become a major economic activity in the early years of the 21st Century and is considered one of the most productive and most employment-oriented global industries. Tourism is one of the most important factors generating wealth and employment in the world. It is necessary to plan for the proper exploitation of this industry, The most important steps to plan are to locate sites for providing tourists with the services they need in the form of tourist villages, This research is for the city of Shush which is one of the most important tourist areas of Khuzestan province And since it has many ancient monuments, it has attracted many tourists, , But the city has been at a very low level in terms of having a space worthy of tourists. Therefore, the conditions reinforced the idea of creating a tourism village. In this research, location-based discussion was conducted through a fuzzy inference system, Finally, the Fuzzy Topsis method has been used to protect the environment and to some extent extend sustainable tourism development. The ranking of these sites is based on environmental criteria. In the fuzzy inference system by applying the layers required in this method, four sites are considered to be very suitable.Then, using Fuzzy Topsis, which includes 10 criteria and 4 options, identified the best site on site 4. This site will bring the least damage to the environment, Located on the banks of the Dez River, most of the area has been covered by ground. In terms of maintaining environmental criteria, the site has a completely organic environment than other sites.