Dr. Mostafa Kabolizadeh, Dr. Sajad Zareie, Mr. Mohammad Foroughi Rad,
Volume 0, Issue 0 (3-1921)
Abstract
| 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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Engineer Elham Azizikhadem, Doctor Kazem Rangzan, Doctor Mostafa Kabolizade, Engineer Ayob Taghizadeh,
Volume 18, Issue 51 (6-2018)
Abstract
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.
Mrs. Zeinab Zaheri Abdehvand, Dr. Mostafa Kabolizadeh,
Volume 25, Issue 78 (9-2025)
Abstract
In vast areas, accessing satellite images with appropriate spatial resolution, such as Landsat images, is often challenging. dditionally, the temporal resolution of the Landsat satellite does not allow for the examination of short-term changes in phenomena such as vegetation. The aim of this research is to utilize temporal and spatial fusion techniques of Landsat-8 and MODIS satellite images to prepare a Normalized Difference Vegetation Index (NDVI) map. For this purpose, six image fusion algorithms—NNDiffuse (Nearest Neighbor Diffusion), PC (Principal Component), Brovey, CN (Color Normalized), Gram-Schmidt, and SFIM—were applied in an experimental area in Khuzestan province. After evaluating the results of these algorithms and selecting the most appropriate algorithm based on statistical indicators (spectral criteria such as the correlation coefficient and spatial criteria such as the Laplacian filter), the spectral and spatial information from the red and near-infrared bands of eight mosaic Landsat-8 images (30 m resolution) were combined with the red and near-infrared bands of one MODIS image (250 m resolution). To investigate vegetation cover, the NDVI was calculated using the fused satellite image for Khuzestan province. The results showed that the NNDiffuse fusion algorithm demonstrated very high accuracy among the tested algorithms in terms of spatial evaluation and spectral quality criteria. Consequently, this algorithm was selected 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 using this algorithm had the lowest statistical errors, with an RMSE (Root Mean Square Error) of 0.1234 and an MAE (Mean Absolute Error) of 0.081.
