فهرست مطالب ayoub taghizadeh

Using water resources require the determination of components of the hydrological cycle. Understanding of natural systems and physical laws that manage each component of the hydrological cycle is important for the water resources management. One of the crucial components of the hydrological cycle is evapotranspiration. Most methods that have been presented in this study use point measurements to estimate evapotranspiration. Due to dynamic and changing nature of regional evapotranspiration is not generalizable to the basin, the applied methods are only suitable for the local areas. Technology of satellite remote sensing and remote sensing-based methods can be used for the temporal and spatial estimation of actual evapotranspiration in the small and large basins. Remote sensing data derived from satellite imagery calculates the amount of actual evapotranspiration using surface energy balances model. In this study, actual evapotranspiration at the Bakhtegan-Maharloo basin were estimated and evaluated using Landsat 8 satellite images sensor OLI/TIRS and SEBS energy balance models. The values of evapotranspiration derived from energy balance model and FAO-Penman-Monteith method were compared.The results showed that the evapotranspiration values obtained from the energy balance model has a root mean square error (RMSE) and mean absolute difference (MAD) equal to 0.62 and 0.49 mm/d, respectively, indicating its acceptable performance to estimate the evapotranspiration

Digging and excessive exploitation of alluvial water sources, create bad condition for many alluvial aquifers in the plains of the country. Given these circumstances, the desire to use the karst sources for which less attention has been paid is increasing. In this study, by considering some karst development criteria such as lithology, altitude, slope, rainfall, temperature, vegetation index, drainage density, lineament density, lineament intersection density, distance from lineament and distance from the intersection of lineaments and applying fuzzy inference system and fuzzy hierarchical analysis, modeling of karst development has been done. The results showed that by the method of fuzzy inference system the development zones with high and very high degrees covered 79.1 percent of the total area, while for the fuzzy hierarchial analysis method it was 26 percent. For weights validation used in the fuzzy hierarchical analysis, ±10%, ±5% sensitivity analyses were done.

Land use changes act as a significant factor in the environmental changes and have become a global threat. Monitoring and prediction these changes by satellite images and models can help the planners and managers to make more conscious planning decisions. In this regard, the current research aimed to monitor, model and predict land use changes using CA-Markov model in Kohmare Sorkhi region, Fars province in 2024 for a period of 25 years (1987-2012). To implement the mentioned model, the land use map was first prepared by ETM and TM sensors during three years (1987, 2000, 2012). Then, validation of maps and change detection process were performed. The results of change detection for the first period (1987-2000) and second period (2000-2012) with an accuracy of 83% and Kappa index of 88% have shown the greatest increase in the rangeland area (4224.24 ha) and the greatest decrease in the forest area (3953.75 ha). In the next stage, in order to calibrate the CA-Markov model, land use map for 2012 was predicted; on the other hand, regarding Error Matrix between the modeling land use map and the reference land use map, the Kappa index wad given as 75%. Finally, considering the previous stage, the land use map for the outlook of 2024 was predicted. The final results for 2024 indicated that the forest area would endure the great amount of changes in comparison with 2012. The forests would change into the irrigated agricultural area and rangelands which can be considered in sustainable development planning by decision makers.

Land surface temperature is a key indicator of energy balance. Besides, it serves as input data for models of climate change, agriculture, meteorology, urban heat islands, choosing the best time to agricultural activities, study of volcanic and geothermal activity, and fire detection. In this study land surface temperature has been extracted by available methods using 4 images of TM and ETM+ sensors of Landsat in span years of 1985 to 2003. The methods of land lurface temperature extraction included landsat project science office, mono window, SEBAL, Stefan-Boltzmann and single channel. Because of the multiplicity of methods and the number of images used in this study using a statistical method is required. It is required to determine the most efficient extraction method of land surface temperature, which is close to the existing field data. The statistical indicator used in this study was a mean absolute error (MAE). The results indicated that Stefan-Boltzmann method was the best method for both TM and ETM+ sensors. The MAE values for TM and ETM+ were 4.3 and 6.8 respectively, which showed a minimum value among other results.