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The basic purpose of this research is to reveal the physical development process of the new city of Sahand, as one of the new cities of the country, using Landsat satellite images during the statistical period of 1401-1373. In this regard, satellite images required for 4 statistical periods of 1373, 1383, 1393, and 1401 were obtained from two Landsat 5 and 8 satellites. By running a band calculation function on the images of TM and OLI sensors, the values ​​of the physical changes of the urban fabric during the investigated time steps in Sahand city were calculated and extracted. The results of this research indicated that the physical growth and development of the city of Sahand has started since 2013. This year, the area of ​​the urban fabric has reached 282 hectares, which is a 28-fold increase compared to 2013. But in the next decade, i.e. 2013, the area of ​​the city reached 570 hectares with a 100% growth compared to the previous decade, and finally, in the last decade, the area of ​​the city reached 850 hectares with a growth rate of 50%. District 6 of Sahand city, which accounts for about 35% of the physical fabric of the city, has been one of the fastest growing areas of the city during the decades of 1393-1400. Considering that a significant correlation at the confidence level of 0.95 (P_value=0.05) was revealed between the population growth and the physical development of Sahand during the statistical period of 1380-1400 (R=0.91), therefore, the fitted regression model between the population growth And the growth of the urban fabric, by placing the proposed population density of this city after the implementation of Mehr housing policies (185 thousand people), it showed that the area of ​​the physical fabric of this city will reach 1181 hectares in the next decade and will face a growth of 38%.
 

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Remote sensing technology is one of the most efficient and innovative technologies for agricultural land use/cover mapping. In this regard, the object-based Image Analysis (OBIA) is known as a new method of satellite image processing which integrates spatial and spectral information for satellite image process. This approach make use of spectral, environmental, physical and geometrical characteristics (e.g. texture, shape) together images contexts for modeling of land use/cover classes. The main objective of this study is to classify micro land use/cover of Meyandoab County by applying appropriate and effective algorithms and parameters in the object based approach. For this goal, Quick Bird and Aster satellite images were used within the integrated approach for processing and land use modeling. Accordingly, the land use map was classified in 9 class based on spectral and spatial characteristics.  In order to perform OBIA, the segmentation was applied in the scale of 10, shape parameter of 0.7 as well as the compactness of 0.3. In terms of the classification task, fuzzy based algorithm and operators (AND, OR) was applied to detriment the membership functionality of segments for each class as well as classifying the related objects.  We also applied textures, geometric, NDVI, GLCM, brightness algorithms based on fuzzy operators and assign class algorithm. In order to applying the validation of results, the accuracy assessment step was performed and the finally overall accuracy of 93.6 was obtained for the derived map. The Kappa coefficient was also detriment to be 0.92. The area under cultivation included respectively for lands of wheat and barley, prunes and plums, apples, vineyards and alfalfa hay2622.42, 4505, 4354.55, 4457.85, 14110.58 hectares.
 

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The cognition of cropping pattern is important for planning and resource management .Remote sensing as a science and technology of spatial information and geographic information system due to having the analytical facilities can play a key role in determining the distribution of crops and their lands under cultivation. In this research, in order to identify and separate the lands under cultivation of the dominant crops in Lenjanat of Isfahan province, the multi-temporal images of Landsat 8 satellite, OLI sensor were used in the dates of April 17, July 6, and August 23 in 2016. Using maximum likelihood classification and normalized difference vegetation index (NDVI) of the agriculture crops in different periods of growth and according to their cropping calendar, the map of the cropping pattern of the area was determined. To evaluate the accuracy of the results, the produced maps were examined with reference data. Kappa coefficient and overall accuracy were 0.88 and 90%, respectively, in maximum likelihood classification, and 0.90 and 93%, respectively, in NDVI. Furthermore, statistics presented by Agricultural Jihad Organization of Isfahan province in the 2015-2016 crop year was used for evaluation. The results showed that there were differences equal to 10.2%, 18.6% and 1.8%, in the area under cultivation of wheat and barley, rice, and potato and forage, respectively, in maximum likelihood classification, comparing with the statistics of Agriculture Jihad while the results of NDVI comparing with Jihad statistics showed the errors equal to 6.6 %, 6.5 % and 3.2%, respectively, that indicated the better performance of temporal vegetation indices in estimation of area under cultivation according to its phenology. Investigation of land use and cropping pattern of this area indicate a high centralization of agricultural lands with high water requirements and industries on the proximity of Zayanderud River which necessitates the spatial analysis of land use in this area.

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This article was written with the aim of revealing land use changes in Urmia city using remote sensing of Landsat satellite images for 4 periods of 8 years between 1990 and 2019. For this purpose, two categories of data will be used in this research. The first category includes data obtained from satellite images and the second category includes ground data taken from Urmia ground station, which includes temperature and other parameters used in this research. The results showed that urban land use in Urmia city has faced significant changes during the statistical period of 30 years. This user has had an increasing trend during all the studied periods, so that during the study period, it has faced a 5-fold increase. Swampy areas and sludge fields east of Lake Urmia have undergone a significant decline during 1990-2019 and has reached less than 6,000 hectares. The citychr('39')s barren lands, which cover a small percentage of the citychr('39')s area, have been declining over the 30-year period under review. The use of gardens has increased during all periods, so that in 2019, its area has reached more than 20,000 hectares. The use of irrigated agriculture has increased during all the studied periods and its area has reached more than 80,000 hectares by 2019. The area of ​​rainfed agricultural lands, after the rangelands, is the widest land use in Urmia, but with a relatively gentle slope has a decreasing trend. Water areas have also been declining, so that in 2019, it has decreased by about 26% compared to 2012. Rangelands, which is the largest land cover in Urmia city, has gone through three different processes during the study period. From 1990 to 1998, these lands did not change significantly, but from 1998 to 2005, the increasing trend and in 2019, with a 10% decrease compared to 2012, reached its lowest area during the statistical period under study, ie less than 20,000 hectares.