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Bakhtiar Feizizadeh, Ali Khedmat Zadeh, Mohammad Reza Nikjoo,,
Volume 18, Issue 48 (3-2018)
Abstract

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.
 


Miss Rahimeh Rostami, Dr. Ali Mohammad Khorshiddust, Dr. Mohammadreza Nikjoo, Dr. Hassan Mahmudzadeh,
Volume 19, Issue 55 (12-2019)
Abstract

The drying of Lake Urmia has had many environmental impacts on the surrounding areas of the lake. In this research, efforts have been made to identify vegetation coverings that are compatible with the study area and then it use of multiplicative decision-making models for identify areas susceptible to cultivation of these products. In the present study, following the study of species in the region of rapeseed, was selected as a suitable halophytes plant. Initially, using Landsat 5 and 8 images, the changes in the land use type and vegetation cover type of the region were investigated from 2000 to 2016, and after calculating the changes, the potential planting of the halophytes plant was sought. The ANP Fuzzy method was used to estimate the ability to cultivate rapeseed. Main criteria used in this research are topography, soil and meteorology. The topographic sub criteria are included: height, slope and tilt direction, soil criteria including soil texture, soil salinity, and soil pH and soil organic matter. Finally, the criteria for meteorological data are total annual precipitation, Relative humidity, average annual temperature, maximum annual temperature and annual minimum temperature. These layers first be changed to fuzzy and then, applying the weight of each of the following criteria, a map of the main criteria of soil, topography and meteorology was prepared and finally, by combining these three main parameters, the potential mapping was obtained. The results indicate a 25.43 percent reduction in water content and an increase of 21.03 percent in saline areas between 2000 and 2016, and the results of identifying areas susceptible to cultivation of halophytes plants have identified 14.28 percent of the study area suitable for rapeseed cultivation.


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