Mohammad Zeinali; Ali Asghar Jaafarzadeh; Farzin Shahbazi; Shahin Oustan; Khalil Valizadeh Kamran
Abstract
Abstract
Soil salinity and salinization of lands as one of the problems facing agriculture, has paramount importance and should be avoided with proper knowledge of its progress. The first step in this way is to identify saline areas and prepare the salinity maps for these soils. With the development ...
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Abstract
Soil salinity and salinization of lands as one of the problems facing agriculture, has paramount importance and should be avoided with proper knowledge of its progress. The first step in this way is to identify saline areas and prepare the salinity maps for these soils. With the development of remote sensing technology and efficient use of satellite imaging, this research aimed to compare the prepared salinity maps with various types of image classification algorithms (Maximum probability, Minimum distance from the mean and Parallelepiped) by Landsat-5 satellite data with TM sensor in a part of the eastern lands of Khoy city. Therefore, 269 soil samples were analyzed with specific geographic coordinates and the results were plotted on TM image. For initial identification, topographic maps and ENVI 4.8 software were used to process satellite images and geometric corrections were made with specific points using GPS. Educational and experimental samples were located on the desired image with an appropriate distribution and salinity classes were determined from 1 to 9. Samples of each class of salinity due to having coordinates were placed accurately and with single pixel size in each image on the corresponding pixel and were stored with ROI format. The results indicate the existence of correlation between bands 1, 4, and 5 of TM image with salinity data, and the highest accuracy of the map among the classification algorithms in the Pixel-based method, is related to the maximum probability. In order to evaluate the accuracy, indices such as error matrix, Producer’s veracity, User’s authenticity, overall accuracy, and kappa Coefficient were extracted. Also, the correspondence of various salinity classes of this map with field observations and measured salinity level indicate the high accuracy of this algorithm in preparing a surface soil salinity map. The aim of the present study is to compare the prepared salinity maps with the results of other researchers by these methods in the area of interest.
