نوع مقاله : پژوهشی
نویسندگان
1 کارشناس ارشد گروه بیابان زدایی دانشکدة کویرشناسی، دانشگاه سمنان، سمنان، ایران
2 استادیار گروه مدیریت مناطق خشک و بیابانی، دانشکدة کویرشناسی، دانشگاه سمنان، سمنان، ایران
3 استادیار گروه بیابان زدایی، دانشکدة کویرشناسی، دانشگاه سمنان، سمنان، ایران
4 دانشیار پژوهشکدة حفاظت خاک و آبخیزداری، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران، ایران
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Introduction and Goal
The process of identifying landforms is a subject that has been studied by many researchers. All geomorphological definitions are based on the study and identification of landforms. Understanding landforms and their distribution is a fundamental need of applied geomorphology and other environmental sciences. In this regard, remote sensing technology, due to the production of satellite images with high spatial and spectral resolution, can be a valuable tool for identifying and classifying landforms. Desert pavement is one of the most important landforms in arid and desert regions. Mapping pavements and their types provides a basis for evaluating the region in terms of its structural and geomorphological characteristics, which is useful in many environmental management and planning issues and can be used as a model for similar regions. In this research, using ETM+ sensor data and based on the considered criteria, the characteristics of desert pavements in Semnan township were identified and classified.
Materials and Methods
The study area, with an area of 47645.98 hectares, is located in Semnan township. The geographical coordinates of the region are 28˚53¢ to 43˚53¢ east longitude and 20˚35¢ to 40˚35¢ north latitude. The aim of this study was to investigate, separate, and identify desert pavement classes as a type of desert landform using remote sensing and Landsat ETM+ satellite images in southern Semnan. Therefore, by conducting field surveys and sampling of the study area, the percentage of desert pavement cover density was measured and the location of each sample was recorded with GPS. Support vector machines, neural networks, spectral angle maps, spectral information divergence, and fuzzy artmaps were used to classify desert pavements in Envi 4.5 and IDRISI Selva software environments. Then, the accuracy of each classification of each method was compared with the training samples using the coefficients of complete accuracy, kappa, user accuracy, and producer accuracy. Finally, the spatial zoning map of each method was drawn in the Arc GIS 10.2 software environment.
Results and discussion
In this study, the best band combination for detecting and separating desert pavements in southern Semnan was the 6-4-3 band combination with an optimal index factor of 45.71 (OIF), which was in the mid-infrared and visible bands (VNIR + TIR). Based on the kappa coefficient, the support vector machine (85.05), fuzzy artmap (81.44), neural network (55.17), spectral angle map (53.89), and spectral information divergence (50.22) methods had the highest ability in spectral separation of different classes of desert pavements in southern Semnan, respectively. The support vector machine and fuzzy artmap classification methods obtained the highest kappa coefficients for the classes, respectively, and the lowest kappa coefficients and complete accuracy were obtained in the neural network, spectral angle map, and spectral information divergence methods, respectively. Since the classes, bands, and other conditions used were the same for all methods, the difference in accuracy depended only on the calculation instructions of the methods.
Conclusions and suggestions
Remote sensing technology, due to the production of satellite images with high spatial and spectral resolution, can be a valuable tool for identifying and classifying landforms. Preparing a map of desert pavements and their types is a basis for evaluating the region in terms of structural and geomorphological characteristics, which can be useful in many environmental management and planning issues. Therefore, it is suggested that other classification methods based on shear orientation, combined methods, frequency coverage, images from sensors with better spatial and spectral resolution, and considering characteristics such as particle diameter be used to be effective in preparing maps of desert pavement layers.
کلیدواژه‌ها [English]
)