Investigating the impact of land use/land cover change trends on the status of groundwater resources using satellite images, GIS, and GS+ | ||
| هیدروژئومورفولوژی | ||
| Article 2, Volume 13, Issue 46, April 2026, Pages 40-20 PDF (1.02 M) | ||
| Document Type: Original Article | ||
| DOI: 10.22034/hyd.2025.66746.1790 | ||
| Authors | ||
| Batool Zeinali* 1; , Maryam Mohammadzadeh shishehgaean2 | ||
| 1Professor of Climatology, Department of physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili | ||
| 2Department of Physical Geography, Faculty of Social Science, University of Mohaghegh Ardabili, Ardabil, Iran | ||
| Abstract | ||
| The research steps were as follows: after preparing piezometric well statistics, the data highlighting method was used to eliminate the deficiencies in the study data. The highlighting method used was the interpolation method, which was performed by Neural Power software (based on artificial neural networks), to eliminate the deficiencies in the data. Logarithmic transformation was used in SPSS software to normalize the data, and GS+ software was used for geostatistical analyses. ENVI5.3 software and radiance and flash methods were used for atmospheric, radiometric, and geometric corrections, and GIS10.5 software was used to extract the desired maps. Object-oriented classification method was used in eCognition Developer64 software to classify land use. In the object-oriented classification method, spectral information is combined with spatial information and pixels are segmented based on shape, texture and gray tone in the image surface with a specific scale and image classification is performed based on these segments (Faizizadeh & Hilali, 2010: 77). In segmentation, pixels are segmented by different algorithms in different sizes, with different spectral and shape ratios and are classified into various objects based on spectral and spatial characteristics. During this process, image objects are created according to their homogeneity or heterogeneity based on scale, color, shape, smoothness coefficient and compression shape parameters. | ||
| Keywords | ||
| Object-oriented classification; groundwater; geostatistical methods; Marand | ||
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