Document Type : Original Article

Authors

• Saeid Hoseinzadeh Khachak ¹
• Omid Rafieyan ²
• Khalil Valizadeh_Kamran ³
• mohammad reza dalalian ⁴
• Gholam hasan Mohammadi ⁵

¹ Department of Environmental science and engineering, Tabriz Branch, Islamic Azad University Tabriz, Iran.

² Department of Environmental Sciences, Tabriz Branch, Islamic Azad University Tabriz, Iran,-Sustainable Development Management Research Center of Urmia lake Basin and Aras River, Tabriz Branch, Islamic Azad University, Tabriz, Iran.

³ Department of Remote Sensing and GIS. Faculty of Planning and Environmental Sciences. University of Tabriz

⁴ Department of Soil Science and Engineering, Tabriz Branch, Islamic Azad University Tabriz, Iran,-Sustainable Development Management Research Center of Urmia lake Basin and Aras River, Tabriz Branch, Islamic Azad University, Tabriz, Iran.

⁵ Ph.D Graduated in Climatology, I.R. of Iran Meteorological Organization, East Azerbaijan Central Bureau, Tabriz. Iran.

Abstract

In addition to natural disasters such as floods, which cause great damage to the environment and human societies, the phenomenon of dust also causes irreparable damage to urban environments, transportation systems, respiratory systems, etc. Identifying Potential Dust Source Areas is considered the first step to control and prevent this phenomenon. Various researches have been conducted to identify dust sources, but most studies have relied on small-scale images. This research aims to use medium-scale satellite images to identify local areas prone to dust production. Google Earth Engine was used to analyze factors influencing dust generation, including slope, Digital Elevation Model (DEM), land use, soil salinity, Normalized Difference Vegetation Index (NDVI), soil moisture, wind speed, precipitation, and Land Surface Temperature (LST). Analytical Hierarchy Process (AHP) was used to assign weights to these elements. The resulting weights were: soil moisture (0.264), NDVI (0.208), wind speed (0.153), precipitation (0.107), land use/soil salinity (0.081), LST (0.064), DEM (0.024), and slope (0.020). The inconsistency index (0.015) indicated a high degree of consistency between the assigned weights, which is below the acceptable threshold (1). Due to the lack of ground-based air quality measurements, the Aerosol Optical Depth product (a satellite-derived measurement of airborne particles) was used to validate the resulting dust source maps. The final map showed that the potential for dust generation decreased closer to the heights of Sahand and increased closer to the lake.

Keywords

• Dust
• Urmia Lake
• Geographical Information System
• Google Earth Engine
• AHP
• Fuzzy Logic

Main Subjects

• Remote sensing and Gis