Spatio-temporal variability of aerosol characteristics in Iran using remotely sensed datasets
Authors
Abstract:
The present study is the first attempt to examine temporal and spatial characteristics of aerosol properties and classify their modes over Iran. The data used in this study include the records of Aerosol Optical Depth (AOD) and Angstrom Exponent (AE) from MODerate Resolution Imaging Spectroradiometer (MODIS) and Aerosol Index (AI) from the Ozone Monitoring Instrument (OMI), obtained from 2005 to 2015. The high concentration of AOD and AI values (representing high-high cluster) have been observed in the southwest and east regions, while their low concentrations (representing low-low cluster) have been found in the high mountainous areas. Based on AE values, Iran has been divided into three distinct regions, including fine, mixture, and coarse aerosol modes in each season. Results show that the maximum/minimum area under fine aerosols mode has occurred in the autumn, covering an area of 84.15% and in the spring, covering an area of 40.5%. In the case of coarse mode, the maximum/minimum area has been found in the spring, covered area=53.5% / in the Autumn covered area=12. 5%. The different aerosol modes regions strongly coincide with the topographical structure. To analyze the relation between aerosol properties and topography, Aerosol Properties Index (API) has been developed by combining OMI and MODIS datasets. API is a simple indicator, capable of showing the degree of aerosol coarseness in each pixel. There is a negative correlation between API and topography over the studied region, meaning that aerosol concentrations are high in the lowlands, but low in the highlands. However, this relation differs in various geographic regions, as Geographically Weighted Regression (GWR) model shows a higher determination coefficient in all seasons, in comparison to Ordinary Least Squares (OLS).
similar resources
Modelling Spatio-temporal Pattern of Landuse Change Using Multitemporal Remotely Sensed Imagery
Remotely sensed data is the most important data source for environmental change study over the past 40 years. Since large collections of remote sensing imagery have been acquired in a time frame of successive years, it is now possible to study long-term spatio-temporal pattern of environmental change and impacts of human activities. This study seeks an efficient and practical methodology for la...
full textSpatio-temporal variability in remotely sensed land surface temperature, and its relationship with physiographic variables in the Russian Altay Mountains
Spatio-temporal variability in energy fluxes at the earth’s surface implies spatial and temporal changes in observed Land Surface Temperatures (LST). These fluxes are largely determined by variation in meteorological conditions, surface cover and soil characteristics. Consequently, a change in these parameters will be reflected in a different temporal LST behavior which can be observed by remot...
full textAn investigation into seasonal and regional aerosol characteristics in East Asia using model-predicted and remotely-sensed aerosol properties
1 Dept. of Environmental Science and Engineering, Gwangju Inst. of Science and Technology (GIST), Gwangju, Korea, and also at Advanced Environmental Monitoring Research Center (ADEMRC), Gwangju Inst. of Science and Technology (GIST), Gwangju, Korea 2 Hazardous Substance Research Center, Korea Inst. of Science and Technology (KIST), Seoul, Korea 3 Earth System Science Interdisciplinary Center (E...
full textA Bayesian Data Fusion Approach to Spatio-Temporal Fusion of Remotely Sensed Images
Remote sensing provides rich sources of data for the monitoring of land surface dynamics. However, single-sensor systems are constrained from providing spatially high-resolution images with high revisit frequency due to the inherent sensor design limitation. To obtain images high in both spatial and temporal resolutions, a number of image fusion algorithms, such as spatial and temporal adaptive...
full textSpatio-temporal analysis of remotely-sensed forest mortality associated with road de-icing salts.
Forest mortality along highways has long been a concern in areas where de-icing compounds are applied during winter. This study combined the spatial advantage of high-resolution remote sensing imagery and the temporal advantage of long-term archival imagery to quantify forest mortality and to detect the subtle and chronic effects of road de-icing salts for a large mountain watershed in the Sier...
full textMy Resources
Journal title
volume 4 issue 1
pages 53- 67
publication date 2018-01-01
By following a journal you will be notified via email when a new issue of this journal is published.
Hosted on Doprax cloud platform doprax.com
copyright © 2015-2023