alexa Estimation of PM10 Concentration using Ground Measureme
ISSN: 2469-4134

Journal of Remote Sensing & GIS
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Research Article

Estimation of PM10 Concentration using Ground Measurements and Landsat 8OLI Satellite Image

Salah Abdul Hameed Saleh1* and Ghada Hasan2
1Nahrain University, Baghdad, Iraq
2University of Technology, Baghdad, Iraq
Corresponding Author : Salah Abdul Hameed Saleh
Nahrain University, Baghdad, Iraq
Tel: 964-1-776-7810
E-mail: [email protected]
Received March 23, 2014; Accepted April 21, 2014; Published April 25, 2014
Citation: Saleh SAH, Hasan G (2014) Estimation of PM10 Concentration using Ground Measurements and Landsat 8 OLI Satellite Image. J Geophys Remote Sens 3:120. doi:10.4172/2169-0049.1000120
Copyright: © 2014 Saleh et al., This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
 

Abstract

The aim of this work is to produce an empirical model for the determination of particulate matter (PM10) concentration in the atmosphere using visible bands of Landsat 8 OLI satellite image over Kirkuk city- Iraq.

The suggested algorithm is established on the aerosol optical reflectance model. The reflectance model is a function of the optical properties of the atmosphere, which can be related to its concentrations.

The concentration of PM10 measurements was collected using Particle Mass Profiler and Counter in a Single Handheld Unit (Aerocet 531) meter simultaneously by the Landsat 8 OLI satellite image date. The PM10 measurement locations were defined by a handheld global positioning system (GPS).

The obtained reflectance values for visible bands (Coastal aerosol, Blue, Green and blue bands) of landsat 8 OLI image were correlated with in-suite measured PM10.

The feasibility of the proposed algorithms was investigated based on the correlation coefficient (R) and rootmean- square error (RMSE) compared with the PM10 ground measurement data. A choice of our proposed multispectral model was founded on the highest value correlation coefficient (R) and lowest value of the root mean square error (RMSE) with PM10 ground data. The outcomes of this research showed that visible bands of Landsat 8 OLI were capable of calculating PM10 concentration to an acceptable level of accuracy.

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