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Estimation of PM10 Distribution using Landsat5 and Landsat8 Remote Sensing

1
Energy Department, University of Oviedo, 33600 Oviedo, Spain
2
Biology of Organisms and Systems Department, University of Oviedo, 33600 Oviedo, Spain
3
TSK Company, Gijón 33203, Spain
*
Author to whom correspondence should be addressed.
Presented at the 2nd International Research Conference on Sustainable Energy, Engineering, Materials and Environment (IRCSEEME), Mieres, Spain, 25–27 July 2018.
Proceedings 2018, 2(23), 1430; https://doi.org/10.3390/proceedings2231430
Published: 31 October 2018
Air pollution is one of the major environmental problems, especially in industrial and highly populated areas. Remote sensing image is a rich source of information with many uses. This paper is focused on estimation of air pollutants using Landsat-5 TM and Landsat-8 OLI satellite images. Particulate Matter with particle size less than 10 microns (PM10) is estimated for the study area of Principado de Asturias (Spain). When a satellite records the radiance of the surface received at sensor, does not represent the true radiance of the surface. A noise caused by Aerosol and Particulate Matters attenuate that radiance. In many applications of remote sensing, that noise called path radiance is removed during pre-processing. Instead, path radiance was used to estimate the PM10 concentration in the air. A relationship between the path radiance and PM10 measurements from ground stations has been established using Random Forest (RF) algorithm and a PM10 map was generated for the study area. The results show that PM10 estimation through satellite image is an efficient technique and it is suitable for local and regional studies.
Keywords: Landsat; PM10; remote sensing; air quality; atmospheric pollution Landsat; PM10; remote sensing; air quality; atmospheric pollution
MDPI and ACS Style

Fernández-Pacheco, V.M.; López-Sánchez, C.A.; Álvarez-Álvarez, E.; López, M.J.S.; García-Expósito, L.; Yudego, E.A.; Carús-Candás, J.L. Estimation of PM10 Distribution using Landsat5 and Landsat8 Remote Sensing. Proceedings 2018, 2, 1430.

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