ORIGINAL RESEARCH
Use of Satellite Images and the Split Window
Algorithm to Detect Fugitive Methane
in Tlalnepantla De Baz Landfill
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1
Facultad de Ingeniería, Universidad Nacional Autónoma de México, Ciudad Universitaria,
Coyoacán, C.P. 04100, Ciudad de México, México
2
Instituto Nacional de Pesca y Acuacultura, SAGARPA, Av. México 190, Del Carmen,
Coyoacán, C.P 04100, Ciudad de México, México
3
Instituto de Geofísica, Universidad Nacional Autónoma de México, Ciudad Universitaria,
Coyoacán, C.P. 04100, Ciudad de México, México
4
Instituto de Ingeniería, Universidad Nacional Autónoma de México, Ciudad Universitaria,
Coyoacán, C.P. 04100, Ciudad de México, México
Submission date: 2022-03-02
Final revision date: 2022-05-25
Acceptance date: 2022-06-24
Online publication date: 2022-10-13
Publication date: 2022-12-08
Pol. J. Environ. Stud. 2022;31(6):5727-5737
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ABSTRACT
Worldwide, the increase in land surface temperature has been attributed to the concentration
of greenhouse gases. However, there is no record of timely information that shows which types of
land cover relate to major increases in surface temperature. The main aim of this paper is to identify
the specific sites in a landfill where biogas is released into the atmosphere. A second objective is
to try to find a spatial correlation between the concentration of methane emitted to the atmosphere
with the observed surface temperature gradients. The recoverable and fugitive methane fluxes were
validated with in situ information, using a LICOR gas accumulation chamber. The surface heat
estimate was obtained from the Split Window algorithm, using the TIRS sensor of the Landsat 8. With
data obtained in previous studies, both in situ and remote, it was possible to spatially correlate the
methane flux released into the atmosphere with the temperature distribution plume within the landfill.
The importance of our research is related to the continuous need for surface temperature monitoring
on the planet. The use of technological tools such as the one presented here reduces the cost
and execution time of environmental studies.