Spatial and temporal changes of land uses and its relationship with surface temperature in western Iran

A split-window algorithm has been used in the Ilam dam watershed to determine the relationship between land surface temperature (LST) and types of land use. Landsat satellite images of the TM sensor for 1990, 1995, 2000, 2005 and 2010 and Landsat 8 (OLI Sensor) for 2015 and 2018 are used. After geom...

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Detalles Bibliográficos
Autores: Rostami, Noredin, Fathizad, Hassan
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:México
Institución:UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO
Repositorio:Atmósfera
Idioma:inglés
OAI Identifier:oai:ojs.pkp.sfu.ca:article/52985
Acceso en línea:https://www.revistascca.unam.mx/atm/index.php/atm/article/view/52985
Access Level:acceso abierto
Palabra clave:Landsat satellite
Split-Window algorithm
Fuzzy ARTMAP
Kappa coefficient
Ilam dam watershed
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spelling Spatial and temporal changes of land uses and its relationship with surface temperature in western IranRostami, NoredinFathizad, HassanLandsat satelliteSplit-Window algorithmFuzzy ARTMAPKappa coefficientIlam dam watershedA split-window algorithm has been used in the Ilam dam watershed to determine the relationship between land surface temperature (LST) and types of land use. Landsat satellite images of the TM sensor for 1990, 1995, 2000, 2005 and 2010 and Landsat 8 (OLI Sensor) for 2015 and 2018 are used. After geometric and radiometric corrections of satellite images, land use maps are extracted by using the fuzzy ARTMAP method. An accuracy assessment showed that the highest value of the kappa coefficient was 94% with a total accuracy of 0.95 for 2015, and the lowest kappa coefficient value was 87% with a total accuracy of 0.9 for 1990. The high values of these coefficients indicate the acceptable accuracy of using Landsat’s remote sensing data for land use detection. The most important land use change is related to dense forest and sparse forest land uses, with decreases of 20.07 and 17.04%, respectively. The minimum LST measures in 1990, 2010, and 2018 in dense forest are 21.27, 30.55 and 33.82 ºC, respectively. The maximum LSTs for the sparse forest land use in 1990 and 2010 are 52.48 and 56.09, and 56.10 ºC for the dense forest land use in 2018. As a result, the average LST in agricultural lands was lower than in sparse forest and rangeland;, which is mainly due to the high moisture content and the greater evapotranspiration rate. Land use/land cover variations from 1990 to 2018 show that all land uses have experienced an increase in LST.Instituto de Ciencias de la Atmósfera y Cambio Climático, Universidad Nacional Autónoma de México2022-04-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://www.revistascca.unam.mx/atm/index.php/atm/article/view/5298510.20937/ATM.52985Atmósfera; Vol. 35 Núm. 4 (2022); 701-717Atmósfera; Vol. 35 No. 4 (2022); 701-7172395-88120187-6236reponame:Atmósferainstname:UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICOinstacron:UNAMenghttps://www.revistascca.unam.mx/atm/index.php/atm/article/view/52985/46778Copyright (c) 2021 Atmósferahttp://creativecommons.org/licenses/by-nc/4.0info:eu-repo/semantics/openAccessoai:ojs.pkp.sfu.ca:article/529852024-08-16T16:52:41Z
dc.title.none.fl_str_mv Spatial and temporal changes of land uses and its relationship with surface temperature in western Iran
title Spatial and temporal changes of land uses and its relationship with surface temperature in western Iran
spellingShingle Spatial and temporal changes of land uses and its relationship with surface temperature in western Iran
Rostami, Noredin
Landsat satellite
Split-Window algorithm
Fuzzy ARTMAP
Kappa coefficient
Ilam dam watershed
title_short Spatial and temporal changes of land uses and its relationship with surface temperature in western Iran
title_full Spatial and temporal changes of land uses and its relationship with surface temperature in western Iran
title_fullStr Spatial and temporal changes of land uses and its relationship with surface temperature in western Iran
title_full_unstemmed Spatial and temporal changes of land uses and its relationship with surface temperature in western Iran
title_sort Spatial and temporal changes of land uses and its relationship with surface temperature in western Iran
dc.creator.none.fl_str_mv Rostami, Noredin
Fathizad, Hassan
author Rostami, Noredin
author_facet Rostami, Noredin
Fathizad, Hassan
author_role author
author2 Fathizad, Hassan
author2_role author
dc.subject.none.fl_str_mv Landsat satellite
Split-Window algorithm
Fuzzy ARTMAP
Kappa coefficient
Ilam dam watershed
topic Landsat satellite
Split-Window algorithm
Fuzzy ARTMAP
Kappa coefficient
Ilam dam watershed
description A split-window algorithm has been used in the Ilam dam watershed to determine the relationship between land surface temperature (LST) and types of land use. Landsat satellite images of the TM sensor for 1990, 1995, 2000, 2005 and 2010 and Landsat 8 (OLI Sensor) for 2015 and 2018 are used. After geometric and radiometric corrections of satellite images, land use maps are extracted by using the fuzzy ARTMAP method. An accuracy assessment showed that the highest value of the kappa coefficient was 94% with a total accuracy of 0.95 for 2015, and the lowest kappa coefficient value was 87% with a total accuracy of 0.9 for 1990. The high values of these coefficients indicate the acceptable accuracy of using Landsat’s remote sensing data for land use detection. The most important land use change is related to dense forest and sparse forest land uses, with decreases of 20.07 and 17.04%, respectively. The minimum LST measures in 1990, 2010, and 2018 in dense forest are 21.27, 30.55 and 33.82 ºC, respectively. The maximum LSTs for the sparse forest land use in 1990 and 2010 are 52.48 and 56.09, and 56.10 ºC for the dense forest land use in 2018. As a result, the average LST in agricultural lands was lower than in sparse forest and rangeland;, which is mainly due to the high moisture content and the greater evapotranspiration rate. Land use/land cover variations from 1990 to 2018 show that all land uses have experienced an increase in LST.
publishDate 2022
dc.date.none.fl_str_mv 2022-04-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://www.revistascca.unam.mx/atm/index.php/atm/article/view/52985
10.20937/ATM.52985
url https://www.revistascca.unam.mx/atm/index.php/atm/article/view/52985
identifier_str_mv 10.20937/ATM.52985
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://www.revistascca.unam.mx/atm/index.php/atm/article/view/52985/46778
dc.rights.none.fl_str_mv Copyright (c) 2021 Atmósfera
http://creativecommons.org/licenses/by-nc/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2021 Atmósfera
http://creativecommons.org/licenses/by-nc/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Instituto de Ciencias de la Atmósfera y Cambio Climático, Universidad Nacional Autónoma de México
publisher.none.fl_str_mv Instituto de Ciencias de la Atmósfera y Cambio Climático, Universidad Nacional Autónoma de México
dc.source.none.fl_str_mv Atmósfera; Vol. 35 Núm. 4 (2022); 701-717
Atmósfera; Vol. 35 No. 4 (2022); 701-717
2395-8812
0187-6236
reponame:Atmósfera
instname:UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO
instacron:UNAM
instname_str UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO
instacron_str UNAM
institution UNAM
reponame_str Atmósfera
collection Atmósfera
repository.name.fl_str_mv
repository.mail.fl_str_mv
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