Pseudo-Invariant Feature-Based Linear Regression Model (PIF-LRM): An Effective Normalization Method to Evaluate Urbanization Impacts on Land Surface Temperature Changes

The Landsat land surface temperature (LST) product is widely used to understand the impact of urbanization on surface temperature changes. However, directly comparing multi-temporal Landsat LST is challenging, as the observed LST might be strongly affected by climatic factors. This study validated t...

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Autores principales:	Zhengwu Cai, Chao Fan, Falin Chen, Xiaoma Li
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	spatiotemporal change pseudo-invariant feature relative normalization urban expansion urban heat island Meteorology. Climatology QC851-999
Acceso en línea:	https://doaj.org/article/6c62c51507b846c7a5357fe9b5709151
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id	oai:doaj.org-article:6c62c51507b846c7a5357fe9b5709151
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spelling	oai:doaj.org-article:6c62c51507b846c7a5357fe9b57091512021-11-25T16:46:01ZPseudo-Invariant Feature-Based Linear Regression Model (PIF-LRM): An Effective Normalization Method to Evaluate Urbanization Impacts on Land Surface Temperature Changes10.3390/atmos121115402073-4433https://doaj.org/article/6c62c51507b846c7a5357fe9b57091512021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4433/12/11/1540https://doaj.org/toc/2073-4433The Landsat land surface temperature (LST) product is widely used to understand the impact of urbanization on surface temperature changes. However, directly comparing multi-temporal Landsat LST is challenging, as the observed LST might be strongly affected by climatic factors. This study validated the utility of the pseudo-invariant feature-based linear regression model (PIF-LRM) in normalizing multi-temporal Landsat LST to highlight the urbanization impact on temperature changes, based on five Landsat LST images during 2000–2018 in Changsha, China. Results showed that LST of PIFs between the reference and the target images was highly correlated, indicating high applicability of the PIF-LRM to relatively normalize LST. The PIF-LRM effectively removed the temporal variation of LST caused by climate factors and highlighted the impacts of urbanization caused land use and land cover changes. The PIF-LRM normalized LST showed stronger correlations with the time series of normalized difference of vegetation index (NDVI) than the observed LST and the LST normalized by the commonly used mean method (subtracting LST by the average, respectively for each image). The PIF-LRM uncovered the spatially heterogeneous responses of LST to urban expansion. For example, LST decreased in the urban center (the already developed regions) and increased in the urbanizing regions. PIF-LRM is highly recommended to normalize multi-temporal Landsat LST to understand the impact of urbanization on surface temperature changes from a temporal point of view.Zhengwu CaiChao FanFalin ChenXiaoma LiMDPI AGarticlespatiotemporal changepseudo-invariant featurerelative normalizationurban expansionurban heat islandMeteorology. ClimatologyQC851-999ENAtmosphere, Vol 12, Iss 1540, p 1540 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	spatiotemporal change pseudo-invariant feature relative normalization urban expansion urban heat island Meteorology. Climatology QC851-999
spellingShingle	spatiotemporal change pseudo-invariant feature relative normalization urban expansion urban heat island Meteorology. Climatology QC851-999 Zhengwu Cai Chao Fan Falin Chen Xiaoma Li Pseudo-Invariant Feature-Based Linear Regression Model (PIF-LRM): An Effective Normalization Method to Evaluate Urbanization Impacts on Land Surface Temperature Changes
description	The Landsat land surface temperature (LST) product is widely used to understand the impact of urbanization on surface temperature changes. However, directly comparing multi-temporal Landsat LST is challenging, as the observed LST might be strongly affected by climatic factors. This study validated the utility of the pseudo-invariant feature-based linear regression model (PIF-LRM) in normalizing multi-temporal Landsat LST to highlight the urbanization impact on temperature changes, based on five Landsat LST images during 2000–2018 in Changsha, China. Results showed that LST of PIFs between the reference and the target images was highly correlated, indicating high applicability of the PIF-LRM to relatively normalize LST. The PIF-LRM effectively removed the temporal variation of LST caused by climate factors and highlighted the impacts of urbanization caused land use and land cover changes. The PIF-LRM normalized LST showed stronger correlations with the time series of normalized difference of vegetation index (NDVI) than the observed LST and the LST normalized by the commonly used mean method (subtracting LST by the average, respectively for each image). The PIF-LRM uncovered the spatially heterogeneous responses of LST to urban expansion. For example, LST decreased in the urban center (the already developed regions) and increased in the urbanizing regions. PIF-LRM is highly recommended to normalize multi-temporal Landsat LST to understand the impact of urbanization on surface temperature changes from a temporal point of view.
format	article
author	Zhengwu Cai Chao Fan Falin Chen Xiaoma Li
author_facet	Zhengwu Cai Chao Fan Falin Chen Xiaoma Li
author_sort	Zhengwu Cai
title	Pseudo-Invariant Feature-Based Linear Regression Model (PIF-LRM): An Effective Normalization Method to Evaluate Urbanization Impacts on Land Surface Temperature Changes
title_short	Pseudo-Invariant Feature-Based Linear Regression Model (PIF-LRM): An Effective Normalization Method to Evaluate Urbanization Impacts on Land Surface Temperature Changes
title_full	Pseudo-Invariant Feature-Based Linear Regression Model (PIF-LRM): An Effective Normalization Method to Evaluate Urbanization Impacts on Land Surface Temperature Changes
title_fullStr	Pseudo-Invariant Feature-Based Linear Regression Model (PIF-LRM): An Effective Normalization Method to Evaluate Urbanization Impacts on Land Surface Temperature Changes
title_full_unstemmed	Pseudo-Invariant Feature-Based Linear Regression Model (PIF-LRM): An Effective Normalization Method to Evaluate Urbanization Impacts on Land Surface Temperature Changes
title_sort	pseudo-invariant feature-based linear regression model (pif-lrm): an effective normalization method to evaluate urbanization impacts on land surface temperature changes
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/6c62c51507b846c7a5357fe9b5709151
work_keys_str_mv	AT zhengwucai pseudoinvariantfeaturebasedlinearregressionmodelpiflrmaneffectivenormalizationmethodtoevaluateurbanizationimpactsonlandsurfacetemperaturechanges AT chaofan pseudoinvariantfeaturebasedlinearregressionmodelpiflrmaneffectivenormalizationmethodtoevaluateurbanizationimpactsonlandsurfacetemperaturechanges AT falinchen pseudoinvariantfeaturebasedlinearregressionmodelpiflrmaneffectivenormalizationmethodtoevaluateurbanizationimpactsonlandsurfacetemperaturechanges AT xiaomali pseudoinvariantfeaturebasedlinearregressionmodelpiflrmaneffectivenormalizationmethodtoevaluateurbanizationimpactsonlandsurfacetemperaturechanges
_version_	1718412959954239488

Pseudo-Invariant Feature-Based Linear Regression Model (PIF-LRM): An Effective Normalization Method to Evaluate Urbanization Impacts on Land Surface Temperature Changes

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