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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| Format: | Article |
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MDPI AG
2021-11-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/12/11/1540 |
| _version_ | 1797511217914314752 |
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| author | Zhengwu Cai Chao Fan Falin Chen Xiaoma Li |
| author_facet | Zhengwu Cai Chao Fan Falin Chen Xiaoma Li |
| author_sort | Zhengwu Cai |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:42:13Z |
| format | Article |
| id | doaj.art-6c62c51507b846c7a5357fe9b5709151 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-10T05:42:13Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-6c62c51507b846c7a5357fe9b57091512023-11-22T22:25:57ZengMDPI AGAtmosphere2073-44332021-11-011211154010.3390/atmos12111540Pseudo-Invariant Feature-Based Linear Regression Model (PIF-LRM): An Effective Normalization Method to Evaluate Urbanization Impacts on Land Surface Temperature ChangesZhengwu Cai0Chao Fan1Falin Chen2Xiaoma Li3Hunan Provincial Key Laboratory of Rural Ecosystem Health in Dongting Lake Area, College of Resources and Environment, Hunan Agricultural University, Changsha 410128, ChinaDepartment of Computer Sciences, University of Idaho, Moscow, ID 83844, USAHunan Provincial Key Laboratory of Rural Ecosystem Health in Dongting Lake Area, College of Resources and Environment, Hunan Agricultural University, Changsha 410128, ChinaHunan Provincial Key Laboratory of Landscape Ecology and Planning & Design in Regular Higher Educational Institutions, College of Landscape Architecture and Art Design, Hunan Agricultural University, Changsha 410128, ChinaThe 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.https://www.mdpi.com/2073-4433/12/11/1540spatiotemporal changepseudo-invariant featurerelative normalizationurban expansionurban heat island |
| spellingShingle | 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 Atmosphere spatiotemporal change pseudo-invariant feature relative normalization urban expansion urban heat island |
| title | 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_short | 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 |
| topic | spatiotemporal change pseudo-invariant feature relative normalization urban expansion urban heat island |
| url | https://www.mdpi.com/2073-4433/12/11/1540 |
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