Multi-Index Assessment of Surface Urban Heat Island (SUHI) Dynamics in Samsun Using Google Earth Engine
Urbanization has emerged as a significant driver of environmental change, particularly impacting local climates through the creation of urban heat islands (SUHIs). SUHIs, characterized by higher temperatures in urban or metropolitan areas than in their rural surroundings, have become a critical focu...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/2073-4433/16/6/712 |
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| author | Yiğitalp Kara Veli Yavuz Anthony R. Lupo |
| author_facet | Yiğitalp Kara Veli Yavuz Anthony R. Lupo |
| author_sort | Yiğitalp Kara |
| collection | DOAJ |
| description | Urbanization has emerged as a significant driver of environmental change, particularly impacting local climates through the creation of urban heat islands (SUHIs). SUHIs, characterized by higher temperatures in urban or metropolitan areas than in their rural surroundings, have become a critical focus of urban climate studies. This study aims to examine the spatial and temporal dynamics of both thermal and vegetative indices (BT, LST, NDVI, NDBI, BUI, ECI, SUHI, UTFVI) across different land cover types in Samsun, Türkiye, in order to assess their contribution to the urban heat island effect. Specifically, brightness temperature (BT), land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), built-up index (BUI), environmental condition index (ECI), urban heat island (SUHI) intensity, and urban thermal field variance index (UTFVI) were calculated and assessed. The analysis utilized cloud-free Landsat 8 imagery sourced from the US Geological Survey via the Google Earth Engine platform, employing a one-year median for each pixel using a cloud masking algorithm. Land use and land cover (LULC) classification was conducted using the random forest (RF) algorithm with satellite composite imagery, achieving an overall accuracy of 85% for 2014 and 86% for 2023. This study provides a detailed analysis of the effects of various land use and cover types on temperature, vegetation, and structural characteristics, revealing the role of changes in different land types on the urban heat island effect. In the LULC classification, water bodies consistently maintained low LST values below 23 °C for both years, while built-up land exhibited the greatest temperature increase, from approximately 25 °C in 2014 to more than 31 °C in 2023. The analysis also revealed that LST varies with the size and type of vegetation, with a mean LST differential between all green spaces and urban areas averaging 7–8 °C, and differences reaching 12 °C in industrial zones. |
| format | Article |
| id | doaj-art-6fddc3eefa204e9fadad2b08c89792f4 |
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| issn | 2073-4433 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Atmosphere |
| spelling | doaj-art-6fddc3eefa204e9fadad2b08c89792f42025-08-20T02:24:22ZengMDPI AGAtmosphere2073-44332025-06-0116671210.3390/atmos16060712Multi-Index Assessment of Surface Urban Heat Island (SUHI) Dynamics in Samsun Using Google Earth EngineYiğitalp Kara0Veli Yavuz1Anthony R. Lupo2Department of Meteorological Engineering, Faculty of Aeronautics and Astronautics, Istanbul Technical University, 34469 Istanbul, TürkiyeDepartment of Meteorological Engineering, Faculty of Aeronautics and Astronautics, Samsun University, 55420 Samsun, TürkiyeAtmospheric Science Program, School of Natural Resources, University of Missouri, 302 E ABNR, Columbia, MO 65211, USAUrbanization has emerged as a significant driver of environmental change, particularly impacting local climates through the creation of urban heat islands (SUHIs). SUHIs, characterized by higher temperatures in urban or metropolitan areas than in their rural surroundings, have become a critical focus of urban climate studies. This study aims to examine the spatial and temporal dynamics of both thermal and vegetative indices (BT, LST, NDVI, NDBI, BUI, ECI, SUHI, UTFVI) across different land cover types in Samsun, Türkiye, in order to assess their contribution to the urban heat island effect. Specifically, brightness temperature (BT), land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), built-up index (BUI), environmental condition index (ECI), urban heat island (SUHI) intensity, and urban thermal field variance index (UTFVI) were calculated and assessed. The analysis utilized cloud-free Landsat 8 imagery sourced from the US Geological Survey via the Google Earth Engine platform, employing a one-year median for each pixel using a cloud masking algorithm. Land use and land cover (LULC) classification was conducted using the random forest (RF) algorithm with satellite composite imagery, achieving an overall accuracy of 85% for 2014 and 86% for 2023. This study provides a detailed analysis of the effects of various land use and cover types on temperature, vegetation, and structural characteristics, revealing the role of changes in different land types on the urban heat island effect. In the LULC classification, water bodies consistently maintained low LST values below 23 °C for both years, while built-up land exhibited the greatest temperature increase, from approximately 25 °C in 2014 to more than 31 °C in 2023. The analysis also revealed that LST varies with the size and type of vegetation, with a mean LST differential between all green spaces and urban areas averaging 7–8 °C, and differences reaching 12 °C in industrial zones.https://www.mdpi.com/2073-4433/16/6/712urban heat islandGoogle Earth Engineland use/land coverland surface temperaturerandom forest algorithm |
| spellingShingle | Yiğitalp Kara Veli Yavuz Anthony R. Lupo Multi-Index Assessment of Surface Urban Heat Island (SUHI) Dynamics in Samsun Using Google Earth Engine Atmosphere urban heat island Google Earth Engine land use/land cover land surface temperature random forest algorithm |
| title | Multi-Index Assessment of Surface Urban Heat Island (SUHI) Dynamics in Samsun Using Google Earth Engine |
| title_full | Multi-Index Assessment of Surface Urban Heat Island (SUHI) Dynamics in Samsun Using Google Earth Engine |
| title_fullStr | Multi-Index Assessment of Surface Urban Heat Island (SUHI) Dynamics in Samsun Using Google Earth Engine |
| title_full_unstemmed | Multi-Index Assessment of Surface Urban Heat Island (SUHI) Dynamics in Samsun Using Google Earth Engine |
| title_short | Multi-Index Assessment of Surface Urban Heat Island (SUHI) Dynamics in Samsun Using Google Earth Engine |
| title_sort | multi index assessment of surface urban heat island suhi dynamics in samsun using google earth engine |
| topic | urban heat island Google Earth Engine land use/land cover land surface temperature random forest algorithm |
| url | https://www.mdpi.com/2073-4433/16/6/712 |
| work_keys_str_mv | AT yigitalpkara multiindexassessmentofsurfaceurbanheatislandsuhidynamicsinsamsunusinggoogleearthengine AT veliyavuz multiindexassessmentofsurfaceurbanheatislandsuhidynamicsinsamsunusinggoogleearthengine AT anthonyrlupo multiindexassessmentofsurfaceurbanheatislandsuhidynamicsinsamsunusinggoogleearthengine |