Assessing Spatiotemporal LST Variations in Urban Landscapes Using Diurnal UAV Thermography
This study investigates the spatiotemporal dynamics of land surface temperature (LST) across five distinct land use/land cover (LULC) classes through high-resolution unmanned aerial vehicle (UAV) thermal remote sensing. Thermal orthomosaics were systematically captured at four diurnal periods (morni...
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MDPI AG
2025-03-01
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| author | Nizar Polat Abdulkadir Memduhoğlu |
| author_facet | Nizar Polat Abdulkadir Memduhoğlu |
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| description | This study investigates the spatiotemporal dynamics of land surface temperature (LST) across five distinct land use/land cover (LULC) classes through high-resolution unmanned aerial vehicle (UAV) thermal remote sensing. Thermal orthomosaics were systematically captured at four diurnal periods (morning, afternoon, evening, and midnight) over an urban university campus environment. Using stratified random sampling in each class with spatial controls to minimize autocorrelation, we quantified thermal signatures across bare soil, buildings, grassland, paved roads, and water bodies. Statistical analyses incorporating outlier management via the Interquartile Range (IQR) method, spatial autocorrelation assessment using Moran’s <i>I</i>, correlation testing, and Geographically Weighted Regression (GWR) revealed substantial thermal variability across LULC classes, with temperature differentials of up to 17.7 °C between grassland (20.57 ± 5.13 °C) and water bodies (7.10 ± 1.25 °C) during afternoon periods. The Moran’s <i>I</i> analysis indicated notable spatial dependence in land surface temperature, justifying the use of GWR to model these spatial patterns. Impervious surfaces demonstrated pronounced heat retention capabilities, with paved roads maintaining elevated temperatures into evening (13.18 ± 3.49 °C) and midnight (2.25 ± 1.51 °C) periods despite ambient cooling. Water bodies exhibited exceptional thermal stability (SD range: 0.79–2.85 °C across all periods), while grasslands showed efficient nocturnal cooling (ΔT = 23.02 °C from afternoon to midnight). GWR models identified spatially heterogeneous relationships between LST patterns and LULC distribution, with water bodies exerting the strongest localized cooling influence (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula>≈ 0.62–0.68 during morning/evening periods). The findings demonstrate that surface material properties significantly modulate diurnal heat flux dynamics, with human-made surfaces contributing to prolonged thermal loading. This research advances urban microclimate monitoring methodologies by integrating high-resolution UAV thermal imagery with robust statistical frameworks, providing empirically-grounded insights for climate-adaptive urban planning and heat mitigation strategies. Future work should incorporate multi-seasonal observations, in situ validation instrumentation, and integration with human thermal comfort indices. |
| format | Article |
| id | doaj-art-4d28e268bda0436a8bb8aec414c25db1 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
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| spelling | doaj-art-4d28e268bda0436a8bb8aec414c25db12025-08-20T03:08:43ZengMDPI AGApplied Sciences2076-34172025-03-01157344810.3390/app15073448Assessing Spatiotemporal LST Variations in Urban Landscapes Using Diurnal UAV ThermographyNizar Polat0Abdulkadir Memduhoğlu1Department of Geomatics Engineering, Faculty of Engineering, Harran University, 63100 Sanliurfa, TürkiyeDepartment of Geomatics Engineering, Faculty of Engineering, Harran University, 63100 Sanliurfa, TürkiyeThis study investigates the spatiotemporal dynamics of land surface temperature (LST) across five distinct land use/land cover (LULC) classes through high-resolution unmanned aerial vehicle (UAV) thermal remote sensing. Thermal orthomosaics were systematically captured at four diurnal periods (morning, afternoon, evening, and midnight) over an urban university campus environment. Using stratified random sampling in each class with spatial controls to minimize autocorrelation, we quantified thermal signatures across bare soil, buildings, grassland, paved roads, and water bodies. Statistical analyses incorporating outlier management via the Interquartile Range (IQR) method, spatial autocorrelation assessment using Moran’s <i>I</i>, correlation testing, and Geographically Weighted Regression (GWR) revealed substantial thermal variability across LULC classes, with temperature differentials of up to 17.7 °C between grassland (20.57 ± 5.13 °C) and water bodies (7.10 ± 1.25 °C) during afternoon periods. The Moran’s <i>I</i> analysis indicated notable spatial dependence in land surface temperature, justifying the use of GWR to model these spatial patterns. Impervious surfaces demonstrated pronounced heat retention capabilities, with paved roads maintaining elevated temperatures into evening (13.18 ± 3.49 °C) and midnight (2.25 ± 1.51 °C) periods despite ambient cooling. Water bodies exhibited exceptional thermal stability (SD range: 0.79–2.85 °C across all periods), while grasslands showed efficient nocturnal cooling (ΔT = 23.02 °C from afternoon to midnight). GWR models identified spatially heterogeneous relationships between LST patterns and LULC distribution, with water bodies exerting the strongest localized cooling influence (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula>≈ 0.62–0.68 during morning/evening periods). The findings demonstrate that surface material properties significantly modulate diurnal heat flux dynamics, with human-made surfaces contributing to prolonged thermal loading. This research advances urban microclimate monitoring methodologies by integrating high-resolution UAV thermal imagery with robust statistical frameworks, providing empirically-grounded insights for climate-adaptive urban planning and heat mitigation strategies. Future work should incorporate multi-seasonal observations, in situ validation instrumentation, and integration with human thermal comfort indices.https://www.mdpi.com/2076-3417/15/7/3448unmanned aerial vehiclethermal infrared imagingland surface temperatureland use/land cover classificationdiurnal thermal variabilityurban heat island |
| spellingShingle | Nizar Polat Abdulkadir Memduhoğlu Assessing Spatiotemporal LST Variations in Urban Landscapes Using Diurnal UAV Thermography Applied Sciences unmanned aerial vehicle thermal infrared imaging land surface temperature land use/land cover classification diurnal thermal variability urban heat island |
| title | Assessing Spatiotemporal LST Variations in Urban Landscapes Using Diurnal UAV Thermography |
| title_full | Assessing Spatiotemporal LST Variations in Urban Landscapes Using Diurnal UAV Thermography |
| title_fullStr | Assessing Spatiotemporal LST Variations in Urban Landscapes Using Diurnal UAV Thermography |
| title_full_unstemmed | Assessing Spatiotemporal LST Variations in Urban Landscapes Using Diurnal UAV Thermography |
| title_short | Assessing Spatiotemporal LST Variations in Urban Landscapes Using Diurnal UAV Thermography |
| title_sort | assessing spatiotemporal lst variations in urban landscapes using diurnal uav thermography |
| topic | unmanned aerial vehicle thermal infrared imaging land surface temperature land use/land cover classification diurnal thermal variability urban heat island |
| url | https://www.mdpi.com/2076-3417/15/7/3448 |
| work_keys_str_mv | AT nizarpolat assessingspatiotemporallstvariationsinurbanlandscapesusingdiurnaluavthermography AT abdulkadirmemduhoglu assessingspatiotemporallstvariationsinurbanlandscapesusingdiurnaluavthermography |