# Assessing Spatiotemporal LST Variations in Urban Landscapes Using Diurnal UAV Thermography
## 4 Results
### Data Pre-Processing and Outlier Management Across LULC Classes  
A comparative boxplot analysis of diurnal LST variations using the initial 500-point dataset reveals considerable variability and distinct thermal behaviors among different surface types before outlier removal (Figure 4). Notably, all LULC classes demonstrate maximum temperature values and highest variability during afternoon periods, with bare soil and grassland exhibiting the most pronounced median temperatures (20.78 \({}^{\circ}\)C and21.33 \({}^{\circ}\)C, respectively) and extensive IQRs. Water bodies display remarkable thermal stability across all temporal periods, characterized by compressed boxplots and minimal outliers, particularly during afternoon (median = 7.20 \({}^{\circ}\)C) and evening (median = 3.52 \({}^{\circ}\)C) periods. The impervious surfaces (paved roads and buildings) show interesting temporal patterns, with paved roads maintaining elevated temperatures during evening hours (median = 13.23 \({}^{\circ}\)C) and displaying several upper outliers, indicating potential urban heat retention hotspots. The midnight period reveals significant thermal differentiation, with grassland demonstrating the lowest median temperature (\(-\)2.50 \({}^{\circ}\)C) and the most compressed distribution, while buildings and paved roads maintain notably higher temperatures, suggesting prolonged heat storage. Several statistical outliers, particularly evident in grassland and bare soil afternoon measurements, warrant further investigation for potential microclimate effects or measurement anomalies.  
To ensure the robustness and reliability of thermal pattern analysis, an outlier removal analysis was applied. Initially, our dataset comprised 500 LST observations across five LULC classes. As highlighted in Figure 4, several statistical outliers were evident, particularly in afternoon measurements from grassland and bare soil, suggesting potential distortions in the characterization of typical thermal patterns. Recognizing that statistical outliers can significantly impede accurate interpretation and obscure underlying patterns, we implemented a stringent IQR method. This process identified anomalous temperature measurements, primarily in afternoon readings from bare soil (\(n=7\)) and grassland (\(n=5\)), which, upon detailed investigation, were attributed to potential sensor anomalies  
Figure 3: Thermal orthophotos of the Harran University Osmanbey Campus illustrating LST distribution at four diurnal periods: (**a**) morning, (**b**) afternoon, (**c**) evening, (**d**) midnight.  
linked to high reflectivity or localized microclimatic effects, particularly at urban-rural transition zones. While less frequent outliers were also detected in other LULC classes and temporal periods, their limited representativeness of typical surface temperature dynamics, coupled with their potential to distort statistical characterization, necessitated their exclusion. Following IQR-based outlier removal, the refined analytical dataset comprised 463 observations, distributed across LULC classes as follows: bare soil (\(n=96\)), buildings (\(n=98\)), grassland (\(n=81\)), paved roads (\(n=99\)), and water bodies (\(n=89\)). The refined dataset, characterized by improved statistical consistency and clearer temporal signatures for each LULC class, provided a robust foundation for subsequent statistical analyses. It ensured that the characterization of thermal patterns remained unaffected by anomalous measurements, enabling a more reliable and accurate quantification of thermal amplitude and variability essential for analyzing urban heat dynamics.