# Assessing Spatiotemporal LST Variations in Urban Landscapes Using Diurnal UAV Thermography
## 4 Results
### Spatial Autocorrelation Analysis (Moran's I) Results  
Given that sampled points span different LULC classes, it is hypothesized that LST values exhibit spatial clustering due to the thermal properties of various surfaces. To assess the overall spatial dependence of LST across the study area, a global Moran's \(I\) analysis was conducted for four diurnal periods. This approach provides insight into the overall spatial clustering of LST, offering a basis for assessing the suitability of spatially explicit modeling techniques, particularly GWR.  
Table 2 presents the [PERSON]'s \(I\) values and their corresponding \(p\)-values for each time period. The results indicate statistically significant positive spatial autocorrelation across all timeframes (\(p<0.001\)), confirming that LST values cluster spatially rather than being randomly distributed.  
The analysis shows that spatial autocorrelation in LST strengthens as the day progresses. The Moran's \(I\) value is lowest in the morning (0.278), suggesting that LST distribution is relatively less structured during early hours, likely due to the effects of nocturnal cooling and minimal solar radiation. However, as solar heating intensifies, spatial cluster  
Figure 6: Diurnal temperature variations across different LULC classes compared to ambient temperature measurements. The graph illustrates the temporal evolution of surface temperatures for bare soil, building, grassland, paved road, and water body surfaces alongside ambient air temperature (dashed line) across four temporal periods (morning, afternoon, evening, and midnight).  
ing strengthens, reaching its peak at midnight (0.604), when impervious surfaces and other heat-retaining materials exhibit their maximum residual thermal loads.  
The increasing [PERSON]'s \(I\) values confirm the presence of structured thermal patterns, highlighting the need for GWR to address spatial dependence in LST variations between LULC categories. The observed clustering indicates that traditional regression models assuming spatial independence would be insufficient, as they would fail to capture the localized effects of different land cover types and their interactions with diurnal temperature variations.  
The strong spatial autocorrelation in LST has key implications for the subsequent analytical approach. The significant spatial autocorrelation observed directly justifies the adoption of GWR as the primary spatial analytical technique in this study. GWR effectively models spatial non-stationarity and dependence, making it well suited for analyzing the spatially varying relationships between LULC and LST.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Time Period** & **Moran’s \(I\)** & \(p\)**-Value** & **Interpretation** \\ \hline Morning & 0.278 & 0.001 & Moderate clustering \\ Afternoon & 0.560 & 0.001 & Strong clustering \\ Evening & 0.592 & 0.001 & Strong clustering \\ Midnight & 0.604 & 0.001 & Strong clustering \\ \hline \hline \end{tabular}
\end{table}
Table 2: Global [PERSON]’s \(I\) values for LST across diurnal periods.  
Figure 7: Correlation matrix depicting the statistical relationships between different LULC classes and ambient temperature. Values range from \(-1\) to \(1\), with darker red indicating stronger positive correlations and blue representing weaker correlations. The diagonal represents perfect self-correlation (1.00).