Utilizing Remotely Sensed Observations to Estimate the Urban Heat Island Effect at a Local Scale: Case Study of a University Campus
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.2.1. Estimating LST
2.2.2. LULC Characteristics
2.2.3. Aggregation to the Hexagon Level and Analysis
- Aggregation to the hexagon unit of analysis (hexagonal tessellation)
- Correlation and multiple regression analysis
- Getis-Ord Gi* for hot spot analysis
3. Results
3.1. Spatial and Temporal Patterns of LST
3.2. The Relation between Patterns of LST and LULC
3.3. Getis-Ord Gi* Hot Spot Analysis
3.4. LST and LULC Correlation and Regression Tests
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix A
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Summer | |||||||
LST | |||||||
2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | |
W | 0.98415 | 0.97712 | 0.97977 | 0.99088 | 0.98885 | 0.99146 | 0.99125 |
p-value | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
NDVI | |||||||
W | 0.84757 | 0.83903 | 0.84063 | 0.8825 | 0.86209 | 0.86565 | 0.86902 |
p-value | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
NDBI | |||||||
W | 0.93511 | 0.93776 | 0.92624 | 0.95469 | 0.9552 | 0.95504 | 0.91619 |
p-value | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
UI | |||||||
W | 0.93363 | 0.93168 | 0.92503 | 0.93798 | 0.94714 | 0.93924 | 0.91778 |
p-value | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Winter | |||||||
LST | |||||||
2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | |
W | 0.98003 | 0.97472 | 0.98669 | 0.99127 | 0.97977 | 0.76887 | |
p-value | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |
NDVI | |||||||
W | 0.84543 | 0.87519 | 0.86753 | 0.9208 | 0.87598 | 0.85601 | |
p-value | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |
NDBI | |||||||
W | 0.93954 | 0.93541 | 0.94447 | 0.95796 | 0.93837 | 0.91166 | |
p-value | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |
Urban Index (UI) | |||||||
W | 0.92928 | 0.93733 | 0.94189 | 0.95279 | 0.9403 | 0.91984 | |
p-value | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Summer | |||||||
2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | |
NDVI | R2 = 0.2445 * | R2 = 0.1946 * | R2 = 0.233 * | R2 = 0.1513 * | R2 = 0.2303 * | R2 = 0.1867 * | R2 = 0.1901 * |
F(1,2544) = 824.6 | F(1,2544) = 615.9 | F(1,2544) = 774.3 | F(1,2544) = 454.9 | F(1,2544) = 762.4 | F(1,2544) = 585.1 | F(1,2544) = 598.4 | |
RMSE = 0.895 | RMSE = 1.162 | RMSE = 1.002 | RMSE = 0.876 | RMSE = 0.954 | RMSE = 0.960 | RMSE = 1.064 | |
NDVI + NDBI | R2 = 0.3704 * | R2 = 0.439 * | R2 = 0.3512 * | R2 = 0.2819 * | R2 = 0.338 * | R2 = 0.2675 * | R2 = 0.3093 * |
F(1,2543) = 749.7 | F(1,2543) = 996.7 | F(1,2543) = 689.9 | F(1,2543) = 500.6 | F(1,2543) = 650.7 | F(1,2543) = 465.8 | F(1,2543) = 570.8 | |
RMSE = 0.817 | RMSE = 0.970 | RMSE = 0.922 | RMSE = 0.806 | RMSE = 0.885 | RMSE = 0.911 | RMSE = 0.983 | |
NDVI + NDBI + UI | R2 = 0.3746 * | R2 = 0.4468 * | R2 = 0.3511 * | R2 = 0.2817 * | R2 = 0.3414 * | R2 = 0.2795 * | R2 = 0.3506 * |
F(1,2542) = 509 | F(1,2542) = 686 | F(1,2542) = 459.9 | F(1,2542) = 333.7 | F(1,2542) = 440.7 | F(1,2542) = 330 | F(1,2542) = 459.1 | |
RMSE = 0.814 | RMSE = 0.963 | RMSE = 0.922 | RMSE = 0.806 | RMSE = 0.883 | RMSE = 0.904 | RMSE = 0.953 | |
Winter | |||||||
NDVI | - | R2 = 0.1168 * | R2 = 0.0715 * | R2 = 0.0856 * | R2 = 0.1352 * | R2 = 0.0466 * | R2 = 0.0356 * |
F(1,2544) = 337.6 | F(1,2544) = 197.1 | F(1,2544) = 239.2 | F(1,2544) = 399 | F(1,2544) = 125.4 | F(1,2544) = 94.93 | ||
RMSE = 1.081 | RMSE = 1.105 | RMSE = 1.05 | RMSE = 0.970 | RMSE = 1.240 | RMSE = 1.658 | ||
NDVI + NDBI | - | R2 = 0.4675 * | R2 = 0.3838 * | R2 = 0.3757 * | R2 = 0.2953 * | R2 = 0.2909 * | R2 = 0.1428 * |
F(1,2543) = 1118 | F(1,2543) = 793.6 | F(1,2543) = 766.8 | F(1,2543) = 534.3 | F(1,2543) = 523.1 | F(1,2543) = 213.1 | ||
RMSE = 0.837 | RMSE = 0.899 | RMSE = 0.865 | RMSE = 0.876 | RMSE = 1.072 | RMSE = 1.563 | ||
NDVI + NDBI + UI | - | R2 = 0.4868 * | R2 = 0.3994 * | R2 = 0.3816 * | R2 = 0.2981 * | R2 = 0.3168 * | R2 = 0.1735 * |
F(1,2542) = 805.7 | F(1,2542) = 565.1 | F(1,2542) = 524.4 | F(1,2542) = 361.4 | F(1,2542) = 394.3 | F(1,2542) = 179.1 | ||
RMSE = 0.822 | RMSE = 0.888 | RMSE = 0.861 | RMSE = 0.874 | RMSE = 1.053 | RMSE = 1.534 |
From | To | NDVI | NDBI | UI | |
---|---|---|---|---|---|
(average) | (average) | Summer | |||
2014–2015 | 2016–2017 | Difference | −0.32 * | 0.26 * | 0.24 * |
2016–2017 | 2018–2019 | Difference | −0.03 ** | 0.31 * | 0.35 * |
2014–2015 | 2018–2019 | Difference | −0.11 * | 0.34 * | 0.37 * |
SOC | −0.10 * | 0.3 * | 0.32 * | ||
Winter | |||||
2014–2015 | 2016–2017 | Difference | - | 0.36 * | 0.330 * |
2016–2017 | 2018–2019 | Difference | 0.05 ** | 0.13 * | 0.21 * |
2014–2015 | 2018–2019 | Difference | - | 0.29 * | 0.32 * |
SOC | 0.01 * | 0.32 * | 0.35 * |
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Addas, A.; Goldblatt, R.; Rubinyi, S. Utilizing Remotely Sensed Observations to Estimate the Urban Heat Island Effect at a Local Scale: Case Study of a University Campus. Land 2020, 9, 191. https://doi.org/10.3390/land9060191
Addas A, Goldblatt R, Rubinyi S. Utilizing Remotely Sensed Observations to Estimate the Urban Heat Island Effect at a Local Scale: Case Study of a University Campus. Land. 2020; 9(6):191. https://doi.org/10.3390/land9060191
Chicago/Turabian StyleAddas, Abdullah, Ran Goldblatt, and Steven Rubinyi. 2020. "Utilizing Remotely Sensed Observations to Estimate the Urban Heat Island Effect at a Local Scale: Case Study of a University Campus" Land 9, no. 6: 191. https://doi.org/10.3390/land9060191