Seasonal and Diurnal Variation of Land Surface Temperature Distribution and Its Relation to Land Use/Land Cover Patterns
Abstract
:1. Introduction
2. Data and Methods
2.1. Study Area
2.2. Data Sets
- For thermal data, we rely on multi-temporal data from the Landsat 8 Thermal Infrared Sensor (TIRS) and Operational Land Imager (OLI) data, which are publicly available (level-1-products http://earthexplorer.usgs.gov/ accessed 20 May 2020). The spatial resolution of Landsat 8 image bands is 30 m except for band 8 (panchromatic, 15 m spatial resolution) and the two thermal bands 10–11 (100 m). Conditions for images to be selected for the study, are: (1) cloud-free; (2) coverage of the entire study area; (3) four-season division covering spring (March–May), summer (June–August), autumn (September–November), and winter (December–February); and (4) daytime (local overpass time before noon) and nighttime (local overpass time after 8 p.m.). Based on these criteria, the following eight images between 2018 and 2019 were selected to retrieve LSTs in spring, summer, autumn, and winter as well as day and nighttime of Berlin (Table 1). The time of data acquisition was approximately 12:00 a.m. and 10:30 p.m. Greenwich Mean Time (GMT) in Berlin.
- The land use/land cover (LU/LC) data (10 m) of Berlin (Figure 2) in 2018 were obtained from the European Urban Atlas (https://land.copernicus.eu/local/urban-atlas accessed 18 February 2020), which provides reliable, inter-comparable, high-resolution data. The LU/LC classification includes six primary types (croplands, woodlands, grasslands, water areas, built-up lands, and unused lands) and 25 secondary types. We identified seven important classes (Table 2): Transportation (15.2%), Commercial and Industrial (6.7%), Residential (35.2%), Sports and Leisure (4.3%), Vegetation (30.2%), Agriculture (2.9%), Wetlands (5.5%) (the percentage of the corresponding class within the study area in 2018).
- Urban morphologic indicators were obtained from building footprints from Open Street Map (https://www.openstreetmap.org/ accessed on 1 January 2020).
2.3. Research Framework
2.4. Retrieving Land Surface Temperature
2.5. Selected LUCP Indicators
2.6. Statistical Analyses Relating Spatial Indicators and LST
3. Results
3.1. Spatial Distribution of LST
- (1)
- Very Hot Spot: LST ≥ LSTmean + 2Std;
- (2)
- Hot Spot: LSTmean + Std ≤ LST ≤ LSTmean + 2Std;
- (3)
- Warm spot: LSTmean ≤ LST ≤ LSTmean + Std;
- (4)
- Cool Spot: LSTmean − Std ≤ LST ≤ LSTmean;
- (5)
- Cold Spot: LSTmean − 2 Std ≤ LST ≤ LSTmean − Std;
- (6)
- Very Cold Spot: LST ≤ LSTmean − 2Std.
3.2. Land Cover Analysis of LST
3.3. Spatial-Temporal Patterns of LUCP Indicators
3.4. Correlation between LST and LUCP Indicators
- In spring, NDBI (0.335) > MNDWI (0.330) > NDVI (0.177) > Albedo (0.158);
- In summer, NDBI (0.660) > MNDWI (0.155) > NDVI (0.112) > Albedo (0.073);
- In autumn, NDBI (0.502) > MNDWI (0.321) > NDVI (0.082) > Albedo (0.095);
- In winter, NDBI (0.388) > MNDWI (0.274) > NDVI (0.251) > Albedo (0.087).
- In summer, BD (0.334) > ISF (0.235) > FAR (0.220) > BH (0.211);
- In autumn, ISF (0.384) > BH (0.249) > FAR (0.227) > BD (0.139);
- In winter, FAR (0.333) > ISF (0.271) > BD (0.212) > BH (0.183).
4. Discussion
4.1. Investigation of Seasonal Variations in Urban Thermal Environment
4.2. Implications for Urban Planning and Management
4.3. Limitations and Future Studies
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Satellite Sensor | Date | Path/Row | Season | Time |
---|---|---|---|---|
Landsat 8 OLI/TIRS | 2018/04/18 | 193/23 | Spring | Day |
2018/09/09 | 193/23 | Autumn | Day | |
2019/02/15 | 49/221 | Winter | Night | |
2019/02/16 | 193/23 | Winter | Day | |
2019/04/20 | 49/221 | Spring | Night | |
2019/06/23 | 49/221 | Summer | Night | |
2019/06/24 | 193/23 | Summer | Day | |
2019/09/27 | 49/221 | Autumn | Night |
Land Cover Type | Description |
---|---|
Transportation | Any type of traffic land, including main roads, highways and airport |
Commercial and Industrial | Urban built-up areas, including commercial land and industrial land |
Residential | Urban built-up areas, including all types of residential land |
Sports and Leisure | Any type of vegetation that provides pervious surface and public service land |
Vegetation | Any type of vegetation that provides shade, including all trees and shrubs |
Agriculture | All agricultural land |
Wetlands | Any type of water body, including lakes, rivers, wetlands, and ponds |
Indicators | Description | Value | |
---|---|---|---|
Land cover factors | NDVI | Measures density of green vegetation, calculated as [40] | [−1, 1] |
NDBI | Measures intensity of imperviousness, calculated as [41] | [−1, 1] | |
MNDWI | Measures characterize the water body features, calculated as [47] | [−1, 1] | |
Albedo | Overall reflectance in all directions [45] | [0, 1] | |
Spatial morphological factors | ISF | Fraction of impervious surface in each grid | [0, 1] |
BH | Average building height in each grid | [0, Max] | |
BD | The building square footage divided by total land area | [0, 1] | |
FAR | The building floor area within each grid | [0, Max] |
Season | Daytime-Date | Maximum (°C) | Minimum (°C) | Mean (°C) | Standard Deviation (°C) |
---|---|---|---|---|---|
Spring | 2018/04/18 | 37.44 | 7.05 | 21.82 | 2.23 |
Summer | 2019/06/24 | 44.89 | 14.51 | 28.26 | 3.34 |
Autumn | 2018/09/09 | 34.62 | 8.04 | 21.57 | 2.39 |
Winter | 2019/02/16 | 15.83 | −10.90 | 7.47 | 1.34 |
Season | Nighttime-Date | Maximum (°C) | Minimum (°C) | Mean (°C) | Standard Deviation (°C) |
Spring | 2019/04/20 | 13.59 | −9.24 | 9.51 | 1.48 |
Summer | 2019/06/23 | 21.29 | 5.53 | 18.08 | 0.81 |
Autumn | 2019/09/27 | 12.58 | −0.31 | 9.33 | 0.81 |
Winter | 2019/02/15 | 6.38 | −17.78 | 0.26 | 1.42 |
Season | Daytime-Date | NDVI | NDBI | MNDWI | Albedo |
---|---|---|---|---|---|
Spring | 2018/04/18 | −0.33 | 0.81 | −0.37 | 0.57 |
0.65 | 0.97 | 0.97 | 0.98 | ||
Summer | 2019/06/24 | −0.32 | 0.86 | −0.18 | 0.34 |
0.63 | 0.97 | 0.97 | 0.97 | ||
Autumn | 2018/09/09 | −0.32 | 0.92 | −0.46 | 0.49 |
0.54 | 0.96 | 0.96 | 0.98 | ||
Winter | 2019/02/16 | 0.31 | 0.72 | −0.62 | 0.59 |
0.06 | 0.93 | 0.92 | 0.96 |
Season | Daytime-Date | ISF | BH | BD | FAR | Nighttime-Date | ISF | BH | BD | FAR |
---|---|---|---|---|---|---|---|---|---|---|
Spring | 20180418 | 0.71 | 0.39 | 0.69 | 0.56 | 20190420 | 0.39 | 0.51 | 0.46 | 0.49 |
0.78 | 0.80 | 0.66 | 0.48 | 0.77 | 0.83 | 0.66 | 0.50 | |||
Summer | 20190624 | 0.69 | 0.43 | 0.69 | 0.59 | 20190623 | 0.31 | 0.38 | 0.35 | 0.38 |
0.79 | 0.81 | 0.67 | 0.49 | 0.74 | 0.79 | 0.62 | 0.45 | |||
Autumn | 20180909 | 0.43 | 0.23 | 0.47 | 0.37 | 20190927 | 0.23 | 0.30 | 0.17 | 0.24 |
0.76 | 0.79 | 0.65 | 0.47 | 0.73 | 0.80 | 0.62 | 0.46 | |||
Winter | 20190216 | 0.44 | 0.33 | 0.50 | 0.43 | 20190215 | 0.16 | 0.56 | 0.43 | 0.54 |
0.77 | 0.81 | 0.66 | 0.49 | 0.13 | 0.32 | 0.28 | 0.38 |
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Dong, R.; Wurm, M.; Taubenböck, H. Seasonal and Diurnal Variation of Land Surface Temperature Distribution and Its Relation to Land Use/Land Cover Patterns. Int. J. Environ. Res. Public Health 2022, 19, 12738. https://doi.org/10.3390/ijerph191912738
Dong R, Wurm M, Taubenböck H. Seasonal and Diurnal Variation of Land Surface Temperature Distribution and Its Relation to Land Use/Land Cover Patterns. International Journal of Environmental Research and Public Health. 2022; 19(19):12738. https://doi.org/10.3390/ijerph191912738
Chicago/Turabian StyleDong, Ruirui, Michael Wurm, and Hannes Taubenböck. 2022. "Seasonal and Diurnal Variation of Land Surface Temperature Distribution and Its Relation to Land Use/Land Cover Patterns" International Journal of Environmental Research and Public Health 19, no. 19: 12738. https://doi.org/10.3390/ijerph191912738
APA StyleDong, R., Wurm, M., & Taubenböck, H. (2022). Seasonal and Diurnal Variation of Land Surface Temperature Distribution and Its Relation to Land Use/Land Cover Patterns. International Journal of Environmental Research and Public Health, 19(19), 12738. https://doi.org/10.3390/ijerph191912738