# Urban Heat Island Analysis Using the Landsat TM Data and ASTER Data: A Case Study in Hong Kong

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Study Area and Data

#### 2.1. Study Area

^{2}is land and 50 km

^{2}is inland water. Situated just south of the Tropic of Cancer, Hong Kong's climate is humid sub-tropical, tending towards temperate for nearly half the year. Hong Kong averages 1,948 hours of sunshine per year, and about 90 percent of its rainfall occurs between April and September. Temperatures can drop below 10 °C in winter and exceed 31 °C in summer.

#### 2.2. Data

#### 2.2.1. Satellite Image Data

Data | Resolution (m) | Time | Date |
---|---|---|---|

Landsat TM | 120 × 120 | 02:40 | 23 November 2005 |

ASTER L1A | 90 × 90 | 03:08 | 1 December 2005 |

#### 2.2.2. Other Auxiliary Data

#### 2.2.3. Data Pre-Processing

## 3. LST Retrieval Methodology

#### 3.1. Brief Introduction to Different LST Retrieval Methods

Algorithm | Temperature/Emissivity Separation | Single-Channel | Mono-Window | Split-Window | Multi-Window | |
---|---|---|---|---|---|---|

Data | ||||||

Landsat TM (1 thermal band) | √ (in situ parameters required) | √ (lower accuracy) | √ (higher accuracy) | |||

ASTER L1A (5 thermal bands) | √ | √ | √ | √ | √ |

#### 3.2. Mono-Window Algorithm and Split-Window Algorithm

- (1)
- Convert the digital number (DN) into spectral radiance:

_{i}is the at-sensor spectral radiance ($MW\cdot c{m}^{-2}\cdot s{r}^{-1}\cdot \mu {m}^{-1}$); L

_{max}is the maximum at-sensor spectral radiance; L

_{min}is the minimum at-sensor spectral radiance; Q

_{max}represents the maximum DN value of pixels and Q

_{dn}represents the DN value of pixel.

_{6}is the at-sensor spectral radiance of Landsat TM 6, and Q

_{dn}is the DN value of pixel.

_{13}= 0.005693Q

_{dn}− 0.005693

_{dn}represents the DN value of pixel.

_{dn}− 0.005225

- (2)
- Convert the spectral radiance into at-sensor brightness temperature:

_{i}is the at-sensor brightness temperature (K); C

_{2}, C

_{1}are constants; λ

_{i}is central wavelength and L

_{i}represents the at-sensor spectral radiance, which can be calculated using Equation (1). For ASTER 13 data, λ

_{13}= 10.6 μm. For ASTER 14 data, λ

_{14}= 11.3 μm.

_{1}and K

_{2}:

_{6}is the at-sensor brightness temperature of Landsat TM 6; K

_{1}, K

_{2}are calibration constants of Landsat TM and L

_{6}represents the at-sensor spectral radiance of Landsat TM 6.

- (3)
- Calculation of land surface emissivity:

- (i)
- Precise calculation of NDVI

- (ii)
- Estimation of emissivity

NDVI | Land surface emissivity (${\epsilon}_{\text{i}}$) |

NDVI < −0.185 | 0.995 |

−0.185 ≤ NDVI < 0.157 | 0.970 |

0.157 ≤ NDVI ≤ 0.727 | 1.009 4 + 0.047ln(NDVI) |

NDVI > 0.727 | 0.990 |

- (4)
- Calculation of atmospheric transmittance:

- (i)
- Calculation of water vapor content

^{2}); ${\text{T}}_{0}$ is the near-surface air temperature in K and RH represents the relative humidity. The water vapor content, near-surface air temperature and relative humidity are all from Hong Kong Observatory. For the Landsat TM 6 data, they are the average values of a total of 29 stations around Hong Kong on 23 November 2005. For ASTER 13 and 14 data, they are the average values of the same 29 stations on 1 December 2005.

- (ii)
- Estimation of atmospheric transmittance

_{6}= 1.031412 − 0.11536 × w

_{6}

Profiles | Water vapor (${w}_{6}$) (g/cm ^{2}) | Transmittance estimation equation (${\tau}_{6}$) | Squared correction | Standard error |
---|---|---|---|---|

High air temperature | 0.4–1.6 | 0.974290−0.08007 ${w}_{6}$ | 0.99611 | 0.002368 |

1.6–3.0 | 1.031412−0.115 36 ${w}_{6}$ | 0.99827 | 0.002539 | |

Low air temperature | 0.4–1.6 | 0.982007−0.09611 ${w}_{6}$ | 0.99563 | 0.003340 |

1.6–3.0 | 1.053710−0.14142 ${w}_{6}$ | 0.99899 | 0.002375 |

_{13}= 1.02 − 0.104 × w

_{13}

_{14}= 1.04 − 0.133 × w

_{14}

- (5)
- Calculation of mean atmospheric temperature:

Area | Atmospheric temperature equation (${\text{T}}_{a}$) (K) |
---|---|

For USA 1976 | 25.9396 + 0.88045 × ${\text{T}}_{0}$ |

For tropical | 17.9769 + 0.91715 × ${\text{T}}_{0}$ |

For mid-latitude summer | 16.0110 + 0.92621 × ${\text{T}}_{0}$ |

For mid-latitude winter | 19.2704 + 0.91118 × ${\text{T}}_{0}$ |

- (6)
- LST retrievals of the two algorithms:temperature:

_{i}× τ

_{i}

_{i})[1 + (1 − ε

_{i}) × τ

_{i}]

_{s}is the LST (K); T

_{i}is the brightness temperature (K), which can be calculated using Equation (5); ε

_{i}is the emissivity, which can be classified and computed by NDVI (Table 3); τ

_{i}is the transmittance, which can be calculated from Equations (14–16), and T

_{a}represents the effective mean atmospheric temperature, which can be calculated using Equation (17).

Parameters | Values | ||||||
---|---|---|---|---|---|---|---|

Land surface emissivity | (ε_{6}) | NDVI | |||||

(ε_{13}) | <−0.185 | [−0.185,0.157] | [0.157,0.727] | >0.727 | |||

(ε_{14}) | 0.995 | 0.970 | 1.0094 + 0.047ln(NDVI) | 0.990 | |||

Atmospheric transmittance | (τ_{6}) | 23 November 2005 | |||||

T_{0} | RH | w_{6} | |||||

20.9 °C | 65% | 1.75 g/cm^{2} | |||||

0.83 | |||||||

(τ_{13}) | 1 December 2005 | ||||||

T_{0} | RH | w_{13} | |||||

21.1 °C | 79% | 2.11 g/cm^{2} | |||||

0.80 | |||||||

(τ_{14}) | 1 December 2005 | ||||||

T_{0} | RH | w_{14} | |||||

21.1 °C | 79% | 2.11 g/cm^{2} | |||||

0.76 | |||||||

Mean atmospheric temperature | (T_{a}) | 23 November 2005 | |||||

T_{0} | |||||||

20.9 °C | |||||||

14.5 °C | |||||||

(T_{b}) | 1 December 2005 | ||||||

T_{0} | |||||||

21.1 °C | |||||||

14.7 °C |

## 4. Results and Discussion

#### 4.1. The Accuracy Verification of LST Retrieval

#### 4.2. The Distribution of Urban Heat Islands in Hong Kong

#### 4.3. The Correlation Analysis between Urban Heat Island, NDVI and NDBI

LST | NDVI | NDBI | |
---|---|---|---|

LST | 1 | ||

NDVI | −0.41 | 1 | |

NDBI | 0.71 | −0.56 | 1 |

#### 4.4. The Ecological Valuation of Hong Kong Urban Heat Island

_{S}is the LST of certain point in K and T

_{MEAN}is the mean LST temperature of the whole study area in K.

Urban thermal field variance index | Urban heat island phenomenon | Ecological evaluation index |
---|---|---|

<0 | None | Excellent |

0.000–0.005 | Weak | Good |

0.005–0.010 | Middle | Normal |

0.015–0.015 | Strong | Bad |

0.015–0.020 | Stronger | Worse |

>0.020 | Strongest | Worst |

## 5. Conclusion

## Acknowledgements

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**MDPI and ACS Style**

Liu, L.; Zhang, Y.
Urban Heat Island Analysis Using the Landsat TM Data and ASTER Data: A Case Study in Hong Kong. *Remote Sens.* **2011**, *3*, 1535-1552.
https://doi.org/10.3390/rs3071535

**AMA Style**

Liu L, Zhang Y.
Urban Heat Island Analysis Using the Landsat TM Data and ASTER Data: A Case Study in Hong Kong. *Remote Sensing*. 2011; 3(7):1535-1552.
https://doi.org/10.3390/rs3071535

**Chicago/Turabian Style**

Liu, Lin, and Yuanzhi Zhang.
2011. "Urban Heat Island Analysis Using the Landsat TM Data and ASTER Data: A Case Study in Hong Kong" *Remote Sensing* 3, no. 7: 1535-1552.
https://doi.org/10.3390/rs3071535