LST in QGIS using Landsat-8
This tutorial is about calculating land surface temperature from Landsat-8 using Semi Automatic Classification plugin (SCP) along use of manual equations in QGIS.
NOTE: The data used here is Landsat-8 Collection 2 Level 1 and it is highly suggested to use cloud free data or very less cloud cover available data for better LST results.
The version of SCP plugin used here is 7.8; installing latest SCP plugin might have different interface so it is suggested to download this plugin version manually.
Download SCP plugin manually
• Install SCP plugin lower version | QGIS
Timestamp
------------------
0:00 Intro
0:18 Data used
0:45 Why Band 11 of Landsat 8 is not used in most of the research related to LST ?
1:09 Data Pre-processing
3:46 SCP-plugin installation
4:47 Data Pre-processing through SCP plugin
7:22 Getting Brightness temperature from Band 10
8:40 Manual equations get NDVI, PoV and LSE
9:30 How to get NDVI from Bands 5 and 4 of Landsat 8 in QGIS ?
10:57 How to get proportion of vegetation from NDVI in QGIS ?
13:24 How to calculate land surface emissivity from proportion of vegetation in QGIS ?
15:19 How to calculate land surface temperature in QGIS ?
19:36 Converting LST in celcius
21:35 How to clip area of interest using vector data ?
22:38 Symbology
23:40 Conclusion and Recommendation
Equations used (Based on Single Channel Algorithm):
1. Top of Atmosphere(ToA) spectral radiance and Brightness temperature(BT) were carried out through SCP plugin.
2. NDVI: (B5 - B4)/(B5 + B4)
3. Proportion of Vegetation(PoV):
PoV= [(NDVI – NDVImin)/(NDVImax – NDVI¬min )]2
5. Land Surface Emissivity(ε):
ε = 0.004 * Pv + 0.986
6. Land Surface Temperature(LST) [ in Kelvin ]
LSTK = BT / (1 + (10.895 * BT / 14380) * ln(ε))
7. LST in celcius
LSTC = LSTK – 273.15
REFERENCES:
https://semiautomaticclassificationma...
https://semiautomaticclassificationma...
Njoku, E. A. (2019). Analysis of spatial-temporal pattern of Land Surface Temperature (LST) due to NDVI and elevation in Ilorin, Nigeria. Master Thesis in Geographical Information Science; (2019), 106, 1–63. http://lup.lub.lu.se/student-papers/r...
Cristina, A., Castro, G., Cristina, A., & Castro, G. (2019). M 2019. 1–28.
Di, D., Civile, I., & Ambientale, E. E. (2023). THE IMPACT OF CLIMATE CHANGE ON SALENTO ’ S URBAN TERRITORIES : MAPPING AND ANALYSIS ON.
Suresh, S., V, A. S., & Mani, K. (2016). Mountain Landscape of Devikulam Taluk Using Landsat 8 Data. International Journal of Research in Engineering and Technology, 5(1), 92–96.
Mathilde, A. (2022). Analysis of temperature variation with respect to the LCZs using in-situ measures and satellite imagery : study case of the Metropolitan City of Milan.
Twumasi, Y. A., Merem, E. C., Namwamba, J. B., Mwakimi, O. S., Ayala-silva, T., Frimpong, D. B., Ning, Z. H., Asare-ansah, A. B., Annan, J. B., Oppong, J., Loh, P. M., Owusu, F., Jeruto, V., Petja, B. M., Okwemba, R., Mcclendon-peralta, J., Akinrinwoye, C. O., & Mosby, H. J. (2021). Estimation of Land Surface Temperature from Landsat-8 OLI Thermal Infrared Satellite Data . A Comparative Analysis of Two Cities in Ghana. 131–149. https://doi.org/10.4236/ars.2021.104009
Di, D., Civile, I., & Ambientale, E. E. (2023). THE IMPACT OF CLIMATE CHANGE ON SALENTO ’ S URBAN TERRITORIES : MAPPING AND ANALYSIS ON.
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