A Comprehensive Guide to Land Surface Temperature Extraction in QGIS

Introduction

In this tutorial, we will explore the process of extracting Land Surface Temperature (LST) using QGIS, a powerful open-source geographic information system. We will guide you through the necessary steps, starting from downloading the appropriate satellite imagery, including Landsat 8 data, to calculating various indices such as Normalized Difference Vegetation Index (NDVI) and ultimately deriving the LST. This comprehensive guide will help both beginners and advanced users effectively understand and apply these techniques in their environmental analysis projects.

Why is LST Important?

Land Surface Temperature (LST) plays a crucial role in a variety of applications:

• Climate Studies: LST is essential for analyzing the Earth's climate and understanding heat distribution patterns.
• Agriculture: Monitoring LST helps in assessing vegetation health and crop yield.
• Urban Planning: Understanding temperature variations aids in urban heat island studies.
• Environmental Monitoring: LST is vital for landscape and ecosystem research.

In this guide, we use Landsat 8 data, which provides us with thermal bands necessary for temperature calculations. Follow along as we break down the steps for extracting LST in QGIS.

Steps for LST Extraction

Step 1: Download the Satellite Image

The first step in our workflow is to download the satellite image from an appropriate source. For this example, we are using Landsat 8. Ensure you also download the associated Metadata file (MTL), as we will reference several values from it for calculations.

Step 2: Calculate Top of Atmospheric (TOA) Spectral Radiance

To compute LST, we begin with the TOA spectral radiance. Here’s how:

1. Open QGIS and load the satellite image.
2. Navigate to the Raster Calculator.
3. Input the formula that employs values from the downloaded MTL file. Specifically, we need the values related to Band 10, as it contains the thermal information.
   • Locate the parameters in your MTL file:
     • RADIANCE_MAX_BAND_10
     • RADIANCE_MIN_BAND_10
4. Insert these values into the calculator following the standard TOA formula.
5. Save your file.

Now, the TOA extraction process is complete.

Step 3: Convert TOA to Brightness Temperature

Next, we will convert the TOA to Brightness Temperature using the following steps:

1. Open the Raster Calculator again.
2. Use the K1 and K2 constants for Band 10, which you can find in your MTL file:
   • K1: Thermal Constant 1 for Band 10
   • K2: Thermal Constant 2 for Band 10
3. Fill in the formula with these constants.
4. Save the output.

We now have the Brightness Temperature extracted.

Step 4: Calculate NDVI (Normalized Difference Vegetation Index)

The NDVI is crucial for understanding the vegetation in our area of interest. Here’s how to calculate it:

1. In your QGIS project, add Band 4 (Red) and Band 5 (NIR) from the Landsat 8 data.
2. Open the Raster Calculator again.
3. Use the NDVI formula: [ NDVI = \frac{(NIR - Red)}{(NIR + Red)} ]
4. Save to a file.

NDVI gives a clear indication of vegetation health.

Step 5: Calculate Proportion of Vegetation

Next, we calculate the proportion of vegetation using NDVI values:

1. Open the Raster Calculator.
2. Use the NDVI ranges (min and max values):
   • Minimum NDVI: 0.172
   • Maximum NDVI: 0.619
3. Perform the calculation using the defined formula and save.

Step 6: Calculate MST (Minimum Surface Temperature)

Following the NDVI calculation, we need to calculate MST to complement our LST analysis:

1. Access the Raster Calculator once more.
2. Enter the appropriate formula based on your NDVI and brightness temperature results.
3. Save the output result.

Step 7: Final Calculation of Land Surface Temperature

The culmination of our work results in the final LST calculation:

1. Open the Raster Calculator.
2. Utilize the established formula for LST.
3. Save the final output, which represents the LST of the area examined.

Step 8: Classification of the Temperature Layers

Finally, we can classify the extracted LST image for better visualization:

1. Access the properties of your raster layer.
2. Select Single Band Pseudo Color.
3. Choose Equal Interval and set the number of classes (e.g., 5 classes).
4. Modify the color ramp for clearer differentiation (e.g., red for high temperature, blue for low temperature).
5. Apply the settings.

Now you have a visually informative map showing the distribution of land surface temperature across the studied area.

Conclusion

In this article, we have systematically outlined the steps required to extract Land Surface Temperature using QGIS from Landsat 8 data. From downloading the satellite images to performing detailed calculations, each step is essential in building a comprehensive understanding of surface temperatures and their implications on our environment.

By mastering these techniques, you can effectively analyze environmental changes, assess vegetation health through NDVI, and better understand heat dynamics in various terrains. Happy mapping!