As of late, I've been exploring Landsat 8 data for professional and personal research projects. This blog post is the first of several planned updates on my exploration and utilization of the Landsat 8 data.

What is Landsat 8?

The Landsat project aims to provide repetitive acquisition of moderate-resolution multispectral data of Earth's surface on a global basis. The Landsat program provides the only publicly accessible source for global, calibrated, moderate spatial resolution measurements of the Earth's surface. The satellite carries the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) instruments.

Project Goals:

To help structure my exploration of the vast volumes of data provided by the Landsat 8 program, I'm creating a rough outline of some general questions to guide the exploration.

For this project, I will begin by looking specifically at the Dallas-Fort Worth (DFW) metroplex. DFW is a massive, sprawling urban area with multiple large cities contained within the metroplex. The DFW area should provide an interesting case study into urban heat islands, tree coverage and other thermal characteristics of the metroplex. I intend to combine various layers of data to identify how tree coverage, surface reflectance and thermal heat gain has changed in DFW between 2015-2023.
Gain a fundamental understanding of Landsat Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS).

Explore multispectral images from the OLI instruments.
Explore thermal images from the TIRS instruments.
Combine and analyze OLI and TIRS images temporally.
Analyze images using specific band combinations, eg. false-color near-infrared

Bellow are some of the images I’ll be using for temporal analysis. These images use the Google Earth Engine’s (GEE) “simple composite” algorithm, to generate composite images based on temporal filtering. The algorithm can be accessed in the GEE code editor with, ee.Algorithms.Landsat.simpleComposite().

The algorithm works by following the these general steps:

Temporal Filtering
- In the code editor, the user can specify a window of time to generate the composite image from. Eg. between 01.01.2023 - 06.01.2023.
Cloud Masking
- The cloud masking function uses various cloud and shadow detection algorithms to mask pixels that are likely to be affected by cloud coverage or heavy shadows. This process is what allows GEE to generate cloud-free composite images.
Quality Assessment Band
- The Landsat data contains a band of information about the quality and conditions of each pixel in the image. This process allows GEE to filter out and mask poor quality and unreliable pixels.
Generate Mosaic
- After the algorithm has masked clouds and removed poor quality pixels, the images are combined into a single composite image.

Isolating Bands

Multispectral images from Landsat allow you to isolate specific bands and set various band combinations that allow you to detect features not visible with standard imagery. The OLI instruments have nine bands, spanning the visible, near-infrared and infrared spectrum. I am particularly interested in what can be learned from the band combination, B5, B4, B3, which generates a false-color near infrared image that allows us to measure vegetation density and health.

Photosynthesis in plants relies on the chemical, Chlorophyll, which reflects near-infrared light wavelengths. This allows the Landsat OLI instruments to capture near-infrared light, reflected by vegetation and measure the health and density of the vegetation. When the near-infrared band (B5) is combined with visible light bands, red (B4) and green (B3), it generates an image like the one below.

This image is of a composite/mosaic image of the Fort Davis Mountains, near Fort Davis Texas. Images in the composite were taken between May 01, 2023 to September 01, 2023. I chose this location because of the pronounce contrast between desert terrain with minimal to no vegetation, and the relatively lush vegetation we can see in in the low lying areas between mountains on the right half of the image. Immediately, we can begin to learn a lot about this terrain, particularly with regard to the collection and flow of water.

NDVI

As mentioned above, the false-color near-infrared band combination (B5, B4, B3) can be used to better understand the health and density of vegetation in a given area. Visualizing vegetation as seen in the image above is one part of that processes, but we also need to quantify vegetation health, which is where NDVI comes in. NDVI stands for Normalized Difference Vegetation Index, which is a commonly used metric in spatial analysis that gives a numeric index between to each pixel which quantifies the amount and health of vegetation in that area.

The resulting value provides and index that can be used to summarize the general amount and health of vegetation in a given location.

A negative value between -1 to 0, indicates the a non-vegetated surface.
A value between 0 to 0.2, indicates vegetation in that area is sparse and/or stressed and not in good health.
A value between 0.5 and 0.8, indicates the vegetation in that area is dense and healthy.
A value greater than 0.81 is rarely ever seen and likely indicates an error in the data or data processing.

Applications

Measuring vegetation with satellite imagery has many applications, especially in the field if agriculture. I am personally interested in applying this type of data to analyzing characteristics of urban environments, such as the relationship between the amount and health of green spaces in cities and the impact on reducing urban heat islands. Bellow are some images extracted using the Google Earth Engine, of various large cities. I am planning on experimenting with Landsat data in personal research efforts and will post more on this topic in the future. If you’re interested in the USGS’s Landsat missions, please see the links below.