Posted on January 2, 2023 (Updated on July 19, 2025)

Produce NDVI on Landsat1 satellite images?

Hiking & Activities

Unearthing Green Secrets: NDVI from Landsat 1 – A Blast from the Past!

Ever wonder what our planet looked like from space way back when? Well, Landsat 1, launched in ’72, gave us our first real peek! It was a game-changer, the original Earth-observer satellite. And even though today’s satellites are packed with fancy tech, Landsat 1’s data is still pure gold for tracking how our environment has changed over time. One super-useful trick we can pull with this old data is calculating the Normalized Difference Vegetation Index – or NDVI, for short. Think of it as a greenness meter! It’s surprisingly simple, yet incredibly powerful for checking up on vegetation health and density. So, how do we actually do this with Landsat 1 images? Let’s dive in.

NDVI and Landsat 1: A Match Made in Heaven

Okay, so what is NDVI, exactly? Simply put, it tells us how green things are by looking at how plants reflect light. Plants are weird – they love bouncing back near-infrared (NIR) light, but they gobble up red light for photosynthesis. NDVI just measures the difference between these two. The formula? Easy peasy:

NDVI = (NIR – Red) / (NIR + Red)

You end up with a number between -1 and +1. Anything negative? Probably water. Close to zero? Think bare ground – rock, sand, maybe snow. But positive numbers? That’s where the magic happens! The higher the number, the thicker and healthier the vegetation.

Now, Landsat 1 wasn’t exactly high-tech by today’s standards. It used something called the Multispectral Scanner System (MSS), which saw the world in four “colors,” or bands:

  • Band 4: Green light (0.5 – 0.6 µm)
  • Band 5: Red light (0.6 – 0.7 µm)
  • Band 6: Near-Infrared (0.7 – 0.8 µm)
  • Band 7: Near-Infrared (0.8 – 1.1 µm)

To get our NDVI, we mainly need Band 5 (Red) and either Band 6 or 7 (NIR).

Digging Up the Data and Getting It Ready

  • Finding the Goods: Landsat 1 was snapping pics from July ’72 to January ’78. Where do you find this stuff? The USGS EROS Center is your best bet. They’ve got all sorts of Landsat data, including the old Landsat 1 MSS images. You can usually download it for free from places like EarthExplorer, LandsatLook, and GloVis.
  • Cleaning It Up (Radiometric Correction): Raw Landsat 1 data comes as digital numbers (DNs). Think of them as raw scores. We need to turn these into something meaningful – like radiance or reflectance. Luckily, the data comes with metadata that tells you how to do this. It basically corrects for quirks in the sensor and how the sun was shining that day.
  • Dealing with the Atmosphere (Atmospheric Correction): The atmosphere is a pain! It messes with the light before it even reaches the satellite. So, we need to try and remove these atmospheric effects to get a clearer picture of what’s actually on the ground. There are fancy techniques for this, like Dark Object Subtraction (DOS). Keep in mind that while later Landsats have fancy “Level-2” data that’s already atmospherically corrected, Landsat 1 doesn’t. We have to do it ourselves!
  • Getting It Straight (Geometric Correction): You want your image to line up properly with maps and other data, right? So, make sure it’s georeferenced correctly. If you’re using Landsat Collection 1 Level-1 Precision and Terrain (L1TP) data, you’re usually in good shape here.

    • Crunching the Numbers and Seeing the Green

  • Pick Your Bands: Find the red and NIR bands in your cleaned-up Landsat 1 image. Band 5 is your red, and Band 6 or 7 is your NIR.
  • Do the Math: Plug those reflectance values into our NDVI formula. You can use image processing software like QGIS or ENVI. Or, if you’re a coding whiz, Python is your friend.
  • Analyze the Results: Your final NDVI image will have values from -1 to +1. Now, look at the patterns! Where are the high NDVI values? That’s where the lush vegetation is hanging out.

    • A Few Bumps in the Road

  • Old Tech: Let’s be real, Landsat 1 is ancient history in satellite terms. The data isn’t perfect. The resolution is lower (80 meters) than what we’re used to today.
  • Comparing Apples and Oranges: Trying to compare NDVI from Landsat 1 with, say, Landsat 8, can be tricky. The sensors are different, the “colors” they see are slightly different… It can skew the results. You might need to do some extra calibration to make the data comparable.
  • Pesky Atmosphere, Again: If you don’t correct for the atmosphere properly, your NDVI values will be off. Garbage in, garbage out, as they say!
  • Waiting Game: Landsat 1 didn’t exactly have a speedy revisit time. It took 18 days to image the same spot again. Add in cloud cover, and you might be waiting a while for a good image.

    • Final Thoughts

    Calculating NDVI from Landsat 1 images is like opening a time capsule. It gives us a glimpse into the past and helps us understand how vegetation has changed over decades. Sure, there are challenges – the data’s old, and you need to be careful with processing. But trust me, it’s worth the effort. For researchers and anyone interested in long-term environmental trends, Landsat 1 NDVI is a goldmine!

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