Libra – The Landsat imagery browser you will love(developmentseed.org)
developmentseed.org
Libra – The Landsat imagery browser you will love
http://www.developmentseed.org/blog/2015/01/22/announcing-libra/
17 comments
I was expecting the Landsat images to be something like satellite view in Google maps. I realize that close-up views in Google maps are collected by airplanes, not satellites. I'm not expecting to see individual cars and houses in the Landsat images, but the images I looked at were very disappointing.
The ones I downloaded were TIFF files, 8k x 8k (or sometimes 16k x 16k), 16-bit grayscale, covering large areas (~100km square), and if I zoomed-in the image quickly became blurry.
With photos that I take with a digital camera that are only 2k x 2k, I can zoom in quite a lot and see a lot of detail. The Landsat images seem to be much larger than they need to be for the level of detail they provide.
EDIT: To give an example, here's San Francisco that I zoomed-in on and cropped:
http://imgur.com/IoRFgCu
You can see that zooming more will not make it better. Is that the best we get from Landsat?
Also, why does the download contain a dozen files (named xxxB1.TIF, xxxB2.TIF, xxxB3.TIF, etc.) that seem to cover the same area?
The ones I downloaded were TIFF files, 8k x 8k (or sometimes 16k x 16k), 16-bit grayscale, covering large areas (~100km square), and if I zoomed-in the image quickly became blurry.
With photos that I take with a digital camera that are only 2k x 2k, I can zoom in quite a lot and see a lot of detail. The Landsat images seem to be much larger than they need to be for the level of detail they provide.
EDIT: To give an example, here's San Francisco that I zoomed-in on and cropped:
http://imgur.com/IoRFgCu
You can see that zooming more will not make it better. Is that the best we get from Landsat?
Also, why does the download contain a dozen files (named xxxB1.TIF, xxxB2.TIF, xxxB3.TIF, etc.) that seem to cover the same area?
First off, you need to realize that the landsat program goes back a _long_ ways (the first generation was launched in the late 70's). Have a look at the Wikipedia article for more details: http://en.wikipedia.org/wiki/Landsat_program
Most of the imagery you're looking at is probably from Landsat 7, which launched in 1999 and is still (partially) functional and active today. (Edit: scratch that, it looks to be all very recent Landsat 8 data.)
Due to the need to have comparable datasets over multiple generations of satellites, newer platforms (e.g. the recently launched Landsat Data Continuity Mission, a.k.a. Landsat 8) deliberately target similar spatial resolutions as the older platforms (This is a good thing!).
Next, Landsat as a program is focused on uses like landcover classification, monitoring broad changes over time, etc. There's a higher-resolution 15m pan-chromatic band on the past few generations, but the real value is in the lower-resolution (30m, 60m, or 100m) multispectral bands. The visible bands are just the tip of the iceberg, and they actually aren't the most useful for the "main" purposes of landsat (land cover classification, etc).
For many uses cases in remote sensing, we're much more interested in having good spectral coverage and a long record of changes than in having very high spatial resolution. Landcover classification has always been a key use case for the Landsat missions. Most of the information is in the spectral signature of a given pixel. (Some recent methods use texture, etc, but it's less useful than you'd think.) There's a trade-off between the wavelength being observed, the spatial resolution, and the exposure time. Because all bands need to be acquired in a similar amount of time, longer wavelength bands (e.g. mid-far infrared) will always have a lower spatial resolution than panchromatic or visual bands. (The transparency of the atmosphere is actually the main player here, but I'll skip the details.)
The Landsat program has been invaluable in documenting and understanding human, ecologic, geologic, and hydrologic changes over the past >30 years. We don't have anything comparable! It's really, really incredible imagery.
Most of the imagery you're looking at is probably from Landsat 7, which launched in 1999 and is still (partially) functional and active today. (Edit: scratch that, it looks to be all very recent Landsat 8 data.)
Due to the need to have comparable datasets over multiple generations of satellites, newer platforms (e.g. the recently launched Landsat Data Continuity Mission, a.k.a. Landsat 8) deliberately target similar spatial resolutions as the older platforms (This is a good thing!).
Next, Landsat as a program is focused on uses like landcover classification, monitoring broad changes over time, etc. There's a higher-resolution 15m pan-chromatic band on the past few generations, but the real value is in the lower-resolution (30m, 60m, or 100m) multispectral bands. The visible bands are just the tip of the iceberg, and they actually aren't the most useful for the "main" purposes of landsat (land cover classification, etc).
For many uses cases in remote sensing, we're much more interested in having good spectral coverage and a long record of changes than in having very high spatial resolution. Landcover classification has always been a key use case for the Landsat missions. Most of the information is in the spectral signature of a given pixel. (Some recent methods use texture, etc, but it's less useful than you'd think.) There's a trade-off between the wavelength being observed, the spatial resolution, and the exposure time. Because all bands need to be acquired in a similar amount of time, longer wavelength bands (e.g. mid-far infrared) will always have a lower spatial resolution than panchromatic or visual bands. (The transparency of the atmosphere is actually the main player here, but I'll skip the details.)
The Landsat program has been invaluable in documenting and understanding human, ecologic, geologic, and hydrologic changes over the past >30 years. We don't have anything comparable! It's really, really incredible imagery.
Landsat images aren't taken with the primary intention of showing human level detail. The imaging sensors are designed to image groundcover. In fact landsat doesn't strictly image visible light, it's imaging bands are in visible, infrared and near infrared depending on the satellite used. [1]
This can be used for all kinds of agricultural, environmental, and climate change research.
Having satellite images that are higher in resolution wouldn't be particularly helpful for these purposes and would instead be counter to their purpose because it would take so much longer to image any given area. With large images you can, within several days have images of the entire earth which is much more useful for any temporally sensitive research. To say nothing for the computing and storage requirements.
http://landsat.usgs.gov/band_designations_landsat_satellites...
This can be used for all kinds of agricultural, environmental, and climate change research.
Having satellite images that are higher in resolution wouldn't be particularly helpful for these purposes and would instead be counter to their purpose because it would take so much longer to image any given area. With large images you can, within several days have images of the entire earth which is much more useful for any temporally sensitive research. To say nothing for the computing and storage requirements.
http://landsat.usgs.gov/band_designations_landsat_satellites...
Thank you, but I still don't get this part:
> The Landsat images seem to be much larger than they need to be for the level of detail they provide.
For the level of detail I'm seeing, the same information could be represented in a 2MB download (rather than 600-700MB that's currently downloaded).
> The Landsat images seem to be much larger than they need to be for the level of detail they provide.
For the level of detail I'm seeing, the same information could be represented in a 2MB download (rather than 600-700MB that's currently downloaded).
Each image is a different band. Landsat imagery is multispectral imagery. For Landsat 8 data there are 11 bands and for Landsat 7 data there are 8 (if I recall correctly?). Each band is typically distributed as an uncompressed, 16-bit tif.
JPEG and similar compression techniques are very effective at compression image data without losing _visual_ information. However, it's very important not to use lossy compression (such as JPEG) for scientific data such as this. Our eyes may not mind, but classification algorithms do.
For various reasons, Landsat data is typically distributed and stored as a zip of uncompressed tiffs. The actual bit depth is often a bit lower than 16 bit (depends on the band), but it's easiest to distribute it as 16 bit to avoid the need for unusual file formats.
At any rate, you can certainly compress these images quite a bit. However, lossless compression algorithms are always at a disadvantage compared to lossy compression (e.g. JPEG). There are plenty of more advanced lossless compression algorithms (e.g. JPEG2000 or MrSid for this exact use case) than putting uncompressed images in a zip file. Nonetheless, they only shave ~10-20% (ballpark: it varies a lot) of the file size off compared to zip (they have other huge advantages when it comes to accessing the data, though).
However, the data needs to be easily used by a wide range of applications and users. It's easy to unzip a file and then have "raw" uncompressed images. For distribution, the drop-off in usability just isn't worth the savings in file size. Also, there are a lot of legacy applications out there that wouldn't react well to the USGS suddenly changing the way it distributes Landsat data.
JPEG and similar compression techniques are very effective at compression image data without losing _visual_ information. However, it's very important not to use lossy compression (such as JPEG) for scientific data such as this. Our eyes may not mind, but classification algorithms do.
For various reasons, Landsat data is typically distributed and stored as a zip of uncompressed tiffs. The actual bit depth is often a bit lower than 16 bit (depends on the band), but it's easiest to distribute it as 16 bit to avoid the need for unusual file formats.
At any rate, you can certainly compress these images quite a bit. However, lossless compression algorithms are always at a disadvantage compared to lossy compression (e.g. JPEG). There are plenty of more advanced lossless compression algorithms (e.g. JPEG2000 or MrSid for this exact use case) than putting uncompressed images in a zip file. Nonetheless, they only shave ~10-20% (ballpark: it varies a lot) of the file size off compared to zip (they have other huge advantages when it comes to accessing the data, though).
However, the data needs to be easily used by a wide range of applications and users. It's easy to unzip a file and then have "raw" uncompressed images. For distribution, the drop-off in usability just isn't worth the savings in file size. Also, there are a lot of legacy applications out there that wouldn't react well to the USGS suddenly changing the way it distributes Landsat data.
Show us / try it yourself! Take one of the bands (~125 Megabytes) and losslessly compress it as small as you can. Then share the results. Science is fun!
Also be aware that TIFF is super fast, which is important for some applications. And the scene compilation is already compressed with bzip2.
Also be aware that TIFF is super fast, which is important for some applications. And the scene compilation is already compressed with bzip2.
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It's essentially the raw sensor data. So each pixel is representing a large area with perhaps several features contained within it (spatial resolution is ~30 or 60 meters depending on the band). The images you are talking about zooming in on have many pixels representing each feature.
Any idea why temporal coverage is so spotty? According to http://landsat.usgs.gov/landsat8.php Landsat 8 repeats every 16 days, but for many of the spots there are only images that are several months old.
Some areas appear spotty because the default filter is set to exclude anything above 20% cloud cover. You can use the filters at the top to broaden the range:
http://cl.ly/image/3U3H120u2H0f/libra-filter-ex.gif
http://cl.ly/image/3U3H120u2H0f/libra-filter-ex.gif
It could be that Libra throws out images with cloud cover percentages that are too high. For some areas with frequent cloud cover, that might mean that there are only a handful of useable Landsat images each year.
Oh how I am not a fan of the "non-descript icon only" trend... I had no idea there was a filter. Thank you!
Try changing the cloud cover filter to see more scenes. By default it is only showing scenes that are relatively free of clouds.
That was it, thanks! I had no idea there was any kind filter active.
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One thing that I don't understand is why coverage appears to be poor or non-existant at high latitudes (say, above 70 degrees). Shouldn't there be a lot of images here because of the near-polar orbit (i.e. high latitudes should be sampled frequently)?
Also, a graticule would be handy, as it can be tricky to find a specific place with the simple basemap.
But thanks very much for this tool!