{"id":2480,"date":"2019-08-09T11:16:07","date_gmt":"2019-08-08T23:16:07","guid":{"rendered":"http:\/\/blogs.lincoln.ac.nz\/gis\/?p=2480"},"modified":"2023-05-07T03:20:59","modified_gmt":"2023-05-07T03:20:59","slug":"join-the-band-combinations","status":"publish","type":"post","link":"https:\/\/blogs.lincoln.ac.nz\/gis\/join-the-band-combinations\/","title":{"rendered":"Join the Band (Combinations)"},"content":{"rendered":"

Most satellite images have multiple bands.\u00a0 We can change the way an image is displayed to highlight certain features in the images.<\/em><\/p>\n

With\u00a0kudos\u00a0to Little Feat’s J<\/a>oin the Band<\/a> album, we’ve been looking at satellite images a lot recently and have seen how many satellite images are composed of multiple bands<\/a>, or layers.\u00a0 When working with imagery, we can change the way\u00a0they are displayed to highlight different features on the land surface.\u00a0 Let’s look at an example using a Landsat\u00a08<\/a> image\u00a0zoomed to an area around Christchurch.\u00a0 This image hosts a whopping 11 bands, designated as shown below:<\/p>\n

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We’re spoilt for choice here.\u00a0 Just a few highlights before we move on.\u00a0 The coastal aerosol band is a new one with Landsat 8 and highlights\u00a0shallow water fine particles like dust and smoke in the atmosphere.\u00a0 Bands 2, 3 and 4 are the visible light bands while 5 in Near Infrared.\u00a0 SWIR stands for shortwave infrared which, among other things, allow us to distinguish between\u00a0wet earth from dry earth, and for seeing differences between surface geology not apparent in visible wavelengths.\u00a0 The panchromatic band we can use for pansharpening <\/a>– note the finer resolution.\u00a0 The cirrus band works only with high altitude cirrus clouds –\u00a0 this is a useful layer for removing clouds from images<\/a>.\u00a0 The final two bands sense thermal heat from the ground over two wavelength ranges, and at a much coarser resolution.<\/p>\n

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(Just another quick aside – this image was downloaded from the USGS’s Earth Explore<\/a>r website.\u00a0 The compressed file was originally just under a Gb in size.\u00a0 When uncompressed it ballooned out to 3Gb and\u00a0included\u00a0a separate image for each band as shown at right.\u00a0 I then used the composite bands too<\/a>l to stack them all into one image layer with 11 bands.\u00a0 There are some extra files in here as well.\u00a0 LC815Dec2016.tif is the image we’ll be looking at today.)<\/em><\/p>\n

Let’s add this to a map in Pro and see what we’ve got:<\/p>\n

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Looks a bit funky, but there’s a good reason why.\u00a0 In the legend we can see entries for Red, Green and Blue and the bands that Pro is using to display them.\u00a0 By default, it just takes the first three bands for RGB.\u00a0 We know from the band table above that these don’t match.\u00a0 In the Symbology pane at right, we can rearrange the bands and get a more natural colour display:<\/p>\n

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(In ArcMap we can do the same from the Symbology tab<\/em>.)\u00a0 That’s a bit more like it – let’s zoom in closer to home:<\/p>\n

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Wow – glorious!\u00a0 We’ve got some clouds to contend with, which is common when working with imagery.\u00a0 Note the sediment coming out of the Waimak – might have been a Norwest day.\u00a0 Also check out the disturbed sediments around the northern bays of Banks Peninsula.\u00a0 This image was captured on 15 Dec 2016 (note: the date is in Greenwich Mean Time which would have been the 16th here<\/em>) – here’s what\u00a0the weather\u00a0was like that day<\/a>.<\/p>\n

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(Ain’t the internet amazing?)\u00a0 So, yes, it was a bit norwest that day and more so the previous day.\u00a0 We could go into the metadata to figure out exactly what time the image was captured but let’s not.\u00a0 Instead, now that we’ve seen we can rearrange the bands, let’s play around with this image.\u00a0 Below, I’ve reset the band combinations to 5, 4, 3 – this is the standard “false colour” combination you may have seen before:<\/p>\n

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Why “false”?\u00a0 We’re using the infrared band for red – our eyes can’t see these wavelengths but Pro simply uses the values in the layer as if they were the red values.\u00a0 This allows us to visualise the infrared and is useful for highlighting vegetation – it’s akin to an NDVI layer in some ways, only completely different.<\/p>\n

The next combination is 7, 6, 4 which is suited to distinguishing urban areas and is less susceptible to haze (though we still have clouds):<\/p>\n

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This doesn’t do a spectacular job – perhaps Christchurch really is the “Garden City” so urban areas don’t stand out so well.\u00a0 \u00a0By comparison, here’s one from LA where things stand out much better:<\/p>\n

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https:\/\/www.harrisgeospatial.com\/Learn\/Blogs\/Blog-Details\/ArtMID\/10198\/ArticleID\/15691\/The-Many-Band-Combinations-of-Landsat-8<\/em><\/figcaption><\/figure>\n

A 6, 5, 2 combination gives us a bit more contrast between different agricultural land covers:<\/p>\n

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This is a nice combination for picking out surface water features – all those darker areas north of the Waimakariri are reservoirs.<\/p>\n

Hopefully, this helps highlight the point that, more than just creating pretty (and odd) pictures, these combinations are useful for\u00a0differentiating between different features.\u00a0 If we’re wanting to pick out all the surface water features, the combination above might suit for further analysis and mapping.\u00a0 Thus far, our wetware has been doing a visual interpretation of what these combinations show.\u00a0 We’ve still got to go a few steps further if we want to extract these into a useful GIS layer.\u00a0 We’ll been slowing building up to this, but next stop: image classification.<\/p>\n

C<\/p>\n","protected":false},"excerpt":{"rendered":"

Most satellite images have multiple bands.\u00a0 We can change the way an image is displayed to highlight certain features in the images. With\u00a0kudos\u00a0to Little Feat’s Join the Band album, we’ve been looking at satellite images a lot recently and have seen how many satellite images are composed of multiple bands, or layers.\u00a0 When working with […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-2480","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/posts\/2480","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/comments?post=2480"}],"version-history":[{"count":1,"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/posts\/2480\/revisions"}],"predecessor-version":[{"id":4105,"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/posts\/2480\/revisions\/4105"}],"wp:attachment":[{"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/media?parent=2480"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/categories?post=2480"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/tags?post=2480"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}