{"id":2435,"date":"2019-07-25T10:00:18","date_gmt":"2019-07-24T22:00:18","guid":{"rendered":"http:\/\/blogs.lincoln.ac.nz\/gis\/?p=2435"},"modified":"2023-05-07T00:47:22","modified_gmt":"2023-05-07T00:47:22","slug":"the-grass-is-always-greener-isnt-it","status":"publish","type":"post","link":"https:\/\/blogs.lincoln.ac.nz\/gis\/the-grass-is-always-greener-isnt-it\/","title":{"rendered":"The grass is always greener, isn’t it?"},"content":{"rendered":"

This post looks at the NDVI vegetation index that can be derived from multispectral images and give us useful information about the state of plant health for an area.<\/em><\/p>\n

We’ve been talking about satellite images recently<\/a> and in this post we’ll start going over some of the beneficial information we can get from those images beyond just a pretty picture.\u00a0 In particular, with the right bands, we can do some simple calculations and that tell us useful things about vegetation health and land cover.\u00a0 Many of you will probably be familiar with the NDVI, or the normalised difference vegetation index.\u00a0 We’ll first cover what it tells us and then cover how we derive it.<\/p>\n

To get our heads around this, we’ve got to go back to the very basics of remote sensing, starting\u00a0with the sun<\/a>.\u00a0 We are literally bathed in radiation across an immensely wide range of wavelengths and frequencies from moment to moment by this essential part of human life.\u00a0 We’ve seen it before, but here’s the electromagnetic spectrum diagram again:<\/p>\n

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As remote sensors, our eyes only work with the small range of visible light but other types of sensors give us access to a wider range of wavelengths (energy).\u00a0 The sun emits radiation across the whole range of frequencies and wavelengths shown above and it is primarily this energy the remote sensing works off of – particularly with how different materials reflect different amounts of energy.\u00a0 (It’s worth a quick note to say that there are two kinds of signals we work with in remotes sensing: passive (reflected energy) and active (a signal is sent out and we work with its reflection, e.g. LiDAR<\/a>, radar, sonar.\u00a0 In this post were working with passive reflected energy.)<\/p>\n

Different materials reflect different amounts of energy.\u00a0 In the\u00a0figure below, the responses of bare soil, vegetation and water\u00a0are shown across a range of wavelengths:\"\"<\/a><\/p>\n

We might refer to these lines as spectral signatures and they allow us to differentiate between the materials.\u00a0 For instance, the range of wavelengths that clear water reflects is much smaller than that for vegetation or soil.\u00a0 In other words, at longer wavelengths,\u00a0water would show very low values of reflectance.\u00a0 Bare soil and vegetation reflect across a wider range of\u00a0wavelengths but at differing amounts.\u00a0 So if we had some imagery for wavelengths between, say, 2.2 and 2.6 micrometers, we should be able to differentiate one from the other.\u00a0 That’s the basis for how we use a lot of remotely sensed imagery.<\/p>\n

Let’s now look at plants specifically.\u00a0 Most plants appear green to us because they reflect a lot of green light (as well as higher wavelengths as we see above) that our eyes detect.\u00a0 It turns out they also reflect a lot of infrared energy.\u00a0 The energy they absorb is used for photosynthesis.\u00a0 When a sensor in a satellite (or a UAV for that matter) captures an image, it stores what it sees as intensities.\u00a0 For instance, have a look at a Landsat 7 satellite image below covering much of the area around Christchurch.\u00a0 This is a six band image but for now we’ll just focus on the blue, green, red and infrared bands.\u00a0 Here’s the RGB image:<\/p>\n

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Below I’ll show all four bands for an\u00a0area zoomed in to the blue box above –\u00a0they are\u00a0greyscale images so a pixel’s level of\u00a0 brightness shows how much energy is reflected in that band: total absorption appears black while total reflection is white.<\/p>\n

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Looking at the intensities, there are subtle differences between the first three, but perhaps the greatest difference is with the infrared band.\u00a0 Lyttelton Harbour and the estuary are black, showing that water absorbs infrared energy.\u00a0 Areas of vegetation in the RGB image appear brighter in the infrared as well and there appears to be more red reflected than blue or green in general.\u00a0 Industrial areas of Christchurch (roofs, mainly) reflect a lot in blue, green and red (i.e. they’re brighter) but absorb\u00a0much\u00a0of the\u00a0infrared.<\/p>\n

As a next step we can do a simple raster calculation that allows us to better distinguish vegetation from other materials.\u00a0 We’ll use a standard vegetation index called the NDVI<\/a><\/p>\n

NDVI stands for normalised difference vegetation index<\/a>.\u00a0 We derive it by subtracting the value of the red band from the near infrared band (NIR) and dividing that by the two bands added together (that’s the normalising bit), pixel by pixel, i.e.<\/p>\n

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As a result, we get pixel values that range between -1 and 1.\u00a0 Going further, we can roughly classify the type of surface material in that pixel based on this value.\u00a0 Negative values can usually be classified as snow, cloud or water while values close to zero are usually rock or bare soil.\u00a0 Shrubs and low vegetation often has values between 0.2 and 0.3 while larger values (0.6 – 0.8) may be temperate or tropical forests.\u00a0 The closer the value is to one, the denser or healthier that vegetation is.\u00a0 If we run\u00a0an NDVI analysis for our Christchurch image, here’s what we get:<\/p>\n

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The legend is quite rough – there’s no set symbology for this index and I’ve taken some liberties in the interpretation.\u00a0 The nice thing about this result is that is allows us to map the state of health of the vegetation while distinguishing other land covers.\u00a0 Which also brings us to a key point in not just NDVI, but remote sensing in general.\u00a0 Any imagery we work with is a snapshot in time – the results above capture the state of vegetation at this moment in time only.\u00a0 As anyone remotely familiar with the growth stages of plants will tell you, things change.\u00a0 Thus, any NDVI result will very\u00a0much depend on the point in the growth stage that the imagery was captured.\u00a0 In the image below, we can see that the amounts of reflected energy can change depending on where we are in that process:<\/p>\n

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https:\/\/earthobservatory.nasa.gov\/features\/MeasuringVegetation\/measuring_vegetation_2.php<\/em><\/a><\/figcaption><\/figure>\n

At the height of this plant’s growth it’s reflecting half of it’s infrared energy and absorbing most of its red.\u00a0 Later, the infrared percentage drops off and the amount of red reflected increases – with noticeable changes in the NDVI value.\u00a0 Of course, the changes could also be due to a\u00a0differences between\u00a0plants’ health from drought or disease, so we’d need a bit\u00a0more context to determine what’s driving the difference.\u00a0 While this is a bit of a blessing and a curse, it does allow us to monitor changes in plant health over time.<\/p>\n

So the NDVI is a powerful index for mapping vegetation health and is widely used.\u00a0 There are two primary ways we can derive this using ArcGIS:<\/p>\n