{"id":1164,"date":"2015-10-16T00:52:01","date_gmt":"2015-10-16T00:52:01","guid":{"rendered":"http:\/\/blogs.lincoln.ac.nz\/gis\/?p=1164"},"modified":"2023-05-07T00:45:12","modified_gmt":"2023-05-07T00:45:12","slug":"lidar-data-thousands-of-tiny-luminous-spheres","status":"publish","type":"post","link":"https:\/\/blogs.lincoln.ac.nz\/gis\/lidar-data-thousands-of-tiny-luminous-spheres\/","title":{"rendered":"LiDAR Data – Thousands of Tiny Luminous Spheres"},"content":{"rendered":"

This post covers the basics of LiDAR data and how very high resolution DEMs can be derived from LiDAR data.<\/em><\/p>\n

With an appropriate nod to The Bats<\/a>, we’ll cover LiDAR data, which, while sort of luminous but not spherical, are certainly tiny and numerous. \u00a0Thousands is a HUGE underestimate.\u00a0 LiDAR is a form of remote sensing and, though this is in dispute, is generally accepted to stand for Light Detection and Ranging.\u00a0 LiDAR uses lasers to measure distances by monitoring how long it takes a beam of laser light to return to its source after bouncing off stuff.\u00a0 Think back to those old Hollywood war movies involving submarines.\u00a0 High tension as they pinged other subs and ships with sonar, and used that as a way to figure out where the enemy was.\u00a0 LiDAR works in a similar way only using light instead of sound.\u00a0 In practice, this usually means mounting a laser system into an airplane equipped with a high-precision GPS system.\u00a0 The laser system fires out a series of beams but also has a receiver that monitors when the beam reflects back\u00a0to its source.\u00a0 Then we’re back to some basic high school physics.\u00a0 Light travels at a roughly constant speed (3 x 10^8 m\/sec for those playing at home) so if we know\u00a0 how long it took for the signal to return, we can also determine how far away that point is from its source (after dividing by 2).\u00a0 The GPS is then used to locate exactly where the airplane is.\u00a0\u00a0 So picture an airplane flying over the ground, shooting out beams of laser light as it goes.\u00a0 Also picture a collection of points on the ground where the laser beam hits the earth.\u00a0 These points also hit trees, and building, and cars, and people and anything on the ground.\u00a0 Those are our luminous spheres (well, points):<\/p>\n

\"lidarimage\"<\/a><\/p>\n

There’s one more aspect of the system.\u00a0 Anyone who’s flown in an airplane will be familiar with turbulence and how that can change the direction the plane is facing.<\/p>\n

\"3554882983_91788f4582_b\"<\/a><\/p>\n

To take this into account, the airplane is also equipped with an Inertial Measurement Unit (IMU) which brings pitch and yaw into the picture.\u00a0 The three components together allow the three dimensional position of all those points on the ground to be calculated, giving us x, y and z coordinates which we can then map.\u00a0 Since laser light is used, we can have millions if not billions of points on the ground. \u00a0The average spacing between these points may be on the order of half a metre, so from LiDAR data we can derive some very high resolution DEMs.\u00a0 And therein lies the real power of LiDAR – the collection of high resolution and high precision elevation points over large areas in relatively short amounts of time.<\/p>\n

As you might imagine, after the Christchurch earthquakes, several sets of LiDAR data were collected, many of them just after major aftershocks.\u00a0 These data have given us an extensive picture of how the topography of the city and region has been changed by the earthquakes.\u00a0 So now we’ll cover some of the basics of how we can work with these data.<\/p>\n

LAS Files – Raw LiDAR Data<\/strong><\/p>\n

Once the raw data have been processed, the output is a “point cloud” and could be as simple as a text file of x, y and z points.\u00a0 Typically, extra data are collected which we’ll talk about later, but because LiDAR datasets can be so huge, they’re typically encoded in a standard binary format, known as LAS files.\u00a0 These are usually broken up into tiles that are subdivisions of the standard 1:50,000 topomap extents.\u00a0 Sorties were flown just after the September 2010 earthquakes – a total of five sorties were flown between September 2010 and December 2011.\u00a0 In the image below, you can see the tiles for a mission flown in February 2011, just after the Lyttelton earthquake.<\/p>\n

\"Tiles\"<\/a><\/p>\n

Quite an extensive coverage.\u00a0 Note that Lincoln is covered so we’ll focus on those tiles as we’re all pretty familiar with the place.\u00a0 For this dataset there are 4994 separate tiles and a total of, get this, 1,078,978,468 points!\u00a0 Yes, that’s a billion points.\u00a0 For the Lincoln subset we’ll deal with two tiles and a mere 2,161,269 points.\u00a0 One handy way to deal with LAS files is to create LAS Datasets, which give us a quick way to visualise the data.\u00a0 We can use the Create LAS Dataset tool on raw LAS tiles and quickly convert that to something useful:<\/p>\n

\"CreateLASD\"<\/a><\/p>\n

We can point this tool as whole folders or individual tiles.\u00a0 We’re managing a lot of data here and it usually pays to keep the input data to just the tiles you need.\u00a0 Processing can take significant time, just ask the ERST310\/607 students.\u00a0 At the end we can add our LAS Dataset to an ArcMap map and get a sense of what’s in there.<\/p>\n

For those of you who would like to play at home, there’s a copy of this LAS Dataset on J:\\Data\\LincolnLiDAR.\u00a0 Below is the LAS Dataset for our two Lincoln tiles:<\/p>\n

\"LASD\"<\/a><\/p>\n

We can visualise these in a number of ways but adding the LAS Toolbar.<\/p>\n

\"LASToolbar\"<\/a><\/p>\n

We can set our layers to show the points, or an elevation surface built off those points.\u00a0 Below I’ve zoomed in to a set of points – their elevations are colour coded.\u00a0 You can get a sense of their spacings with the satellite image underneath (that’s the Forbes Building we’re looking at).<\/p>\n

\"Points\"<\/a><\/p>\n

Before we turn the elevation surface on, we can go a step further with these points.\u00a0 Zooming out\u00a0and clicking the 3D viewer, we get a window that allows us to move the point cloud around to see more detail – recognise where we are?.<\/p>\n

\"3D\"<\/a><\/p>\n

Maybe not – it’s the Forbes lawn looking toward Ivey Hall, trees and all. \u00a0We can also get a profile by clicking the 3D Profile tool and drawing a line on the points.\u00a0 We can also give it a bit of width and see what that looks like:<\/p>\n

\"Profile\"<\/a><\/p>\n

Ivey Hall and Memorial Hall and the trees at right. \u00a0We should now delve a bit deeper in to the data.\u00a0 Imagine a laser beam encountering a tree.\u00a0 Parts of the beam perhaps hit a leaf.\u00a0 Some of that light gets absorbed, some bounces off and returns to the airplane.\u00a0 This is our so called “first return”.\u00a0 Parts of the beam may continue on through the tree, maybe encountering a branch or another leaf, and as above, some gets absorbed, and some bounces back to the plane – our “second return”.\u00a0 Eventually, the beam hits the ground, some gets absorbed, some bounces back to the airplane – our “last return”.\u00a0 So one beam may have multiple returns as shown below:<\/p>\n

\"Multip8\"<\/a><\/p>\n

If the beams bounce off a hard surface like a building roof, or a car, or your head, there may only be one return.\u00a0 These returns are recorded in the data and can be mapped.\u00a0 If you click on the points button on the LAS toolbar, you’ll see you can show the points as elevation, class or return.\u00a0 Here’s what the returns look like:<\/p>\n

\"returns\"<\/a><\/p>\n

Mostly first returns in these data. \u00a0As to class, the points can also be classified into a set of predefined classes as outlined by the American\u00a0Society for Photogrammetry and Remote Sensing (ASPRS)\u00a0protocol into these classes:<\/p>\n

\"Classifications\"<\/a><\/p>\n

This isn’t even all of them – check here<\/a> for the whole set. \u00a0These classes can be terribly useful – unfortunately, the classes of the earthquake LiDAR sets is pretty inconsistent across the datasets but at a minimum we tend to have ground and non-ground.\u00a0 We’ll see later how this can be quite useful.<\/p>\n

Let’s now turn to the elevation surface:<\/p>\n

\"Elevation\"<\/a><\/p>\n

We can clearly make out buildings and roof shapes now – that’s the Forbes Lawn again. \u00a0The surface button allows us to visualise the elevations, aspect, slope and contour lines.\u00a0 To see the real impact of the returns, let’s look at the Filter menu.\u00a0 Notice that when we click it we’ve got a few options:<\/p>\n

\"FilterMenu\"<\/p>\n

We can choose All, Ground, Non Ground and First Return.\u00a0 All will show us all returns, i.e. everything.\u00a0 This allows us to see the trees and the buildings as well as the ground level.\u00a0 Setting this to Ground will display only those areas classified as ground level, as if we could just sweep everything on the surface aside. \u00a0Here’s what that looks like for the same view as above:<\/p>\n

\"Ground\"<\/a><\/p>\n

The surface has done its best to interpolate the ground surface where the buildings are. \u00a0The Non Ground setting shows us what’s been classified as not ground while Last Return shows only the areas derived from the last returns (which usually doesn’t show major differences for these data).<\/p>\n

While LAS Datasets are quite useful for visualising the raw data, they’re not able to be used for analysis.\u00a0 To do further useful stuff with this we’re best to convert the LAS Datasets to raster grids.\u00a0 There are two main grids we’d want to derive from these data: grids that show the “bare earth” elevations (DEMs) and grids that also incorporate surface features like buildings and trees (DTMs – Digital Terrain Models).\u00a0 It’s a two-step process to get each.\u00a0 We can use the LAS Dataset to Raster tool to create the grid, but we need to first filter the data to use the correct returns.\u00a0 We can do this from the LAS Dataset Properties Filter tab.\u00a0 There are some predefined settings which make life a bit easier.\u00a0 For the bare earth DEM, set this to “Ground”.\u00a0 We also need to set the Filter on the LAS Toolbar to Ground.<\/p>\n

\"Filter\"<\/a><\/p>\n

Next we can use the LAS Dataset to Raster tool.\u00a0 Given the high resolution of these data, we can easily get grids with a 2 m resolution:<\/p>\n

\"BEtoRaster\"<\/a><\/p>\n

This will create a “Bare Earth” raster grid for use with further analysis.<\/p>\n

\"BareEarthDEM\"<\/a><\/p>\n

If we next reset our filters to Non Ground and the run the tool again we’ll get a DTM which includes the surface features:<\/p>\n

\"DTM\"<\/a><\/p>\n

This could be a useful grid for more realistic viewsheds.\u00a0 One of the things we’ll cover later on is how grids like this will allow us to build realistic and accurate 3D models using CityEngine<\/a>.\u00a0 This is one of the promises of LiDAR – accurate extraction of building from the data, still a bit of a holy grail but we’re edging ever closer.<\/p>\n

So using LAS Datasets is one way of creating DEMs\/DTMs.\u00a0 There is another way which is arguably a bit better (i.e. less “noisy”) which involves using Terrain Datasets<\/a>.\u00a0 Contact me if you’d like to hear more about this.<\/p>\n

LiDAR data is a powerful (though not inexpensive) way of creating high resolution elevation grids using lasers, GPS, IMUs and a whole lot of number crunching.\u00a0 I’d hazard to say that most people will probably end up working with the end products of work such as what I’ve outlined above.\u00a0 For example, the LiDAR derived 2 m DEM is available on ECan’s data service<\/a>:<\/p>\n

\"KaikouraDEM\"<\/a><\/p>\n

Stay tuned over the next few weeks for some interesting applications of LiDAR data.<\/p>\n

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

This post covers the basics of LiDAR data and how very high resolution DEMs can be derived from LiDAR data. With an appropriate nod to The Bats, we’ll cover LiDAR data, which, while sort of luminous but not spherical, are certainly tiny and numerous. \u00a0Thousands is a HUGE underestimate.\u00a0 LiDAR is a form of remote […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5],"tags":[],"class_list":["post-1164","post","type-post","status-publish","format-standard","hentry","category-interesting-problems"],"_links":{"self":[{"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/posts\/1164","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=1164"}],"version-history":[{"count":1,"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/posts\/1164\/revisions"}],"predecessor-version":[{"id":4014,"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/posts\/1164\/revisions\/4014"}],"wp:attachment":[{"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/media?parent=1164"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/categories?post=1164"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/tags?post=1164"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}