Introduction to LiDAR

Transcription

1 Introduction to LiDAR Our goals here are to introduce you to LiDAR data, to show you how to download it for an area of interest, and to better understand the data and uses through some simple manipulations. In our project we ll be calculating building and tree heights for your project area. The state on Minnesota has flown LiDAR for the entire state. Data may be interactively downloaded via the MN DNR MNTopo viewer ( or for larger areas via the MNGEO ftp site (ftp://ftp.lmic.state.mn.us/pub/data/elevation/lidar/). Raster DEMs, hillshades, contours, and the raw data or point cloud data may be downloaded, in ESRI geodatabases or in commonly supported formats. We will download LiDAR data and derived layers and prepare them for inclusion in our Campus GIS. We will first work with the processed layers, and later the raw data, primarily for building heights. Since the data are very detailed we often need to merge together several tiles to obtain the complete St. Paul Campus. This is common when working with LiDAR. The data is so detailed that 16 tiles of LiDAR data fit within a typical USGS 1:24,000 scale topo map. Our individual study areas are small, so we shouldn t have that problem here. Open the MnTOPO website, and familiarize yourself with the tools near the upper right. Use the default display and the zoom button in the upper left, and click-hold-drag to pan, to get the approximate Campus area displayed. You may select different basemaps with one of the tools in the upper right, the image basemap is perhaps most useful. You may also turn off the contours via the stacked layers icon. Use the download data tool (disk drive with arrow icon, in the toolbar row, upper/left), specify a polygon that is centered on your individual project area, but about 1.5 to double the height and width you need to include your entire area. Download the 1 m dem, 1m hillshade the raw lidar point cloud data (these will be.las or.laz (a compressed form) You ll see the DEM and hillshade are rasters, and the raw lidar point data are in files with strange codes and a.laz extension. The codes are tile number, las datasets are typically large, and so are usually tiled, and the laz is a compressed format that typically reduces the file size by about 90%. You should copy the DEM and hillshade, and the relevant laz/las files from your download directory to a new directory, in this example we ll call it \LASData. Note that you don t need all the.laz/.las point files that MnTOPO downloaded in the tile. You should refer to the image below, and only copy the files you need for your project area. The boundaries correspond to the tile borders, and the labels the.laz/.las file names. The files are large, will multiply through processing, slow things down, and clutter your workspace. 1

2 Display the DEM and hillshade surfaces. These are largely from an automated process that first tries to identify the bare earth, or ground only returns, and then build a DEM and a hillshade. Notice the odd shapes, sometimes like triangles, on the hillshade surface within the building footprints. These are artifacts of lidar data processing. The algorithms have a hard time identifying the ground near buildings perfectly, so there are often triangular shapes on or near buildings. You also see a roughness and occasional small bumps over grass or forest areas, these are also artifacts of the processing. Also notice the DEM rasters are in UTM15 NAD83(1986). You will need to transform them to CORS96 if you wish to avoid the warnings about datum conflicts when displayed with your other project data. You can do all the following steps in ArcGIS, we will be using LASTools, a widely-used, general purpose lidar processing toolbox that is much faster than processing in Arc, and quite flexible. The UMN has a site licence for LASTools, so we ll be using it for this small exercise. Before we start processing, you should copy the LASTools software to the directory containing your data. The LASTools executable files are on the lab drive under 4295W\LASTools\bin directory You should copy the las2las.exe and las2shp.exe from the \bin directory to your las data directory (\LASData in our example) and the _README.txt files also, if you wish to read all the options available. If you used these tools frequently, then we d add the \bin to the path, so programs would look there automatically, but for lab management purposes, we haven t done that. You should also make two sub-directories within your \LasData directory, one named buildings, and one named tallveg, into which we ll be subsetting the las data. 2

3 When you re done, your directory should look something like this window below. You notice we have the laz files, the executable tools las2las and las2shp, the Readme files, and the buildings and tallveg directories. I ve also converted copies of the.laz to.las files. You don t need to, but notice the.laz files are much smaller than the.las. Because lidar data are often so large, there is usually a tradeoff between saving space and processing speed. We re working with a small area, so it doesn t much matter, but in many real projects it does. You should know a few more things about LiDAR data. Remember, these LiDAR are from pulses of light sent down from a plane, then reflected back from a target. Precise geometry allows operators to determine the x, y, and z coordinates to within a few inches of each object that reflects back a pulse. There can be many returns measured from each pulse, although we re most often interested in the first returns, to measure object heights, and the last returns, to measure ground elevation. The data are processed before they re delivered, and the collecting organization usually applies algorithms to try to detect ground hits (not all last returns are from the ground), object heights (not all first returns are from the tops of objects), and to provide other information about each data point. Most times each return point contains information on the return number (first, last, or in between), the feature classification (2 = ground, 4 = mid-height vegetation, 5 = high vegetation, 6 = building, 9= water, 0 = unclassified), as well as the scan angle, the strength of the return signal, and other information about the point. LASTools, and most other lidar processing tools, allow us to access these data. 3

4 We ll use the command prompt to run LAStools. The command prompt is found via the Windows Start button (pink arrow, right), then in the Accessories folder > Command Prompt (green arrow and circled, at right) This will open a small window that only allows only text entry (see below). The prompt shows the current path string. Want to set the path to your data location. First type in the drive letter, in my case the Y drive, so I type in the drive letter and then hit return to change to that drive: Y: The prompt should now show the drive, in my case: Y:\ I then use the CD (Change Directory) command to change the active directory, typing in: CD downloads\lasdata So the prompt now becomes: Y:\downloads\lasdata I can type DIR (for directory) and then hit return, it should list the directory contents. I can check it 4

5 against the Windows Explorer view of the directory, show at the bottom of page 2, to verify I m at the correct subdirectory. Now, on to processing. LASTools is a bit old school, you don t have access to a GUI, you specify options through flags on a command line. Each flag in LASTools starts with a dash, e.g., -i means the following text specifies the input file(s). This is a bit cumbersome for single file processing, but it is great for writing small programs to do repetitive tasks. Another key bit of information is that the asterisk * stands for match everything, and helps when specifying multiple files The first command we ll type is: Las2las i *.laz odir buildings keep_class 6 This uses the las2las tool to: Read all the.laz files as input (-i means input, the *.laz uses the wildcard symbol * to match anything that comes before a.laz extension). NOTE HERE: IF I ve already uncompressed my.laz files to.las files, I HAVE TO SPECIFY -i *.las as the input Output them to the directory named buildings ( -odir buildings), and Only keep the class labeled 6 (-keep_class 6), in this case the points classified as building points You may get a comment saying the use of LasTools is unauthorized, ignore it. We do have a license, but setting it up requires a few more steps, and the changes they might warn about are minor, and don t change our outcome. We can use CD change to that buildings directory after the command runs, and verify the command worked: 5

6 Notice the files have the same name as the originals (that s why we put them in a clearly labeled sub-directory), but that they re much smaller, the subset building file is 16,337,316 bytes (16.3 Mb), while the full data set was almost 166 Mb; we ve sampled out only the building returns, and the file is less than 10% of the original size. We can apply a similar command to specify keeping only the vegetation classes (3, low veg, 4, medium trees, and 5, high trees): Las2las i *.laz odir tallveg keep_class Note the output directory is set to tallveg here. You should check yourself to make sure the command worked, and note the decrease in size. I convert these las points to ESRI shapefiles with the las2shp command. First, change directories to the buildings sub-directory you created (with cd and the path) Verify from the prompt (type in DIR) that you are in the right location. When in the correct directory, type in:..\las2shp i *.las single_points and hit return. Again, you may get a warning that you re unauthorized, and they re adding small errors to the data, you can ignore them here. The command accesses the las2shp command in the parent directory above the current directory (the..\las2shp), tells it to use all.las files as input ( i *.las), and to output single-point shape files (-single_points), not multipoint files, which cluster points in blocks, reducing file size, but encumbering access. 6

7 Verify the above command worked, first by looking in the buildings directory and checking that shape file(s) appeared, and then by displaying the data in an ArcMap view: You can see the points, here in yellow, more or less match the building locations. The slight mismatch is because of building lean in the aerial photographs. You might remember that orthophotos correct all locations as if they were on the ground, and the tops of buildings aren t. Thus, the tops get displaced, usually outward from the imaging center relative to the camera position. That s why skyscrapers seem to lean in most vertical orthophotos. Now we should look at heights. Open the table for your point shapefile, and add a new field named zheight, double or float, precision 12, scale 1 (it may take a while). When it is done, calculate the z value (remember, right click on column, calculate geometry, z- coordinate of point. Again, this may take a while, there are many points). When the process is finished, use the identify button to query a few building heights. Remember, the values are in meters. Do these values make sense? Are the buildings over 200 meters (650 ft) tall? Why do you think you re getting these values? The Z value represents the orthometric height of the top of the building. This is the height relative to our standard surface, near sea level, and not relative to the local ground surface. We need to subtract the local ground surface from the building z value to get building height. One way is to query the raster below each point, and add it to the table record for each point. We can then subtract the DEM height from the zheight, and get a building height. The Extract Values to Points tool, found at ArcToolbox-> Spatial Analyst Tools-> Extraction does just this. Specifying the input and outputs will query each cell below each point, and add a column to the new data layer specified. This may take quite a long time, so be patient. 7

8 This generates a new column named RASTEVAL that contains the ground elevations near at each point: Now you can calculate the building heights for each point. Do this using what you know about table calculations using ArcMap, creating a new variable called something like build_hght for the height aboveground sampled at each point. You should have a table that looks something like the one to the right: Now, calculate the average height 8

9 from point samples for one of your buildings. There are several ways to do this. Perhaps the easiest is a spatial join. First, create a polygon that represents the building you ve chosen. You may have to display both the LiDAR points and an image of the study area with your building, and adjust the building footprint polygon a bit to account for building lean. After digitizing and saving the building, use Select By Location in the main ArcMap screen to select points from your layer with heights (figure at left). This will only select points that intersect the building footprint polygon. You may then open the table for the output point file (pointheights here), then right click on the build_hght column, and specify Statistics: and it will display the mean of the selected points, among other statistics. 9

10 There are a bit more complicated ways to summarize statistics to multiple polygons, but since this is just to give you short introduction to LiDAR, this manual method will suffice. You would need to do quite a bit of additional processing to clean up the delivered LiDAR data prior to extracting accurate average building heights for multiple buildings, and one building gives you an idea of the data structure and use. Turn in a map of showing your subset LiDAR data for your project area, your building polygon, and your calculated average building height. Use an image for the background, either the HiRez images on the class drive, or one of the WMS layers. Include the usual title, legend, scale, and other standard map elements. Do this same exercise for a single large tree or patch of trees in your project area. Again, create and turn in a second map of your results, with an image background, the tree polygon, your LiDAR vegetation points, the measured average height for one of the trees. 10