LiDAR Surface Models in ArcGIS Pro

this video will show you how to take a lidar point clouds and turn them into raster surface models using ArcGIS Pro ArcGIS pro does allow you to work directly with point clouds in their native llas format simply add them to the map or scene from the catalog and you can get in to view them adjusting attributes note that when you're zoomed out the point cloud won't display it will simply display the bounding box I can choose to view only a subset of points by their classification or return attributes here you can see me going into the layer properties going into the la s filter dialog and there I'm turning off all the classifications except those points assigned too close to the ground class the points are still being displayed by elevation but only those points with the ground cloths are displayed now I'll go back and activate all points regardless of their classification you can do the same things with return values or classification flags by going into the symbology menu we can symbolize the points based on elevation cloths return her intensity here I'm switching from elevation which is the default symbology to displaying it by the cloth which is the point classification now switching it over again to the return information so you can see the return number for each point and then finally back again to elevation although you can work with point clouds directly if your workflow involves generating surface models it's likely you'll want to put your point clouds within an L a s data set an LS data set is simply a container for point clouds it exists outside of a geo database and you can create one within any folder point cloud stored within lis datasets have additional functionality that point clouds stored natively don't have you can populate your la s data set with one or more point clouds using the add files to la s data set to your processing tool simply select the input Elias data set that you created and then choose the le s files or folder containing the OAS files and then click run to add those files to your la s data set with our point cloud populated within our la s data set we'll now take advantage of some of the functionality within the la s data set layer tools dialog instead of using the symbology dialog I'm going to go over under the appearance tab and use the la s points dialog to control the points that I'm viewing note that the la s points dialog is not a replacement for the symbology dialog but it's complimentary and very useful for example quickly sharing the ground only points or the non ground points computing statistics on your la s data set can give you greater insight into your point clouds and help you determine crucial factors when you convert from point cloud to raster formats you can compute statistics using the la s data set statistics tool by default statistics are stored within the la s data set but you also have the option to export them to an external text file the external file is simply tabular data and the benefit of having it in this format is you can open it up in a spreadsheet database or data visualization software package for further analysis as you can see the lis dataset statistics contain information on both the returns and the classifications including both total point counts percents Z values and other useful information typically when generating a surface model you only want it to contain a subset of the points this could be by return information or by classification code the first step in this is to use the make la S dataset layer tool in this particular case I'm selecting my input la s dataset I'm creating a new layer this is not an output file but a virtual layer and I'm going to only include ground points there's a design the la s classification of two in this case into this lis layer clicking run will produce a new virtual layer similar to a standard layer that you would create an ArcGIS pro that contains a subset of these la s dataset in this case all returns but only those points that are classified as ground I'm now creating a second la s dataset layer this time for those points they're first returns either first of many or standalone first returns but they can have any class code for their classification value when I generate my raster datasets from these point clouds I'll use the ground points layer to generate a de M or digital elevation model representing the Bharath topographic surface and then I'll use the first return layer to generate a dsm or digital surface model a raster representation of the true 3d surface of all features including buildings and trees which wouldn't exist in the ground point classification and hence wouldn't be present in the de M prior to interpolating your point clouds into a raster data set it's crucial that you understand the approximate resolution of your lidar point cloud to do this I'm going to access the la s dataset statistics that we computed in an earlier step by right-clicking on my la s data set going into the properties and selecting statistics a point spacing of 0.5 59 in this case means that every 1/2 meter because our coordinate system is a meters we have a lidar point in this particular data set when I computed the LA s data set statistics earlier we examine the tabular output we can view their same statistics located here within the lis data set properties information on the classification attributes return and classification flags is all available to convert my first return and ground point layers to respective raster surface models I'm going to go up to the data menu click on the export button and choose raster this will launch the geoprocessing tool to export the lis datasets to raster format starting off I'm going to use the la s data set to raster geoprocessing tool to take my ground points layer to produce a de m or digital elevation model once again this represents the Bharath topographic surface I'm storing my output raster in a geodatabase would be very appropriate to store it in a folder just by adding the dot TI f or IMG extension you'll notice that I have options to select the interpolation type you'll probably want to play around with these but I've had good success using triangulation and natural neighbor with no thinning when I get down to the South link type I'm using the cell size and this is where I want to apply my knowledge of the la s dataset statistics specifically the point spacing my point spacing was 0.5 59 for this point cloud I'm gonna drop the cell resolution down to 0.5 because my point cloud included some water areas where there are no lidar returns it's the absence of those returns that allows me to make an educated guess as to what the actual sampling value or cell size should be I'm now going to replicate that entire workflow with the only change being that my input la s dataset is now going to be my light our first returns layer when the signal emanating from the lidar sensor comes into contact with a hard service like a building or pavement it's going to generate a single return but when it comes into contact with something a little bit more porous like tree canopy there can be multiple returns from a single pulse as a result by using the first returns will have a highest hit or digital surface model dsm representing the highest point of all the features surfaces across the landscape you may find it useful to generate other surface models such as a surface model from the lost returns or from even the intensity values but for this particular example we'll stop which is the de M digital elevation model and the DSM the digital surface model once those two processes are complete I have my de M which represents the bare earth and I have my DSM which represents the top of all features I'm now going to use some raster functions and subtract the de M from the DSM to create an N DSM or normalized digital surface model by subtracting the de M from the DSM I'll produce a layer that represents the height above ground and that's what the normalized digital surface model is as raster functions create virtual layers this new output layer is not a raster file but rather a virtual data set that exists within my arcgis pro project now that I have my output and DSM layer I can go into the table of contents and give it a more meaningful name to improve the calligraphic appearance I'm going to go back into my raster functions generate a hill shade put that hill shade underneath my ndsm layer apply some transparency and then symbology to my ndsm layer the result is that my data are now displayed in a way that makes it much more easy for me to identify features on the landscape jumping ahead you'll see that I've used the same approach from my de M of my DSM I've generated a hill shade using a raster function and applied symbology from the symbology menu turning on and off these layers allows us to explore the differences between the de m the DSM and the ndsm the de M is the bare earth typographic surface and the pixel values represent the absolute Heights relative to the vertical datum the DSM is the true 3d surface including features such as trees and buildings and utility lines and the pixel values are also the absolute values relative to the vertical datum because the ndsm is action between the DSM and the DM the pixel values represent the height of features above ground so clicking on the roof of an individual building the pixel value represents that height in map units which in this case 4 meters to summarize the steps we first populated our point cloud into an L a s data set we then generated la s data set layers for the appropriate points that we wanted to use in the raster surface models and then we converted those layers to raster surface models before symbolizing them

Channel: Jarlath O'Neil-Dunne

Views: 14,115

Keywords: ArcGIS, LiDAR, Remote Sensing, GIS

Id: L4tVXARSrUo

Length: 10min 9sec (609 seconds)

Published: Sat Jan 05 2019