Using Saga GIS' Terrain Analysis Swath Profile (interactive) function

Saga GIS has a couple of interactive terrain profiling functions, a single profile and a swath profile. This post shows how to use the interactive swath terrain profile command.

Load a grid file such as a USGS SRTM file

1. Run Saga GIS. Select Geoprocessing | File | Grid | Import | Import USGS SRTM Grid.
   
   The Import USGS SRTM Grid dialog box appears.
   
2. Click the Browse button in the Files field. Choose an SRTM file e.g. N21E093.hgt.
   
   
3. Click Open.
   
   
4. Click Okay.
   
   The SRTM file is loaded and shown in the Data tab.
   

Start the swath profile command

1.  Under the Data tab, mouse right click on the loaded grid file e.g. N21E093.
2. In the pop up context menu, choose Add to Map.
   
   The grid file is displayed in a map window.
   
3. Select Geoprocessing | Terrain Analysis | Profiles | Swath Profle [Interactive].
   
   The Swath Profile dialog box appears.
   
4. In the Grid system field, choose the loaded grid file's system e.g. 0.000833; 1201x 1201y; 93x 21y option.
5. In the DEM field, choose the loaded grid file, e.g. N21E093.
6. Optional. Change the Swath Width if necessary.
   
   The message Interactive tool execution has been started is displayed in the Messages pane.

Digitize the swath profile

1. In the Toolbar, click the Action icon (that looks like a black NW arrow).
2. In the map window, click a few points to draw the swath profile.
   
   
   
3. To complete the drawing, press the mouse right button.
4. To exit the interactive command, select Geoprocessing | Swath Profle [Interactive].
   
   The Tool Execution prompt appears.
   
5. Click Yes.
   
   The message: "Interactive tool execution has been stopped" is shown in the Messages pane.
   
   Note: This may take a while as the command will sample the terrain to calculate the points.

Display the swath profile graphically

1.  In the Data pane, mouse right click on the newly created profile points e.g. Profile [N21E093].
   
   
2. In the pop up menu, choose Attributes | Diagram.
   
   The Properties dialog box appear.
   
3. In the X Axis Values field, choose D (for Distance).
4. In the X Axis Label field, choose D.
5. In the Attributes field, toggle on Z, Z [min], Z[max].
6. Set other options if necessary.
7. Click Okay.
   
   The profile line(s) are displayed.
   

Calculate the difference between ground control points and a DEM using Global Mapper

Somebody asked me how to find out the elevation difference between the digital elevation model (DEM) and independently surveyed ground control points (GCP). Any terrain software such as Global Mapper should have the tools to calculate the differences. In this post, I shall illustrate the steps to use Global Mapper to find out the elevation differences.

It is not as straightforward as other software as there is no single tool in Global Mapper. Basically, the DEM and the ground control points layer must be loaded together first; then the DEM elevation under the control point must be added to the GCP layer as an ELEVATION database attribute field. With the database attributes having the GCP elevation and DEM elevation fields, a database field subtraction can be done to calculate the difference between the DEM elevation and the GCP elevation.

Load the DEM and GCPs

1. Start Global Mapper. Load in a DEM layer e.g. dtm.asc.
   
   
   
   
2. Load and display the vector GCP layer e.g. gcp.shp.
   
   
3. Optional. Select Search | Search by Attributes, Names, and Description.
   
   The Search by Attributes, Names, and Description dialog box appears.
   
   Note the GCP elevation field e.g. GPS_ELEVAT. 

Append the DEM elevation to the GCP

1. Select File | Export Vector Format.
   
   The Select Export Format dialog box appears.
   
2. Choose a format e.g. Shapefile. Click OK.
   
   The Shapefile Export Options dialog box appears.
   
3. Toggle on Generate 3D Features Using Loaded Elevation data.
   
4. Toggle on Export Points.
   
   The Save As dialog box appears.
   
5. Type in the new GCP layer name e.g. gcp_dsm_accuracy.shp. Click Save.
   
   
   The GCP layer is exported out to a new vector file. The DEM elevation for each GCP point is appended to the output file
   .
6. Press ALT+C.
   
   The Overlay Control Center dialog box appears.
   
7. Select the loaded GCP layer e.g. gcp.shp. Click Close Overlay.
   
   The GCP layer is unloaded.

Calculate the difference between GCP and DEM elevations

1. Load in the GCP layer appended with the DEM elevations exported previously e.g. gcp_dsm_accuracy.shp.
   
   
2. Press ALT_C.
   
   The Overlay Control Center appears.
   
   
3. Right click on the GCP layer.
   
   A pop up menu appears.
   
   
4. Choose CALC_ATTR.
   
   The Setup Attribute Calculation dialog box appears.
   
5. In the Select Existing or Create New Attribute to Assign Calculated Values to field, type in a name e.g. hgtdiff.
   
6. In the Source Attribute field, choose the GCP elevation field, e.g GPS_ELEVAT.
   
7. In the Operation field, choose Subtract.
   
8. Toggle on Use Attribute Value. Choose the DEM elevation field e.g. ELEVATION.
   
   
   
9. Click OK.
   
   The differences are calculated and inserted into the hgtdiff field.
   
   
   The Feature Information dialog box below shows the new field with the calculated difference for one of the GCP feature.
   

Remove noisy spikes from LiDAR data using SAGA GIS and SRTM data

LiDAR data collected from the field contains noise in the form of spikes or zingers (extremely high or low points) and/or clouds. Typically these erroneous points are reclassified as noise from the point cloud by using high-low filters, median filters or other statistical methods. An example is shown in the screenshot below.


I have in mind using the globally available Shuttle Radar Topography Mission (SRTM) data to form an envelope to filter away the extreme noise points using SAGA GIS. SRTM data can be downloaded from http://www2.jpl.nasa.gov/srtm/.

From the SRTM elevation data, a height value can be added and subtracted to form a volume envelope. Any LiDAR points inside the envelope are valid points while any points outside the envelope are noise. The following illustrates a possible workflow.

Load and reproject an SRTM tile to match the LiDAR data

1. Start SAGA GIS.
2. Select Modules | File | Grid | Import | Import USGS SRTM Grid.
   
   The Import USGS SRTM Grid dialog appears.
   
   
3. Click the Files field. Then click the browse [...] button. Browse and select an SRTM file e.g. N39W084.hgt. Click Open. Click Okay.
   
   The SRTM data is loaded.
   
   Note: the SRTM data is in a geographical latitude-longitude coordinate system while the LiDAR data is in a projected coordinate system e.g. UTM 17 North.
   
4. Select Modules | Projection | Coordinate Transformation (Grid).
   
   The Coordinate Transformation (Grid) dialog box appears.
   
   
5. In the EPSG Code | Projected Coordinate Systems field, choose a coordinate system e.g. WGS 84 / UTM zone 17N.
6. In the Data Objects | Grid system field, choose the SRTM grid system e.g. 0.000833; 1201x 1201y; -84x 39y.
7. In the Data Objects | Grid system | source field, choose the SRTM grid layer e.g. 01.N39W084.
   
   
   
8. Click Okay.
   
   The User Defined Grid dialog box appears.
   
   
   
9. Click Okay.
   
   The SRTM grid is reprojected into UTM 17 North.
   
   Note: there are now two SRTM grid layers with the same name. For clarity, we shall remove the first SRTM grid layer.
   
10. In the Data tab of the Workspace pane, select the first SRTM grid layer e.g. 01. N39W084.
    
11. Press the mouse right click button. In the pop up menu, select Close.
    
    
    
12. Click Yes and Okay.
    
    The original SRTM grid layer is removed.

Load the LiDAR LAS file

1. Select Modules | File | Shapes | Import | Import LAS Files.
   
   The Import LAS Files dialog box appears.
   
2. Click the Input file field. Click the browse [...] button and select and open a LiDAR LAS file .e.g. noisy_serpent.las.
   
3. In the Attributes to import besides x,y,z list, toggle on all the attributes you want to retain.
   
   
   
4. Click Okay.
   
   The LAS file is loaded as a PointCloud.
   

Assign the SRTM elevations to each LiDAR point

1. Select Modules | Shapes | Grid | Grid Values | Add Grid Values to Shapes.
   
   The Add Grid Values to Shapes dialog box appears.
   
2. In the Shapes field, choose the LiDAR point cloud layer e.g. 01. noisy_serpent.
   
3. In the Grids field, click the [...] button. Choose the reprojected SRTM grid layer e.g. 01.N39W084.
   
   
   
4. Click Okay.
   
   The SRTM elevation values are added as a new attribute to the LiDAR point cloud layer.
   

Create a valid elevation indicator attribute from the LiDAR point and the SRTM elevation values
This part is the key to the workflow. The Calculator is used to create a field that counts two tests: (1) if a point is above the bottom of the SRTM envelope and (2) if a point is below the top of the SRTM envelope. A count of 2 means the point is within the SRTM envelope. 

1. Select Modules | Shapes | Point Clouds | Tools | Point Cloud Attribute Calculator.
   
   The Point Cloud Attribute Calculator dialog box appears.
   
2. In the Point Cloud field, choose the LiDAR point cloud layer e.g. 01. noisy_serpent.
3. In the Result field, choose [create].
4. In the formula field, type in the following (without spaces):
   
   ifelse(gt(c,n-100),1,0)+ifelse(lt(c,n+100),1,0)
   
   Note: the point cloud attribute fields are in alphabetical order a, b, c....etc. c indicates the z attribute, n indicates the SRTM elevation field in this example and 100 is half the thickness of the envelope around the SRTM elevation.
   
   Note: the statement says that if the point z is greater than the bottom of the SRTM envelope (n-100) then assign 1, otherwise assign 0; and if the point z is less than the top of the envelope (n+100), then add another 1. Otherwise add 0.
   
5. In the Output Field Name field, type in valid.
6. In the Field data type field, change to 2 byte signed integer.
   
   
   
   
7. Click Okay.
   
   The Shapes point layer is created with a new attribute field valid containing values 1 and 2.
   

Filter out the LiDAR points outside the SRTM envelope

1. Select Module | Shapes | Points | Point Filter.
   
   The Points Filter dialog box appears.
   
2. In the Points field, choose the previously created point cloud layer e.g. 02. noisy_serpent_valid.
   
3. In the Attribute field, choose the field created previously e.g. valid.
   
4. In the Filtered Points field, choose [create].
   
5. In the Filter Criterion field, choose keep maxima (with tolerance). Leave the tolerance at 0.
   
   
   
   
6. Click Okay.
   
   The filtered Shapes point layer 01.noisy_serpent_valid[Filtered] is created. This layer contains only the LiDAR points inside the SRTM envelope.
   

Save as LAS

Before the filtered points can be saved as a LAS file, the Shapes point layer has to be converted to a point cloud layer. 

1. Select Module | Shapes | Point Cloud | Conversion | Point Cloud from Shapes.
   
   The Point Cloud from Shapes dialog box appears.
   
2. In the Shapes field, choose the Shapes point layer e.g. 01. noisy_serpent_valid[Filtered].
   
3. In the Z Value field, choose Z.
   
4. In the Output field, choose all attributes.
   
   
   
5. Click Okay.
   
   The Shapes point layer is converted to a point cloud layer 03. noisy_serpent_valid[Filtered].
   
   
6. Select Modules | File | Shapes | Export | Export LAS Files.
   
   The Export LAS Files dialog box appears.
   
7. In the Point Cloud field, choose the filtered point cloud layer e.g. 03. noisy_serpent_valid[Filtered].
   
8.  For each LAS field to export out, change the [not set] value to the appropriate attribute field.
   
9. If necessary, enter values in the Offset X, Offset Y fields that match the original LAS file.
   
10. Optional. Change the Point Data Record field accordingly e.g. to version 2.
    
11. In the Output field, define the output file name e.g. clean_serpent.las.
    
    
    
12. Click Okay.
    
    The filtered output LAS file is created without the zingers.
    

Create geo-referenced heat maps Google Mapplet

Comma-separated-values (CSV) of statistical data in the format latitude, longitude, and magnitude can be imported and visualized as heat maps in Google Maps using this custom mapplet. An example screenshot is shown below.



While the heat maps feature is already possible using the Google Docs FusionTable object, the heat maps created from this Mapplet is done using the HTML5 canvas object via the Javascript heatmap.js library. On  top of that, the Mapplet provides the option to export out the heat map image file along with supporting geo-referenced information in the form of world and projection files.

To run the Mapplet, click this link http://dominoc925-pages.appspot.com/mapplets/vheatmap.html.

The Mapplet's sidebar contains a few button commands that should be obvious - Import, Export, Fit and Clear.

Clicking the Import button brings up the Import Points dialog. 

Copy and paste your comma-separated-values data into the text box. The data must be comma delimited and in  the following order: latitude, longitude, and magnitude. Alternatively, click Use random samples to let the Mapplet randomly create CSV data for demonstration purposes. 

Then click Start Import to create the heat map. 

Click  the Export button to export the heat map and supporting files. This will bring up the Export Heatmap dialog box. 

To save out the heat map image, right click on the image preview and choose Save image as. 

Next, click on the World file text box and press +C to copy the contents to the clipboard. Then paste inside a text editor and save the world file with the same file name but with the prefix *.pgw. 

Similarly, click on the Projection text box and press +C. Then paste in a text editor and save the contents into a projection file with the same file name prefix but with the extension *.prj. 

Once the image, world file and projection have been exported, the heat map can be displayed and overlaid with other geo-spatial data in any GIS software e.g. Global Mapper as shown below.