The biggest problem of generating a Digital Terrain Model (DTM) from the photogrammetric point clouds that are produced from aerial imagery with dense-matching software such as SURE, Pix4D, or Photoscan is dense vegetation: when plants completely cover the terrain not a single point is generated on the ground. This is different for LiDAR point clouds as the laser can even penetrate dense multi-level tropical forests. The complete lack of ground points in larger vegetated areas such as closed forests or dense plantations means that the many processing workflows for vegetation analysis that have been developed for LiDAR cannot be used for photogrammetric point clouds … unless … well unless we are getting those missing ground points some other way. In the following we see how to integrate external ground points to generate a reasonable DTM under a dense forest with LAStools. See this, this, this, this, and this article for further reading.
Here you can download the dense matching point cloud, the manually collected ground points, and the forest stand delineating polygon that we are using in the following example work flow:
We leave the usual inspection of the content with lasinfo and lasview that we always recommend on newly obtained data as an exercise to the reader. Using las2dem and lasgrid we created the Google Earth overlays shown above to visualize the extent of the dense matched point cloud and the distribution of the manually collected ground points:
las2dem -i DenseMatching.laz ^ -thin_with_grid 1.0 ^ -extra_pass ^ -step 2.0 ^ -hillshade ^ -odix _hill_2m -opng lasgrid -i ManualGround.laz ^ -set_RGB 255 0 0 ^ -step 10 -rgb ^ -odix _grid_10m -opng
Attempts to ground-classify the dense matching point cloud directly are futile as there are no ground points under the canopy in the heavily forested area. Therefore 558 ground points were manually surveyed in the forest of interest that are around 50 to 120 meters apart from another. We show how to integrate these points into the dense matching point cloud such that we can successfully extract bare-earth information from the data.
In the first step we “densify” the manually collected ground points by interpolating them with triangles onto a raster of 2 meter resolution that we store as LAZ points with las2dem. You could consider other interpolation schemes to “densify” the ground points, here we use simple linear interpolation to prove the concept. Due to the varying distance between the manually surveyed ground points we allow interpolating triangles with edge lengths of up to 125 meters. These triangles then also cover narrow open areas next to the forest, so we clip the interpolated ground points against the forest stand delineating polygon with lasclip to classify those points that are really in the forest as “key points” (class 8) and all others as “noise” (class 7).
las2dem -i ManualGround.laz ^ -step 2 ^ -kill 125 ^ -odix _2m -olaz lasclip -i ManualGround_2m.laz ^ -set_classification 7 ^ -poly forest.shp ^ -classify_as 8 -interior ^ -odix _forest -olaz
Below we show the resulting densified ground points colored by elevation that survive the clipping against the forest stand delineating polygon and were classified as “key points” (class 8). The interpolated ground points in narrow open areas next to the forest that fall outside this polygon were classified as “noise” (class 7) and are shown in violet. They will be dropped in the next step.
We then merge the dense matching points with the densified manual ground points (while dropping all the violet points marked as noise) as input to lasthin and reclassify the lowest point per 1 meter by 1 meter with a temporary code (here we use class 9 that usually refers to “water”). Only the subset of lowest points that receives the temporary classification code 9 will be used for ground classification later.
lasthin -i DenseMatching.laz ^ -i ManualGround_2m_forest.laz ^ -drop_class 7 ^ -merged ^ -lowest -step 1 -classify_as 9 ^ -o DenseMatchingAndDensifiedGround.laz
We use the GUI of lasview to pick several interesting areas for visual inspection. The selected points load much faster when the LAZ file is spatially indexed and therefore we first run lasindex. For better orientation we also load the forest stand delineating polygon as an overlay into the GUI.
lasindex -i DenseMatchingAndDensifiedGround.laz lasview -i DenseMatchingAndDensifiedGround.laz -gui
We pick the area shown below that contains the target forest with manually collected and densified ground points and a forested area with only dense matching points. The difference could not be more drastic as the visualizations show.
Now we run ground classification using lasground with option ‘-town’ using only the points with the temporary code 9 by ignoring all other classifications 0 and 8 in the file. We leave the temporary classification code 9 unchanged for all the points that were not classified with “ground” code 2 so we can visualize later which ones those are.
lasground -i DenseMatchingAndDensifiedGround.laz ^ -ignore_class 0 8 ^ -town ^ -non_ground_unchanged ^ -o GroundClassified.laz
We again use the GUI of lasview to pick several interesting areas after running lasindex and again load the forest stand delineating polygon as an overlay into the GUI.
lasindex -i GroundClassified.laz lasview -i GroundClassified.laz -gui
We pick the area shown below that contains all three scenarios: the target forest with manually collected and densified ground points, an open area with only dense matching points, and a forested area with only dense matching points. The result is as expected: in the target forest the manually collected ground points are used as ground and in the open area the dense-matching points are used as ground. But there is no useful ground in the other forested area.
Now we can compute the heights of the points above ground for our target forest with lasheight and either replace the z elevations in the file of store them separately as “extra bytes”. Then we can compute, for example, a Canopy Height Model (CHM) that color codes the height of the vegetation above the ground with lasgrid. Of course this will only be correct in the target forest where we have “good” ground but not in the other forested areas. We also compute a hillshaded DTM to be able to visually inspect the topography of the generated terrain model.
lasheight -i GroundClassified.laz ^ -store_as_extra_bytes ^ -o GroundClassifiedWithHeights.laz lasgrid -i GroundClassifiedWithHeights.laz ^ -step 2 ^ -highest -attribute 0 ^ -false -set_min_max 0 25 ^ -o chm.png las2dem -i GroundClassified.laz ^ -keep_class 2 -extra_pass ^ -step 2 ^ -hillshade ^ -o dtm.png
Here you can download the resulting color-coded CHM and the resulting hill-shaded DTM as Google Earth KMZ overlays. Clearly the resulting CHM is only meaningful in the target forest where we used the manually collected ground points to create a reasonable DTM. In the other forested areas the ground is only correct near the forest edges and gets worse with increasing distance from open areas. The resulting DTM exhibits some interesting looking bumps in the middle of areas with manually collected ground point. Those are a result of using the dense-matching points as ground whenever their elevation is lower than that of the manually collected points (which is decided in the lasthin step). Whether those bumps represent true elevations of are artifacts of low erroneous elevation from dense-matching remains to be investigated.
For forests on complex and steep terrain the number of ground points that needs to be manually collected may make such an approach infeasible in practice. However, maybe you have another source of elevation, such as a low-resolution DTM of 10 or 25 meter provided by your local government. Or maybe even a high resolution DTM of 1 or 2 meter from a LiDAR survey you did several years ago. While the forest may have grown a lot in the past years, the ground under the forest will probably not have changed much …
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