Removing Excessive Low Noise from Dense-Matching Point Clouds

Point clouds produced with dense-matching by photogrammetry software such as SURE, Pix4D, or Photoscan can include a fair amount of the kind of “low noise” as seen below. Low noise causes trouble when attempting to construct a Digital Terrain Model (DTM) from the points as common algorithm for classifying points into ground and non-ground points – such as lasground – tend to “latch onto” those low points, thereby producing a poor representation of the terrain. This blog post describes one possible LAStools workflow for eliminating excessive low noise. It was developed after a question in the LAStools user forum by LASmoons holder Muriel Lavy who was able to share her noisy data with us. See this, this, this, thisthis, and this blog post for further reading on this topic.

Here you can download the dense matching point cloud that we are using in the following work flow:

We leave the usual inspection of the content with lasinfolasview, and lasvalidate that we always recommend on newly obtained data as an exercise to the reader. Note that a check for proper alignment of flightlines with lasoverlap that we consider mandatory for LiDAR data is not applicable for dense-matching points.

With lastile we turn the original file with 87,261,083 points into many smaller 500 by 500 meter tiles for efficient multi-core processing. Each tile is given a 25 meter buffer to avoid edge artifacts. The buffer points are marked as withheld for easier on-the-fly removal. We add a (terser) description of the WGS84 UTM zone 32N to each tile via the corresponding EPSG code 32632:
lastile -i muriel\20161127_Pancalieri_UTM.laz ^
        -tile_size 500 -buffer 25 -flag_as_withheld ^
        -epsg 32632 ^
        -odir muriel\tiles_raw -o panca.laz
Because dense-matching points often have a poor point order in the files they get delivered in we use lassort to rearrange them into a space-filling curve order as this will speed up most following processing steps:
lassort -i muriel\tiles_raw\panca*.laz ^
        -odir muriel\tiles_sorted -olaz ^
        -cores 7
We then run lasthin to reclassify the highest point of every 2.5 by 2.5 meter grid cell with classification code 8. As the spacing of the dense-matched points is around 40 cm in both x and y, around 40 points will fall into each such grid cell from which the highest is then classified as 8:
lasthin -i muriel\tiles_sorted\panca*.laz ^
        -step 2.5 ^
        -highest -classify_as 8 ^
        -odir muriel\tiles_thinned -olaz ^
        -cores 7
Considering only those points classified as 8 in the last step we then run lasnoise to find points that are highly isolated in wide and flat neighborhoods that are then reclassified as 7. See the README file of lasnoise for a detailed explanation of the different parameters:
lasnoise -i muriel\tiles_thinned\panca*.laz ^
         -ignore_class 0 ^
         -step_xy 5 -step_z 0.1 -isolated 4 ^
         -classify_as 7 ^
         -odir muriel\tiles_isolated -olaz ^
         -cores 7
Now we run a temporary ground classification of only (!!!) on those points that are still classified as 8 using the default parameters of lasground. Hence we only use the points that were the highest points on the 2.5 by 2.5 meter grid and that were not classified as noise in the previous step. See the README file of lasground for a detailed explanation of the different parameters:
lasground -i muriel\tiles_isolated\panca*.laz ^
          -city -ultra_fine -ignore_class 0 7 ^
          -odir muriel\tiles_temp_ground -olaz ^
          -cores 7
The result of this temporary ground filtering is then merely used to mark all points that are 0.5 meter below the triangulated TIN of these temporary ground points with classification code 12 using lasheight. See the README file of lasheight for a detailed explanation of the different parameters:
lasheight -i muriel\tiles_temp_ground\panca*.laz ^
          -do_not_store_in_user_data ^
          -classify_below -0.5 12 ^
          -odir muriel\tiles_temp_denoised -olaz ^
          -cores 7
In the resulting tiles the low noise (but also many points above the ground) are now marked and in a final step we produce properly classified denoised tiles by re-mapping the temporary classification codes to conventions that are more consistent with the ASPRS LAS specification using las2las:
las2las -i muriel\tiles_temp_denoised\panca*.laz ^
        -change_classification_from_to 1 0 ^
        -change_classification_from_to 2 0 ^
        -change_classification_from_to 7 0 ^
        -change_classification_from_to 12 7 ^
        -odir muriel\tiles_denoised -olaz ^
        -cores 7
Let us visually check what each of the above steps has produced by zooming in on a 300 meter by 100 meter strip of points with the bounding box (388500,4963125) to (388800,4963225) in tile ‘panca_388500_4963000.laz’:
The final classification of all points that are not already classified as noise (7) into ground (2) or non-ground (1) was done with a final run of lasground. See the README file of lasground for a detailed explanation of the different parameters:
lasground -i muriel\tiles_denoised\panca*.laz ^
          -ignore_class 7 ^
          -city -ultra_fine ^
          -odir muriel\tiles_ground -olaz ^
          -cores 7
Then we create a seamless hill-shaded DTM tiles by triangulating all the points classified as ground into a temporary TIN (including those in the 25 meter buffer) and then rasterizing only the inner 500 meter by 500 meter of each tile with option ‘-use_tile_bb’ of las2dem. For more details on the importance of buffers in tile-based processing see this blog post here.
las2dem -i muriel\tiles_ground\panca*.laz ^
        -keep_class 2 ^
        -step 1 -hillshade ^
        -use_tile_bb ^
        -odir muriel\tiles_dtm -opng ^
        -cores 7

And here the original DSM side-by-side with resulting DTM after low noise removal. One dense forested area near the center of the data was not entirely removed due to the lack of ground points in this area. Integrating external ground points or manual editing with lasview are two possible way to rectify these few remaining errors …

Integrating External Ground Points in Forests to Improve DTM from Dense-Matching Photogrammetry

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 …

LASmoons: Gudrun Norstedt

Gudrun Norstedt (recipient of three LASmoons)
Forest History, Department of Forest Ecology and Management
Swedish University of Agricultural Sciences, Umeå, Sweden

Until the end of the 17th century, the vast boreal forests of the interior of northern Sweden were exclusively populated by the indigenous Sami. When settlers of Swedish and Finnish ethnicity started to move into the area, colonization was fast. Although there is still a prospering reindeer herding Sami culture in northern Sweden, the old Sami culture that dominated the boreal forest for centuries or even millenia is to a large extent forgotten.
Since each forest Sami family formerly had a number of seasonal settlements, the density of settlements must have been high. However, only very few remains are known today. In the field, old Sami settlements can be recognized through the presence of for example stone hearths, storage caches, pits for roasting pine bark, foundations of certain types of huts, reindeer pens, and fences. Researchers of the Forest History section of the Department of Forest Ecology and Management have long been surveying such remains on foot. This, however, is extremely time consuming and can only be done in limited areas. Also, the use of aerial photographs is usually difficult due to dense vegetation. Data from airborne laser scanning should be the best way to find remains of the old forest Sami culture. Previous research has shown the possibilities of using airborne laser scanning data for detecting cultural remains in the boreal forest (Jansson et al., 2009; Koivisto & Laulamaa, 2012; Risbøl et al., 2013), but no studies have aimed at detecting remains of the forest Sami culture. I want to test the possibilities of ALS in this respect.

DTM from the Krycklan catchment, showing a row of hunting pits and (larger) a tar pit.

The goal of my study is to test the potential of using LiDAR data for detecting cultural and archaeological remains on the ground in a forest area where Sami have been known to dwell during historical times. Since the whole of Sweden is currently being scanned by the National Land Survey, this data will be included. However, the average point density of the national data is only 0,5–1 pulses/m^2. Therefore, the study will be done in an established research area, the Krycklan catchment, where a denser scanning was performed in 2015. The Krycklan data set lacks ground point classification, so I will have to perform such a classification before I can proceed to the creation of a DTM. Having tested various kind of software, I have found that LAStools seems to be the most efficient way to do the job. This, in turn, has made me aware of the importance of choosing the right methods and parameters for doing a classification that is suitable for archaeological purposes.

The data was acquired with a multi-spectral airborne LiDAR sensor, the Optech Titan, and a Micro IRS IMU, operated on an aircraft flying at a height of about 1000 m and positioning was post-processed with the TerraPos software for higher accuracy.
The average pulse density is 20 pulse/m^2.
+ About 7 000 hectares were covered by the scanning. The data is stored in 489 tiles.

LAStools processing:
1) run a series of classifications of a few selected tiles with both lasground and lasground_new with various parameters [lasground and lasground_new]
2) test the outcomes by comparing it to known terrain to find out the optimal parameters for classifying this particular LiDAR point cloud for archaeological purposes.
3) extract the bare-earth of all tiles (using buffers!!!) with the best parameters [lasground or lasground_new]
4) create bare-earth terrain rasters (DTMs) and analyze the area [lasdem]
5) reclassify the airborne LiDAR data collected by the National Land Survey using various parameters to see whether it can become more suitable for revealing Sami cultural remains in a boreal forest landscape  [lasground or lasground_new]

Jansson, J., Alexander, B. & Söderman, U. 2009. Laserskanning från flyg och fornlämningar i skog. Länsstyrelsen Dalarna (PDF).
Koivisto, S. & Laulamaa, V. 2012. Pistepilvessä – Metsien arkeologiset kohteet LiDAR-ilmalaserkeilausaineistoissa. Arkeologipäivät 2012 (PDF).
Risbøl, O., Bollandsås, O.M., Nesbakken, A., Ørka, H.O., Næsset, E., Gobakken, T. 2013. Interpreting cultural remains in airborne laser scanning generated digital terrain models: effects of size and shape on detection success rates. Journal of Archaeological Science 40:4688–4700.

Plots to Stands: Producing LiDAR Vegetation Metrics for Imputation Calculations

Some professionals in remote sensing find LAStools a useful tool to extract statistical metrics from LiDAR that are used to make estimations about a larger area of land from a small set of sample plots. Common applications are prediction of the timber volume or the above-ground biomass for entire forests based on a number of representative plots where exact measurements were obtained with field work. The same technique can also be used to make estimations about animal habitat or coconut yield or to classify the type of vegetation that covers the land. In this tutorial we describe the typical workflow for computing common metrics for smaller plots and larger areas using LAStools.

Download these six LiDAR tiles (1, 2, 3, 4, 5, 6) from a Eucalyptus plantation in Brazil to follow along the step by step instructions of this tutorial. This data is courtesy of Suzano Pulp and Paper. Please also download the two shapefiles that delineate the plots where field measurements were taken and the stands for which predictions are to be made. You should download version 170327 (or higher) of LAStools due to some recent bug fixes.

Quality Checking

Before processing newly received LiDAR data we always perform a quality check first. This ranges from visual inspection with lasview, to printing textual content reports and attribute histograms with lasinfo, to flight-line alignment checks with lasoverlap, pulse density and pulse spacing checks with lasgrid and las2dem, and completeness-of-returns check with lassort followed by lasreturn.

lasinfo -i tiles_raw\CODL0003-C0006.laz ^
        -odir quality -odix _info -otxt

The lasinfo report tells us that there is no projection information. However, we remember that this Brazilian data was in the common SIRGAS 2000 projection and try for a few likely UTM zones whether the hillshaded DSM produced by las2dem falls onto the right spot in Google Earth.

las2dem -i tiles_raw\CODL0003-C0006.laz ^
        -keep_first -thin_with_grid 1 ^
        -hillshade -epsg 31983 ^
        -o epsg_check.png

Hillshaded DSM and Google Earth imagery align for EPSG code 31983

The lasinfo report also tells us that the xyz coordinates are stored with millimeter resolution which is a bit of an overkill. For higher and faster LASzip compression we will later lower this to a more appropriate centimeter resolution. It further tells us that the returns are stored using point type 0 and that is a bit unfortunate. This (older) point type does not have a GPS time stamp so that some quality checks (e.g. “completeness of returns” with lasreturn) and operations (e.g. “resorting of returns into acquisition order” with lassort) will not be possible. Fortunately the min-max range of the ‘point source ID’ suggests that this point attribute is correctly populated with flightline numbers so that we can do a check for overlap and alignment of the different flightlines that contribute to the LiDAR in each tile.

lasoverlap -i tiles_raw\*.laz ^
           -min_diff 0.2 -max_diff 0.4 ^
           -epsg 31983 ^
           -odir quality -opng ^
           -cores 3

We run lasoverlap to visualize the amount of overlap between flightlines and the vertical differences between them. The produced images (see below) color code the number of flightlines and the maximum vertical difference between any two flightlines as seen below. Most of the area is cyan (2 flightlines) except in the bottom left where the pilot was sloppy and left some gaps in the yellow seams (3 flightlines) so that some spots are only blue (1 flightline). We also see that two tiles in the upper left are partly covered by a diagonal flightline. We will drop that flightline later to create a more uniform density.across the tiles. The mostly blue areas in the difference are mostly aligned with features in the landscape and less with the flightline pattern. Closer inspection shows that these vertical difference occur mainly in the dense old growth forests with species of different heights that are much harder to penetrate by the laser than the uniform and short-lived Eucalyptus plantation that is more of a “dead forest” with little undergrowth or animal habitat.

Interesting observation: The vertical difference of the lowest return from different flightlines computed per 2 meter by 2 meter grid cell could maybe be used a new forestry metric to help distinguish mono cultures from natural forests.

lasgrid -i tiles_raw\*.laz ^
        -keep_last ^
        -step 2 -point_density ^
        -false -set_min_max 10 20 ^
        -epsg 31983 ^
        -odir quality -odix _d_2m_10_20 -opng ^
        -cores 3

lasgrid -i tiles_raw\*.laz ^
        -keep_last ^
        -step 5 -point_density ^
        -false -set_min_max 10 20 ^
        -epsg 31983 ^
        -odir quality -odix _d_5m_10_20 -opng ^
        -cores 3

We run lasgrid to visualize the pulse density per 2 by 2 meter cell and per 5 by 5 meter cell. The produced images (see below) color code the number of last return per square meter. The impact of the tall Eucalyptus trees on the density per cell computation is evident for the smaller 2 meter cell size in form of a noisy blue/red diagonal in the top right as well as a noisy blue/red area in the bottom left. Both of those turn to a more consistent yellow for the density per cell computation with larger 5 meter cells. Immediately evident is the higher density (red) for those parts or the two tiles in the upper left that are covered by the additional diagonal flightline. The blueish area left to the center of the image suggests a consistently lower pulse density whose cause remains to be investigated: Lower reflectivity? Missing last returns? Cloud cover?

The lasinfo report suggests that the tiles are already classified. We could either use the ground classification provided by the vendor or re-classify the data ourselves (using lastilelasnoise, and lasground). We check the quality of the ground classification by visually inspecting a hillshaded DTM created with las2dem from the ground returns. We buffer the tiles on-the-fly for a seamless hillshade without artifacts along tile boundaries by adding ‘-buffered 25’ and ‘-use_orig_bb’ to the command-line. To speed up reading the 25 meter buffers from neighboring tiles we first create a spatial indexing with lasindex.

lasindex -i tiles_raw\*.laz ^
         -cores 3

las2dem -i tiles_raw\*.laz ^
        -buffered 25 ^
        -keep_class 2 -thin_with_grid 0.5 ^
        -use_orig_bb ^
        -hillshade -epsg 31983 ^
        -odir quality -odix _dtm -opng ^
        -cores 3

hillshaded DTM tiles generated with las2dem and on-the-fly buffering

The resulting hillshaded DTM shows a few minor issues in the ground classification but also a big bump (above the mouse cursor). Closer inspection of this area (you can cut it from the larger tile using the command-line below) shows that there is a group of miss-classified points about 1200 meters below the terrain. Hence, we will start from scratch to prepare the data for the extraction of forestry metrics.

las2las -i tiles_raw\CODL0004-C0006.laz ^
        -inside_tile 207900 7358350 100 ^
        -o bump.laz

lasview -i bump.laz

bump in hillshaded DTM caused by miss-classified low noise

Data Preparation

Using lastile we first tile the data into smaller 500 meter by 500 meter tiles with 25 meter buffer while flagging all points in the buffer as ‘withheld’. In the same step we lower the resolution to centimeter and put nicer a coordinate offset in the LAS header. We also remove the existing classification and classify all points that are much lower than the target terrain as class 7 (aka noise). We also add CRS information and give each tile the base name ‘suzana.laz’.

lastile -i tiles_raw\*.laz ^
        -rescale 0.01 0.01 0.01 ^
        -auto_reoffset ^
        -set_classification 0 ^
        -classify_z_below_as 500.0 7 ^
        -tile_size 500 ^
        -buffer 25 -flag_as_withheld ^
        -epsg 31983 ^
        -odir tiles_buffered -o suzana.laz

With lasnoise we mark the many isolated points that are high above or below the terrain as class 7 (aka noise). Using two tiles we played around with the ‘step’ parameters until we find good parameter settings. See the README of lasnoise for the exact meaning and the choice of parameters for noise classification. We look at one of the resulting tiles with lasview.

lasnoise -i tiles_buffered\*.laz ^
         -step_xy 4 -step_z 2 ^
         -odir tiles_denoised -olaz ^
         -cores 3

lasview -i tiles_denoised\suzana_206000_7357000.laz ^
        -color_by_classification ^
        -win 1024 192

noise points shown in pink: all points (top), only noise points (bottom)

Next we use lasground to classify the last returns into ground (2) and non-ground (1). It is important to ignore the noise points with classification 7 to avoid the kind of bump we saw in the vendor-delivered classification. We again check the quality of the computed ground classification by producing a hillshaded DTM with las2dem. Here the las2dem command-line is sightly different as instead of buffering on-the-fly we use the buffers stored with each tile.

lasground -i tiles_denoised\*.laz ^
          -ignore_class 7 ^
          -nature -extra_fine ^
          -odir tiles_ground -olaz ^
          -cores 3

las2dem -i tiles_ground\*.laz ^
        -keep_class 2 -thin_with_grid 0.5 ^
        -hillshade ^
        -use_tile_bb ^
        -odir quality -odix _dtm_new -opng ^
        -cores 3

Finally, with lasheight we compute how high each return is above the triangulated surface of all ground returns and store this height value in place of the elevation value into the z coordinate using the ‘-replace_z’ switch. This height-normalizes the LiDAR in the sense that all ground returns are set to an elevation of 0 while all other returns get an elevation relative to the ground. The result are height-normalized LiDAR tiles that are ready for producing the desired forestry metrics.

lasheight -i tiles_ground\*.laz ^
          -replace_z ^
          -odir tiles_normalized -olaz ^
          -cores 3
Metric Production

The tool for computing the metrics for the entire area as well as for the individual field plots is lascanopy. Which metrics are well suited for your particular imputation calculation is your job to determine. Maybe first compute a large number of them and then eliminate the redundant ones. Do not use any point from the tile buffers for these calculations. We had flagged them as ‘withheld’ during the lastile operation, so they are easy to drop. We also want to drop the noise points that were classified as 7. And we were planning to drop that additional diagonal flightline we noticed during quality checking. We generated two lasinfo reports with the ‘-histo point_source 1’ option for the two tiles it was covering. From the two histograms it was easy to see that the diagonal flightline has the number 31.

First we run lascanopy on the 11 plots that you can download here. When running on plots it makes sense to first create a spatial indexing with lasindex for faster querying so that only those tiny parts of the LAZ file need to be loaded that actually cover the plots.

lasindex -i tiles_normalized\*.laz ^
         -cores 3

lascanopy -i tiles_normalized\*.laz -merged ^
          -drop_withheld ^
          -drop_class 7 ^
          -drop_point_source 31 ^
          -lop WKS_PLOTS.shp ^
          -cover_cutoff 3.0 ^
          -cov -dns ^
          -height_cutoff 2.0 ^
          -c 2.0 999.0 ^
          -max -avg -std -kur ^
          -p 25 50 75 95 ^
          -b 30 50 80 ^
          -d 2.0 5.0 10.0 50.0 ^
          -o plots.csv

The resulting ‘plots.csv’ file you can easily process in other software packages. It contains one line for each polygonal plot listed in the shapefile that lists its bounding box followed by all the requested metrics. But is why is there a zero maximum height (marked in orange) for plots 6 though 10? All height metrics are computed solely from returns that are higher than the ‘height_cutoff’ that was set to 2 meters. We added the ‘-c 2.0 999.0’ absolute count metric to keep track of the number of returns used in these calculations. Apparently in plots 6 though 10 there was not a single return above 2 meters as the count (also marked in orange) is zero for all these plots. Turns out this Eucalyptus stand had recently been harvested and the new seedlings are still shorter than 2 meters.

more plots.csv

Then we run lascanopy on the entire area and produce one raster per tile for each metric. Here we remove the buffered points with the ‘-use_tile_bb’ switch that also ensures that the produced rasters have exactly the extend of the tiles without buffers. Again, it is imperative that you download the version 170327 (or higher) of LAStools for this to work correctly.

lascanopy -version
LAStools (by version 170327 (academic)

lascanopy -i tiles_normalized\*.laz ^
          -use_tile_bb ^
          -drop_class 7 ^
          -drop_point_source 31 ^
          -step 10 ^
          -cover_cutoff 3.0 ^
          -cov -dns ^
          -height_cutoff 2.0 ^
          -c 2.0 999.0 ^
          -max -avg -std -kur ^
          -p 25 50 75 95 ^
          -b 30 50 80 ^
          -d 2.0 5.0 10.0 50.0 ^
          -odir tile_metrics -oasc ^
          -cores 3

The resulting rasters in ASC format can easily be previewed using lasview for some “sanity checking” that our metrics make sense and to get a quick overview about what these metrics look like.

lasview -i tile_metrics\suzana_*max.asc
lasview -i tile_metrics\suzana_*p95.asc
lasview -i tile_metrics\suzana_*p50.asc
lasview -i tile_metrics\suzana_*p25.asc
lasview -i tile_metrics\suzana_*cov.asc
lasview -i tile_metrics\suzana_*d00.asc
lasview -i tile_metrics\suzana_*d01.asc
lasview -i tile_metrics\suzana_*b30.asc
lasview -i tile_metrics\suzana_*b80.asc

The maximum height rasters are useful to inspect more closely as they will immediately tell us if there was any high noise point that slipped through the cracks. And indeed it happened as we see a maximum of 388.55 meters for of the 10 by 10 meter cells. As we know the expected height of the trees we could have added a ‘-drop_z_above 70’ to the lascanopy command line. Careful, however, when computing forestry metrics in strongly sloped terrains as the terrain slope can significantly lift up returns to heights much higher than that of the tree. This is guaranteed to happen for LiDAR returns from branches that are extending horizontally far over the down-sloped part of the terrain as shown in this paper here.

We did not use the shapefile for the stands in this exercise. We could have clipped the normalized LiDAR points to these stands using lasclip as shown in the GUI below before generating the raster metrics. This would have saved space and computation time as many of the LiDAR points lie outside of the stands. However, it might be better to do that clipping step on the rasters in whichever GIS software or statistics package you are using for the imputation computation to properly account for partly covered raster cells along the stand boundary. This could be subject of another blog article … (-:

not all LiDAR was needed to compute metrics for

LASmoons: Jesús García Sánchez

Jesús García Sánchez (recipient of three LASmoons)
Landscapes of Early Roman Colonization (LERC) project
Faculty of Archaeology, Leiden University, The Netherlands

Our project Landscapes of Early Roman Colonization (LERC) has been studying the hinterland of the Latin colony of Aesernia (Molise region, Italy) using several non-destructive techniques, chiefly artefactual survey, geophysics, and interpretation of aerial photographs. Nevertheless large areas of the territory are covered by the dense forests of the Matese mountains, a ridge belonging the Apennine chain, or covered by bushes due to the abandonment of the countryside. The project won’t be complete without integrating the marginal, remote and forested areas into our study of the Roman hinterland. Besides, it’s also relevant to discuss the feasibility of LiDAR data sets in the study of Mediterranean landscapes and its role within contemporary Landscape Archaeology.

some clever caption

LiDAR coverage in Molise region, Italy.

+ to study in detail forested areas in the colonial hinterland of Aesernia.
+ to found the correct parameters of the classification algorithm to be able to locate possible archaeological structures or to document appropriately those we already known.
+ to document and create new visualization of hill-top fortified sites that belong to the indigenous population and are currently poorly studied due to inaccessibility and forest coverage (Monte San Paolo, Civitalla, Castelriporso, etc.)
+ to demonstrate the archaeological potential of LiDAR data in Italy and help other scholars to work with that kind of data, explaining basic information about data quality, where and how to acquire imagery and examples of application in archaeology. A paper entitled “Working with ALS – LiDAR data in Central South Italy. Tips and experiences”, will be presented in the International Mediterranean Survey Workshop by the end of February in Athens.

Civitella hillfort (Longano, IS) and its local context: ridges and forest belonging to the Materse mountains and the Appenines.

Recently the LERC project has acquired a large LiDAR dataset created by the Italian Geoportale Nazionale and the Minisstero dell’Ambiente e della Tutella del Territorio e del Mare. The data was produced originally to monitor land-slides and erosive risk.
The average point resolution is 1 meter.
+ The data sets were cropped originally in 1 sq km. tiles by the Geoportale Nazionale for distribution purposes.

LAStools processing:
1) data is provided in *.txt files thus the first step is to create appropriate LAS files to work with [txt2las]
2) combine areas of circa 16 sq km (still fewer than 20 million points to be processed in one piece with LAStools) in the surroundings of the colony of Aesernia and in the Matese mountains [lasmerge]
3) assign the correct projection to the data [lasmerge or las2las]
4) extract the care-earth with the best-fitting parameters [lasground or lasground_new]
5) create bare-earth terrain rasters as a first step to visualize and analyze the area [lasdem]

LASmoons: Rachel Opitz

Rachel Opitz (recipient of three LASmoons)
Center for Virtualization and Applied Spatial Technologies
Department of Anthropology, University of South Florida, USA

In Spring 2017 Rachel Opitz will be teaching a course on Remote Sensing for Human Ecology and Archaeology at the University of South Florida. The aim of the course is to provide students with the practical skills and knowledge needed to work with contemporary remote sensing data. The course focuses on airborne laser scanning and hyper-spectral data and their application in Human Ecology and Archaeology. Through the course students will be introduced to a number of software packages commonly used to process and interpret these data, with an emphasis on free and/or open source tools.

Classification parameters and the resolution at which the DTM is interpolated both have a significant impact on our ability to recognize anthropogenic features in the landscape. Here we see a small quarry. More aggressive filtering and a coarser DTM resolution (left) makes it difficult to recognize that this is a quarry. Less aggressive filtering and a higher resolution (right) leaves some vegetation behind, but makes the edges of the quarry and some in-situ blocks clearly visible.

The students will develop practical skills in applied remote sensing through hands-on exercises. Learning to assess, manage and process large data sets is essential. In particular, the students in the course will learn to:
+ Identify the set of techniques needed to solve a problem in applied remote sensing
+ Find public imagery and specify acquisitions
+ Assess data quality
+ Process airborne LiDAR data
+ Combine complementary remote sensing data sources
+ Create effective data visualizations
+ Analyze digital topographic and spectral data to answer questions in human ecology and archaeology

The course will include case studies that draw on a variety of publicly available data sets that will all be used in the exercises:
+ the PNOA data from Spain
+ data held by NOAA
+ data collected using NASA’s GLiHT platform

LAStools processing:
LAStools will be used throughout the course, as students learn to assess the quality of LiDAR data, classify raw LiDAR point clouds, create raster terrain and canopy models, and produce visualizations. The online tutorials and videos available via the company website and the over 50 hours of video on YouTube as well as the LAStools user forum will be used as resources during the course.

LASmoons: Stéphane Henriod

Stéphane Henriod (recipient of three LASmoons)
National Statistical Committee of the Kyrgyz Republic
Bishkek, Kyrgyzstan

This pilot study is part of the International Climate Initiative project called “Ecosystem based Adaptation to Climate change in the high mountainous regions of Central Asia” that is funded by the Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety (BMU) of Germany and implemented by the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH in Kyrgyzstan, Tajikistan and Kazakhstan.


The ecosystems in high mountainous regions of Central Asia are characterized by a unique diversity of flora and fauna. In addition, they are the foundation of the livelihoods of the local population. Specific benefits include clean water, pasture, forest products, protection against floods and landslides, maintenance of soil fertility, and ecotourism. However, the consequences of climate change such as melting glaciers, changing river runoff regimes, and weather anomalies including sharp temperature fluctuations and non-typical precipitation result in negative impacts on these ecosystems. Coupled with unwise land use, these events damage fragile mountain ecosystems and reduce their regeneration ability undermining the local population’s livelihoods. Therefore, people living in rural areas and directly depending on natural resources must adapt to adverse impacts of climate change. This can be done through a set of measures, known in the world practice as ecosystem-based adaptation (EbA) approach. It promotes the sustainable use of natural resources to sustain and enhance the livelihood of the population depending on those resources.

lasmoons_Stephane_Henriod_2 Goal:
In two selected pilot regions of Kyrgyzstan and Tajikistan, planned measures will concentrate on climate-informed management of ecosystems in order to maintain their services for the rural population. EbA always starts with identifying the vulnerability of the local population. Besides analyzing the socio-economic situation of the local population, this includes (1) assessing the ecological conditions of the ecosystems in the watershed and the related ecosystem services people benefit from, (2) identifying potential disaster risks, and (3) analyzing glacier dynamics with its response to water runoff. As a baseline to achieve this and to get spatially explicit, remote sensing based techniques and mapping activities need to be utilized.

A first UAV (unmanned aerial vehicle) mission has taken place in the Darjomj watershed of the Bartang valley in July 2016. RGB-NIR images as well as a high-resolution Digital Surface Model have been produced that now need to be segmented and analysed in order to produce comprehensive information. The main processing that will take advantage of LAStools is the generation of a DTM from the DSM that will then be used for identifying risk areas (flood zones, landslides and avalanches, etc.). The results of this approach will ultimately be compared with lower-cost satellite images (RapidEye, Planet, Sentinel).

+ High-resolution RGB and NIR image (10 cm) from a SenseFly Ebee
+ High-resolution DSM (10 cm) from a SenseFly Ebee

LAStools processing:
classify DSM points obtained via dense-matching photogrammetry into a SenseFly Ebee imagery into ground and non-ground points via processing pipelines as described here and here [lastile, lassort, lasnoise, lasground]
2) create a DTM [las2dem, lasgrid, blast2dem]
3) produce 3D visualisations to facilitate the communication around adaptation to climate change [lasview]