LASmoons: Martin Buchauer

Martin Buchauer (recipient of three LASmoons)
Cartography & Geomedia Technology
University of Applied Science Munich, GERMANY

Background:
Salt marsh areas provide numerous services such as natural flood defenses, carbon sequestration, agricultural services, and are a valuable coastal habitat for flora, fauna and humans. However, they are not only threatened by the constant rise of sea levels caused by global warming but also by human settlement in coastal areas. A sensible local coastal development is important as it may help to support the development and progression of stressed salt marshes.

Looking South you can see the salt marsh area next to a famous golf course with St Andrews in the background.

Goal:

This research aims to visualize and extract vegetation metrics as well as the temporal analysis of four salt marsh data sets which are derived from terrestrial laser scanning. Located at the South and North shore of the Eden Estuary near St Andrews, Scotland, the scans were acquired in the summer and winter of 2016. Ground based laser scanning is an ideal method of fully reconstructing vegetation structures as well as having the ability to retrieve meaningful metrics such as height, area, and vegetation density. Although this technology has frequently been applied in the area of forestry, its application to salt marsh areas has not yet fully explored.

Data:
+
 TLS data acquired with a Leica HDS6100 (average density of 38000 points/m²)
+ ground control points (field data)

LAStools processing:
1) check the quality of the LiDAR data [lasinfo, lasoverlap, lasgrid]
2) merge and retile the original data with buffers [lastile]
3) classify point clouds into ground and non-ground [lasthin, lasground]
4) create digital terrain (DTM) and digital surface models (DSM) [lasthin, las2dem, blast2dem]

Removing Low Noise from RIEGL’s VUX-1 UAV LiDAR flown in the Philippines

In this tutorial we are removing some “tricky” low noise from LiDAR point clouds in order to produce a high-resolution Digital Terrain Model (DTM). The data was flown above a tropical beach and mangrove area in the Philippines using a VUX-1 UAV based system from RIEGL mounted on a helicopter. The survey was done as a test flight by AB Surveying who have the capacity to fly such missions in the Philippines and who have allowed us to share this data with you for educational purposes. You can download the data (1 GB) here. It covers a popular twin beach knows as “Nacpan” near El Nido in Palawan (that we happen to have visited in 2014).

A typical beach fringed by coconut palms in Palawan, Philippines.

We start our usual quality check with a run of lasinfo. We add the ‘-cd’ switch to compute an average point density and the ‘-histo gps_time 1’ switch to produce a 1 second histogram for the GPS time stamps.

lasinfo -i lalutaya.laz ^
        -cd ^
        -histo gps_time 1 ^
        -odix _info -otxt

You can download the resulting lasinfo report here. It tells us that there are 118,740,310 points of type 3 (with RGB colors) with an average density of 57 last returns per square meter. The point coordinates are in the “PRS92 / Philippines 1” projection with EPSG code 3121 that is based on the “Clarke 1866” ellipsoid.

Datum Transform

We prefer to work in an UTM projection based on the “WGS 1984” ellipsoid, so we will first perform a datum transform based on the seven parameter Helmert transformation – a capacity that was recently added to LAStools. For this we first need a transform to get to geocentric or Earth-Centered Earth-Fixed (ECEF) coordinates on the current “Clarke 1866” ellipsoid, then we apply the Helmert transformation that operates on geocentric coordinates and whose parameters are listed in the TOWGS84 string of EPSG code 3121 to get to geocentric or ECEF coordinates on the “WGS 1984” ellipsoid. Finally we can convert the coordinates to the respective UTM zone. These three calls to las2las accomplish this.

las2las -i lalutaya.laz ^
        -remove_all_vlrs ^
        -epsg 3121 ^
        -target_ecef ^
        -odix _ecef_clark1866 -olaz

las2las -i lalutaya_ecef_clark1866.laz ^
        -transform_helmert -127.62,-67.24,-47.04,-3.068,4.903,1.578,-1.06 ^
        -wgs84 -ecef ^
        -ocut 10 -odix _wgs84 -olaz
 
las2las -i lalutaya_ecef_wgs84.laz ^
        -target_utm auto ^
        -ocut 11 -odix _utm -olaz

In these steps we implicitly reduced the resolution that each coordinate was stored with from quarter-millimeters (i.e. scale factors of 0.00025) to the default of centimeters (i.e. scale factors of 0.01) which should be sufficient for subsequent vegetation analysis. The LAZ files also compress better when coordinates a lower resolution so that the ‘lalutaya_utm.laz’ file is over 200 MB smaller than the original ‘lalutaya.laz’ file. The quantization error this introduces is probably still below the overall scanning error of this helicopter survey.

Flightline Recovery

Playing back the file visually with lasview suggests that it contains more than one flightline. Unfortunately the point source ID field of the file is not properly populated with flightline information. However, when scrutinizing the GPS time stamp histogram in the lasinfo report we can see an occasional gap. We highlight two of these gaps in red between GPS second 540226 and 540272 and GPS second 540497 and 540556 in this excerpt from the lasinfo report:

gps_time histogram with bin size 1
[...]
 bin 540224 has 125768
 bin 540225 has 116372
 bin 540226 has 2707
 bin 540272 has 159429
 bin 540273 has 272049
 bin 540274 has 280237
[...]
 bin 540495 has 187103
 bin 540496 has 180421
 bin 540497 has 126835
 bin 540556 has 228503
 bin 540557 has 275025
 bin 540558 has 273861
[...]

We can use lasplit to recover the original flightlines based on gaps in the continuity of GPS time stamps that are bigger than 10 seconds:

lassplit -i lalutaya_utm.laz ^
         -recover_flightlines_interval 10 ^
         -odir strips_raw -o lalutaya.laz

This operation splits the points into 11 separate flightlines. The points within each flightline are stored in the order that the vendor software – which was RiPROCESS 1.7.2 from RIEGL according to the lasinfo report – had written them to file. We can use lassort to bring them back into the order they were acquired in by sorting first on the GPS time stamp and then on the return number field:

lassort -i strips_raw\*.laz ^
        -gps_time -return_number ^
        -odir strips_sorted -olaz ^
        -cores 4

Now we turn the sorted flightlines into tiles (with buffers !!!) for further processing. We also erase the current classification of the data into ground (2) and medium vegetation (4) as a quick visual inspection with lasview immediately shows that those are not correct:

lastile -i strips_sorted\*.laz ^
        -files_are_flightlines ^
        -set_classification 0 ^
        -tile_size 250 -buffer 30 -flag_as_withheld ^
        -odir tiles_raw -o lalu.laz

Quality Checking

Next comes the standard check of flightline overlap and alignment check with lasoverlap. Once more it become clear why it is so important to have flightline information. Without we may have missed what we are about to notice. We create false color images load into Google Earth to visually assess the situation. We map all absolute differences between flightlines below 5 cm to white and all absolute differences above 30 cm to saturated red (positive) or blue (negative) with a gradual shading from white to red or blue for any differences in between. We also create an overview KML file that lets us quickly see in which tile we can find the points for a particular area of interest with lasboundary.

lasoverlap -i tiles_raw\*.laz ^
           -step 1 -min_diff 0.05 -max_diff 0.30 ^
           -odir quality -opng ^
           -cores 4

lasboundary -i tiles_raw\*.laz ^
            -use_tile_bb -overview -labels ^
            -o quality\overview.kml

The resulting visualizations show (a) that our datum transform to the WGS84 ellipsoid worked because the imagery aligns nicely with Google Earth and (b) that there are several issues in the data that require further scrutiny.

In general the data seems well aligned (most open areas are white) but there are blue and red lines crossing the survey area. With lasview have a closer look at the visible blue lines running along the beach in tile ‘lalu_765000_1252750.laz’ by repeatedly pressing ‘x’ to select a different subset and ‘x’ again to view this subset up close while pressing ‘c’ to color it differently:

lasview -i tiles_raw\lalu_765000_1252750.laz

These lines of erroneous points do not only happen along the beach but also in the middle of and below the vegetation as can be seen below:

Our initial hope was to use the higher than usual intensity of these erroneous points to reclassify them to some classification code that we would them exclude from further processing. Visually we found that a reasonable cut-off value for this tile would be an intensity above 35000:

lasview -i tiles_raw\lalu_765000_1252750.laz ^
        -keep_intensity_above 35000 ^
        -filtered_transform ^
        -set_classification 23

However, while this method seems successful on the tile shown above it fails miserably on others such as ‘lalu_764250_1251500.laz’ where large parts of the beach are very reflective and result in high intensity returns to to the dry white sand:

lasview -i tiles_raw\lalu_764250_1251500.laz ^
        -keep_intensity_above 35000 ^
        -filtered_transform ^
        -set_classification 23

Low Noise Removal

In the following we describe a workflow that can remove the erroneous points below the ground so that we can at least construct a high-quality DTM from the data. This will not, however, remove the erroneous points above the ground so a subsequent vegetation analysis would still be affected. Our approach is based on two obervations (a) the erroneous points affect only a relatively small area and (b) different flightlines have their erroneous points in different areas. The idea is to compute a set of coarser ground points separately for each flightline and – when combining them in the end – to pick higher ground points over lower ones. The combined points should then define a surface that is above the erroneous below-ground points so that we can mark them with lasheight as not to be used for the actual ground classification done thereafter.

The new huge_las_file_extract_ground_points_only.bat example batch script that you can download here does all the work needed to compute a set of coarser ground points for each flightline. Simply edit the file such that the LAStools variable points to your LAStools\bin folder and rename it to end with the *.bat extension. Then run:

huge_las_file_extract_ground_points_only strips_sorted\lalutaya_0000001.laz strips_ground_only\lalutaya_0000001.laz
huge_las_file_extract_ground_points_only strips_sorted\lalutaya_0000002.laz strips_ground_only\lalutaya_0000001.laz
huge_las_file_extract_ground_points_only strips_sorted\lalutaya_0000003.laz strips_ground_only\lalutaya_0000001.laz
...
huge_las_file_extract_ground_points_only strips_sorted\lalutaya_0000009.laz strips_ground_only\lalutaya_0000009.laz
huge_las_file_extract_ground_points_only strips_sorted\lalutaya_0000010.laz strips_ground_only\lalutaya_0000010.laz
huge_las_file_extract_ground_points_only strips_sorted\lalutaya_0000011.laz strips_ground_only\lalutaya_0000011.laz

The details on how this batch script works – a pretty standard tile-based multi-core processing workflow – are given as comments in this batch script. Now we have a set of individual ground points computed separately for each flightline and some will include erroneous points below the ground that the lasground algorithm by its very nature is likely to latch on to as you can see here:

The trick is now to utilize the redundancy of multiple scans per area and – when combining flightlines – to pick higher rather than lower ground points in overlap areas by using the ground point closest to the 75th elevation percentile per 2 meter by 2 meter area with at least 3 or more points with lasthin:

lasthin -i strips_ground_only\*.laz -merged ^
        -step 2 -percentile 75 3 ^
        -o lalutaya_ground_only_2m_75_3.laz

There are still some non-ground points in the result as ground-classifying of flightlines individually often results in vegetation returns being included in sparse areas along the edges of the flight lines but we can easily get rid of those:

lasground_new -i lalutaya__ground_only_2m_75_3.laz ^
              -town -hyper_fine ^
              -odix _g -olaz

We sort the remaining ground points into a space-filling curve order with lassort and spatially index them with lasindex so they can be efficiently accessed by lasheight in the next step.

lassort -i lalutaya__ground_only_2m_75_3_g.laz ^
        -keep_class 2 ^
        -o lalutaya_ground.laz

lasindex -i lalutaya_ground.laz

Finally we have the means to robustly remove the erroneous points below the ground from all tiles. We use lasheight with the ground points we’ve just so painstakingly computed to classify all points 20 cm or more below the ground surface they define into classification code 23. Later we simply can ignore this classification code during processing:

lasheight -i tiles_raw\*.laz ^
          -ground_points lalutaya_ground.laz ^
          -do_not_store_in_user_data ^
          -classify_below -0.2 23 ^
          -odir tiles_cleaned -olaz ^
          -cores 4

Rather than trying to ground classify all remaining points we run lasground on a thinned subset of all points. For this we mark the lowest point in every 20 cm by 20 cm grid cell with some temporary classification code such as 6.

lasthin -i tiles_cleaned\*.laz ^
        -ignore_class 23 ^
        -step 0.20 -lowest -classify_as 6 ^
        -odir tiles_thinned -olaz ^
        -cores 4

Finally we can run lasground to compute the ground classification considering all points with classification code 6 by ignoring all points with classification codes 23 and 0.

lasground_new -i tiles_thinned\*.laz ^
              -ignore_class 23 0 ^
              -city -hyper_fine ^
              -odir tiles_ground_new -olaz ^
              -cores 4

And finally we can create a DTM with a resolution of 25 cm using las2dem and the result is truly beautiful:

las2dem -i tiles_ground_new\*.laz ^
        -keep_class 2 ^
        -step 0.25 -use_tile_bb ^
        -odir tiles_dtm_25cm -obil ^
        -cores 4

We have to admit that a few bumps are left (see mouse cursor below) but adjusting the parameters presented here is left as an exercise to the reader.

We would again like to acknowledge AB Surveying whose generosity has made this blog article possible. They have the capacity to fly such missions in the Philippines and who have allowed us to share this data with you for educational purposes.

LASmoons: Sebastian Kasanmascheff

Sebastian Kasanmascheff (recipient of three LASmoons)
Forest Inventory and Remote Sensing
Georg-August-Universität Göttingen, GERMANY

Background:
Forest inventories are the backbone of forest management in Germany. In most federal forestry administrations in Germany, they are performed every ten years in order to assure that logging activities are sustainable. The process involves trained foresters who visit each stand (i.e. an area where the forest is similar in terms of age structure and tree species) and perform angle count sampling as developed by Walter Bitterlich in 1984. In a second step the annual growth is calculated using yield tables and finally a harvest volume is derived. There are three particular reasons to investigate how remote sensing can be integrated in the current inventory system:

  1. The current process does not involve random sampling of the sampling points and thus does not offer any measure of the accuracy of the data.
  2. Forest engineers hardly ever rely on the inventory data as a stand-alone basis for logging planning. Most often they rely on intuition alone and on the total volume count that they have to deliver for a wider area every year.
  3. In the last ten years, the collection of high-resolution LiDAR data has become more cost-effective and most federal agencies in Germany have access to it.

In order to be able to integrate the available remote-sensing data for forest inventories in Germany, it is important to tell apart different tree species as well as estimate their volumes.

Hesse is one of the most forested federal states in Germany.

Goal:
The goal of this project is to perform an object-based classification of conifer trees in Northern Hesse based on high-resolution LiDAR and multi-spectral orthophotos. The first step is to delineate the tree crowns. The second step is to perform a semi-automated classification using the spectral signature of the different conifer species.

Data:
+
 DSM (1m), DTM (1m), DSM (0.2 m) of the study area
+ Stereo images with 0.2 m resolution
+ high-resolution LiDAR data (average 10 points/m²)
+ forest inventory data
+ vector files of the individual forest stands
+ ground control points (field data)
All of this data is provided by the Hessian Forest Agency (HessenForst).

LAStools processing:
1) merge and clip the LAZ files [las2las]
2) classify ground and non-ground points [lasground]
3) remove low and high outliers [lasheight, lasnoise]
4) identify buildings within the study area [lasclassify]
5) create a normalized point cloud [lasheight]
6) create a highest-return canopy height model (CHM) [lasthin, las2dem]
7) create a pit-free (CHM) with the spike-free algorithm [las2dem]

LASmoons: Manuel Jurado

Manuel Jurado (recipient of three LASmoons)
Departamento de Ingeniería Topográfica y Cartografía
Universidad Politécnica de Madrid, SPAIN

Background:
The availability of LiDAR data is creating a lot of innovative possibilities in different fields of science, education, and other field of interests. One of the areas that has been deeply impacted by LiDAR is cartography and in particular one highly specialized sub-field of cartography in the domain of recreational and professional orienteering running: the production of high-quality maps for orienteering races (Ditz et al., 2014). These are thematic maps with a lot of fine detail which demands many hours of field work for the map maker. In order to reduce the fieldwork, LiDAR data obtained from Airborne Research Australia (ARA) is going to be used in order to obtain DEM and to extract features that must be included in these maps. The data will be filtered and processed with the help of LAStools.

Final map with symbolism typical for use in orienteering running

Goal:
The goal of this project is to extract either point (boulders, mounds), linear (contours, erosion gullies, cliffs) and area features (vegetation density) that should be drawn in a orienteering map derived from high-resolution LiDAR. Different LiDAR derived raster images are being created: 0.5m DTM, vegetation density (J. Ryyppo, 2013), slope, Sky-View factor (Ž. Kokalj et al., 2011), and shaded relief. The area used is in Renmark, South Australia and the produced map is going to be used for the Australian Orienteering Championships 2018.

Sky-View factor of DTM for same area as shown above.

Data:
+
4 square kilometers of airborne LiDAR data produced by Airborne Research Australia at 18 pulses per square meter using the full waveform scanning LiDAR Q680i-S laser scanner from RIEGL
+ 60 hours of check and validation work in the field

LAStools processing:
1) tile into 500 by 500 meter tiles with 20 meter buffer [lastile]
2) classify isolated points as noise [lasnoise]
3) classify point clouds into ground and non-ground [lasground]
4) create a Digital Terrain Model (DTM) [las2dem]
5) normalize height of points above the ground [lasheight]
6) compute vegetation density metrics [lascanopy]
7) create hillshades of the raster DTMs [blast2dem or GDAL]

References:
Ditz, Robert, Franz Glaner, and Georg Gartner. (2014). “Laser Scanning and Orienteering Maps.” Scientific Journal of Orienteering 19.1.
JRyyppo, Jarkko. (2013). “Karttapullautin vegetation mapping guide”.
Kokalj, Žiga, Zaksek, Klemen, and Oštir, Krištof. (2011). Application of sky-view factor for the visualization of historic landscape features in lidar-derived relief models. Antiquity. 85. 263-273.

LASmoons: Chris J. Chandler

Chris J. Chandler (recipient of three LASmoons)
School of Geography
University of Nottingham, UNITED KINGDOM

Background:
Wetlands provide a range of important ecosystem services: they store carbon, regulate greenhouse gas emissions, provide flood protection as well as water storage and purification. Preserving these services is critical to achieve sustainable environmental management. Currently, mangrove forests are protected in Mexico, however, fresh water wetland forests, which also have high capacity for storing carbon both in the trees and in the soil, are not protected under present legislation. As a result, coastal wetlands in Mexico are threatened by conversion to grazing areas, drainage for urban development and pollution. Given these threats, there is an urgent need to understand the current state and distribution of wetlands to inform policy and protect the ecosystem services provided by these wetlands.
In this project we will combine field data collection, satellite data (i.e. optical remote sensing, radar and LiDAR remote sensing) and modelling to provide an integrated technology for assessing the value of a range of ecosystem services, tested to proof of concept stage based on carbon storage. The outcome of the project will be a tool for mapping the value of a range of ecosystem services. These maps will be made directly available to local stakeholders including policy makers and land users to inform policy regarding forest protection/legislation and aid development of financial incentives for local communities to protect these services.

Wetland classification in the Chiapas region of Mexico

Goal:
At this stage of the project we have characterized wetlands for three priority areas in Mexico (Pantanos de Centla, La Encrucijada and La Mancha). Next stage is the up scaling of the field data at the three study sites using LiDAR data for producing high quality Canopy Height Model (CHM), which has been of great importance for biomass estimation (Ferraz et al., 2016). A high quality CHM will be achieved using LAStools software.

Data:
+
LiDAR provided by the Mexican National Institute of Statistics and Geography (INEGI)
+ average height: 5500 m, mirror angle: +/- 30 degrees, speed: 190 knots
+ collected with Cessna 441, Conquest II system at 1 pts/m².

LAStools processing:
1)
create 1000 meter tiles with 35 meter buffer to avoid edge artifacts [lastile]
2) classify point clouds into ground and non-ground [lasground]
3) normalize height of points above the ground [lasheight]
4) create a Digital Terrain and Surface Model (DTM and DSM) [las2dem]
5) generate a spike-free Canopy Height Model (CHM) as described here and here [las2dem]
6) compute various metrics for each plot and the normalized tiles [lascanopy]

References:
Ferraz, A., Saatchi, S., Mallet, C., Jacquemoud S., Gonçalves G., Silva C.A., Soares P., Tomé, M. and Pereira, L. (2016). Airborne Lidar Estimation of Aboveground Forest Biomass in the Absence of Field Inventory. Remote Sensing, 8(8), 653.

LASmoons: Huaibo Mu

Huaibo Mu (recipient of three LASmoons)
Environmental Mapping, Department of Geography
University College London (UCL), UK

Background:
This study is a part of the EU-funded Metrology for Earth Observation and Climate project (MetEOC-2). It aims to combine terrestrial and airborne LiDAR data to estimate biomass and allometry for woodland trees in the UK. Airborne LiDAR can capture large amounts of data over large areas, while terrestrial LiDAR can provide much more details of high quality in specific areas. The biomass and allometry for individual specific tree species in 1 ha of Wytham Woods located about 5km north west of the University of Oxford, UK are estimated by combining both airborne and terrestrial LiDAR. Then the bias will be evaluated when estimation are applied on different levels: terrestrial LiDAR level, tree level, and area level. The goal are better insights and a controllable error range in the bias of biomass and allometry estimates for woodland trees based on airborne LiDAR.

Goal:
The study aims to find the suitable parameters of allometric equations for different specific species and establish the relationship between the tree height and stem diameter and crown diameter to be able to estimate the above ground biomass using airborne LiDAR. The biomass estimates under different levels are then compared to evaluate the bias and for the total 6ha of Wytham Woods for calibration and validation. Finally the results are to be applied to other woodlands in the UK. The LiDAR processing tasks for which LAStools are used mainly center around the creation of suitable a Canopy Height Model (CHM) from the airborne LiDAR.

Data:
+ Airborne LiDAR data produced by Professor David Coomes (University of Cambridge) with Airborne Research and Survey Facility (ARSF) Project code of RG13_08 in June 2014. The average point density is about 5.886 per m^2.
+ Terrestrial LiDAR data collected by UCL’s team leader by Dr. Mat Disney and Dr. Kim Calders in order to develop very detailed 3D models of the trees.
+ Fieldwork from the project “Initial Results from Establishment of a Long-term Broadleaf Monitoring Plot at Wytham Woods, Oxford, UK” by Butt et al. (2009).

LAStools processing:
1) check LiDAR quality as described in these videos and articles [lasinfo, lasvalidate, lasoverlap, lasgrid, las2dem]
2) classify into ground and non-ground points using tile-based processing  [lastile, lasground]
3) generate a Digital Terrain Model (DTM) [las2dem]
4) compute height of points and delete points higher than maximum tree height obtained from terrestrial LiDAR [lasheight]
5) convert points into disks with 10 cm diameter to conservatively account for laser beam width [lasthin]
6) generate spike-free Digital Surface Model (DSM) based on algorithm by Khosravipour et al. (2016) [las2dem]
7) create Canopy Height Model (CHM) by subtracting DTM from spike-free DSM [lasheight].

References:
Butt, N., Campbell, G., Malhi, Y., Morecroft, M., Fenn, K., & Thomas, M. (2009). Initial results from establishment of a long-term broadleaf monitoring plot at Wytham Woods, Oxford, UK. University Oxford, Oxford, UK, Rep.
Khosravipour, A., Skidmore, A.K., Isenburg, M., Wang, T.J., Hussin, Y.A., (2014). Generating pit-free Canopy Height Models from Airborne LiDAR. PE&RS = Photogrammetric Engineering and Remote Sensing 80, 863-872.
Khosravipour, A., Skidmore, A.K., Isenburg, M. and Wang, T.J. (2015) Development of an algorithm to generate pit-free Digital Surface Models from LiDAR, Proceedings of SilviLaser 2015, pp. 247-249, September 2015.
Khosravipour, A., Skidmore, A.K., Isenburg, M (2016) Generating spike-free Digital Surface Models using raw LiDAR point clouds: a new approach for forestry applications, (journal manuscript under review).

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 …