Another European Country Opens LiDAR: Welcome to the Party, Slovakia!

We got a little note from Vítězslav Moudrý from CULS pointing out that the Geodesy, Cartography and Cadastre Authority of the Slovak Republic has started releasing LiDAR as open data on their interactive Web portal. Congratulations, Slovakia!!! Welcome to the Open Data Party!!! We managed to download some data starting from this Web portal link and describe the process of obtaining LiDAR data from the Low Tatras mountain range in central Slovakia with pictures below.

open_data_portal_slovakia_01

(1) click the new “data export” link

open_data_portal_slovakia_02

(2) change the export selection to “Shape”

open_data_portal_slovakia_03

(3) change the file format to “LAZ”

open_data_portal_slovakia_04

(4) zoom to a colored area-of-interest

open_data_portal_slovakia_05

(5) zoom further and draw a nice polygon

open_data_portal_slovakia_06

(6) edit polygon into nice shape and realize heart is red because area is too big

open_data_portal_slovakia_07

(7) zoom further and draw polygon smaller than 2 square kilometer

open_data_portal_slovakia_08

(8) when polygon turns green, accept license, enter email address and export

open_data_portal_slovakia_09

(9) short wait and you get download link to such an archive

open_data_portal_slovakia_10

(10) license conditions: PDF auto-translated from Slovak to English

 

open_data_portal_slovakia_11

(11) LiDAR are spatially indexed flight lines clipped to area-of-interest

open_data_portal_slovakia_12_density_all_returns_20_50

(12) all return density: blue = 20 and red = 50 returns per square meter

lasgrid -i LowTatras\*.laz -merged ^
        -step 2 -point_density_16bit ^
        -false -set_min_max 20 50 ^
        -o LowTatras\density_all_returns_20_50.png
open_data_portal_slovakia_13_density_last_returns_4_40

(13) last return density: blue = 4 and red = 40 last returns per square meter

lasgrid -i LowTatras\*.laz -merged ^
        -keep_last ^
        -step 2 -point_density_16bit ^
        -false -set_min_max 4 40 ^
        -o LowTatras\density_last_returns_4_40.png
open_data_portal_slovakia_14_density_ground_returns_4_40

(14) ground return density: blue = 4 and red = 40 ground returns per square meter

lasgrid -i LowTatras\*.laz -merged ^
        -keep_classification 2 ^
        -step 2 -point_density_16bit ^
        -false -set_min_max 4 40 ^
        -o LowTatras\density_ground_returns_4_40.png
open_data_portal_slovakia_14_overlap_10cm_20cm_diff

(15) flight line difference image: white <= +/- 10 cm and red/blue >= +/- 20 cm

lasoverlap -i LowTatras\*.laz -faf ^
           -drop_classification 7 18 ^
           -min_diff 0.1 -max_diff 0.2 ^
           -o LowTatras\overlap_10cm_20cm.png

Finally we compute a DSM and a corresponding DTM using the already existing ground classification with BLAST using the command sequence shown below.

 

lasthin -i LowTatras\*.laz -merged ^
        -drop_classification 7 18 ^
        -step 0.5 -highest ^
        -o LowTatras\highest_50cm.laz

blast2dem -i LowTatras\highest_50cm.laz ^
          -hillshade ^
          -o LowTatras -o dsm_1m_hillshaded.png

blast2dem -i LowTatras\*.laz -merged ^
          -keep_classification 2 ^
          -thin_with_grid 0.5 ^
          -hillshade ^
          -o LowTatras\dtm_1m_hillshaded.png

We thank the Geodesy, Cartography and Cadastre Authority of the Slovak Republic for providing their LiDAR as open data for both commercial and non-commercial purposes and name the source of the data used above (as the license requires) as the Office of Geodesy, Cartography and Cadastre of the Slovak Republic (GCCA SR) or – in Slovak – the Úrad geodézie, kartografie a katastra Slovenskej republiky (ÚGKK SR).

Which European country goes next? Czech Republic? Poland? Hungary? Switzerland?

 

 

Completeness and Correctness of Discrete LiDAR Returns per Laser Pulse fired

Again and again we have preached about the importance of quality checking when you first get your expensive LiDAR data from the vendor or your free LiDAR data from an open data portal. The minimal quality check we usually advocate consists of lasinfo, lasvalidate, lasoverlap, and lasgrid. The information computed by these LAStools can reassure you that the data contains the right information, is specification conform, has properly aligned flight lines, and has the density distribution you expect. For deliveries or downloads in LAZ format we in addition recommend running laszip with the option ‘-check’ to find the rare file that might have gotten bit-corrupted or truncated during the transfer or the download. Today we learn about a more advanced quality check that can be done by running lassort followed by lasreturn.

For every laser shot fired there are usually between one to five discrete LiDAR returns and some full-waveform systems may even deliver up to fifteen returns. Each of these one to fifteen returns is then given the exact same GPS time stamp that corresponds to the moment in time the laser pulse was fired. By having these unique GPS time stamps we can always recover the set of returns that come from the same laser shot. This makes it possible to check completeness (are all the returns in the file) and correctness (is the returns numbering correct) for the discrete returns of each laser pulse.

optech_galaxy_issue

Showing all sets of returns in the file that do not have an unique GPS time stamp because the set has one or more duplicate returns (e.g. two first returns, two second returns, … ).

With LAStools we can do this by running lassort followed by lasreturn for any LiDAR that comes from a single beam system. For LiDAR that comes from some multi-beam system, such as the Velodyne 16, 32, 64, or 128, the Optech Pegasus, the RIEGL LMS 1560 (aka “crossfire”), or the Leica ALS70 or ALS80 we first need to seperate the files into one file per beam, which can be done with lassplit.  In the following we investigate data coming from an Optech Galaxy single-beam system. First we sort the returns by GPS time stamp using lassort (this step can be omitted if the data is already sorted in acquisition order (aka by increasing GPS time stamps)) and then we check the return numbering with lasreturn:

lassort -i L001-1-M01-S1-C1_r.laz -gps_time -odix _sorted -olaz

lasreturn -i L001-1-M01-S1-C1_r_sorted.laz -check_return_numbering
checked returns of 11809046 multi and 8585573 single return pulses. took 26.278 secs
missing: 0 duplicate: 560717 too large: 0 zero: 0
duplicate
========
200543 returns with n = 1 and r = 1 are duplicate
80548 returns with n = 2 and r = 1 are duplicate
80548 returns with n = 2 and r = 2 are duplicate
41962 returns with n = 3 and r = 1 are duplicate
41962 returns with n = 3 and r = 2 are duplicate
41962 returns with n = 3 and r = 3 are duplicate
13753 returns with n = 4 and r = 1 are duplicate
13753 returns with n = 4 and r = 2 are duplicate
13753 returns with n = 4 and r = 3 are duplicate
13753 returns with n = 4 and r = 4 are duplicate
3636 returns with n = 5 and r = 1 are duplicate
3636 returns with n = 5 and r = 2 are duplicate
3636 returns with n = 5 and r = 3 are duplicate
3636 returns with n = 5 and r = 4 are duplicate
3636 returns with n = 5 and r = 5 are duplicate
WARNING: there are 59462 GPS time stamps that have returns with different number of returns

The output we see above indicates a problem in the return numbering. A recently added new options to lasreturn that allow to reclassify those returns that seem to be part of a problematic set of returns that either contains missing returns, duplicate returns, or returns with different values for the “numbers of returns of given pulse” attribute. This allows us to visualize the issue with lasview. All returns whose are part of a problematic set is shown in the image above.

lasreturn -i L001-1-M01-S1-C1_r_sorted.laz ^
          -check_return_numbering ^
          -classify_as 8 ^
          -classify_duplicate_as 9 ^
          -classify_violation_as 7 ^
          -odix _marked -olaz

This command will mark all sets of returns (i.e. returns that have the exact same GPS time stamp) that have missing returns as 8, that have duplicate returns as 9, and that have returns which different “number of returns per pulse” attribute as 7. The data we have here has no missing returns (no returns are classified as 8) but we have duplicate (9) and violating (7) returns. We look at them closely in single scan lines to conclude.

It immediately becomes obvious that the same GPS time stamp was assigned to the returns of pair of subsequent shots. If the subsequent shots have the same number of returns per shot they are classified as duplicate (9 or blue). If the subsequent shots have different number of returns per shot they are marked as violating (7 or violett) but the reason for the issue is the same. We can look at a few of these return sets in ASCII. Here two subsequent four return shots that have the same GPS time stamp.

237881.011730 4 1 691602.736 5878246.425 141.992 6 79
237881.011730 4 2 691602.822 5878246.415 141.173 6 89
237881.011730 4 3 691603.051 5878246.389 138.993 6 44
237881.011730 4 4 691603.350 5878246.356 136.150 6 169
237881.011730 4 1 691602.793 5878246.439 142.037 6 114
237881.011730 4 2 691602.883 5878246.429 141.185 6 96
237881.011730 4 3 691603.109 5878246.404 139.033 6 50
237881.011730 4 4 691603.414 5878246.370 136.129 6 137

Here a four return shot followed by a three return shot that have the same GPS time stamp.

237881.047753 4 1 691603.387 5878244.501 140.187 6 50
237881.047753 4 2 691603.602 5878244.476 138.141 6 114
237881.047753 4 3 691603.776 5878244.456 136.490 6 60
237881.047753 4 4 691603.957 5878244.436 134.767 6 116
237881.047753 3 1 691603.676 5878244.492 138.132 6 97
237881.047753 3 2 691603.845 5878244.473 136.534 6 90
237881.047753 3 3 691604.034 5878244.452 134.739 6 99

It appears the GPS time counter in the LMS export software did not store the GPS time with sufficient resolution to always distinguish subsequent shots. The issue was confirmed by Optech and was already fixed a few months ago.

We should point out that these double-used GPS time stamps have zero impact on the geometric quality of the point cloud or the distribution of returns. The drawback is that not all returns can easily be grouped into one unique set per laser shot and that the files are not entirely specification conform. Any software that relies on accurate and unique GPS time stamps (such as flight line alignment software) may potentially struggle as well. The bug of the twice-used GPS time stamps was a discovery that is probably of such low consequence that no user of Optech Galaxy data had noticed it in the 4 years that Galaxy had been sold … until we really really scrutinized some data from one of our clients. Optech reports that the issue has been fixed now. But there are other vendors out there with even more serious GPS time and return numbering issues … to be continued.

Another German State Goes Open LiDAR: Saxony

Finally some really good news out of Saxony. 😊 After North Rhine-Westphalia and Thuringia released the first significant amounts of open geospatial data in Germany in a one-two punch in January 2017, we now have a third German state opening their entire tax-payer-funded geospatial data holdings to the tax-paying public via a simple and very easy-to-use online download portal. Welcome to the open data party, Saxony!!!

Currently available via the online portal are the LiDAR-derived raster Digital Terrain Model (DTM) at 1 meter resolution (DGM 1m) for everything flown since 2015 and and at 2 meter resolution (DGM 2m) or 20 meter resolution (DGM 20m) for the entire state. The horizontal coordinates use UTM zone 33 with ETRS89 (aka EPSG code 25833) and the vertical coordinate uses the “Deutsche Haupthöhennetz 2016” or “DHHN2016” (aka EPSG code 7837). Also available are orthophotos at 20 cm (!!!) resolution (DOP 20cm).

dgm_1000_rdax_87

Overview of current LiDAR holdings. Areas flown 2015 or later have LAS files and 1 meter rasters. Others have LiDAR as ASCII files and lower resolution rasters.

Offline – by ordering through either this online form or that online form – you can also get the 5 meter DTM and the 10 meter DTM, the raw LiDAR point clouds, LiDAR intensity rasters, hill-shaded DTM rasters, as well as the 1 meter and the 2 meter Digital Surface Model (DSM) for a small administrative fee that ranges between 25 EUR and 500 EUR depending on the effort involved.

Our immediate thought is to get a copy on the entire raw LiDAR points clouds (available as LAS 1.2 files for all  data acquired since 2015 and as ASCII text for earlier acquisitions) and find some portal willing to hosts this data online. We are already in contact with the land survey of Saxony to discuss this option and/or alternate plans.

Let’s have a look at the data. First we download four 2 km by 2 km tiles of the 1 meter DTM raster for an area surrounding the so called “Greifensteine” using the interactive map of the download portal, which are provided as simple XYZ text. Here a look at the contents of one ot these tiles:

more Greifensteine\333525612_dgm1.xyz
352000 5613999 636.26
352001 5613999 636.27
352002 5613999 636.28
352003 5613999 636.27
352004 5613999 636.24
[...]

Note that the elevation are not sampled in the center of every 1 meter by 1 meter cell but exactly on the full meter coordinate pair, which seems especially common  in German-speaking countries. Using txt2las we convert these XYZ rasters to LAZ format and add geo-referencing information for more efficient subsequent processing.

txt2las -i greifensteine\333*_dgm1.xyz ^
        -set_scale 1 1 0.01 ^
        -epsg 25833 ^
        -olaz

Below you see that going from XYZ to LAZ reduces the amount of  data from 366 MB to 10.4 MB, meaning that the data on disk becomes over 35 times smaller. The ability of LASzip to compress elevation rasters was first noted during the search for missing airliner MH370 and resulted in our new LAZ-based compressor for height grid called DEMzip.  The resulting LAZ files now also include geo-referencing information.

96,000,000 333525610_dgm1.xyz
96,000,000 333525612_dgm1.xyz
96,000,000 333545610_dgm1.xyz
96,000,000 333545612_dgm1.xyz
384,000,000 bytes

2,684,820 333525610_dgm1.laz
2,590,516 333525612_dgm1.laz
2,853,851 333545610_dgm1.laz
2,795,430 333545612_dgm1.laz
10,924,617 bytes

Using blast2dem we then create a hill-shaded version of the 1 meter DTM in order to overlay a visual representation of the DTM onto Google Earth.

blast2dem -i greifensteine\333*_dgm1.laz ^
          -merged ^
          -step 1 ^
          -hillshade ^
          -o greifensteine.png

Below the result that nicely shows how the penetrating laser of the LiDAR allows us to strip away the forest to see interesting geological features in the bare-earth terrain.

In a second exercise we use the available RGB orthophoto images to color one of the DTM tiles and explore it using lasview. For this we download the image for the top left of the four tiles that covers the area containing the “Greifensteine” from the interactive download portal for orthophotos. As the resolution of the TIF image is 20 cm and that of the DTM is only 1 meter, we first down-sample the TIF using gdalwarp of GDAL.

gdalwarp -tr 1 1 ^
         -r cubic ^
         greifensteine\dop20c_33352_5612.tif ^
         greifensteine\dop1m_33352_5612.tif

If you are not yet using GDAL today is a good day to start. It nicely complements the point cloud processing functionality of LAStools for raster inputs. Next we use lascolor to give each elevation pixel of the DTM stored in LAZ format its corresponding color from the orthophoto.

lascolor -i greifensteine\333525612_dgm1.laz ^
         -image greifensteine\dop1m_33352_5612.tif ^
         -odix _rgb -olaz

Now we can view the colored DTM in LAZ format interactively with lasview or any other LiDAR viewing software and turn on the RGB colors from the orthophoto as needed to understand the scene.

lasview -i greifensteine\333525612_dgm1_rgb.laz

We thank the “Staatsbetrieb Geobasisinformation und Vermessung Sachsen (GeoSN)” for giving us easy access to the 1 meter DTM and the 20 cm orthophoto that we have used in this article through their new open geodata portal as open data under the user-friendly license “Datenlizenz Deutschland – Namensnennung – Version 2.0.

National Open LiDAR Strategy of Latvia humiliates Germany, Austria, and other European “Closed Data” States

Latvia, officially the Republic of Latvia, is a country in the Baltic region of Northern Europe has around 2 million inhabitants, a territory of 65 thousand square kilometers and – since recently – also a fabulous open LiDAR policy. Here is a list of 65939 tiles in LAS format available for free download that cover the entire country with airborne LiDAR with a density from 4 to 6 pulses per square meters. The data is classified into ground, building, vegetation, water, low noise, and a few other classifications. It is licensed Creative Commons CC0 1.0 – meaning that you can copy, modify, and distribute the data, even for commercial purposes, all without asking permission. And there is a simple and  functional interactive download portal where you can easily download individual tiles.

latvia_open_data_portal_01

Interactive open LiDAR download portal of Latvia.

We downloaded the 5 by 5 block of square kilometer tiles matching “4311-32-XX.las” for checking the quality and creating a 1m DTM and a 1m DSM raster. You can follow along after downloading the latest version of LAStools.

Quality Checking

We first run lasvalidate and lasinfo on the downloaded LAS files and then immediately compress them with laszip because multi-core processing of uncompressed LAS files will quickly overwhelm our file system, make processing I/O bound, and result in overall longer processing times with CPUs waiting idly for data to be loaded from the drives.

lasinfo -i 00_tiles_raw\*.las ^
        -compute_density ^
        -histo z 5 ^
        -histo intensity 256 ^
        -histo user_data 1 ^
        -histo scan_angle 1 ^
        -histo point_source 1 ^
        -histo gps_time 10 ^
        -odir 01_quality -odix _info -otxt ^
        -cores 3
lasvalidate -i 00_tiles_raw\*.las ^
            -no_CRS_fail ^
            -o 01_quality\report.xml

Despite already excluding a missing Coordinate Reference System (CRS) from being a reason to fail (the lasinfo reports show that the downloaded LAS files do not have any geo-referencing information) lasvalidate still reports a few failing files, but scrutinizing the resulting XML file ‘report.xml’ shows only minor issues.

Usually during laszip compression we do not alter the contents of a file, but here we also add the EPSG code 3059 for CRS “LKS92 / Latvia TM” as we turn bulky LAS files into slim LAZ files so we don’t have to specify it in all future processing steps.

laszip -i 00_tiles_raw\*.las ^
       -epsg 3059 ^
       -cores 2

Compression reduces the total size of the 25 tiles from over 4.1 GB to below 0.6 GB.

Next we use lasgrid to visualize the last return density which corresponds to the pulse density of the LiDAR survey. We map each 2 by 2 meter pixel where the last return density is 2 or less to blue and each 2 by 2 meter pixel it is 8 or more to red.

lasgrid -i 00_tiles_raw\*.laz ^
        -keep_last ^
        -step 2 ^
        -density_16bit ^
        -false -set_min_max 2 8 ^
        -odir 01_quality -odix _d_2_8 -opng ^
        -cores 3

This we follow by the mandatory lasoverlap check for flight line overlap and alignment where we map the number of overlapping swaths as well as the worst vertical difference between overlapping swaths to a color that allows for quick visual quality checking.

lasoverlap -i 00_tiles_raw\*.laz ^
           -step 2 ^
           -min_diff 0.1 -max_diff 0.2 ^
           -odir 01_quality -opng ^
           -cores 3

The results of the quality checks with lasgrid and lasoverlap are shown below.

Raster Derivative Generation

Now we use first las2dem to create a Digital Terrain Model (DTM) and a Digital Surface Model (DSM) in RasterLAZ format and then use blast2dem to create merged and hill-shaded versions of both. Because we will use on-the-fly buffering to avoid edge effects along tile boundaries we first spatially index the data using lasindex for more efficient access to the points from neighboring tiles.

lasindex -i 00_tiles_raw\*.laz ^
         -cores 3

las2dem -i 00_tiles_raw\*.laz ^
        -keep_class 2 9 ^
        -buffered 25 ^
        -step 1 ^
        -use_orig_bb ^
        -odir Latvia\02_dtm_1m -olaz ^
        -cores 3

blast2dem -i 02_dtm_1m\*.laz ^
          -merged ^
          -hillshade ^
          -step 1 ^
          -o dtm_1m.png

las2dem -i 00_tiles_raw\*.laz ^
        -drop_class 1 7 ^
        -buffered 10 ^
        -spike_free 1.5 ^
        -step 1 ^
        -use_orig_bb ^
        -odir 03_dsm_1m -olaz ^
        -cores 3

blast2dem -i 03_dsm_1m\*.laz ^
          -merged ^
          -hillshade ^
          -step 1 ^
          -o dsm_1m.png

Because the overlaid imagery does not look as nice in our new Google Earth installation, below are the DTM and DSM at versions down-sampled to 25% of their original size.

Many thanks to SunGIS from Latvia who tweeted us about the Open LiDAR after we chatted about it during the Foss4G 2019 gala dinner. Kudos to the Latvian Geospatial Information Agency (LGIA) for implementing a modern national geospatial policy that created opportunity for maximal return of investment by opening the expensive tax-payer funded LiDAR data for re-purposing and innovation without barriers. Kudos!

LASmoons: Gabriele Garnero

Gabriele Garnero (recipient of three LASmoons)
Interuniversity Department of Regional and Urban Studies and Planning
Politecnico e Università degli Studi, Torino
ITALY

Background:
Last spring, the LARTU research group produced a laser scanner survey of the Abbey of Sant’Andrea in Vercelli, on the occasion of the VIII centenary of the dedication (1219). The database produced with a topographic tool that integrates the potential of a total station with laser scanner and photogrammetric sensors (Trimble SX 10), has been used to produce representations that can be consulted in interactive mode, navigating within the point clouds and producing a consultation platform that can also be accessed by non-specialist users such as art historians or archaeologists.

lasmoons_gabriele_garnero_0

Goal:
The LAStools software will be used to improve both the point cloud produced by eliminating the remaining noises, and check other ways of publishing the data, so as to make it usable from outside, to the community of researchers.

Data:
+
laser scanner and photogrammetric acquisitions of the interior of the building (150 millions of points)
+ laser scanner and photogrammetric acquisitions of the outside of the building (210 millions of points)
+ drone-based shooting of outdoor areas processed with Pix4D (23 millions of points)

LAStools processing:
1) tile large point cloud into tiles with buffer [lastile]
2) mark set of points whose z coordinate is a certain percentile of that of their neighbors [lasthin]
3) remove isolated low points from the set of marked points [lasnoise]
4) classify marked points into ground and non-ground [lasground]
5) creates a LiDAR portal for 3D visualization of LAS files [laspublish]

Removing Low Noise in LiDAR Points with Median Ground Surface

Recently a user of LAStools asked a question in our user forum about how to classify LiDAR data that contains lots of low noise. A sample screen shot of the user’s failed attempt to correctly classify the noise using lasnoise and the ground with lasground is shown below: red points are noise, brown points are ground, and grey points are unclassified. In this article we show how to remove this low noise using a temporary ground surface that we construct from a subset of points at a certain elevation percentile. You can follow along by downloading the data and the sequence of command lines used.

example of miss-classified low noise points: ground points (brown) below ground

Download the LiDAR data set that was apparently flown with a RIEGL “crossfire” Q1560. You can also download the command line sequence here. We first run lasinfo with option ‘-compute_density’ (or ‘-cd’ for short) to get a rough idea about the last return density which is quite high with an average of over 31 last returns per square meter. We then use lasthin to classify one last return per square meter with the temporary classification code 8, namely the one whose elevation is closest to the 20th percentile per 1 meter by 1 meter grid cell. We then repeat this command line for the 30th, 40th, 50th percentile modifying the command line accordingly. You must use this version of lasthin that will part of a future LAStools release as options ‘-ignore_first_of_many’ and ‘-ignore_intermediate’ were just added this weekend.

lasthin -i crossfire.laz ^
        -ignore_first_of_many -ignore_intermediate ^
        -step 1 ^
        -percentile 20 15 ^
        -classify_as 8 ^
        -odix _p20 -olaz

Below you see the resulting subset of points marked with the temporary classification code 8 for the four different percentiles 20th, 30th, 40th, and 50th triangulated into a surface and hill-shaded.

Next we reclassify only those points marked with the temporary classification code 8 into ground (2) and unclassified (1) points using lasground by ignoring all points that still have the original classification code 0.

lasground -i crossfire_p20.laz ^
          -ignore_class 0 ^
          -wilderness ^
          -odix g -olaz

Below you see the resulting ground points computed from the subsets of points at four different percentiles 20th, 30th, 40th, and 50th triangulated into a surface and hill-shaded.

Both the ground classification of the 40th and the 50th percentile look reasonable. Only a few down spikes remain in the 40th percentile surface and a few additional bumps appear in the 50th percentile surface. Next we use lasheight with those two reasonable-looking ground surfaces to classify all points that are 20 centimeter below the triangulated ground surface into the noise classification code 7.

lasheight -i crossfire_p40g.laz ^
          -classify_below -0.2 7 ^
          -do_not_store_in_user_data ^
          -odix h -olaz

Now that the low noise points were removed (or rather classified as noise) we start the actual ground classification process. In this example we want to create a 50 cm DTM, hence it is more than sufficient to find one ground point per 25 cm cell. Therefore we first move all lowest non-noise last return per 25 cm cell to the temporary classification code 8.

Side note: One might also consider to modify the following workflow to run the ground classification on more than just the last returns by omitting ‘-ignore_first_of_many’ and ‘-ignore_intermediate’ from the lasthin call and by adding ‘-all_returns’ to the lasground call. Why? Because for all laser shots that resulted in a low noise point, this noise point will usually be the last return, so that the true ground hit could be the second to last return.

lasthin -i crossfire_p40gh.laz ^
        -ignore_first_of_many -ignore_intermediate ^
        -ignore_class 7 ^
        -step 0.25 ^
        -lowest ^
        -classify_as 8 ^
        -odix _low25 -olaz

The final ground classification is obtained by running lasground only on the points with temporary classification code 8 by ignoring all others, namely the noise points (7) and the unclassified points (0 and 1).

lasground -i crossfire_p40gh_low25.laz ^
          -ignore_class 0 1 7 ^
          -wilderness ^
          -odix g -olaz

We then use las2dem to create the 50 cm DTM from the points classified as ground. We store this DTM raster to the LAZ format which has shown to be the most efficient format for storing elevation or height rasters. We have started calling this format RasterLAZ. It is supported by all LAStools and the new DEMzip tool. One advantage is that we can feed RasterLAZ directly back into LAStools, for example as done below, for a second call to las2dem that computes a hill-shaded DTM.

las2dem -i crossfire_p40gh_low25g.laz ^
        -keep_class 2 ^
        -step 0.5 ^
        -ocut 9 -odix _dtm50 -olaz

las2dem -i crossfire_p40_dtm50.laz ^
        -step 0.5 ^
        -hillshade ^
        -odix _hill -opng

Below the resulting hill-shaded DTMs computed for the 40th and the 50th elevation percentile – as well as for the 45th elevation percentile that we’ve added for comparison.

Below we finally take a closer look at an example 1 meter profile line through the LiDAR classified by the 45th percentile workflow. There is a small stretch of ground points that was incorrectly classified as noise points (find the mouse cursor) so it might be worthwhile to change parameters slightly to make the noise classification less aggressive.

Side note follow-up: The return coloring shows there are indeed some ‘intermediate’ as well some ‘first of many returns’ just where we expect the bare terrain to be. However, there are not so many that the results can be expected to drastically change by including them into the ground finding process.

LASmoons: Nicolas Barth

Nicolas Barth (recipient of three LASmoons)
Department of Earth & Planetary Sciences
University of California, Riverside
UNITED STATES

Background:
The 850 km-long Alpine Fault (AF) is one of the world’s great laterally-slipping active faults (like California’s San Andreas Fault), which currently accommodates about 80% of the motion between the Australian and Pacific tectonic plates in the South Island of New Zealand (NZ). Well-dated sedimentary layers preserved in swamps and lakes adjacent to the AF currently provide one of the world’s most spatially and temporally complete record of large ground rupturing earthquakes (Howarth et al., 2018). Importantly these records reveal that major earthquakes occur with greater regularity on the AF than any other known fault, releasing a Magnitude (Mw) 7 to 8 earthquake on average every 249 ± 58 years and that the most recent earthquake was around Mw 8 in 1717 AD prior to European arrival. This computes to a conditional probability of 69% that the AF will rupture in the next 50 years. For a country that has recently had several notable earthquakes (e.g. 2010 Mw 7.1 Canterbury, 2016 Mw 7.8 Kaikoura) and has an economy heavily reliant on tourism, the next AF earthquake is the one NZ is trying to prepare for (note that a Mw 8 earthquake is about thirty times the energy release of a Mw 7).

The more data we can gather as scientists to constrain (1) the magnitude of the next AF earthquake, (2) the amount of lateral and vertical slip (offset roads, powerlines, etc.), (3) the coseismic effects (ground shaking, landslides, liquefaction), and (4) the duration it takes the landscape to recover (muddy rivers, increased sediment supply, prolonged landsliding), the more we can anticipate expected hazards and foster societal resilience.

Despite its name, the AF is almost completely obscured beneath a dense temperate rain-forest canopy, which has hindered fine-scale geomorphic studies. Relatively low quality airborne LiDAR (2 m-resolution bare-earth model) was first collected in 2010 for a 32 km-length of the central AF. Despite being the best studied portion of the AF, 82 % of the fault traces identified in the LiDAR were previously unmapped (Barth et al., 2012). The LiDAR reveals the width and style of ground deformation. Interpretation of the bare-earth landscape in combination with on the ground sampling, allows single earthquake displacements, uplift rates, recurrence of landslides, and post-earthquake sedimentation rates to be quantified. A new 2019 airborne LiDAR dataset collected along 230 km-length of the southern AF has great potential to improve our understanding of this relatively “well-behaved” fault system, what to expect from its next earthquake, and to give us insight into considerably more complex fault systems like the San Andreas.

(A) Aerial view of the South Island of New Zealand highlighting the boundary between the Pacific and Australian plates (white) and the Alpine Fault in particular (red). (B) View showing the extent of the 2019 airborne LiDAR survey to be processed by this lasmoons proposal. (C) Aerial imagery over Franz Josef, site of a 2010 airborne LiDAR survey. (D) 2010 Franz Josef LiDAR DTM hillshade (GNS Science). LiDAR has revolutionized our ability to map fault offsets and other earthquake ground deformation beneath this dense temperate rainforest.

Goal:
The LAStools software will be used to check the quality of the data (reclassing ground points and removing any low ground classed outliers if needed) and create a seamless digital terrain model (DTM) from the 1695 tiled LAS files provided. The DTM will be used to create derivative products including contours, slope map, aspect map, single direction B&W hillshades, multi-directional hillshades, and slope-colored hillshades to interpret the fault and landslide related landscape features hidden beneath the dense temperate rain-forest. The results will be used as seed data to seek national-level science funding to field verify interpretations and collect samples to determine ages of features (geochronology). The ultimate goal is to improve our understanding of the Alpine Fault prior to its next major earthquake and to communicate those findings effectively through publications in open access peer-reviewed journal articles and meetings with NZ regional councils.

Data:
+
airborne LiDAR survey collected in 2019 using a Riegl LSM-Q780 sensor by AAM New Zealand
+ provided data are as 1695 LAS files organized into 500 m x 500 m tiles and classified as ground and non-ground points (75 pts/m2 or ~0.8 ground-classed pts/m2; 320 GB total)

LAStools processing:
1) check the quality of the ALS data [lasinfo, lasoverlap, lasgrid]
2) [if needed] remove any low and high ground-classed outliers [lasnoise]
3) [if needed] reclassify ground and non-ground points [lasground]
4) create Digital Terrain Model (DTM) from ground points [blast2dem]

References:
Howarth, J.D., Cochran, U.A., Langridge, R.M., Clark, K.J., Fitzsimons, S.J., Berryman, K.R., Villamor, P., Strong, D.T. (2018) Past large earthquakes on the Alpine Fault: paleosismological progress and future directions. New Zealand Journal of Geology and Geophysics, v. 61, 309-328, doi: 10.1080/00288306.2018.1465658
Barth, N.C., Toy, V.G., Langridge, R.M., Norris, R.J. (2012) Scale dependence of oblique plate-boundary partitioning: new insights from LiDAR, central Alpine Fault, New Zealand. Lithosphere 4(5), 435-448, doi: 10.1130/L201.1