No Sugarcoating: Sweet LiDAR from RiCOPTER carrying VUX-1UAV over Sugarcane

Recently we saw an interesting LiDAR data set talked about on social media by Chad Netto from Chustz Surveying in New Roads, Louisiana and asked for a copy. It is a LiDAR scan of a sugarcane plantation in Assumption Parish, Louisiana carried out with the VUX-1UAV by RIEGL mounted onto a RiCOPTER and guided by an Applanix IMU and a Trimble base station. That is probably one of the sweetest (but also one of the most expensive) UAV LiDAR system you can buy today. I received this LiDAR file and this trajectory file. In the following we talk a detailed look at this data set.

First we run lasinfo to get an idea of the contents of the data set. We create various histograms as those can often help understand an unfamiliar data set:

lasinfo -i sugarcane\181026_163424.laz ^
        -cd ^
        -histo gps_time 5 ^
        -histo intensity 64 ^
        -histo point_source 1 ^
        -histo z 5 ^
        -odix _info -otxt

You can download the resulting report here. For the 84,751,955 points we notice that

  1. both horizontal and vertical coordinates are stored in US survey feet
  2. with scale factors of 0.00025 this means a resolution of 76 micrometer
  3. there is no explicit flight line information (all point source IDs are zero)
  4. gaps in the GPS time stamp histogram are suggesting multiple lines

First we use las2las to lower the insanely high resolution from 0.00025 US survey feet to something more reasonable for an airborne UAV scan, namely to 0.01 or 1 hundredths of a US survey foot or centi-US-survey-feet:

las2las -i sugarcane\181026_163424.laz ^
        -rescale 0.01 0.01 0.01 ^
        -odix _cft -olaz

I have already done this for you. The file that is online is already in “centi-US-survey-feet” because it reduced the file size from the original 678 MB file that we got from Netto to the 518 MB file that is online, meaning that you had 160 MB less data to download.

Next we use lassplit to recover the original flight lines as follows:

lassplit -i sugarcane\181026_163424.laz ^
         -recover_flightlines ^
         -odir sugarcane\0_recovered_strips ^
         -o assumption.laz

This results in 5 strips. We then use lassort to bring the strips back into their original acquisition order by sorting first based on the GPS time stamp (which brings all returns of one pulse together) and second on the return number (which sorts them in ascending order). We do this on 3 cores in parallel with this command:

lassort -i sugarcane\0_recovered_strips\*.laz ^
        -gps_time ^
        -return_number ^
        -odir sugarcane\1_sorted_strips -olaz ^
        -cores 3

We also create a spatial index for each of these strips using lasindex so that any area-of-interest query that we do later will be significantly accelerated. See the README file for the meaning of the parameters:

lasindex -i sugarcane\1_sorted_strips\*.laz ^
         -tile_size 10 -maximum -100 ^
         -cores 3

Then we check for flight line alignment using lasoverlap by comparing – per 2 feet by 2 feet area – the lowest elevation value of points from different strips wherever there is overlap. Cells with an absolute vertical difference of less than a quarter of a foot are mapped to white. Cells with vertical differences of more (or less) than a quarter foot are mapped to an increasingly red (or blue) color that is saturated red (or blue) when one full foot is reached.

lasoverlap -i sugarcane\1_sorted_strips\*.laz ^
           -files_are_flightlines ^
           -step 2.0 ^
           -min_diff 0.25 -max_diff 1.0 ^
           -o sugarcane\2_quality\overlap.png

The resulting image looks dramatic at first glance. But we have to remember that this is sugarcane. The penetration of the laser can vary greatly depending on the direction from which the beam hits the densely standing stalks. Large differences between flight lines can be expected where sugarcane stands tall. We need to focus our visual quality checks on the few open areas, namely the access roads and harvested areas.

Color-mapped highest vertical difference in lowest point per 2 feet by 2 feet area between overlapping flight lines.

We use las2las via its native GUI to cut out several suspicious-looking open areas with overly red or overly blue shading. By loading the resulting image into the GUI these areas-of-interest are easy to target and cut out.

las2las -i sugarcane\1_sorted_strips\*.laz -gui

Overlaying the difference image in the GUI of las2las to cut out suspicious areas for closer inspection.

We cut out four square 100 by 100 meter tiles in open areas that show a suspiciously strong pattern of red or blue colors for closer inspection. The command lines for these four square areas are given below and you can download them here:

  1. assumption_3364350_534950_100.laz
  2. assumption_3365600_535750_100.laz
  3. assumption_3364900_535500_100.laz
  4. assumption_3365500_535600_100.laz
las2las -i sugarcane\1_sorted_strips\*.laz ^
        -merged -faf ^
        -inside_tile 3364350 534950 100 ^
        -o sugarcane\assumption_3364350_534950_100.laz

las2las -i sugarcane\1_sorted_strips\*.laz ^
        -merged -faf ^
        -inside_tile 3365600 535750 100 ^
        -o sugarcane\assumption_3365600_535750_100.laz

las2las -i sugarcane\1_sorted_strips\*.laz ^
        -merged -faf ^
        -inside_tile 3364900 535500 100 ^
        -o sugarcane\assumption_3364900_535500_100.laz

las2las -i sugarcane\1_sorted_strips\*.laz ^
        -merged -faf ^
        -inside_tile 3365500 535600 100 ^
        -o sugarcane\assumption_3365500_535600_100.laz

In the image sequence below we scrutinize these differences which will lead us to notice two things:

  1. There are vertical miss-alignments of around one foot. These big difference can especially be observed between flight lines that cover an area with a very high point density and those that cover the very same area with a very low point density.
  2. There are horizontal miss-alignments of around one foot. Again these differences seem somewhat correlated to the density that these flight lines cover a particular area with.

For the most part the miss-aligned points come from a flight line that has only sparse coverage in that area. In a flat terrain the return density per area goes down the farther we are from the drone as those areas are only reached with higher and higher scan angles. Hence an immediate idea that comes to mind is to limit the scan angle to those closer to nadir and lower the range from -81 to 84 degrees reported in the lasinfo report to something like -75 to 75, -70 to 70, or -65 to 65 degrees. We can check how this will improve the alignment with these lasoverlap command lines:

lasoverlap -i sugarcane\1_sorted_strips\*.laz ^
           -files_are_flightlines ^
           -keep_scan_angle -75 75 ^
           -step 2.0 ^
           -min_diff 0.25 -max_diff 1.0 ^
           -o sugarcane\2_quality\overlap75.png

lasoverlap -i sugarcane\1_sorted_strips\*.laz ^
           -files_are_flightlines ^
           -keep_scan_angle -70 70 ^
           -step 2.0 ^
           -min_diff 0.25 -max_diff 1.0 ^
           -o sugarcane\2_quality\overlap70.png

lasoverlap -i sugarcane\1_sorted_strips\*.laz ^
           -files_are_flightlines ^
           -keep_scan_angle -65 65 ^
           -step 2.0 ^
           -min_diff 0.25 -max_diff 1.0 ^
           -o sugarcane\2_quality\overlap65.png

lasoverlap -i sugarcane\1_sorted_strips\*.laz ^
           -files_are_flightlines ^
           -keep_scan_angle -60 60 ^
           -step 2.0 ^
           -min_diff 0.25 -max_diff 1.0 ^
           -o sugarcane\2_quality\overlap60.png

This simple technique significantly improves the difference image. Based on these images would suggest to only use returns with a scan angle between -70 and 70 degrees for any subsequent analysis. This seems to remove most of the discoloring in open areas without loosing too many points. Note that only using returns with a scan angle between -60 and 60 degrees means that some flight lines are no longer overlapping each other.

Note also that by limiting the scan angle we get suddenly get white areas even in incredible dense vegetation. The more horizontal a laser shoot is the more likely it will only hit higher up sugarcane plants and the less likely it will penetrate all the way to the ground. The white areas coincide with where laser pulses are close to nadir which is in the flight line overlap areas that directly below the drone’s trajectory.

Can we improve alignment further? Not with LAStools, so I turned to Andre Jalobeanu, a specialist on that particular issue, who I have known for many years. Andre has developed BayesStripAlign – a software by his company BayesMap that is quite complementary to LAStools and does exactly what the name suggests: it align strips. I gave Andre the five flight lines and he aligned them for me. Below the new difference images:

We cut out the very same four square areas from the realigned strips for closer inspection and you may investigate them on your own. You can download them here.

  1. assumption_3364350_534950_100_realigned.laz
  2. assumption_3365600_535750_100_realigned.laz
  3. assumption_3364900_535500_100_realigned.laz
  4. assumption_3365500_535600_100_realigned.laz

In the image sequence below we are just looking at the last of the four square areas and you can see that most of the miss-alignment we saw earlier between the flight lines was removed.

We would like to thank Chad Netto from Chustz Surveying to make this data set available to us and Andre Jalobeanu from BayesMap to align the flight lines for us.

LASmoons: David Bandrowski

David Bandrowski (recipient of three LASmoons)
Yurok Tribe
Native American Indian Tribe in Northern California, USA

Wild spring-run Chinook salmon populations on the South Fork Trinity River in Northern California are near the brink of extinction. The South Fork Trinity River is the most remote and the largest un-dammed river in the State of California, federally designated as a wild and scenic river, and is a keystone watershed within the Klamath River basin supporting one of the last remaining populations of wild spring-run Chinook salmon. Ecosystem restoration is urgently needed to improve watershed health in the face of climate change, land use, and water diversions. This drastic decline of the wild salmon species motivated the Yurok Tribe and its partners to take action and implement this project as a last opportunity to save this species before extinction. Spring-run Chinook are extremely important for the Yurok people culturally, spiritually, and for a subsistence food source.

sample of the available photogrammetry data

Due to budgetary constraints, airborne LiDAR is not available; therefore the Yurok Tribe has been using aerial drones and Structure for Motion (SfM) photogrammetry to develop DTM models that can be used in determining available salmon habitat and to develop prioritized locations for restoration. The watershed has extremely heavy vegetation, and obtaining bare-earth surfaces for hydraulic modeling is difficult without the proper tools. The goal is to use LAStools to further restoration science and create efficient workflows for DTM development.

 length of river mapped: 8 Kilometers
+ number of points: 150,856,819
+ horizontal datum: North American Datum 83 – California State Plane – Zone 1 (usft)
+ vertical datum: North American Vertical Datum 88

LAStools processing:
1) data quality checking [lasinfo, lasview, lasgrid]
2) classify ground and non-ground points [lasground and lasground_new]
3) remove low and high outliers [lasheight, lasnoise]
4) create DTM tiles at appropriate resolution [las2dem]
5) create a normalized point cloud [lasheight]

LASmoons: Sebastian Flachmeier

Sebastian Flachmeier (recipient of three LASmoons)
UniGIS Master of Science, University of Salzburg, AUSTRIA
Bavarian Forest National Park, administration, Grafenau, GERMANY

The Bavarian Forest National Park is located in South-Eastern Germany, along the border with the Czech Republic. It has a total area of 240 km² and its elevation ranges from 600 to 1453 m. In 2002 a project called “High-Tech-Offensive Bayern” was started and a few first/last return LiDAR transects were flown to compute some forest metrics. The results showed that LiDAR has an advantage over other methods, because the laser was able to get readings from below the canopy. New full waveform scanner were developed that produced many more returns in the lower canopy. The National Park experimented with this technology in several projects and improved their algorithms for single tree detection. In 2012 the whole park was flown with full waveform and strategies for LiDAR based forest inventory for the whole National Park were developed. This is the data that is used in the following workflow description.

The whole Bavarian Forest National Park (black line), 1000 meter tiles (black dotted lines), the coverage of the recovered flight lines (light blue). In the area marked yellow within the red frame there are gaps in some of the flightlines. The corresponding imagery in Google Earth shows that this area contains a water reservoir.

Several versions of the LiDAR existed on the server of the administration that didn’t have the attributes we needed to reconstruct the original flight lines. The number of returns per pulse, the flight line IDs, and the GPS time stamps were missing. The goal was a workflow to create a LAStools workflow to convert the LiDAR from the original ASCII text files provided by the flight company into LAS or compressed LAZ files with all fields properly populated.

 ALS data flown in 2012 by Milan Geoservice GmbH 650 m above ground with overlap.
+ full waveform sensor (RIEGL 560 / Q680i S) with up to 7 returns per shot
+ total of 11.080.835.164 returns
+ in 1102 ASCII files with *.asc extension (changed to *.txt to avoid confusion with ASC raster)
+ covered area of 1.25 kilometers
+ last return density of 17.37 returns per square meter

This data is provided by the administration of Bavarian Forest National Park. The workflow was part of a Master’s thesis to get the academic degree UniGIS Master of Science at the University of Salzburg.

LAStools processing:

The LiDAR was provided as 1102 ASCII text files named ‘spur000001.txt’ to ‘spur001102.txt’ that looked like this:

more spur000001.txt
4589319.747 5436773.357 685.837 49 106 1 215248.851500
4589320.051 5436773.751 683.155 46 24 2 215248.851500
4589320.101 5436773.772 686.183 66 87 1 215248.851503

Positions 1 to 3 store the x, y, and z coordinate in meter [m]. Position 4 stores the “echo width” in 0.1 nanoseconds [ns], position 5 stores the intensity, position 6 stores the return number, position 7 stores the GPS time stamp in seconds [s] of the current GPS week. The “number of returns (of given pulse)” information is not explicitly stored and will need to be reconstructed in order, for example, to identify which returns are last returns. The conversion from ASCII text to LAZ was done with the txt2las command line shown below that incorporates these rationals:

  • Although the ASCII files list the three coordinates with millimeter resolution (three decimal digits), we store only centimeter resolution which is sufficient to capture all the precision in a typical airborne LiDAR survey.
  • After computing histograms of the “return number” and the “echo width” for all points with lasinfo and determining their maximal ranges it was decided to use point type 1 which can store up to 7 returns per shot and store the “echo width” as an additional attribute of type 3 (“unsigned short”) using “extra bytes”.
  • The conversion from GPS time stamp in GPS week time to Adjusted Standard time was done by finding out the exact week during which Milan Geoservice GmbH carried out the survey and looking up the corresponding GPS week 1698 using this online GPS time calculator.
  • Information about the Coordinate Reference System “DHDN / 3-degree Gauss-Kruger zone 4” as reported in the meta data is added in form of EPSG code 31468 to each LAS file.
txt2las -i ascii\spur*.txt ^
        -parse xyz0irt ^
        -set_scale 0.01 0.01 0.01 ^
        -week_to_adjusted 1698 ^
        -add_attribute 3 "echo width" "of returning waveform [ns]" 0.1 0 0.1 ^
        -epsg 31468 ^
        -odir spur_raw -olaz ^
        -cores 4

The 1102 ASCII files are now 1102 LAZ files. Because we switched from GPS week time to Adjusted Standard GPS time stamps we also need to set the “global encoding” flag in the LAS header from 0 to 1 (see ASPRS LAS specification). We can do this in-place (i.e. without creating another set of files) using the following lasinfo command:

lasinfo -i spur_raw\spur*.laz ^
        -nh -nv -nc ^
        -set_global_encoding 1

To reconstruct the missing flight line information we look for gaps in the sequence of GPS time stamps by computing GPS time histograms with lasinfo and bins of 10 seconds in size:

lasinfo -i spur_raw\spur*.laz -merged ^
        -histo gps_time 10 ^
        -o spur_raw_all.txt

The resulting histogram exhibits the expected gaps in the GPS time stamps that happen when the survey plane leaves the target area and turns around to approach the next flight line. The subsequent histogram entries marked in red show gaps of 120 and 90 seconds respectively.

more spur_raw_all.txt
bin [27165909.595196404,27165919.595196255) has 3878890
bin [27165919.595196255,27165929.595196106) has 4314401
bin [27165929.595196106,27165939.595195957) has 435788
bin [27166049.595194317,27166059.595194168) has 1317998
bin [27166059.595194168,27166069.595194019) has 4432534
bin [27166069.595194019,27166079.59519387) has 4261732
bin [27166239.595191486,27166249.595191337) has 3289819
bin [27166249.595191337,27166259.595191188) has 3865892
bin [27166259.595191188,27166269.595191039) has 1989794
bin [27166349.595189847,27166359.595189698) has 2539936
bin [27166359.595189698,27166369.595189549) has 3948358
bin [27166369.595189549,27166379.5951894) has 3955071

Now that we validated their existence, we use these gaps in the GPS time stamps to split the LiDAR back into the original flightlines it was collected in. Using lassplit we produce one file per flightline as follows:

lassplit -i spur_raw\spur*.laz -merged ^
         -recover_flightlines_interval 10 ^
         -odir strips_raw -o strip.laz

In the next step we repair the missing “number of returns (per pulse)” field that was not provided in the ASCII file. This can be done with lasreturn assuming that the point records in each file are sorted by increasing GPS time stamp. This happens to be true already in our case as the original ASCII files where storing the LiDAR returns in acquisition order and we have not changed this order. If the point records are not yet in this order it can be created with lassort as follows. As these strips can have many points per file it may be necessary to run the new 64 bit executables by adding ‘-cpu64’ to the command line in order to avoid running out of memory.

lassort -i strips_raw\strips*.laz ^
        -gpstime -return_number ^
        -odir strips_sorted -olaz ^
        -cores 4 -cpu64

An order sorted by GPS time stamp is necessary as lasreturn expects point records with the same GPS time stamp (i.e. returns generated by the same laser pulse) to be back to back in the input file. To ‘-repair_number_of_returns’ the tool will load all returns with the same GPS time stamp  and update the “number of returns (per pulse)” attribute of each return to the highest “return number” of the loaded set.

lasreturn -i strips_sorted\strips*.laz ^
          -repair_number_of_returns ^
          -odir strips_repaired -olaz ^
          -cores 4

In a final step we use las2las with the ‘-files_are_flightlines’ option (or short ‘-faf’) to set the “file source ID” field in the LAS header and the “point source ID” attribute of every point record in the file to the same unique value per strip. The first file in the folder will have all its field set to 1, the next file will have all its field set to 2, the next file to 3 and so on. Please do not run this on multiple cores for the time being.

las2las -i strips_repaired\strips*.laz ^
        -files_are_flightlines ^
        -odir strips_final -olaz

It’s always useful to run a final validation of the files using lasvalidate to reassure yourself and the people you will be sharing the data with that nothing funky has happened during any of these conversion steps.

lasvalidate -i strips_final\strip*.laz ^
            -o strips_final\report.xml

And it can also be useful to add an overview in SHP or KML format to the delivery that can be created with lasboundary as follows:

lasboundary -i strips_final\strip*.laz ^
            -overview -labels ^
            -o strips_final\overview.kml

The result was 89 LAZ files (each containing one complete flightline) totaling 54 GB compared to 1102 ASCII files (each containing a slice of a flightline) totaling 574 GB.

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

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.

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.

 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

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

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.

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]

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: Alen Berta

Alen Berta (recipient of three LASmoons)
Department of Terrestrial Ecosystems and Landscape, Faculty of Forestry
University of Zagreb and Oikon Ltd Institute for Applied Ecology, CROATIA

After becoming the EU member state, Croatia is obliged to fulfill the obligation risen from the Kyoto protocol: National Inventory Report (NIR) of the Green House Gasses according to UNFCCC. One of the most important things during the creation of the NIR is to know how many forested areas there are and their wood stock and increment. This is needed to calculate the size of the existing carbon pool and its potential for sequestration. Since in Croatia, according to legislative, it is not mandatory to calculate the wood stock and yield of the degraded forest areas (shrubbery and thickets) during the creation of the usual forest management plans, this data is missing. So far, only a rough approximation of the wood stock and increment is used during the creation of NIR. However, these areas are expanding every year due to depopulation of the rural areas and the cessation of traditional farming.

very diverse stand structure of degraded forest areas (shrubbery and thickets)

This study will focus on two things: (1) Developing regression models for biomass volume estimation in continental shrubberies and thickets based on airborne LiDAR data. To correlate LiDAR data with biomass volume, over 70 field plots with a radius of 12 meters have been established in more than 550 ha of the hilly and lowland shrubberies in Central Croatia and all trees and shrubberies above 1 cm Diameter at Breast Height (DBH) were recorded with information about tree species, DBH and height. Precise locations of the field plots are measured with survey GNNS and biomass is calculated with parameters from literature. For regression modeling, various statistics from the point clouds matching the field plots will be used (i.e. height percentiles, standard deviation, skewness, kurtosis, …). 2) Testing the developed models for different laser pulse densities to find out if there is a significant deviation from results if the LiDAR point cloud is thinner. This will be helpful for planning of the later scanning for the change detection (increment or degradation).

641 square km of discrete returns LiDAR data around the City of Zagreb, the capitol of Croatia (but since it is highly populated area, only the outskirts of the area will be used)
+ raw geo-referenced LAS files with up to 3 returns and an average last return point density of 1 pts/m².

LAStools processing:
extract area of interest [lasclip or las2las]
2) create differently dense versions (for goal no. 2) [lasthin]
3) remove isolated noise points [lasnoise]
4) classify point clouds into ground and non-ground [lasground]
5) create a Digital Terrain Model (DTM) [las2dem]
6) compute height of points above the ground [lasheight]
7) classify point clouds into vegetation and other [lasclassify]
8) normalize height of the vegetation points [lasheight]
9) extract the areas of the field plots [lasclip]
10) compute various metrics for each plot [lascanopy]
11) convert LAZ to TXT for regression modeling in R [las2txt]