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

LASmoons: David Bandrowski

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

Background:
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

Goal:
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.

Data:
+
 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

Background:
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.

Goal:
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.

Data:
+
 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.

LASmoons: Maria Kampouri

Maria Kampouri (recipient of three LASmoons)
Remote Sensing Laboratory, School of Rural & Surveying Engineering
National and Technical University of Athens, GREECE

Background:
The Aralar Natural Park, famous for its stunning landscapes, is located in the southeast of the province of Gipuzkoa, sharing a border with the neighboring province of Navarre. Inside the park there are nature reserves of exceptional importance, such as beech woods, large number of yew trees, very singular species of flora and fauna and areas of exceptional geological interest. Griffon vultures, Egyptian vultures, golden eagles and even bearded vultures (also known as lammergeier) can be seen flying over this area. European minks and Pyrenean desmans can be found in the streams and rivers that descend from the mountain tops.

The concept of biodiversity is based on inter- and intra-species genetic variation and has been evolving over the past 25 years. The importance of mapping biodiversity in order to plan its conservation, as well as identifying patterns in endemism and biodiversity hot-spots, have been pillars for EU and global environmental policy and legislation. The coupling of remote sensing and field data can increase reliability, periodicity and reproduce-ability of ecosystem process and biodiversity monitoring, leading to an increasing interest in environmental monitoring, using data for the same areas over time. Natural processes and complexity are best explored by observing ecosystems or landscapes through scale alteration, using spatial analysis tools, such as LAStools.

DTM generated with restricted version of las2dem above point limits

Goal:
The aim of this study is to investigate the potential use of LiDAR data for the identification and determination of forest patches of particular interest, with respect to ecosystem dynamics and biodiversity and to produce a relevant biodiversity map, based on Simpson’s Diversity Index for Aralar Natural Park.

Data:
+
 approximately 123 km^2 of LiDAR in 1km x 1km LAS tiles
+ Average point density: 2 pts/m^2
+ Spatial referencing system: ETRS89 UTM zone 30N with elevations on the EGM08 geoid. Data from LiDAR flights are These files were obtained from the LiDAR flight carried out in 2008 by the Provincial Council of Gipuzkoa and the LiDAR flights of the Basque Government.

LAStools processing:
1) data quality checking [lasinfolasoverlaplasgridlasreturn]
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 DTM tiles with 0.5 step in ‘.bil’ format [las2dem]
6) create DSM tiles with 0.5 step in ‘.bil’ format [las2dem]
7) create a normalized point cloud [lasheight]
8) create a highest-return canopy height model (CHM) [lasthin, las2dem]
9) create a pit-free (CHM) with the spike-free algorithm [las2dem]
10) create various rasters with forest metrics [lascanopy]

The generated elevation and forest metrics rasters are then combined with satellite data to create a biodiversity map, using Simpson’s Diversity Index.

Complete LiDAR Processing Pipeline: from raw Flightlines to final Products

This tutorial serves as an example for a complete end-to-end workflow that starts with raw LiDAR flightlines (as they may be delivered by a vendor) to final classified LiDAR tiles and derived products such as raster DTM, DSM, and SHP files with contours, building footprint and vegetation layers. The three example flightlines we are using here were flown in Ayutthaya, Thailand with a RIEGL LMS Q680i LiDAR scanner by Asian Aerospace Services who are based at the Don Mueang airport in Bangkok from where they are serving South-East-Asia and beyond. You can download them here:

Quality Checking

The minimal quality checks consist of generating textual reports (lasinfo & lasvalidate), inspecting the data visually (lasview), making sure alignment and overlap between flightlines fulfill expectations (lasoverlap), and measuring pulse density per square meter (lasgrid). Additional checks for points replication (lasduplicate), completeness of all returns per pulse (lasreturn), and validation against external ground control points (lascontrol) may also be performed.

lasinfo -i Ayutthaya\strips_raw\*.laz ^
        -cd ^
        -histo z 5 ^
        -histo intensity 64 ^
        -odir Ayutthaya\quality -odix _info -otxt ^
        -cores 3

lasvalidate -i Ayutthaya\strips_raw\*.laz ^
            -o Ayutthaya\quality\validate.xml

The lasinfo report generated with the command line shown computes the average density for each flightline and also generates two histograms, one for the z coordinate with a bin size of 5 meter and one for the intensity with a bin size of 64. The resulting textual descriptions are output into the specified quality folder with an appendix ‘_info’ added to the original file name. Perusing these reports tells you that there are up to 7 returns per pulse, that the average pulse density per flightline is between 7.1 to 7.9 shots per square meter, that the point source IDs of the points are already populated correctly, that there are isolated points far above and far below the scanned area, and that the intensity values range from 0 to 1023 with the majority being below 400. The warnings in the lasinfo and the lasvalidate reports about the presence of return numbers 6 and 7 have to do with the history of the LAS format and can safely be ignored.

lasoverlap -i Ayutthaya\strips_raw\*.laz ^
           -files_are_flightlines ^
           -min_diff 0.1 -max_diff 0.3 ^
           -odir Ayutthaya\quality -o overlap.png

This results in two color illustrations. One image shows the flightline overlap with blue indicating one flightline, turquoise indicating two, and yellow indicating three flightlines. Note that wet areas (rivers, lakes, rice paddies, …) without LiDAR returns affect this visualization. The other image shows how well overlapping flightlines align. Their vertical difference is color coded with while meaning less than 10 cm error while saturated red and blue indicate areas with more than 30 cm positive or negative difference.

One pixel wide red and blue along building edges and speckles of red and blue in vegetated areas are normal. We need to look-out for large systematic errors where terrain features or flightline outlines become visible. If you click yourself through this photo album you will eventually see typical examples (make sure to read the comments too). One area slightly below the center looks suspicious. We load the PNG into the GUI to pick this area for closer inspection with lasview.

lasview -i Ayutthaya\strips_raw\*.laz -gui

Why these flightline differences exist and whether they are detrimental to your purpose are questions that you will have to explore further. For out purpose this isolated difference was noted but will not prevent us from proceeding further. Next we want to investigate the pulse density. We do this with lasgrid. We know that the average last return density per flightline is between 7.1 to 7.9 shots per square meter. We set up the false color map for lasgrid such that it is blue when the last return density drops to 5 shots (or less) per square meter and such that it is red when the last return density reaches 10 shots (or more).

lasgrid -i Ayutthaya\strips_raw\*.laz -merged ^
        -keep_last ^
        -step 2 -density ^
        -false -set_min_max 4 8 ^
        -odir Ayutthaya\quality -o density_4ppm_8ppm.png

The last return density per square meter mapped to a color: blue is 5 or less, red is 10 or more.

The last return density image clearly shows how the density increases to over 10 pulses per square meter in all areas of flightline overlap. However, as there are large parts covered by only one flightline their density is the one that should be considered. We note great variations in density along the flightlines. Those have to do with aircraft movement and in particular with the pitch. When the nose of the plane goes up even slightly, the gigantic “fan of laser pulses” (that can be thought of as being rigidly attached at the bottom perpendicular to the aircraft flight direction) is moving faster forward on the ground far below and therefore covers it with fewer shots per square meter. Conversely when the nose of the plane goes down the spacing between scan lines far below the plane are condensed so that the density increases. Inclement weather and the resulting airplane pitch turbulence can have a big impact on how regular the laser pulse spacing is on the ground. Read this article for more on LiDAR pulse density and spacing.

LiDAR Preparation

When you have airborne LiDAR in flightlines the first step is to tile the data into square tiles that are typically 1000 by 1000 or – for higher density surveys – 500 by 500 meters in size. This can be done with lastile. We also add a buffer of 30 meters to each tile. Why buffers are important for tile-based processing is explained here. We choose 30 meters as this is larger than any building we expect in this area and slightly larger than the ‘-step’ size we use later when classifying the points into ground and non-ground points with lasground.

lastile -i Ayutthaya\strips_raw\*.laz ^
        -tile_size 500 -buffer 30 -flag_as_withheld ^
        -odir Ayutthaya\tiles_raw -o ayu.laz

NOTE: Usually you will have to add ‘-files_are_flightlines’ or ‘-apply_file_source_ID’ to the lastile command shown above in order to preserve the information which points is from which flightline. We do not have to do this here as evident from the lasinfo reports we generated earlier. Not only is the file source ID in the LAS header is correctly set to 2, 3, or 4 reflecting the intended flightline numbering evident from the file names. Also the point source ID of each point is already set to the correct value 2, 3, or 4. For more info see this or this discussion from the LAStools user forum.

Next we classify isolated points that are far from most other points with lasnoise into the (default) classification code 7. See the README file for the meaning of the parameters and play around with different setting to get a feel for how to make this process more or less aggressive.

lasnoise -i Ayutthaya\tiles_raw\ayu*.laz ^
         -step_xy 4 -step_z 2 -isolated 5 ^
         -odir Ayutthaya\tiles_denoised -olaz ^
         -cores 4

Especially for ground classification it is important that low noise points are excluded. You should inspect a number of tiles with lasview to make sure the low noise are all pink now if you color them by classification.

lasview -i Ayutthaya\tiles_denoised\ayu*.laz -gui

While the algorithms in lasground are designed to withstand a few noise points below the ground, you will find that it will include them into the ground model if there are too many of them. Hence, it is important to tell lasground to ignore these noise points. For the other parameters used see the README file of lasground.

lasground -i Ayutthaya\tiles_denoised\ayu*.laz ^
          -ignore_class 7 ^
          -city -ultra_fine ^
          -compute_height ^
          -odir Ayutthaya\tiles_ground -olaz ^
          -cores 4

You should visually check the resulting ground classification of each tile with lasview by selecting smaller subsets (press ‘x’, draw a rectangle, press ‘x’ again, use arrow keys to walk) and then switch back and forth between a triangulation of the ground points (press ‘g’ and then press ‘t’) and a triangulation of last returns (press ‘l’ and then press ‘t’). See the README of lasview for more information on those hotkeys.

lasview -i Ayutthaya\tiles_ground\ayu*.laz -gui

This way I found at least one tile that should be reclassified with ‘-metro’ instead of ‘-city’ because it still contained one large building in the ground classification. Alternatively you can correct miss-classifications manually using lasview as shown in the next few screen shots.

This is an optional step for advanced users who have a license. In case you managed to do such a manual edit and saved it as a LAY file using LASlayers (see the README file of lasview) you can overwrite the old file with:

laslayers -i Ayutthaya\tiles_ground\ayu_669500_1586500.laz -ilay ^
          -o Ayutthaya\tiles_ground\ayu_669500_1586500_edited.laz

move Ayutthaya\tiles_ground\ayu_669500_1586500_edit.laz ^
     Ayutthaya\tiles_ground\ayu_669500_1586500.laz

The next step classifies those points that are neither ground (2) nor noise (7) into building (or rather roof) points (class 6) and high vegetation points (class 5). For this we use lasclassify with the default parameters that only considers points that are at least 2 meters above the classified ground points (see the README for details on all available parameters).

lasclassify -i Ayutthaya\tiles_ground\ayu*.laz ^
            -ignore_class 7 ^
            -odir Ayutthaya\tiles_classified -olaz ^
            -cores 4

We  check the classification of each tile with lasview by selecting smaller subsets (press ‘x’, draw a rectangle, press ‘x’ again) and by traversing with the arrow keys though the point cloud. You will find a number of miss-classifications. Boats are classified as buildings, water towers or complex temple roofs as vegetation, … and so on. You could use lasview to manually correct any classifications that are really important.

lasview -i Ayutthaya\tiles_classified\ayu*.laz -gui

Before delivering the classified LiDAR tiles to a customer or another user it is imperative to remove the buffers that were carried through all computations to avoid artifacts along the tile boundary. This can also be done with lastile.

lastile -i Ayutthaya\tiles_classified\ayu*.laz ^
        -remove_buffer ^
        -odir Ayutthaya\tiles_final -olaz ^
        -cores 4

Together with the tiling you may want to deliver a tile overview file in KML format (or in SHP format) that you can easily generate with lasboundary using this command line:

lasboundary -i Ayutthaya\tiles_final\ayu*.laz ^
            -use_bb ^
            -overview -labels ^
            -o Ayutthaya\tiles_overview.kml

The small KML file generated b lasboundary provides a quick overview where tiles are located, their names, their bounding box, and the number of points they contain.

Derivative production

The most common derivative product produced from LiDAR data is a Digital Terrain Model (DTM) in form of an elevation raster. This can be obtained by interpolating the ground points with a triangulation (i.e. a Delaunay TIN) and by sampling the TIN at the center of each raster cell. The pulse density of well over 4 shots per square meter definitely supports a resolution of 0.5 meter for the raster DTM. From the ground-classified tiles with buffer we compute the DTM using las2dem as follows:

las2dem -i Ayutthaya\tiles_ground\ayu*.laz ^
        -keep_class 2 ^
        -step 0.5 -use_tile_bb ^
        -odir Ayutthaya\tiles_dtm -obil ^
        -cores 4

It’s important to add ‘-use_tile_bb’ to the command line which limits the raster generation to the original tile sizes of 500 by 500 meters in order not to rasterize the buffers that are extending the tiles 30 meters in each direction. We used the BIL format so that we inspect the resulting elevation rasters with lasview:

lasview -i Ayutthaya\tiles_dtm\ayu*.bil -gui

To create a hillshaded version of the DTM you can use your favorite raster processing package such as GDAL or GRASS but you could also use the BLAST extension of LAStools and create a large seamless image with blast2dem as follows:

blast2dem -i Ayutthaya\tiles_dtm\ayu*.bil -merged ^
          -step 0.5 -hillshade -epsg 32647 ^
          -o Ayutthaya\dtm_hillshade.png

Because blast2dem does not parse the PRJ files that accompany the BIL rasters we have to specify the EPSG code explicitly to also get a KML file that allows us to visualize the LiDAR in Google Earth.

A a hillshading of the merged DTM rasters produced with blast2dem.

Next we generate a Digital Surface Model (DSM) that includes the highest objects that the laser has hit. We use the spike-free algorithm that is implemented in las2dem that creates a triangulation of the highest returns as follows:

las2dem -i Ayutthaya\tiles_denoised\ayu*.laz ^
        -drop_class 7 ^
        -step 0.5 -spike_free 1.2 -use_tile_bb ^
        -odir Ayutthaya\tiles_dsm -obil ^
        -cores 4

We used 1.0 as the freeze value for the spike free algorithm because this is about three times the average last return spacing reported in the individual lasinfo reports generated during quality checking. Again we inspect the resulting rasters with lasview:

lasview -i Ayutthaya\tiles_dsm\ayu*.bil -gui

For reason of comparison we also generate the DSM rasters using a simple first-return interpolation again with las2dem as follows:

las2dem -i Ayutthaya\tiles_denoised\ayu*.laz ^
        -drop_class 7 -keep_first ^
        -step 0.5 -use_tile_bb ^
        -odir Ayutthaya\tiles_dsm -obil ^
        -cores 4

A few direct side-by-side comparison between a spike-free DSM and a first-return DSM shows the difference that are especially noticeable along building edges and in large trees.

Another product that we can easily create are building footprints from the automatically classified roofs using lasboundary. Because the tool is quite scalable we can simply on-the-fly merge the final tiles. This also avoids including duplicate points from the tile buffer whose classifications are also often less accurate.

lasboundary -i Ayutthaya\tiles_final\ayu*.laz -merged ^
            -keep_class 6 ^
            -disjoint -concavity 1.1 ^
            -o Ayutthaya\buildings.shp

Similarly we can use lasboundary to create a vegetation layer from those points that were automatically classified as high vegetation.

lasboundary -i Ayutthaya\tiles_final\ayu*.laz -merged ^
             -keep_class 5 ^
             -disjoint -concavity 3 ^
             -o Ayutthaya\vegetation.shp

We can also produce 1.0 meter contour lines from the ground classified points. However, for nicer contours it is beneficial to first generate a subset of the ground points with lasthin using option ‘-contours 1.0’ as follows:

lasthin -i Ayutthaya\tiles_final\ayu*.laz ^
        -keep_class 2 ^
        -step 1.0 -contours 1.0 ^
        -odir Ayutthaya\tiles_temp -olaz ^
        -cores 4

We then merge all subsets of ground points from those temporary tiles on-the-fly into one (using the ‘-merged’ option) and let blast2iso from the BLAST extension of LAStools generate smoothed and simplified 1 meter contours as follows:

blast2iso -i Ayutthaya\tiles_temp\ayu*.laz -merged ^
          -iso_every 1.0 ^
          -smooth 2 -simplify_length 0.5 -simplify_area 0.5 -clean 5.0 ^
          -o Ayutthaya\contours_1m.shp

Finally we composite all of our derived LiDAR products into one map using QGIS and then fuse it with data from OpenStreetMap that we’ve downloaded from BBBike. Here you can download the OSM data that we use.

It’s in particular interesting to compare the building footprints that were automatically derived from our LiDAR processing pipeline with those mapped by OpenStreetMap volunteers. We immediately see that there is a lot of volunteering work left to do and the LiDAR-derived data can assist us in directing those mapping efforts. A closer look reveals the (expected) quality difference between hand-mapped and auto-generated data.

The OSM buildings are simpler. These polygons are drawn and divided into logical units by a human. They are individually verified and correspond to actual buildings. However, they are less aligned with the Google Earth satellite image. The LiDAR-derived buildings footprints are complex because lasboundary has no logic to simplify the complicated polygonal chains that enclose the points that were automatically classified as roof into rectilinear shapes or to break directly adjacent roof points into separate logical units. However, most buildings are found (but also objects that are not buildings) and their geospatial alignment is as good as that of the LiDAR data.

LASmoons: Maeva Dang

Maeva Dang (recipient of three LASmoons)
Industrial Building and interdisciplinary Planning, Faculty of Civil Engineering
Vienna University of Technology, AUSTRIA

Background:
After centuries of urbanization and industrialization the green landscape of Rio de Janeiro in Brazil must be regenerated. The forests and other green areas, providers of ecosystem services, are fragmented and surrounded by dense urban occupation [1]. The loss of vegetation in the city reduces the amount of cooling and increases the urban heat islands effect. The metropolis also has a chronic problem with floods as a result of the lack of sustainable planning in urban areas of low permeability. A well-designed green infrastructure system is highly needed, since it would help the city to mitigate the negative effects of its urbanization and to be more resilient to environmental changes [2]. Intensive green roofs provide a large range of benefits from enhancing biodiversity in the city to reducing flood risks and mitigating the urban heat islands effect. The present research aims to quantitatively and accurately assess the intensive greening potential of the roof landscape of Rio de Janeiro based on LIDAR data.

A view of the roof landscape of the Urca district. Rio de Janeiro has high contrasts of forests and dense urban environments.

Goal:
The LAStools software will be used to check the quality of the data and create a Digital Terrain Model (DTM) and Digital Surface Model (DSM) for the city of Rio de Janeiro. The goal of the study is to identify the existing flat roof surfaces suitable for intensive greening (i.e. that have a slope between 0 and 5 degrees). The results will be provided for free to the public.

Data:
+
 Airborne LiDAR data provided by the City hall of Rio de Janeiro, Instituto Municipal de Urbanismo Pereira Passos (IPP)
+ Average pulse density 2 pulses per square meter
+ Sensor System: Leica ALS60

LAStools processing:
1) check the quality of the LiDAR data [lasinfo, lasoverlap, lasgrid]
2) classify into ground and non-ground points using tile-based processing [lastilelasground]
3) remove low and high outliers [lasheight, lasnoise]
4) identify buildings within the study area [lasclassify]
5) normalize LiDAR heights [lasheight]
6) generate DTM and DSM [las2dem, lasgrid]

References:
[1] Herzog C. (2012). Connecting the wonderful Landscapes of Rio de Janeiro. Available online . Accessed on 07/06/18.
[2] European Commission (2011). Communication from the Commission to the European Parliament, the Council, the
Economic and Social Committee and the Committee of the Regions: Our life insurance, our natural capital: an EU
biodiversity strategy to 2020. Available online. Accessed on 07/06/18.

LASmoons: Alex S. Olpenda

Alex S. Olpenda (recipient of three LASmoons)
Department of Geomatics and Spatial Planning, Faculty of Forestry
Warsaw University of Life Sciences, POLAND

Background:
The Bialowieza Forest is a trans-boundary property along the borders of Poland and Belarus consisting of diverse Central European lowland forest covering a total area of 141,885 hectares. Enlisted as one of the world’s biosphere reserves and a UNESCO World Heritage Site, the Bialowieza Forest conserves a complex ecosystem that supports vast wildlife including at least 250 species of birds and more than 50 mammals such as wolf, moose, lynx and the largest free-roaming population of the forest’s iconic species, the European bison [1]. The area is also significantly rich in dead wood which becomes a home for countless species of mushrooms, mold, bacteria and insects of which many of them are endangered of extinction [2]. Another factor, aside from soil type, that impacts the species of plant communities growing in the area is humidity [3] which can be considered as a function of solar radiation. Understanding the interactions and dynamics of these elements within the environment is vital for proper management and conservation practices. Sunlight below canopies is a driving force that affects the growth and survival of both fauna and flora directly and indirectly. Measurement and monitoring of this variable is crucial.

The European bison  (image credit to Frederic Demeuse).

Goal:
Remote sensing technology can describe the light condition inside the forest with relatively high spatial and temporal resolutions at large scale. The goal of this research is to develop a predictive model to estimate sub-canopy light condition of Bialowieza Forest inside Poland’s territory using LiDAR data. Aside from common metrics based on heights and intensities, extraction of selected metrics known to infer transmitted light are also to be done. Returns that belong or are close to the ground are a good substitute for sun-rays that reach the forest floor while vegetation-classified returns could be assumed as the ones impeding the light. Relationships between these metrics and to both direct and diffuse sunlight derived from hemispherical photographs will be explored. Furthermore, multiple regression shall then be conducted between the parameters. Previous similar studies have been done successfully but mostly in homogeneous forest. The task might pose a challenge as Bialowieza Forest is a mixture of conifers and broad-leaved trees.

Location map of the study site with 100 random sample plots.

Data:
+
2015 ALS data set obtained using full waveform sensor (Riegl LMS-Q680i)
+ discrete point clouds (average pulse density: 6 points/m²)
+ 134 flightlines with 40% overlap
+ forest inventory data (100 circular plots, 12.62 m radius)
+ colored hemispherical photographs
All of this data is provided by the Forest Research Institute through the ForBioSensing project.

LAStools processing:
1) data quality checking [lasinfo, lasoverlap, lasgrid, lasreturn]
2) merge and clip the LAZ files [las2las]
3) classify ground and non-ground points [lasground]
4) remove low and high outliers [lasheight, lasnoise]
5) create a normalized point cloud [lasheight]
6) compute forestry metrics for each plot [lascanopy]

References:
[1] UNESCO. World Heritage List. Available online (accessed on 2 October 2017).
[2] Polish Tourism Organization. Official Travel Website. Available online (accessed on 3 October 2017).
[3] Bialowieza National Park. Available online (accessed on 3 October 2017).