LASmoons: Olumese Efeovbokhan

Olumese Efeovbokhan (recipient of another three LASmoons)
Geosciences, School of Geography
University of Nottingham, UK

Background:
Hydrological models require various input data for flood vulnerability mapping. An important input data for flood vulnerability mapping is the DTM over which flow is being routed. DTMs are generated using cartography, ground surveying, digital aerial photogrammetry, interferometric SAR (InSAR), LiDAR amongst other means. The accuracy of high resolution DTMs minimize errors that may emanate from input data when conducting hydrological modelling, especially in small built-up catchment areas. This research involves the application of digital aerial photogrammetry to generate point clouds which can subsequently be utilized for flood vulnerability mapping.

Goal:
To consolidate on previous gains in using LAStools to generate DTMs required for flood vulnerability mapping. The suitability of these DTMs will be subsequently validated for flood vulnerability analysis. These results will be compared with other DTMs in order to determine the uncertainty associated with the use of such DTMs for flood vulnerability mapping.

Data:
+
high-resolution photogrammetry point cloud and DSM for Lagos Island, Ikorodu and Ajah Nigeria
– – – imagery obtained with an Ebee Sensefly drone flight
– – – photogrammetry point cloud generated with Photoscan by AgiSoft 
+ rainfall data
+ classified LiDAR point cloud with a resolution of 1 pulse per square meter obtained for the study area from the Lagos State Government

LAStools processing:
1) tile large photogrammetry 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) pull in points close above and below the ground [lasheight]
6) create Digital Terrain Model (DTM) from ground points [las2dem]
7) merge and hillshade individual raster DTMs [blast2dem]

Preparing Drone LiDAR from Snoopy by LidarUSA carrying a Velodyne HDL-32E

In March 2019 I was welcoming Nelson Mattie from LiDAR Latinoamerica to Samara who brought along his versatile Snoopy A-Series scanning system by LidarUSA that is based on the Velodyne HDL-32E scanner. We mounted it to his truck for a mobile scan of the core downtown block, Nelson carried it on his shoulder through “Samara Jungle” for a pedestrian scan and we strapped it onto a DJI Matrice 600 to scan this cute beach and surf town from above.

The scanning part was easy. Getting sensible data out of the ScanLookPC software proved to be quite an Odyssee as I neither had access to nor training with the ScanLookPC software. Hard surfaces such as the roof of the “New China” supermarket looked this wobbly when looking at the points from individual beams of this 32 beam scanner and turned into a complete fuzz when using all points from all beams.

I could only scrutinize the LAS files and I found several unrelated errors, such as duplicate returns and non-unique GPS time stamps, but I was unable to fix the wobbles. Frustrated with the vendor support I was ready to give up when out-of-nowhere I suddenly got an email from Luis Hernandez Perez who also worked with these system. He sent me a link to properly exported flight strips and suggested “it was a problem of poor GNSS signal and the solution for me was use the base station IND1 (of IGS) that was 3.2 kilometers away”. After months of struggles I finally had LiDAR data from downtown Samara and look how crisp the roof of the supermarket suddenly was.

As always my standard quality checks are running lasview, lasinfo, lasgrid but most importantly lasoverlap which would reveal the typical flight line misalignments that we can find in most airborne surveys.

lasoverlap ^
-i Samara\Drone\00_raw\*.laz -faf ^
-step 1 -min_diff 0.1 -max_diff 0.2 -no_over ^
-o Samara\Drone\01_quality\overlap_10_20_before.png

As usually, I contacted Andre Jalobeanu from Bayesmap and asked for help with the alignment. A few days later he returned a new and improved set of flight lines to me that he had run through his stripalign software. So I performed the same quality check with lasoverlap once again.

lasoverlap ^
-i Samara\Drone\00_raw_aligned\*.laz -faf ^
-step 1 -min_diff 0.1 -max_diff 0.2 -no_over ^
-o Samara\Drone\01_quality\overlap_10_20_after.png

Vertical differences of more than 20 centimeters are mapped to saturated blue and red and the improvement in alignment though stripalign is impressive. Note that the road is not white because it was perfectly aligned but because there was no data. A few days before the scan, Samara got a new tar road from the municipality. As we were flying at 70 meters above ground – a little too high for this LiDAR scanner – we did not capture surfaces with low reflectivity and the fresh black tar did not reflect enough photons.

The Velodyne HDL-32E scanner rotates shooting laser beams from 32 different heads and the information which return comes from which head was stored into the “user data” field and into the “point_source ID” field of each point by the exporting software. The LAS format does not have a dedicated field for this information as the supported maximum number of different laser beams is 4 in the new point types 6 through 10 of the latest LAS 1.4 specification (where this field is called the “scanner channel”). But when we use lastile to turn the flight lines into square tiles we will override the “point_source ID” with the flight line number. Also the “user data” field is a fragile place to store important information as lasheight, for example, will store temporary data there. The “extra bytes” concept of the LAS format is perfect to store such an additional attribute and the ASPRS LAS Working Group is currently discussing to have standardized “extra bytes” for exactly such laser beam IDs.

We at rapidlasso have already implemented this a while ago into our LAStools software. So before processing the data any further we copy the beam index that ranges from 0 to 31 from the “user data” field into an unsigned 8 bit integer “extra byte” with these two las2las commands.

las2las ^
-i Samara\Drone\00_raw_aligned\*.laz ^
-add_attribute 1 "laser beam ID" "which beam ranged this return" ^
-odir Samara\Drone\00_raw_temp -olaz

las2las ^
-i Samara\Drone\00_raw_temp\*.laz ^
-copy_user_data_into_attribute 0 ^
-set_user_data 0 ^
-set_point_source 0 ^
-odir Samara\Drone\00_raw_ready -olaz

In a future article we will process these aligned and prepared flight lines into a number of products. We thank Nelson Mattie from LiDAR Latinoamerica, Luis Hernandez Perez and Andre Jalobeanu from Bayesmap to help me acquire and fix this data. Several others helped with experiments using their own software and data or contributed otherwise to the discussions in the LAStools user forum. Thanks, guys. This data will soon be available as open data but a sample lasinfo report is already below.

lasinfo (210128) report for 'Samara\Drone\00_raw_ready\flightline_01.laz'
reporting all LAS header entries:
file signature: 'LASF'
file source ID: 1
global_encoding: 0
project ID GUID data 1-4: 00000000-0000-0000-0000-000000000000
version major.minor: 1.2
system identifier: 'LAStools (c) by rapidlasso GmbH'
generating software: 'las2las (version 210315)'
file creation day/year: 224/2019
header size: 227
offset to point data: 527
number var. length records: 2
point data format: 1
point data record length: 29
number of point records: 6576555
number of points by return: 6576555 0 0 0 0
scale factor x y z: 0.001 0.001 0.001
offset x y z: 661826 1092664 14
min x y z: 660986.622 1092595.013 11.816
max x y z: 661858.813 1092770.575 95.403
variable length header record 1 of 2:
reserved 0
user ID 'ScanLook'
record ID 25
length after header 0
description 'ScanLook Point Cloud'
variable length header record 2 of 2:
reserved 0
user ID 'LASF_Spec'
record ID 4
length after header 192
description 'by LAStools of rapidlasso GmbH'
Extra Byte Descriptions
data type: 1 (unsigned char), name "laser beam ID", description: "which beam ranged this return", scale: 1 (not set), offset: 0 (not set)

LASzip compression (version 3.4r3 c2 50000): POINT10 2 GPSTIME11 2 BYTE 2
reporting minimum and maximum for all LAS point record entries …
X -839378 32813
Y -68987 106575
Z -2184 81403
intensity 4 255
return_number 1 1
number_of_returns 1 1
edge_of_flight_line 0 0
scan_direction_flag 0 0
classification 1 1
scan_angle_rank -12 90
user_data 0 0
point_source_ID 0 0
gps_time 537764.192416 537854.547507
attribute0 0 31 ('laser beam ID')
number of first returns: 6576555
number of intermediate returns: 0
number of last returns: 6576555
number of single returns: 6576555
overview over number of returns of given pulse: 6576555 0 0 0 0 0 0
histogram of classification of points:
6576555 unclassified (1)

Strip Aligning of Drone LiDAR flown with Livox MID-40 over destroyed Mangrove

September 11th 2020 seemed like a fitting day to hunt down – with a powerful drone – those who destroy our common good. The latest DJI M300 RTK drone came to visit me in Samara, Guanacaste, Costa Rica and it was carrying the gAirHawk GS-MID40 UAV laser scanning system by Geosun featuring the light-weight Livox Mid 40 LiDAR. The drone is owned and operated by my friends at LiDAR Latinoamerica.

We flew a two-sortie mission covering a destroyed mangrove lagoon that was illegally poisoned, cut-down and filled in with the intention to construct a fancy resort in its place some 25 years ago. For future environmental work I wanted to get a high-resolution baseline scan with detailed topography of the meadow and what now-a-days remains of the mangroves that are part of the adjacent “Rio Lagarto” estuary. Recently the area was illegally treated with herbicides to eliminate the native herbs and the wild flowers and improve grazing conditions for cattle. Detailed topography can show how the heavy rains have washed these illegal substances into the ocean and further prove that the application of agro-chemicals in this meadow causes harm to marine life.

Here you can see a sequence of video about the LiDAR system, the preparations and the survey flights. Shortly after the flight I obtained the LiDAR from Nelson Mattie, the CEO of LiDAR Latinoamerica and ran the usual quality checks with LAStools.

lasinfo ^
-i Samara\Livox\00_raw_laz\*.laz ^
-histo intensity 16 ^
-histo gps_time 10 ^
-histo z 5 ^
-odir Samara\Livox\01_quality -odix _info -otxt ^
-cores 3

lasgrid ^
-i Samara\Livox\00_raw_laz\*.laz ^
-utm 16north ^
-merged ^
-keep_last ^
-step 0.5 ^
-density ^
-false -set_min_max 100 1000 ^
-odir Samara\Livox\01_quality ^
-o density_050cm_100_1000.png

For the density image, lasgrid counts how many last return from all flight lines fall into each 50 cm by 50 cm area, computes the desnity per square meter and maps this number to a color ramp that goes from blue via cyan, yellow and orange to red. The overall density of our survey is in the hundred of laser pulses per square meters with great variations where flight line overlap and at the survey boundary. The start and landing area as well as the place where the first flight ended and the second flight started are the two red spots of maximum density that can easily be picked out.

blue: 100 or fewer laser pulses per square meters, red: 1000 or more laser pulses per square meter

lasoverlap ^
-i Samara\Livox\00_raw_laz\*.laz ^
-utm 16north ^
-merged -faf ^
-step 0.5 ^
-min_diff 0.10 -max_diff 0.25 ^
-elevation -lowest ^
-odir Samara\Livox\01_quality ^
-o overlap_050cm_10cm_25cm.png

For the overlap image lasoverlap counts how many different flight lines overlap each 50 cm by 50 cm area and maps the counter to a color: 1 = blue, 2 = cyan, 3 = yellow, 4 = orange, and 5 of more = red. Here the result suggests that the 27 delivered LAS files do not actually correspond to the logical flight lines but that the files are chopped up in some other way. We will have Andre Jalobeanu from Bayesmap repair this for us later.

number of flight lines covering each area: blue = 1, cyan = 2, yellow – 3, orange = 4, red = 5 or more

For the difference image, lasoverlap finds the maximal vertical difference between the lowest points from all flight lines that overlap for each 50 cm by 50 cm area and maps it to a color. If this difference is less than 10 cm, the area is colored white. Differences of 25 cm or more are colored either red or blue. All open areas such as roads, meadows and rooftops should be white here we definitely have way to much red and blue in the open areas.

vertical differences below 10 cm are white but red or blue if above 25 cm

There is way too much red and blue in areas that are wide open or on roof tops. We inspect this in further detail by taking a closer look at some of these red and blue areas. For this we first spatially index the strips with lasindex so that area-of-interest queries are accelerated, then load the strips into the GUI of lasview and add the difference image into the background via the overlay option.

lasindex ^
-i Samara\Livox
\00_raw_laz\*.laz ^
-tile_size 10 -maximum -100 ^
-cores 3

lasview ^
-i Samara\Livox
\00_raw_laz\*.laz ^
-gui

using the difference image as an overlay to inspect troublesome areas

This way is easy to lasview or clip out (with las2las) those areas that look especially troublesome. We do this here for the large condominium “Las Palmeras” whose roofline and pool provide perfect features to illustrate the misalignment. As you can see in the image sequence below, there is a horizontal shift of up to 1 meter that can be nicely visualized with a cross section drawn perpendicular across the gable of the roof and – due to the inability to get returns from water – in the area without points where the pool is.

The misalignments between flight lines are too big for the data to be useful as is, so we do what we always do when we have this problem: We write an email to Andre Jalobeanu from Bayesmap and ask for help.

After receiving the LAZ files and the trajectory file Andre repaired the misalignment in two steps. The first call to his software stripalign in mode ‘-cut’ recovered a proper set of flight lines and removed most of the LiDAR points from the moments when the drone was turning. The second call to his software stripalign in mode ‘-align’ computed the amount of misalignment in this set of flight lines and produced a new set of flight lines with these errors corrected as much as possible. The results are fabulous.

lasmerge ^
-i Samara_MID40\*.laz ^
-o samaramid40.laz

stripalign ^
-uav -cut ^
-i samaramid40.laz ^
-po Samara_MID40\*.txt -po_parse ntxyzwpk ^
-G2 -cut_dist 50 ^
-O Samara_MID40\cut

stripalign ^
-uav -align ^
-i Samara_MID40\cut\*.laz ^
-po Samara_MID40\*.txt -po_parse ntxyzwpk ^
-A -G2 -full -smap -rmap -sub 2 ^
-O Samara_MID40\corr

As you can see above, the improvements are incredible. The data seems now sufficiently aligned to be useful for being processed into a number of products. One last thing to do is the removal of spurious scan lines that seem to stem from an unusual movement of the drone, like the beginning or the end of a turn.

We use lasview with option ‘-load_gps_time’ to determine the GPS time stamps of these spurious scan lines and remove them manually using las2las with option ‘-drop_gps_time_between t1 t2’ or similar. As the points are ordered in acquisition order, we can simply replay the flight by pressing ‘p’ and step forward and backward with ‘s’ and ‘S’.

Using lasview with hot keys ‘i’, ‘p’, ‘s’ and ‘S’ we find the GPS time of points from the last reasonable scan line.

Once we determined a suitable set of GPS times to remove from a flight lines we first verify our findings once more visually using lasview before actually creating the final cut with las2las.

lasview ^
-i Samara\Livox
\02_strips_aligned\samaramid40_c_13_i_13.laz ^
-drop_gps_time_below 283887060 ^
-drop_gps_time_above 283887123 ^
-filtered_transform ^
-set_classification 8 ^
=color_by_classification

visualizing which points we keep by mapping them on-the-fly to classification 8 with a filtered transform

las2las ^
-i Samara\Livox
\02_strips_aligned\samaramid40_c_13_i_13.laz ^
-drop_gps_time_below 283887060 ^
-drop_gps_time_above 283887123 ^
-odix _cut -olaz

After spending several hours of manually removing these spurious scan lines as well as deciding to remove a few short scan lines in areas of exzessive overlap we have a sufficiently aligned and cleaned data set to start the actual post-processing.

A big “Thank You!” to Andre Jalobeanu from Bayesmap for his help in aligning the data and to Nelson Mattie from LiDAR Latinoamerica for bringing his fancy drone to Samara. You can download the data here.

final density after removing turns, spurious scan lines and redundant scan lines

LASmoons: Leonidas Alagialoglou

Leonidas Alagialoglou (recipient of three LASmoons)
Multimedia Understanding Group, Aristotle University of Thessaloniki
Thessaloniki, GREECE

Background:
Canopy height is a fundamental geometric tree parameter in supporting sustainable forest management. Apart from the standard height measurement method using LiDAR instruments, other airborne measurement techniques, such as very high-resolution passive airborne imaging, have also shown to provide accurate estimations. However, both methods suffer from high cost and cannot be regularly repeated.

Preliminary results of predicted CHE based on multi-temporal satellite images against ground-truth LiDAR measurements. The 3rd column depicts pixel-wise absolute error of prediction. Last column depicts pixel-wise uncertainty estimation of the prediction (in means of 3 standard deviations).

Goal:
In our study, we attempt to substitute airborne measurements with widely available satellite imagery. In addition to spatial and spectral correlations of a single-shot image, we seek to exploit temporal correlations of sequential lower resolution imagery. For this we use a convolutional variant of a recurrent neural network based model for estimating canopy height, based on a temporal sequence of Sentinel-2 images. Our model’s performance using sequential space borne imagery is shown to outperform the compared state-of-the-art methods based on costly airborne single-shot images as well as satellite images.

Digital Terrain Model of a part of the study area

Data:
The experimental study area of approximately 940 squared km is includes two national parks, Bavarian Forest National Park and Šumava National Park, which are located at the border between Germany and Czech Republic. LiDAR measurements of the area from 2017 and 2019 will be used as ground truth height measurements that have been provided by the national park’s authorities. Temporal sequences of Sentinel-2 imagery will be acquired from the Copernicus hub for canopy height estimation.

LAStools processing:
Accurate conversion of LAS files into DEM and DSM in order to acquire ground truth canopy height model.
1) Remove noise [lasthin, lasnoise]
2) Classify points into ground and non-ground [lasground, lasground_new]
3) Create DTMs and DSMs [lasthin, las2dem]

LASmoons: Zak Kus

Zak Kus (recipient of three LASmoons)
Topology Enthusiast
San Francisco, USA

Background:
While LiDAR data enables a lot of research and innovation in a lot of fields, it can also be used to create unique and visceral art. Using the high resolution data available, a 3D printer, and a long tool chain, we can create a physical, 3D topological map of the San Francisco bay area that shows off both the city’s hilly geology, and its unique skyline.

lasmoons_zak_kus_0

Test print of San Francisco’s Golden Gate Park.

lasmoons_zak_kus_1

Test print of San Francisco’s Golden Gate Park.

Goal:
The ultimate goal of this project is to create an accurate, unique physical map of San Francisco, and the surrounding areas, which will be given to a loved one as a birthday gift. Using the data from the 2010 ARRA-CA GoldenGate survey, we can filter and process the raw lidar data into a DEM format using LAStools, which can be converted using a python script into a “water tight” 3D printable STL file.

While the data works fairly well out of the box, it does require a lot of manual editing, to remove noise spikes, and to delineate the coast line from the water in low lng areas. Interestingly, while many sophisticated tools exist to edit STLs that could in theory be used to clean up and prepare the files at the STL stage, few are capable of even opening files with so much detailed data. Using LAStools to manually classify, and remove unwanted data is the only way to achieve the desired level of detail in the final piece.

Data:
+
LiDAR data provided through USGS OpenTopography, using the ARRA-CA GoldenGate 2010 survey
+ Average point density of 3.33 pts/m^2 (though denser around SF)
+ Covers 2638 km^2 in total (only a ~100 km^2 subset is used)

LAStools processing:
1)
Remove noise [lasnoise]
2) Manually clean up shorelines and problematic structures [lasview, laslayers]
3) Combine multiple tiles (to fit 3d printer) [lasmerge]
4) Create DEMs (asc format) for external tool to process [las2dem]

LASmoons: Martin Romain

Martin Romain (recipient of three LASmoons)
Marshall Islands Conservation Society
Majuro, Republic of the MARSHALL ISLANDS

Background:
As a low-lying coastal nation, the Republic of the Marshall Islands (RMI) is at the forefront of exposure to climate change impacts. RMI has a strong dependence on natural resources and biodiversity not only for food and income but also for culture and livelihood. However, these resources are threatened by rising sea levels and associated coastal hazards (king tides, storm surges, wave run-up, saltwater intrusion, erosion). This project aims at addressing the lack of technical capacity and available data to implement effective risk reduction and adaptation measures, with a particular focus on inundation mapping and local evacuation planning in population centers.

DCIM100MEDIADJI_0507.JPG

Typical low-lying coastal area of the Republic of the Marshall

Goal:
This project intends to use LAStools to generate a DEM of the inhabited sections of 3 remote atolls (Aur, Ebon, Likiep) and 1 island (Mejit). The resulting DEM will be used to produce an inundation exposure model (and map) under variable sea level rise projections for each site. The ultimate goal is to integrate the results into each site’s disaster risk reduction strategy (long-term outcome) and present it through community consultations in schools, community centers, and council houses.

Data:
+
Aerial imagery of 11.5 square kilometers of land (6.3% of total national landmass) using DJI Matrice 200 V2 & DJI Zenmuse X5S with a minimum overlap of 75/75 and maximum altitude of 120m.

LAStools processing:
1) tile large point cloud into tiles with buffer [lastile]
2) remove noise points [lasthin, lasnoise]
3) classify points into ground and non-ground [lasground]
4) create Digital Terrain Models and Digital Surface Models [lasthin, las2dem]

Potential LAStools pipelines:
1)
Removing Excessive Low Noise from Dense-Matching Point Clouds
2)
Digital Pothole Removal: Clean Road Surface from Noisy Pix4D Point Cloud
3)
Creating DTMs from dense-matched points of UAV imagery from SenseFly’s eBee

LASmoons: Volga Lipwoni

Volga Lipwoni (recipient of three LASmoons)
Department of Geography, School of Earth and Environment
University of Canterbury, NEW ZEALAND

Background:
Structure from motion (SfM) photogrammetry, has emerged as an effective tool to accurately extract three-dimensional (3D) structures from a series of overlapping two-dimensional (2D) Unmanned aerial vehicles (UAVs) images. The bid to switch from the current labour-intensive, and time consuming forestry inventory practices has seen a lot of interest geared towards understanding the use of SfM photogrammetry to derive forest metrics (Iglhaut et al., 2019). There are a range of commercial, free and open source SfM photogrammetric software packages that can be used to process UAV images into 3D point clouds. Selection of the most appropriate package has become an important issue for most projects (Turner, Lucieer, & Wallace, 2013). A comparison of software performance in terms of accuracy, processing times and related costs would help foresters in deciding the best tool for the job.

lasmoons_Volga_Lipwoni

Typical point cloud derived with SfM software from UAV imagery.

Goal:
The study will generate 3D point clouds of images of a young forest trial and LAStools will be used to derive canopy height models (CHM) for computing tree heights. Tree heights from LiDAR data will serve as a baseline for accuracy assessment of heights derived from the point clouds.

Data:
+
422 UAV images processed into 3D point clouds using ten (10) different commercial and open source SfM software packages

LAStools processing:
1) tile large point cloud into tiles with buffer [lastile]
2) remove noise points [lasthin, lasnoise]
3) classify points into ground and non-ground [lasground]
4) create Digital Terrain Modelsand Digital Surface Models [lasthin, las2dem]
5) produce Canopy Height Models for computing tree heights [lasheight, las2dem]

References:
Iglhaut, J., Cabo, C., Puliti, S., Piermattei, L., O’Connor, J., & Rosette, J. (2019). Structure from motion photogrammetry in forestry: A review. Current Forestry Reports, 5(3), 155-168. doi:https://doi.org/10.1007/s40725-019-00094-3
Turner, D., Lucieer, A., & Wallace, L. (2013). Direct georeferencing of ultrahigh-resolution UAV imagery. EEE Transactions on Geoscience and Remote Sensing, 52(5), 2738-2745. doi:10.1109/TGRS.2013.2265295

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]

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: Olumese Efeovbokhan

Olumese Efeovbokhan (recipient of three LASmoons)
Geosciences, School of Geography
University of Nottingham, UK

Background:
One of the vital requirements to successfully drive and justify favorable flood risk management policies is the availability of reliable data for hydrological modelling. Unfortunately, this poses a big challenge in data-sparse regions and has resulted in uncoordinated and ineffective flood risk management policies with some areas left at the mercy of the floods they are exposed to. This research is focused on the ability to successfully generate data required for hydrological modelling using affordable and easy-to-replicate methods. The research will utilize unmanned aerial vehicles (UAVs) for the generation of bare earth models (DTMs) from photogrammetry points, which will be subsequently used for flood vulnerability mapping.

Photogrammetry point cloud of Tafawa Balewa Square in Lagos Island, Nigeria

Goal:
Generate a bare earth model using a combination of Agisoft Photoscan and LAStools and then validate its suitability for hydrological modelling. Should the generated model prove to be suitable we will use it to conduct flood sensitivity analysis and inundation modelling in other data-sparse regions using high resolution bare earth models generated the same way.

Data:
+
high-resolution photogrammetry point cloud for a portion of the study area
– – – imagery obtained with an Ebee Sensefly drone flight
– – – photogrammetry point cloud generated with Photoscan by AgiSoft 
+ classified LiDAR point cloud with a resolution of 1 pulse per square meter obtained for the study area from the Lagos State Government

LAStools processing:
1) tile large photogrammetry 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) pull in points close above and below the ground [lasheight]
6) create Digital Terrain Model (DTM) from ground points [las2dem]
7) merge and hillshade individual raster DTMs [blast2dem]