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

Background:
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)

Goal:
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).

Data:
+
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:
1)
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]

RIEGL Becomes LASzip Sponsor for LAS 1.4 Extension

PRESS RELEASE (for immediate release)
August 31, 2015
rapidlasso GmbH, Gilching, Germany

We are happy to announce that RIEGL Laser Measurement Systems, Austria has become a sponsor of the award-winning LASzip compressor. Their contribution at the Silver level will kick-off the actual development phase of the “native LAS 1.4 extension” that had been discussed with the LiDAR community over the past two years. This “native extension” for LAS 1.4 complements the existing “compatibility mode” for LAS 1.4 that was supported by Gold sponsor NOAA and Bronze sponsors Quantum Spatial and Trimble Geospatial. The original sponsor who initiated and financed the open sourcing of the LASzip compressor was USACE – the US Army Corps of Engineers (see http://laszip.org).

The existing “LAS 1.4 compatibility mode” in LASzip was created to provide immediate support for compressing the new LAS 1.4 point types by rewriting them as old point types and storing their new information as “Extra Bytes”. As an added side-benefit this has allowed legacy software without LAS 1.4 support to readily read these newer LAS files as most of the important fields of the new point types 6 to 10 can be mapped to fields of the older point types 1, 3, or 5.

In contrast, the new “native LAS 1.4 extension” of LASzip that is now sponsored in part by RIEGL will utilize the “natural break” in the format due to the new point types of LAS 1.4 to introduce entirely new features such as “selective decompression”, “rewritable classifications and flags”, “integrated spatial indexing”, … and other functionality that has been brain-stormed with the community since rapidlasso GmbH had issued the open “call for input” on native LASzip compression for LAS 1.4 in January 2014. We invite you to follow the progress or contribute to the development via the discussions in the “LAS room“.

silverLASzip_m60_512_275

About rapidlasso GmbH:
Technology powerhouse rapidlasso GmbH specializes in efficient LiDAR processing tools that are widely known for their high productivity. They combine robust algorithms with efficient I/O and clever memory management to achieve high throughput for data sets containing billions of points. The company’s flagship product – the LAStools software suite – has deep market penetration and is heavily used in industry, government agencies, research labs, and educational institutions. Visit http://rapidlasso.com for more information.

About RIEGL:
Austrian based RIEGL Laser Measurement Systems is a performance leader in research, development and production of terrestrial, industrial, mobile, bathymetric, airborne and UAS-based laser scanning systems. RIEGL’s innovative hard- and software provides powerful solutions for nearly all imaginable fields of application. Worldwide sales, training, support and services are delivered from RIEGL‘s Austrian headquarters and its offices in Vienna, Salzburg, and Styria, main offices in the USA, Japan, and in China, and by a worldwide network of representatives covering Europe, North and South America, Asia, Australia and Africa. Visit http://riegl.com for more information.

Beautiful Full-Waveform LiDAR of a Tropical Rainforest

Scanning forests with LiDAR is done to predict timber yield, estimate biomass, and monitor ecosystems. A detailed Digital Terrain Model (DTM) is needed to accurately compute forestry metrics such as tree height. For generating a DTM it is important to have enough “ground returns” that measure the elevation of the bare-earth for which the laser pulse needs to penetrate through the vegetation down to the forest floor and reflect back to the plane. This can be difficult in a dense tropical forest with tall trees and many layers of canopy as the light of the laser pulse has many opportunities to be reflected or absorbed before reaching the ground. Often none of the light energy will reach the forest floor or the returning ground echo will be too weak to be registered back at the plane.

The "airborne sensors" of  Asian Aerospace Services is equpiied with a RIEGL Q680i airborne LiDAR scanner.

The “airborne sensors” aircraft of Asian Aerospace Services is equipped with a RIEGL Q680i airborne LiDAR scanner.

Asian Aerospace Services Ltd. and rapidlasso GmbH flew a RIEGL LMS Q680i LiDAR system out of a Diamond DA42 “Airborne Sensors” aircraft above dense tropical rainforest in Thailand. We did a short test flight to determine whether the LiDAR would be able to penetrate the canopy of Khao Yai National Forest. The original objective was to demonstrate that the scanner is able to “see through” the canopy in order to produce data for the TREEMAPS project of WWF Thailand funded with EUR 1,730,205 through the International Climate Initiative of the German government. Unfortunately Germany had to pull the funding for TREEMAPS after political tinkering started to interfere with technical objectives, and the LiDAR produced by our “pro-bono flight” has become the only tangible outcome produced by TREEMAPS. We are now making this data available under a Creative Commons Public Domain license (for download see below).

Above Khao Yao National Forest scanning for the pro-bono test flight to investigate the canopy penetration of our LiDAR.

Above Khao Yao National Forest during a test scan to investigate the canopy penetration of the RIEGL Q680i LiDAR scanner.

Low clouds disrupted our original plans but we had one cloud-free stretch during a ferry flight between target locations. We were scanning for 90 seconds at 1200 meters above ground with a speed of 220 km/h. The Q680i was set to operate at a laser pulse rate of 80 kHz with the polygon mirror rotating 10 times per second. The effective scan rate was 240 / 360 * 80,000 = 53,333 shots per second, as each of the 4 mirror facets covers 60 degrees – summing up to 240 degrees – and there are no laser shots leaving the aircraft for the remaining 120 degrees of between facets.

The 53,333 shots per second are reflected over the length of 10 * 4 = 40 mirror facets so that a single scan line contains a sequence of 53,333 / 40 = 1333 shots spaced about 12.4 microseconds apart. After each scan line comes a short pause of 8.3 milliseconds during which the mirror rotates 30 degrees to the next facet. Flying at 220 km/h and 1200 meters above ground means that each second we scanned 62 meters of a 1430 meters wide swath. This resulted in a pulse spacing of 62 / 40 = 1.55 meters between subsequent scan lines and a pulse spacing of 1430 / 1333 = 1.07 meters along each scan line. The 90 second scan resulted in a strip length of about 5.5 km.

The PulseWaves export options available in RIEGL's RiPROCESS.

The PulseWaves export options available in RIEGL’s RiPROCESS.

For each shot the outgoing as well as the returning waveform are digitized and stored to RIEGL’s proprietary SDF format. We used RIEGL’s RiPROCESS software (version 1.5.8) to export the 4,770,152 shots as full waveform LiDAR to PulseWaves and to extract a total of 7,956,587 returns as LASzip-compressed discrete LiDAR to the LAZ format.

The DSM, DTM, and CHM of the 5.5 km long strip scanned above Khao Yai National Forest.

The DSM, DTM, and CHM of the 5.5 km long strip scanned above Khao Yai National Forest.

The Q680i produced sufficiently many ground returns to compute a “plausible” DTM (using lasground and las2dem of LAStools) despite the dense 45 meter tropical canopy during leaf-on season and high humidity. Typical terrain features such as streams and slopes are clearly visible in the hillshading. The corresponding Canopy Height Model (CHM) uses a false coloring that maps vegetation heights of 0 meters to blue and heights of 45 meters to red. There is less vegetation (darker blue) in areas that appear to be in the steeper parts of the DTM.

Only 5 % of pulses emitted produced a discrete return on the ground … this is rather low. It means that the generated DTM will be of much coarser resolution than the number of pulses shot per square meter would typically suggest. Of the 250,473 points classified as ground only 30,931 are single returns where the laser had an unobstructed view of the bare earth. More than half of the ground returns come from laser shots that had already produced two or more vegetation returns and 7,665 of them had their light energy weakened by grazing four or more layers before hitting the forest floor. Scrutinizing the full waveform LiDAR might lead to more ground information to improve the accuracy of the DTM.

We are happy to provide both – the discrete return as well as the full-waveform LiDAR – under a Creative Commons Public Domain license to researchers world-wide. You can download the data here:

You can decompress the pair of compressed PulseWaves files PLZ + WVZ into an pair of uncompressed PulseWaves files PLS + WVS with pulsezip.exe – a recent addition to PulseTools. We would be happy to hear back about your experiences with the data. You could join the PulseWaves user forum at http://pulsewaves.org and share your insights with the group. Below a visualization of a small part of the full waveforms …

Full Waveform LiDAR from Khao Yai National forest.

Full Waveform LiDAR from Khao Yai National forest.

Density and Spacing of LiDAR

Recently I worked with LiDAR from an Optech Gemini scanner in the Philippines and with LiDAR from a RIEGL Q680i scanner in Thailand. The two devices scan the terrain below with a very different pattern: the Optech uses an oscillating mirror producing zig-zag scan lines, whereas the RIEGL uses a rotating polygon producing parallel scan lines. In the following we investigate the point distribution in a small piece cut from each flightline.

First we compute an estimate for the average point density and the average point spacing with lasinfo:

D:\lastools\bin>lasinfo -i optech.laz ^
                        -nh -nv -nmm -cd
number of last returns: 2330671
covered area in square units/kilounits: 1280956/1.28
point density: all returns 2.25 last only 1.82 (per unit^2)
      spacing: all returns 0.67 last only 0.74 (in units)

The option ‘-nh’ asks lasinfo not to print the header (aka ‘no header’), the option ‘-nv’ means not to print the variable length records (aka ‘no vlr’), the option ‘-nmm’ supresses the output of minimum and maximum point values (aka ‘no min max’), and the option ‘-cd’ requests that an average point density is computed (aka ‘compute density’) from which the average point spacing is derived.

D:\lastools\bin>lasinfo -i riegl.laz ^
                        -nh -nv -nmm -cd
number of last returns: 3707217
covered area in square units/kilounits: 889816/0.89
point density: all returns 4.58 last only 4.17 (per unit^2)
      spacing: all returns 0.47 last only 0.49 (in units)

What we really want to know are pulse density and pulse spacing which we get by only counting one return from every pulse. Commonly one uses the last return, which is reported as ‘last only’ values by lasinfo. According to lasinfo the Optech and the RIEGL scan have an average pulse density of 1.82 and 4.17 [pulses per square meter] and an average pulse spacing of 0.74 and 0.49 [meters] respectively.

Single number averages do not capture anything about the actual distribution of the last returns in the swath of the scan. Let us compute density rasters with lasgrid. Because the density of the two scans is different we use 3 by 3 meter cells for the Optech, which should average 3 * 3 * 1.82 = 16.38 points per cell, and 2 by 2 meter cells for the RIEGL, which should average 2 * 2 * 4.17 = 16.68 points per cell.

lasgrid -i optech.laz -last_only ^
        -density -step 3 ^
        -odix _density3x3 -obil
lasview -i optech_density3x3.bil

lasgrid -i riegl.laz -last_only ^
        -density -step 2 ^
        -odix _density2x2 -obil
lasview -i riegl_density2x2.bil

A visual comparison of the two resulting density rasters in BIL format with lasview shows first differences in pulse distribution for the two scanners.

Using 'lasview' to inspect a BIL density raster: Elevation and colors show number of points per 3 by 3 meter cell for Optech (top) and per 2 by 2 meter cell for RIEGL (bottom).

Using ‘lasview’ to inspect a BIL density raster: Elevations and colors illustrate the number of last returns per 3 by 3 meter cell for Optech (top) and per 2 by 2 meter cell for RIEGL (bottom). The images use differently scaled color ramps.

It is noticable that the number of last returns per cell increases at the edges of the swath for the Optech whereas it decreases for the RIEGL. For the Optech it is higher due to the slowing down of the oscillating mirror and the reversing of scan direction when reaching either side of the scanline. This decreases the distances between pulses at the edges of the zig-zag scan and increases the number of last returns falling into cells there. For the rotating polygon scan of the RIEGL these pulse distances are more uniform. Here the numbers are lower because many cells along the edges of the swath are only partly covered by the scan and therefore receive fewer last returns.

lasgrid -i optech.laz -last_only ^
        -density -step 3 ^
        -false -set_min_max 0 20 ^
        -odix _density3x3 -opng

lasgrid -i riegl.laz -last_only ^
        -density -step 2 ^
        -false -set_min_max 0 20 ^
        -odix _density2x2 -opng

Here another visualization of the pulse distribution by letting lasgrid false-color density rasters to a fixed range of 0 to 20. This range is based on the near identical expected values of around 16.38 and 16.68 last returns per cell based on the average densities that lasinfo reported and the cell sized used here.

A more quantitative check of how well distributed the pulse are is to generate histograms. You can use lasinfo with the ‘-histo z 1’ option to print a histogram of the z values of the BIL rasters and import these statistics into your favorite software to create a chart.

lasinfo -i optech_density3x3.bil ^
        -nh -nv -nmm -histo z 1
lasinfo -i riegl_density2x2.bil ^
        -nh -nv -nmm -histo z 1

Both distributions have their peaks at the expected 16 to 17 last returns per cell although the Optech distribution has a significantly wider spead. The odd two-peak distribution of the Optech scan seems puzzling at first but looking at the density image shows that this is an aliasing artifact of putting a fixed 3 by 3 meter grid over zig-zagging scanlines.

The next experiments uses the new ‘-edge_shortest’ and ‘-edge_longest’ options available in las2dem that compute for each point the length of its shortest or longest edge in a Delaunay triangulation and then rasters these lengths. Below you see the command lines to do this and generate histograms of the rastered edge lengths.

las2dem -i optech.laz -last_only ^
        -step 1.5 ^
        -edge_longest ^
        -odix _edge_longest -obil
lasinfo -i optech_edge_longest.bil ^
        -nh -nv -nmm -histo z 0.1 ^
        -o optech_edge_shortest_0_10.txt
las2dem -i optech.laz -last_only ^
        -step 1.5 ^
        -edge_shortest ^
        -odix _edge_shortest -obil
lasinfo -i optech_edge_shortest.bil ^
        -nh -nv -nmm -histo z 0.05 ^
        -o optech_edge_shortest_0_05.txt
las2dem -i riegl.laz -last_only ^
        -edge_longest ^
        -odix _edge_longest -obil
lasinfo -i riegl_edge_longest.bil ^
        -nh -nv -nmm -histo z 0.05 ^
        -o riegl_edge_longest_0_05.txt
las2dem -i riegl.laz -last_only ^
        -edge_shortest ^
        -odix _edge_shortest -obil
lasinfo -i riegl_edge_shortest.bil ^
        -nh -nv -nmm -histo z 0.05 ^
        -o riegl_edge_shortest_0_05.txt

These resulting histograms do look quite different.

The more important histograms are those of longest edge lengths. They illustrate the observed spacing between pulses showing us the maximal distance of each pulse from its surrounding pulses. A perfect pulse distribution that samples the ground evenly in all directions would have one narrow peak. The Optech scan is far from this ideal with a wide and flat peak that tells us that pulses are spaced apart anywhere from 1.2 to 2.2 meters. Visualizing the scan pattern shows that the 2.2 meter spacings happen at the tips of zig-zag and the 1.2 meter spacings at nadir. The RIEGL scan has much narrower peak with most pulses spaced apart at most 60 to 80 centimeters.

The histograms of shortest edge lengths illustrate how close pulses are spaced. Again, it helps to visualize the zig-zag scan pattern to explain the odd distribution of shortest edge lengths in the Optech scan: as we get closer to the edges of the flightline the pulse spacing along the scanline becomes increasingly dense. This is represented on the left side in the histogram that is slowly leveling off to zero. Again, the RIEGL scan has a narrower peak with most pulses spaced no closer than 35 to 55 centimeters.

To confirm our findings we illustrate where the scanners produce longest or shortest edges using ranges that we find in the histograms above and create false-colored rasters:

las2dem -i optech.laz -last_only ^
        -step 1.5 -edge_longest ^
        -false -set_min_max 1 2.5 ^
        -odix _edge_longest -opng
las2dem -i optech.laz -last_only ^
        -step 1.5 -edge_shortest ^
        -false -set_min_max 0 0.7 ^
        -odix _edge_shortest -opng
las2dem -i riegl.laz -last_only ^
        -edge_longest ^
        -false -set_min_max 0.5 1.0 ^
        -odix _edge_longest -opng
las2dem -i riegl.laz -last_only ^
        -edge_shortest ^
        -false -set_min_max 0.1 0.7 ^
        -odix _edge_shortest -opng

The color-codings clearly illustrate that the Optech has a much wider pulse spacing on both edges of the flightline but also a much narrower pulse spacing. That reads contradictory but one are the “within-zig-zag” spacings and the other are the “between-zig-zag” spacings. The RIEGL has an overall much more even distribution in pulse spacings.

As a final confirmation to what causes the pulse spacings to be both widest and narrowest at the edges of the flightline for the Optech scan we scrutinizing the distribution of last returns and their triangulation visually.

Now it should be clear. At the edge of the flightline the Optech scanner has increasingly close-spaced pulses within each zig-zagging scan line but also increasingly distant-spaced pulses between subsequent pairs of zig-zagging scanlines. At the very edge of the flightline the scanner is almost sampling a “linear area” twice but leaves unsampled gaps that are twice as wide as in the center of the flightline. I believe this is one of the reasons why people “cut off” the edges of the flightlines based on the scan angle when using oscillating mirror systems. Now let’s took at the RIEGL.

There is no obvious difference in pulse distribution at the edges and the center of the flightline. The entire width of the swath seems more or less evenly sampled.

The above results show that “average point density” and “average point spacing” do not capture the whole picture. A quality check that properly verifies that a scan has the desired point / pulse density and spacing should measure the actual spacings – especially when operating a scanner with oscillating mirrors. We have done it here by computing for each last return its longest surrounding edge in the Delaunay triangulation, rasterizing these edge length, and then generating a histogram of the raster values.

 

Good follow-up reads are Dr. Ullrich’s “Impact of point distribution on information content of point clouds of airborne LiDAR” presented at ELMF 2013 and “Assessing the Information Content of LiDAR Point Clouds” from PhoWo 2013.

clone wars and drone fights

NEWSFLASH: update on Feb 5th and 6th and 17th (see end of article)

The year 2014 shapes to be a fun one in which the LiDAR community will see some major laser battles. (-: First, ESRI starts a “lazer clone war” with the open source community saddening your very own LAS clown here at rapidlasso with a proprietary LAZ clone. Then AHAB and Optech start to squirmish over the true penetration depth of their bathymetric systems in various forums. And now RIEGL – just having launched their Q1560 “crossfire” for battling Leica and Optech in the higher skies – is heading into an all-out “laser drone war” with FARO and Velodyne over arming UAVs and gyrocopters with lasers by rolling out their new (leaked?) RIEGL VUX-1 scanner … (-;

Some specs below, but no details yet on power-consumption.

  • about 3.85 kilograms
  • 225mm x 180mm x 125mm
  • survey-grade accuracy of 25mm
  • echo signal digitisation
  • online waveform processing
  • measurement rate up to 500 kHz
  • field of view 300 degrees
  • internal 360 GB SSD storage or real-time data via LAN-TCP/IP
  • collects data at altitudes or ranges of more than 1,000 ft
  • will be on display during ILMF 2014 (Feb 17-19, Denver, USA)
The new RIEGL VUX-1 laser scanner for UAVs ...

The new RIEGL VUX-1 laser scanner for UAVs

Below a proof-of-concept concept video by Phoenix Aerial on how such a system (here based on a Velodyne HDL-32E scanner) may look and operate.

A similar system introduced by Airbotix is shown below.

Aibot X6 with laser scanner from Velodyne HDL-32E

Aibot X6 with laser scanner from Velodyne HDL-32E

But before trusting these kind of drones with your expensive hardware you should make plenty of test flights with heavy payloads. Here a suggestion to make this more fun:

UPDATE (February 5th): It is reported by SPAR Point Group that the RIEGL VUX-1 LiDAR scanner for drones is to be shown at International LiDAR Mapping Forum in Denver later this month. Given the ceremony that the Q1560 “crossfire” was introduced with, it surely seems as if Geospatial World Magazine spilled the news early …

UPDATE (February 6th): RIEGL officially press-released the VUX-1 LiDAR. Its weight is below 4kg and its range up to 1000 ft …

UPDATE (February 17th): Today the new VUX-1 finally made it’s much anticipated world-premiere during ILMF 2014 in Denver. For more images of the official unveiling see RIEGL’s blog.

The VUX-1 LiDAR scanner for UAVs unveiled at ILMF 2014.

The VUX-1 LiDAR scanner for UAVs unveiled at ILMF 2014.

new scanner cross examines terrain

It seems Leica and Optech may have come a bit under “crossfire” (-: by the other big news (besides adding PulseWaves support to RiPROCESS) at the RIEGL LiDAR 2013 user conference in Vienna, namely the unveiling of the new LMS-Q1560 airborne laser scanner. Below you see the management team doing the ceremonial act.Image

The specification looks great and will especially make those happy looking for dense surveys in complex terrain or for more LiDAR returns from building facades in urban environments. By employing two units that “crossfire” at a particular angle while one unit looks forward and one backward, the combined system can provide a better point spacing on the ground and a better point coverage on vertical surfaces.

The new system combines two LMS-Q780 units each firing at rates of up 400 kHz via a shared rotating mirror in the center. The scan lines of the two laser-beam-emitting units are not perpendicular to the flight direction but angled at +12 and -12 degrees (actual number may be different) in the x-y plane to assure that they independently and consistently sample the covered terrain – unaffected from flying height or aircraft speed. Furthermore, the two fanning sheets of laser pulses are angled at +7 and -7 degrees (actual number may be different) in flight direction, meaning that one unit is forward-looking and the other backward-looking. This increases the likelihood that the sides of buildings are scanned as well. Colloquially put, those back-facing facades perpendicular to the flight direction that are not seen by the first round of laser pulses from the forward-looking unit will be hit by the second round of laser pulses from the  backward-looking unit and vice-versa. The above details may not be 100% correct but are what I (wrongly?) remember from the technical presentation that Dr. Ullrich gave (see below).

Image

The overall ability to sample so rapidly is realized by combining the “crossfire” that I have just described with the “rapidfire” of the earlier Q780 units. Each unit is able to operate with up to 10 pulses simultaneously in the air by subsequently resolving ambiguities off-line with the multiple-time-around (MTA) processing technique introduced for earlier models (see the product descriptions for the Q680i or the Q780 for more on that topic).

Hence, this new scanner with “crossfire technology” promises to give your terrain a dense cross examination … and yes, it is a full waveform scanner. (-: