Two ASPRS awards for “pit-free” CHM algorithm

PRESS RELEASE (for immediate release)
July 29, 2015
rapidlasso GmbH, Gilching, Germany

The paper “Generating Pit-free Canopy Height Models from Airborne LiDAR” co-authored by rapidlasso GmbH and published in the September 2014 issue of PE&RS (the journal of the ASPRS) was awarded twice at the IGTF 2015 – ASPRS Annual Conference in Tampa, Florida last May. The paper took home the John I. Davidson President’s Award for Practical Papers (2nd Place) as well as the Talbert Abrams Award (2nd Honorable Mention).

The John I. Davidson President’s Award for Practical Papers (2nd Place).

The “pit-free” CHM paper wins the John I. Davidson President’s Award for Practical Papers (2nd Place) and the Talbert Abrams Award (Second Honorable Mention).

The “pit-free” CHM paper is joint work with Anahita Khosravipour, Andrew K. Skidmore, Tiejun Wang, and Yousif A. Hussin of ITC and University of Twente. It describes a technique that can create raster Canopy Height Models (CHMs) without the so called “pits” that tend to hamper subsequent extraction of individual tree attributes such as number, location, height, and crown diameter. The paper uses data measured in the field by ITC researchers to show that “pit-free” CHMs significantly lower the commission and omission errors in single tree detection.

Side-by-side comparison of a "standard" CHM and a "pit-free" CHM.

Visual side-by-side comparison of a “standard” versus a “pit-free” CHM.

The “pit-free” CHM algorithm can easily be implemented with LAStools either by modifying an available batch script or by executing the LAStools Pipelines distributed with the toolboxes for ArcGIS and QGIS. A detailed blog article that compares various different methods for creating CHMs is available via the Web pages of rapidlasso GmbH.

We at rapidlasso GmbH like to especially congratulate the main author, Ms. Anahita Khosravipour, who managed to get two awards with her very first academic publication. Those who like our “pit-free” CHM algorithm will probably also love the new technique that our team will introduce later this year at SilviLaser 2015 in France.

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.

LiDAR heights of burial mounds and cairns

[contributed by guest blogger Lars Forseth]

Archaeologists are increasingly finding ALS/LiDAR useful for making better surveys of archaeological sites and monuments. This is also done where these sites are in danger of being developed, and thus destroyed, see i.e (Risbøl 2011; Gustavsen et al. 2013). Norwegian archaeologists at several county councils and museums have detected unknown sites in woodland or areas previously not surveyed. LiDAR is now available for large areas, as the national mapping authority of Norway, Statens Kartverk, is using this data as a source for generating elevation and contour maps.

aerial view of a mound in North-Trøndelag

aerial view of a mound in North-Trøndelag

Working in North-Trøndelag county, my colleague Kristin Foosnæs at NTNU and I have embarked on a project to create a survey of the larger burial mounds and burial cairns of the county. North-Trøndelag seems to have an unusual large amount of such mounds larger than 20 meters (462 such are so far identified, far more than in any other county). We have gathered exact survey data for a sample of 2900 mounds/cairns. For these we have the exact polygon describing the area of the mounds. LiDAR and LAStools have been extensively used in the creation of this database.

the same  mound seen from the ground

the same mound seen from the ground

The height of the monuments however could only be gathered from the national monuments and sites database where the heights are stored as text. These were gathered by field surveys in the 1970s to 1990s. Then the only tools available to archaeologists for estimating the height were yardsticks or soil probes. Mapping the sites was done on aerial photos at a scale of 1:16.000. The height data gathered from the database is very variable in quality, which has to do with how they were generated. Mostly those that did the original surveys had to estimate the height of the monuments.

a first-return DSM and a ground-return DTM of the same mound

a first-return DSM and a ground-return DTM of the same mound

This summer I discovered that the lascanopy tool of LAStools could measure the min/max elevation for an area-of-interest. Using lascanopy I generated a csv report of elevations (min/max) within a polygon in a shapefile:

lascanopy -lof steinkjer.txt ^
          -keep_class 2 ^
          -lop tessem.shp ^
          -height_offset -1000 ^
          -centroids -min -max ^
          -o tessem.csv

Here I’m inputting a text file ‘steinkjer.txt’ with the list of LAS files to be queried and a shapefile ‘tessem.shp’ with the polygons of the mounds I want to know the height above the ground for. The output ‘tessem.csv’ looks like this:

index,min_x,min_y,max_x,max_y,centroid_x,centroid_y,min,max
0,616277.76,7108569.01,616290.33,7108581.37,616284.04,7108575.19,76.72,77.98
1,616292.40,7108572.37,616299.98,7108580.46,616296.19,7108576.41,77.83,78.96
2,616310.04,7108585.13,616320.96,7108596.96,616315.50,7108591.05,79.93,81.15
3,616714.65,7108371.35,616734.75,7108392.03,616724.70,7108381.69,83.47,86.73
4,616681.74,7108412.71,616699.80,7108429.61,616690.77,7108421.16,86.13,87.97
5,616672.13,7108436.30,616694.56,7108453.78,616683.34,7108445.04,86.55,89.19
6,616666.01,7108449.99,616696.89,7108475.04,616681.45,7108462.52,85.74,90.79
7,616665.14,7108471.25,616687.86,7108494.26,616676.50,7108482.76,86.81,90.35
8,616673.88,7108488.44,616691.35,7108505.91,616682.61,7108497.18,86.72,89.35
9,616695.43,7108602.90,616724.26,7108632.90,616709.85,7108617.90,81.18,85.27
10,617066.09,7108807.01,617080.97,7108819.01,617073.53,7108813.01,87.44,89.59
11,616010.62,7108764.98,616025.46,7108780.39,616018.04,7108772.68,88.3,90.78
12,621229.46,7111180.27,621246.66,7111197.69,621238.06,7111188.98,111.3,112.86
13,621196.44,7111192.72,621216.57,7111208.55,621206.50,7111200.63,110.18,112.08
14,621183.77,7111206.97,621206.39,7111226.65,621195.08,7111216.81,109.69,111.89

The resulting CSV file can be imported to QGIS with the centroid x/y as point location. In QGIS I can then generate a spatial join between the CSV file and the shapefile containing the surveyed mounds/cairns. Then using the field calculator on the table, I can compute the height as a difference of max and min elevation for the each mound/cairn. About 2600 of the 2900 monuments could get their height measured using lascanopy.

The results are shown in the three plots. These have been generated in R and ggplot2. The figure below shows the diameter plotted against the height gathered by lascanopy.

Diameter plotted against height

Diameter plotted against height

Height and diameter correspond to a large degree. One marked difference between mounds and cairns is that some of the larger mounds are lower than their expected height. This can have two explanations; one is that mounds are more likely to be affected by cultivation activities (i.e. they were plowed over by farmers) that have reduced their height. Mounds are more likely to be situated close to farms, while cairns are more likely to be sited along the coast or on hills.

histogram of diameter of monuments

histogram of diameter of monuments

The above histogram of the diameters of the monuments shows a skewing of the data towards the left. Mounds above 20 m of diameter are considered to be large, while those above 40 m are called “kongshauger” or “Kings mounds”. There are 19 such in North-Trøndelag. A normal – Gaussian – curve is fitted over the histogram.

histogram of heights of monuments

histogram of heights of monuments

Finally, the above histogram of the heights – as measured by lascanopy – for aproximately 2600 monuments. This shows that the maximum height lies at about 12.5 meters. The histogram of heights is again skewed to the left. The large mounds mostly seems to be above 20 m of diameter and above 4 m of height.

References:
Gustavsen, L., Paasche, K. 1964-, & Risbøl, O. 1963. 2013. Arkeologiske undersøkelser: vurdering av nyere avanserte arkeologiske registreringsmetoder. Oslo: Statens vegvesen.
Risbøl, O. 1963-. 2011. Flybåren laserskanning til bruk i forskning og til forvaltning av kulturminner og kulturmiljøer: dokumentasjon og overvåking av kulturminner. Oslo: NIKU.

Using LAStools on Mac OS X with “Wine”

[contributed by guest blogger Yuriy Czoli]

If you want to use LAStools on a Mac running OS X you will have to do some preparations. This is a brief introduction to get you up and running with LAStools on a Mac in the terminal. You may have heard that you can use “Wine” to run LAStools on OS X. Depending on your experience this might sound intuitive, or like utter jibberish. For those who feel more like the latter, let’s walk through this.

homebrew

If you don’t have Homebrew go ahead and install that now by following the instructions on the site. It should be one line found at the bottom of the page, entered into the terminal. It is a fantastic package manager which has saved me the trouble of dealing with unruly libraries, paths, dependencies, etc.

wine2

What is Wine? Wine allows for Windows programs to run on Mac OS X (and other non-Windows platforms like Linux). That is all we are interested in here. Read more about Wine here, if you’d like. Side note: You might see something called WineBottler in your search for information on Wine. You can use WineBottler to transform *.exe files to *.app files. I found it did not work with LAStools, but good to know about for other applications.

Follow these steps!

1. Let’s install Wine with Homebrew:

brew install wine

My build took 3.7 minutes. Time will vary. This next part is based off the code on this site.

2. Download LAStools:

http://lastools.org/download/lastools.zip

3. Place the download where you like (but avoid spaces and funny symbols in the directory names). Then change directories in the terminal to where the zipped folder is located. Unzip the LAStools distribution:

unzip lastools.zip

4. Enter the unzipped folder:

cd lastools

5. Now enter the ‘bin’ directory where the LAStools modules are located:

cd bin

6. Run some tool (here: lasview) by calling wine before the LAStools command:

wine lasview -i pathToYourFile/yourFile.laz

For lasview an OpenGL window should open up and you should see your LiDAR data being rendered (see the README file for all the different visualization options or follow this tutorial). Go ahead and start exploring your data. You can use any of the many LAStools modules by preceding the command with “wine”. Today I happened to be looking at a section of Helsinki:

helsinki

Then get going with LAStools and follow along the 6 new videos or the 4 step by step tutorials (1: quality checking, 2: LiDAR preparation, 3: derivative generation, 4: manual editing). After having installed Wine you will also be able to use LAStools via the QGIS toolboxes.

For the geospatially inclined, check out Homebrew for installing other libraries. If you are working with geospatial data, you can use brew to install GDAL, Postgres SQL, PostGIS, and many more.

First ever LiDAR Processing Model in QGIS using LAStools

We at rapidlasso had finally the chance to meet Victor Olaya of Boundless who created the Processing framework (formerly know as Sextante) that is now integral part of QGIS. On the last day of the joint workshop organized by Dr. Lene Fischer at the Forest and Landscape department of the University of Copenhagen that saw 2 days of LiDAR processing with LAStools followed by 2 days of GIS exercises with QGIS and Processing, we decided to do an impromptu, unscripted, and unplanned experiment in front of the course participants, doing something none of us had tested before:

We wanted to see if we would be able to use the existing LAStools and QGIS Processing functionalities to create a LiDAR Processing Model that would take a single LAZ or LAS file as input, find the bare-earth points with lasground, compute the height of each point above the ground with lasheight, look in the remaining points for buildings and vegetation with lasclassify, generate a set of polygons around the building points with lasboundary, and open the resulting shapefile in QGIS.

After some discussion a Processing Model was decided upon. Holding our breath we started it. SUCCESS! On the first try! It was a nice way to conclude our 4 days of LAStools and QGIS training as you see below … (-:

See this blog entry (also see the blog’s comment section) for information on how to add LAStools to your QGIS Processing toolboxes.

LASmoons: Asger S. Petersen

Asger S. Petersen (recipient of three LASmoons)
Copenhagen, Denmark
Partner & Consultant, Septima

Background:
The Danish Geodata Agency has acquired country wide LiDAR coverage. Approximately 1/4th of the country has been flown this spring and the GST has published preliminary classified point cloud data. There are a lot of people in the Danish geodata community who are very excited to get to know this new data.

lasmoons_asger_petersen_0

Goal:
I would like to produce a hill shaded DTM and DSM from these data and publish them as a freely available web map for all to give those interested a first glance at this new and exciting data set.

Data:
+ 400 square kilometres of LIDAR from the western part of Zealand
+ average point density: 4 pts/m2

LAStools processing:
1)
unzip LAS (unfortunately compressed as *.las.zip instead of *.laz files)
2) lower excessive resolution from [mm] to [cm] with ‘-rescale 0.01 0.01 0.01’, add missing projection information with ‘-utm 32N’, re-tile with ‘-buffer 35’, and compress with ‘-olaz’ (lastile)
3) create 0.4 meter DTM and DSM elevation raster tiles (las2dem)
4) calculate hill shade and pseudo color rasters for presentation using QGIS

Using the unlicensed version of LAStools in combination with QGIS I was able to produce hillshaded DTMs at a resolution of 40 cm (without edge artifacts) from approximately 80 GBs of zipped LAS (sic!) files in an afternoon.

References:
Geodatastyrelsen (2013), Danmarks Heojdemodel bliver bedre og mere noejagtig, Web post from 29 November 2013 (available here on 14 May 2014).
Geodatastyrelsen (2014), Twitter message from 28 April 2014 (available here on 14 May 2014).

lasmoons_asger_petersen_2

lasmoons_asger_petersen_1Results:
Asger has been hard at work to set up the infrastucture for the Web map which “has taken 10 times as long as the data processing.” The landing page can be found here. It describes the process and the data in Google translated Danish-English. If you want to skip directly to the big map it can be found here.