LASmoons: Gudrun Norstedt

Gudrun Norstedt (recipient of three LASmoons)
Forest History, Department of Forest Ecology and Management
Swedish University of Agricultural Sciences, Umeå, Sweden

Background:
Until the end of the 17th century, the vast boreal forests of the interior of northern Sweden were exclusively populated by the indigenous Sami. When settlers of Swedish and Finnish ethnicity started to move into the area, colonization was fast. Although there is still a prospering reindeer herding Sami culture in northern Sweden, the old Sami culture that dominated the boreal forest for centuries or even millenia is to a large extent forgotten.
Since each forest Sami family formerly had a number of seasonal settlements, the density of settlements must have been high. However, only very few remains are known today. In the field, old Sami settlements can be recognized through the presence of for example stone hearths, storage caches, pits for roasting pine bark, foundations of certain types of huts, reindeer pens, and fences. Researchers of the Forest History section of the Department of Forest Ecology and Management have long been surveying such remains on foot. This, however, is extremely time consuming and can only be done in limited areas. Also, the use of aerial photographs is usually difficult due to dense vegetation. Data from airborne laser scanning should be the best way to find remains of the old forest Sami culture. Previous research has shown the possibilities of using airborne laser scanning data for detecting cultural remains in the boreal forest (Jansson et al., 2009; Koivisto & Laulamaa, 2012; Risbøl et al., 2013), but no studies have aimed at detecting remains of the forest Sami culture. I want to test the possibilities of ALS in this respect.

DTM from the Krycklan catchment, showing a row of hunting pits and (larger) a tar pit.

Goal:
The goal of my study is to test the potential of using LiDAR data for detecting cultural and archaeological remains on the ground in a forest area where Sami have been known to dwell during historical times. Since the whole of Sweden is currently being scanned by the National Land Survey, this data will be included. However, the average point density of the national data is only 0,5–1 pulses/m^2. Therefore, the study will be done in an established research area, the Krycklan catchment, where a denser scanning was performed in 2015. The Krycklan data set lacks ground point classification, so I will have to perform such a classification before I can proceed to the creation of a DTM. Having tested various kind of software, I have found that LAStools seems to be the most efficient way to do the job. This, in turn, has made me aware of the importance of choosing the right methods and parameters for doing a classification that is suitable for archaeological purposes.

Data:
The data was acquired with a multi-spectral airborne LiDAR sensor, the Optech Titan, and a Micro IRS IMU, operated on an aircraft flying at a height of about 1000 m and positioning was post-processed with the TerraPos software for higher accuracy.
The average pulse density is 20 pulse/m^2.
+ About 7 000 hectares were covered by the scanning. The data is stored in 489 tiles.

LAStools processing:
1) run a series of classifications of a few selected tiles with both lasground and lasground_new with various parameters [lasground and lasground_new]
2) test the outcomes by comparing it to known terrain to find out the optimal parameters for classifying this particular LiDAR point cloud for archaeological purposes.
3) extract the bare-earth of all tiles (using buffers!!!) with the best parameters [lasground or lasground_new]
4) create bare-earth terrain rasters (DTMs) and analyze the area [lasdem]
5) reclassify the airborne LiDAR data collected by the National Land Survey using various parameters to see whether it can become more suitable for revealing Sami cultural remains in a boreal forest landscape  [lasground or lasground_new]

References:
Jansson, J., Alexander, B. & Söderman, U. 2009. Laserskanning från flyg och fornlämningar i skog. Länsstyrelsen Dalarna (PDF).
Koivisto, S. & Laulamaa, V. 2012. Pistepilvessä – Metsien arkeologiset kohteet LiDAR-ilmalaserkeilausaineistoissa. Arkeologipäivät 2012 (PDF).
Risbøl, O., Bollandsås, O.M., Nesbakken, A., Ørka, H.O., Næsset, E., Gobakken, T. 2013. Interpreting cultural remains in airborne laser scanning generated digital terrain models: effects of size and shape on detection success rates. Journal of Archaeological Science 40:4688–4700.

LASmoons: Muriel Lavy

Muriel Lavy (recipient of three LASmoons)
RED (Risk Evaluation Dashboard) project
ISE-Net s.r.l, Aosta, ITALY.

Background:
The Aosta Valley Region is a mountainous area in the heart of the Alps. This region is regularly affected by hazard natural phenomena connected with the terrain geomorphometry and the climate change: snow avalanche, rockfalls and landslide.
In July 2016 a research program, funded by the European Program for the Regional Development, aims to create a cloud dashboard for the monitoring, the control and the analysis of several parameters and data derived from advanced sensors: multiparametrical probes, aerial and oblique photogrammetry and laser scanning. This tool will help the territory management agencies to improve the risk mitigation and management system.

The RIEGL VZ-4000 scanning the Aosta Valley Region in Italy.

Goal:
This study aims to classify the point clouds derived from aerial imagery integrated with laser scanning data in order to generate accurate DTM, DSM and Digital Snow Models. The photogrammetry data set was acquired with a Nikon D810 camera from an helicopter survey. The aim of further analysis is to detect changes of natural dynamic phenomena that have occurred via volume analysis and mass balance evaluation.

Data:
+ The photogrammetry data set was acquired with an RGB camera (Nikon D810) with a focal length equivalent of 50 mm from a helicopter survey: 1060 JPG images
+ The laser scanner data set was acquired using a Terrestrial Laser Scanner (RIEGL VZ-4000) combined with a Leica GNSS device (GS25) to georeference the project. The TLS dataset was then used as base reference to properly align and georeference the photogrammetry point cloud.

LAStools processing:
1) check the reference system and the point cloud density [lasinfo, lasvalidate]
2) remove isolated noise points [lasnoise]
3) classify point into ground and non-ground [lasground]
4) classify point clouds into vegetation and other [lasclassify]
5) create DTM and DSM  [las2dem, lasgrid, blast2dem]
6) produce 3D visualizations to facilitate the communication and the interaction [lasview]

Plots to Stands: Producing LiDAR Vegetation Metrics for Imputation Calculations

Some professionals in remote sensing find LAStools a useful tool to extract statistical metrics from LiDAR that are used to make estimations about a larger area of land from a small set of sample plots. Common applications are prediction of the timber volume or the above-ground biomass for entire forests based on a number of representative plots where exact measurements were obtained with field work. The same technique can also be used to make estimations about animal habitat or coconut yield or to classify the type of vegetation that covers the land. In this tutorial we describe the typical workflow for computing common metrics for smaller plots and larger areas using LAStools.

Download these six LiDAR tiles (1, 2, 3, 4, 5, 6) from a Eucalyptus plantation in Brazil to follow along the step by step instructions of this tutorial. This data is courtesy of Suzano Pulp and Paper. Please also download the two shapefiles that delineate the plots where field measurements were taken and the stands for which predictions are to be made. You should download version 170327 (or higher) of LAStools due to some recent bug fixes.

Quality Checking

Before processing newly received LiDAR data we always perform a quality check first. This ranges from visual inspection with lasview, to printing textual content reports and attribute histograms with lasinfo, to flight-line alignment checks with lasoverlap, pulse density and pulse spacing checks with lasgrid and las2dem, and completeness-of-returns check with lassort followed by lasreturn.

lasinfo -i tiles_raw\CODL0003-C0006.laz ^
        -odir quality -odix _info -otxt

The lasinfo report tells us that there is no projection information. However, we remember that this Brazilian data was in the common SIRGAS 2000 projection and try for a few likely UTM zones whether the hillshaded DSM produced by las2dem falls onto the right spot in Google Earth.

las2dem -i tiles_raw\CODL0003-C0006.laz ^
        -keep_first -thin_with_grid 1 ^
        -hillshade -epsg 31983 ^
        -o epsg_check.png

Hillshaded DSM and Google Earth imagery align for EPSG code 31983

The lasinfo report also tells us that the xyz coordinates are stored with millimeter resolution which is a bit of an overkill. For higher and faster LASzip compression we will later lower this to a more appropriate centimeter resolution. It further tells us that the returns are stored using point type 0 and that is a bit unfortunate. This (older) point type does not have a GPS time stamp so that some quality checks (e.g. “completeness of returns” with lasreturn) and operations (e.g. “resorting of returns into acquisition order” with lassort) will not be possible. Fortunately the min-max range of the ‘point source ID’ suggests that this point attribute is correctly populated with flightline numbers so that we can do a check for overlap and alignment of the different flightlines that contribute to the LiDAR in each tile.

lasoverlap -i tiles_raw\*.laz ^
           -min_diff 0.2 -max_diff 0.4 ^
           -epsg 31983 ^
           -odir quality -opng ^
           -cores 3

We run lasoverlap to visualize the amount of overlap between flightlines and the vertical differences between them. The produced images (see below) color code the number of flightlines and the maximum vertical difference between any two flightlines as seen below. Most of the area is cyan (2 flightlines) except in the bottom left where the pilot was sloppy and left some gaps in the yellow seams (3 flightlines) so that some spots are only blue (1 flightline). We also see that two tiles in the upper left are partly covered by a diagonal flightline. We will drop that flightline later to create a more uniform density.across the tiles. The mostly blue areas in the difference are mostly aligned with features in the landscape and less with the flightline pattern. Closer inspection shows that these vertical difference occur mainly in the dense old growth forests with species of different heights that are much harder to penetrate by the laser than the uniform and short-lived Eucalyptus plantation that is more of a “dead forest” with little undergrowth or animal habitat.

Interesting observation: The vertical difference of the lowest return from different flightlines computed per 2 meter by 2 meter grid cell could maybe be used a new forestry metric to help distinguish mono cultures from natural forests.

lasgrid -i tiles_raw\*.laz ^
        -keep_last ^
        -step 2 -point_density ^
        -false -set_min_max 10 20 ^
        -epsg 31983 ^
        -odir quality -odix _d_2m_10_20 -opng ^
        -cores 3

lasgrid -i tiles_raw\*.laz ^
        -keep_last ^
        -step 5 -point_density ^
        -false -set_min_max 10 20 ^
        -epsg 31983 ^
        -odir quality -odix _d_5m_10_20 -opng ^
        -cores 3

We run lasgrid to visualize the pulse density per 2 by 2 meter cell and per 5 by 5 meter cell. The produced images (see below) color code the number of last return per square meter. The impact of the tall Eucalyptus trees on the density per cell computation is evident for the smaller 2 meter cell size in form of a noisy blue/red diagonal in the top right as well as a noisy blue/red area in the bottom left. Both of those turn to a more consistent yellow for the density per cell computation with larger 5 meter cells. Immediately evident is the higher density (red) for those parts or the two tiles in the upper left that are covered by the additional diagonal flightline. The blueish area left to the center of the image suggests a consistently lower pulse density whose cause remains to be investigated: Lower reflectivity? Missing last returns? Cloud cover?

The lasinfo report suggests that the tiles are already classified. We could either use the ground classification provided by the vendor or re-classify the data ourselves (using lastilelasnoise, and lasground). We check the quality of the ground classification by visually inspecting a hillshaded DTM created with las2dem from the ground returns. We buffer the tiles on-the-fly for a seamless hillshade without artifacts along tile boundaries by adding ‘-buffered 25’ and ‘-use_orig_bb’ to the command-line. To speed up reading the 25 meter buffers from neighboring tiles we first create a spatial indexing with lasindex.

lasindex -i tiles_raw\*.laz ^
         -cores 3

las2dem -i tiles_raw\*.laz ^
        -buffered 25 ^
        -keep_class 2 -thin_with_grid 0.5 ^
        -use_orig_bb ^
        -hillshade -epsg 31983 ^
        -odir quality -odix _dtm -opng ^
        -cores 3

hillshaded DTM tiles generated with las2dem and on-the-fly buffering

The resulting hillshaded DTM shows a few minor issues in the ground classification but also a big bump (above the mouse cursor). Closer inspection of this area (you can cut it from the larger tile using the command-line below) shows that there is a group of miss-classified points about 1200 meters below the terrain. Hence, we will start from scratch to prepare the data for the extraction of forestry metrics.

las2las -i tiles_raw\CODL0004-C0006.laz ^
        -inside_tile 207900 7358350 100 ^
        -o bump.laz

lasview -i bump.laz

bump in hillshaded DTM caused by miss-classified low noise

Data Preparation

Using lastile we first tile the data into smaller 500 meter by 500 meter tiles with 25 meter buffer while flagging all points in the buffer as ‘withheld’. In the same step we lower the resolution to centimeter and put nicer a coordinate offset in the LAS header. We also remove the existing classification and classify all points that are much lower than the target terrain as class 7 (aka noise). We also add CRS information and give each tile the base name ‘suzana.laz’.

lastile -i tiles_raw\*.laz ^
        -rescale 0.01 0.01 0.01 ^
        -auto_reoffset ^
        -set_classification 0 ^
        -classify_z_below_as 500.0 7 ^
        -tile_size 500 ^
        -buffer 25 -flag_as_withheld ^
        -epsg 31983 ^
        -odir tiles_buffered -o suzana.laz

With lasnoise we mark the many isolated points that are high above or below the terrain as class 7 (aka noise). Using two tiles we played around with the ‘step’ parameters until we find good parameter settings. See the README of lasnoise for the exact meaning and the choice of parameters for noise classification. We look at one of the resulting tiles with lasview.

lasnoise -i tiles_buffered\*.laz ^
         -step_xy 4 -step_z 2 ^
         -odir tiles_denoised -olaz ^
         -cores 3

lasview -i tiles_denoised\suzana_206000_7357000.laz ^
        -color_by_classification ^
        -win 1024 192

noise points shown in pink: all points (top), only noise points (bottom)

Next we use lasground to classify the last returns into ground (2) and non-ground (1). It is important to ignore the noise points with classification 7 to avoid the kind of bump we saw in the vendor-delivered classification. We again check the quality of the computed ground classification by producing a hillshaded DTM with las2dem. Here the las2dem command-line is sightly different as instead of buffering on-the-fly we use the buffers stored with each tile.

lasground -i tiles_denoised\*.laz ^
          -ignore_class 7 ^
          -nature -extra_fine ^
          -odir tiles_ground -olaz ^
          -cores 3

las2dem -i tiles_ground\*.laz ^
        -keep_class 2 -thin_with_grid 0.5 ^
        -hillshade ^
        -use_tile_bb ^
        -odir quality -odix _dtm_new -opng ^
        -cores 3

Finally, with lasheight we compute how high each return is above the triangulated surface of all ground returns and store this height value in place of the elevation value into the z coordinate using the ‘-replace_z’ switch. This height-normalizes the LiDAR in the sense that all ground returns are set to an elevation of 0 while all other returns get an elevation relative to the ground. The result are height-normalized LiDAR tiles that are ready for producing the desired forestry metrics.

lasheight -i tiles_ground\*.laz ^
          -replace_z ^
          -odir tiles_normalized -olaz ^
          -cores 3
Metric Production

The tool for computing the metrics for the entire area as well as for the individual field plots is lascanopy. Which metrics are well suited for your particular imputation calculation is your job to determine. Maybe first compute a large number of them and then eliminate the redundant ones. Do not use any point from the tile buffers for these calculations. We had flagged them as ‘withheld’ during the lastile operation, so they are easy to drop. We also want to drop the noise points that were classified as 7. And we were planning to drop that additional diagonal flightline we noticed during quality checking. We generated two lasinfo reports with the ‘-histo point_source 1’ option for the two tiles it was covering. From the two histograms it was easy to see that the diagonal flightline has the number 31.

First we run lascanopy on the 11 plots that you can download here. When running on plots it makes sense to first create a spatial indexing with lasindex for faster querying so that only those tiny parts of the LAZ file need to be loaded that actually cover the plots.

lasindex -i tiles_normalized\*.laz ^
         -cores 3

lascanopy -i tiles_normalized\*.laz -merged ^
          -drop_withheld ^
          -drop_class 7 ^
          -drop_point_source 31 ^
          -lop WKS_PLOTS.shp ^
          -cover_cutoff 3.0 ^
          -cov -dns ^
          -height_cutoff 2.0 ^
          -c 2.0 999.0 ^
          -max -avg -std -kur ^
          -p 25 50 75 95 ^
          -b 30 50 80 ^
          -d 2.0 5.0 10.0 50.0 ^
          -o plots.csv

The resulting ‘plots.csv’ file you can easily process in other software packages. It contains one line for each polygonal plot listed in the shapefile that lists its bounding box followed by all the requested metrics. But is why is there a zero maximum height (marked in orange) for plots 6 though 10? All height metrics are computed solely from returns that are higher than the ‘height_cutoff’ that was set to 2 meters. We added the ‘-c 2.0 999.0’ absolute count metric to keep track of the number of returns used in these calculations. Apparently in plots 6 though 10 there was not a single return above 2 meters as the count (also marked in orange) is zero for all these plots. Turns out this Eucalyptus stand had recently been harvested and the new seedlings are still shorter than 2 meters.

more plots.csv
index,min_x,min_y,max_x,max_y,max,avg,std,kur,p25,p50,p75,p95,b30,b50,b80,c00,d00,d01,d02,cov,dns
0,206260.500,7358289.909,206283.068,7358312.477,11.23,6.22,1.91,2.26,4.71,6.01,7.67,9.5,26.3,59.7,94.2,5359,18.9,41.3,1.5,76.3,60.0
1,206422.500,7357972.909,206445.068,7357995.477,13.54,7.5,2.54,1.97,5.32,7.34,9.65,11.62,26.9,54.6,92.2,7030,12.3,36.6,13.3,77.0,61.0
2,206579.501,7358125.909,206602.068,7358148.477,12.22,5.72,2.15,2.5,4.11,5.32,7.26,9.76,46.0,73.7,97.4,4901,24.8,28.7,2.0,66.8,51.2
3,206578.500,7358452.910,206601.068,7358475.477,12.21,5.68,2.23,2.64,4.01,5.14,7.18,10.04,48.3,74.1,95.5,4861,25.7,26.2,2.9,68.0,50.2
4,206734.501,7358604.910,206757.068,7358627.478,15.98,10.3,2.18,2.64,8.85,10.46,11.9,13.65,3.3,27.0,91.0,4946,0.6,32.5,44.5,91.0,77.5
5,207043.501,7358761.910,207066.068,7358784.478,15.76,10.78,2.32,3.43,9.27,11.03,12.49,14.11,3.2,20.7,83.3,4819,1.5,24.7,51.0,91.1,76.8
6,207677.192,7359630.526,207699.760,7359653.094,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0
7,207519.291,7359372.366,207541.859,7359394.934,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0
8,207786.742,7359255.850,207809.309,7359278.417,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0
9,208159.017,7358997.344,208181.584,7359019.911,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0
10,208370.909,7358602.565,208393.477,7358625.133,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0

Then we run lascanopy on the entire area and produce one raster per tile for each metric. Here we remove the buffered points with the ‘-use_tile_bb’ switch that also ensures that the produced rasters have exactly the extend of the tiles without buffers. Again, it is imperative that you download the version 170327 (or higher) of LAStools for this to work correctly.

lascanopy -version
LAStools (by martin@rapidlasso.com) version 170327 (academic)

lascanopy -i tiles_normalized\*.laz ^
          -use_tile_bb ^
          -drop_class 7 ^
          -drop_point_source 31 ^
          -step 10 ^
          -cover_cutoff 3.0 ^
          -cov -dns ^
          -height_cutoff 2.0 ^
          -c 2.0 999.0 ^
          -max -avg -std -kur ^
          -p 25 50 75 95 ^
          -b 30 50 80 ^
          -d 2.0 5.0 10.0 50.0 ^
          -odir tile_metrics -oasc ^
          -cores 3

The resulting rasters in ASC format can easily be previewed using lasview for some “sanity checking” that our metrics make sense and to get a quick overview about what these metrics look like.

lasview -i tile_metrics\suzana_*max.asc
lasview -i tile_metrics\suzana_*p95.asc
lasview -i tile_metrics\suzana_*p50.asc
lasview -i tile_metrics\suzana_*p25.asc
lasview -i tile_metrics\suzana_*cov.asc
lasview -i tile_metrics\suzana_*d00.asc
lasview -i tile_metrics\suzana_*d01.asc
lasview -i tile_metrics\suzana_*b30.asc
lasview -i tile_metrics\suzana_*b80.asc

The maximum height rasters are useful to inspect more closely as they will immediately tell us if there was any high noise point that slipped through the cracks. And indeed it happened as we see a maximum of 388.55 meters for of the 10 by 10 meter cells. As we know the expected height of the trees we could have added a ‘-drop_z_above 70’ to the lascanopy command line. Careful, however, when computing forestry metrics in strongly sloped terrains as the terrain slope can significantly lift up returns to heights much higher than that of the tree. This is guaranteed to happen for LiDAR returns from branches that are extending horizontally far over the down-sloped part of the terrain as shown in this paper here.

We did not use the shapefile for the stands in this exercise. We could have clipped the normalized LiDAR points to these stands using lasclip as shown in the GUI below before generating the raster metrics. This would have saved space and computation time as many of the LiDAR points lie outside of the stands. However, it might be better to do that clipping step on the rasters in whichever GIS software or statistics package you are using for the imputation computation to properly account for partly covered raster cells along the stand boundary. This could be subject of another blog article … (-:

not all LiDAR was needed to compute metrics for

NRW Open LiDAR: Merging Points into Proper LAS Files

In the first part of this series we downloaded, compressed, and viewed some of the newly released open LiDAR data for the state of North Rhine-Westphalia. In the second part we look at how to merge the multiple point clouds provided back into single LAS or LAZ files that are as proper as possible. Follow along with a recent version of LAStools and a pair of DGM and DOM files for your area of interest. For downloading the LiDAR we suggest using the wget command line tool with option ‘-c’ that after interruption in transmission will restart where it left off.

In the first part of this series we downloaded the pair of DGM and DOM files for the City of Bonn. The DGM file and the DOM file are zipped archives that contain the points in 1km by 1km tiles stored as x, y, z coordinates with centimeter resolution. We had already converted these textual *.xyz files into binary *.laz files. We did this with the open source LASzip compressor that is distributed with LAStools as described in that blog post. We continue now with the assumption that you have converted all of the *.xyz files to *.laz files as described here.

Mapping from tile names of DGM and DOM archives to classification and return type of points.

The mapping from tile names in DGM and DOM archives to the classification and return type of points: lp = last return. fp = first return, ab,aw,ag = synthetic points

There are multiple tiles for each square kilometer as the LiDAR has been split into different files based on classification and return type. Furthermore there are also synthetic points that were created by the land survey department to replace LiDAR under bridges and along buildings for generating higher quality rasters. We want to combine all points of a square kilometer into a single LAZ tile as it is usually expected. Simply merging the multiple files per tile is not an option as this would result in loosing point classifications and return type information as well as in duplicating all single returns that are stored in more than one file. The folks at OpenNRW offer this helpful graphic to know what the acronyms above mean:

Illustration of how acronyms used in tile names correspond to point classification and type.

Illustration of how acronyms used in tile names correspond to point classification and type.

In the following we’ll be looking at the set of files corresponding to the UTM tile 32366 / 5622. We wanted an interesting area with large buildings, a bridge, and water. We were looking for a suitable area using the KML overlays generated in part one. The tile along the Rhine river selected in the picture below covers the old city hall, the opera house, and the “Kennedy Bridge” has a complete set of DGM and DOM files:

      3,501 dgm1l-ab_32366_5622_1_nw.laz
     16,061 dgm1l-ag_32366_5622_1_nw.laz
      3,269 dgm1l-aw_32366_5622_1_nw.laz
    497,008 dgm1l-brk_32366_5622_1_nw.laz
  7,667,715 dgm1l-lpb_32366_5622_1_nw.laz
 12,096,856 dgm1l-lpnb_32366_5622_1_nw.laz
     15,856 dgm1l-lpub_32366_5622_1_nw.laz

      3,269 dom1l-aw_32366_5622_1_nw.laz
 21,381,106 dom1l-fp_32366_5622_1_nw.laz
We find the name of the tiles that cover the "Kennedy Bridge" using the KML overlays generated in part one.

We find the name of the tile that covers the “Kennedy Bridge” using the KML overlays generated in part one.

We now assign classification codes and flags to the returns from the different files using las2las, merge them together with lasmerge, and recover single, first, and last return information with lasduplicate. We set classifications to bridge deck (17), ground (2), to unclassified (1), and to noise (7) for all returns in the files with the acronym ‘brk’ (= bridge points), the acronym ‘lpb’ (= last return ground), the acronym ‘lpnb’ (= last return non-ground), and the acronym ‘lpub’ (= last return under ground). with las2las and store the resulting files to a temporary folder.

las2las -i dgm1l-brk_32366_5622_1_nw.laz ^
        -set_classification 17 ^
        -odir temp -olaz

las2las -i dgm1l-lpb_32366_5622_1_nw.laz ^
        -set_classification 2 ^
        -odir temp -olaz

las2las -i dgm1l-lpnb_32366_5622_1_nw.laz ^
        -set_classification 1 ^
        -odir temp -olaz

las2las -i dgm1l-lpub_32366_5622_1_nw.laz ^
        -set_classification 7 ^
        -odir temp -olaz

Next we use the synthetic flag of the LAS format specification to flag any additional points that were added (no measured) by the survey department to generate better raster products. We set classifications to ground (2) and the synthetic flag for all points of the files with the acronym ‘ab’ (= additional ground) and the acronym ‘ag’ (= additional building footprint). We set classifications to water (9) and the synthetic flag for all points of the files with the acronym ‘aw’ (= additional water bodies). Files with acronym ‘aw’ appear both in the DGM and DOM archive. Obviously we need to keep only one copy.

las2las -i dgm1l-ab_32366_5622_1_nw.laz ^
        -set_classification 2 ^
        -set_synthetic_flag 1 ^
        -odir temp -olaz

las2las -i dgm1l-ag_32366_5622_1_nw.laz ^
        -set_classification 2 ^
        -set_synthetic_flag 1 ^
        -odir temp -olaz

las2las -i dgm1l-aw_32366_5622_1_nw.laz ^
        -set_classification 9 ^
        -set_synthetic_flag 1 ^
        -odir temp -olaz

Using lasmerge we merge all returns from files with acronyms ‘brk’ (= bridge points), ‘lpb’ (= last return ground),  ‘lpnb’ (= last return non-ground), and ‘lpub’ (= last return under ground) into a single file that will then contain all of the (classified) last returns for this tile.

lasmerge -i temp\dgm1l-brk_32366_5622_1_nw.laz ^
         -i temp\dgm1l-lpb_32366_5622_1_nw.laz ^
         -i temp\dgm1l-lpnb_32366_5622_1_nw.laz ^
         -i temp\dgm1l-lpub_32366_5622_1_nw.laz ^
         -o temp\dgm1l-lp_32366_5622_1_nw.laz

Next we run lasduplicate three times to recover which points are single returns and which points are the first and the last return of a pair of points generated by the same laser shot. First we run lasduplicate with option ‘-unique_xyz’ to remove any xyz duplicates from the last return file. We also mark all surviving returns as the second of two returns. Similarly, we remove any xyz duplicates from the first return file and mark all survivors as the first of two returns. Finally we run lasduplicate with option ‘-single_returns’ with the unique last and the unique first return files as ‘-merged’ input. If a return with the exact same xyz coordinates appears in both files only the first copy is kept and marked as a single return. In order to keep the flags and classifications from the last return file, the order in which the last and first return files are listed in the command line is important.

lasduplicate -i temp\dgm1l-lp_32366_5622_1_nw.laz ^
             -set_return_number 2 -set_number_of_returns 2 ^
             -unique_xyz ^
             -o temp\last_32366_5622_1_nw.laz

lasduplicate -i dom1l-fp_32366_5622_1_nw.laz ^
             -set_return_number 1 -set_number_of_returns 2 ^
             -unique_xyz ^
             -o temp\first_32366_5622_1_nw.laz

lasduplicate -i temp\last_32366_5622_1_nw.laz ^
             -i temp\first_32366_5622_1_nw.laz ^
             -merged ^
             -single_returns ^
             -o temp\all_32366_5622_1_nw.laz

We then add the synthetic points with another call to lasmerge to obtain a LAZ file containing all points of the tile correctly classified, flagged, and return-numbered.

lasmerge -i temp\dgm1l-ab_32366_5622_1_nw.laz ^
         -i temp\dgm1l-ag_32366_5622_1_nw.laz ^
         -i temp\dgm1l-aw_32366_5622_1_nw.laz ^
         -i temp\all_32366_5622_1_nw.laz ^
         -o temp\merged_32366_5622_1_nw.laz

Optional: For more efficient use of this file in subsequent processing – and especially to accelerate area-of-interest queries with lasindex – it is often of great advantage to reorder the points in a spatially coherent manner. A simple call to lassort will rearrange the points along a space-filling curve such as a Hilbert curve or a Z-order curve.

lassort -i temp\merged_32366_5622_1_nw.laz ^
        -o bonn_32366_5622_1_nw.laz

Note that we also renamed the file because a good name can be useful if you find that file again in two years from now. Let’s have a look at the result with lasview.

lasview -i bonn_32366_5622_1_nw.laz

In lasview you can press <c> repeatedly to switch through all available coloring modes until you see the yellow (single) / red (first) / last (blue) coloring of the returns. The recovered return types are especially evident under vegetation, in the presence of wires, and along building edges. Press <x> to select an area of interest and press <x> again to inspect it more closely. Press <i> while hovering above a point to show its coordinates, classification, and return type.

We did each processing in separate steps to illustrate the overall workflow. The above sequence of LAStools command line calls can be shortened by combining multiple processing steps into one operation. This is left as an exercise for the advanced LAStools user … (-;

Acknowledgement: The LiDAR data of OpenNRW comes with a very permissible license. It is called “Datenlizenz Deutschland – Namensnennung – Version 2.0” or “dl-de/by-2-0” and allows data and derivative sharing as well as commercial use. It only requires us to name the source. We need to cite the “Land NRW (2017)” with the year of the download in brackets and specify the Universal Resource Identification (URI) for both the DOM and the DGM. Done. So easy. Thank you, OpenNRW … (-:

LASmoons: Chloe Brown

Chloe Brown (recipient of three LASmoons)
Geosciences, School of Geography
University of Nottingham, UK

Background:
Malaysia’s North Selangor peat swamp forest is experiencing rapid and large scale conversion of peat swampland to oil palm agriculture, contrary to prevailing environmental guidelines. Given the global importance of tropical peat lands, and the uncertainties surrounding historical and future oil palm development, quantifying the spatial distribution of ecosystem service values, such as climate mitigation, is key to understanding the trade-offs associated with anthropogenic land use change.
The study explores the capabilities and methods of remote sensing and field-based data sets for extracting relevant metrics for the assessment of carbon stocks held in North Selangor peat swamp forest reserve, estimating both the current carbon stored in the above and below ground biomass, as well as the changes in carbon stock over time driven by anthropogenic land use change. Project findings will feed directly into peat land management practices and environmental accounting in Malaysia through the Tropical Catchments Research Initiative (TROCARI), and support the Integrated Management Plan of the Selangor State Forest Department (see here for a sample).

some clever caption

Goal:
LiDAR data is now seen as the practical option when assessing canopy height over large scales (Fassnacht et al., 2014), with Lucas et al., (2008) believing LiDAR data to produce more accurate tree height estimates than those derived from manual field based methods. At this stage of the project, the goal is to produce a high quality LiDAR-derived Canopy Height Model (CHM) following the “pit-free” algorithm of Khosravipour et.al., 2014 using the LAStools software.

Data:
+ LiDAR provided by the Natural Environment Research Council (NERC) Airborne Research and Survey Facility’s 2014 Malaysia Campaign.
+ covers 685 square kilometers (closed source)
+ collected with Leica ALS50-II LiDAR system
+ average pulse spacing < 1 meter, average pulse density 1.8 per square meter

LAStools processing:
1) Create 1000 meter tiles with 35 meter buffer to avoid edge artifacts [lastile]
2) Remove noise points (class 7) that are already classified [las2las]
3) Classify point clouds into ground (class 2) and non-ground (class 1) [lasground]
4) Generate normalized above-ground heights [lasheight]
5) Create DSM and DTM [las2dem]
6) Generate a pit-free Canopy Height Model (CHM) as described here [lasthin, las2dem, lasgrid]
7) Generate a spike-free Canopy Height Model (CHM) as described here for comparison [las2dem]

References:
Fassnacht, F.E., Hartig, F., Latifi, H., Berger, C., Hernández, J., Corvalán, and P., Koch, B. (2014). Importance of sample size, data type and prediction method for remote sensing-based estimations of above-ground forest biomass. Remote Sensing. Environment. 154, 102–114.
Khosravipour, A., Skidmore, A. K., Isenburg, M., Wang, T., and Hussin, Y. A. (2014). Generating pit-free canopy height models from airborne LiDAR. Photogrammetric Engineering & Remote Sensing, 80(9), 863-872.
Lucas, R. M., Lee, A. C., and Bunting, P. J., (2008). Retrieving forest biomass through integration of casi and lidar data. International Journal of Remote Sensing, 29 (5), 1553-1577.

LASmoons: Elia Palop-Navarro

Elia Palop-Navarro (recipient of three LASmoons)
Research Unit in Biodiversity (UO-PA-CSIC)
University of Oviedo, SPAIN.

Background:
Old-growth forests play an important role in biodiversity conservation. However, long history of human transformation of the landscape has led to the existence of few such forests nowadays. Its structure, characterized by multiple tree species and ages, old trees and abundant deadwood, is particularly sensible to management practices (Paillet et al. 2015) and requires long time to recover from disturbance (Burrascano et al. 2013). Within protected areas we would expect higher proportions of old-growth forests since these areas are in principle managed to ensure conservation of natural ecosystems and processes. Nevertheless, most protected areas in the EU sustained use and exploitation in the past, or even still do.

lasmoons_elia_palopnavarro_0

Part of the study area. Dotted area corresponds to forest surface under protection.

Goal:
Through the application of a model developed in the study area, using public LiDAR and forest inventory data (Palop-Navarro et al. 2016), we’d like to know how much of the forest in a network of mountain protected areas retains structural attributes compatible with old-growth forests. The LiDAR processing tasks which LAStools will be used for involve a total of 614,808 plots in which we have to derive height metrics, such as mean or median canopy height and its variability.

Vegetation profile colored by height in a LiDAR sample of the study area.

Vegetation profile colored by height in a LiDAR sample of the study area.

Data:
+ Public LiDAR data that can be downloaded here with mean pulse density 0.5 points per square meter. This data has up to 5 returns and is already classified into ground, low, mid or high vegetation, building, noise or overlapped.
+ The area covers forested areas within protected areas in Cantabrian Mountains, occupying 1,207 km2.

LAStools processing:
1) quality checking of the data as described in several videos and blog posts [lasinfo, lasvalidate, lasoverlap, lasgrid, las2dem]
2) use existing ground classification (if quality suffices) to normalize the elevations of to heights above ground using tile-based processing with on-the-fly buffers of 50 meters to avoid edge artifacts [lasheight]
3) compute height-based forestry metrics (e.g. ‘-avg’, ‘-std’, and ‘-p 50’) for each plot in the study area [lascanopy]

References:
Burrascano, S., Keeton, W.S., Sabatini, F.M., Blasi, C. 2013. Commonality and variability in the structural attributes of moist temperate old-growth forests: a global review. Forest Ecology and Management 291:458-479.
Paillet, Y., Pernot, C., Boulanger, V., Debaive, N., Fuhr, M., Gilg, O., Gosselin, F. 2015. Quantifying the recovery of old-growth attributes in forest reserves: A first reference for France. Forest Ecology and Management 346:51-64.
Palop-Navarro, E., Bañuelos, M.J., Quevedo, M. 2016. Combinando datos lidar e inventario forestal para identificar estados avanzados de desarrollo en bosques caducifolios. Ecosistemas 25(3):35-42.

Prototype for “native LAS 1.4 extension” of LASzip LiDAR Compressor Released

PRESS RELEASE (for immediate release)
February 13, 2017
rapidlasso GmbH, Gilching, Germany

Just in time for ILMF 2017 in Denver, the makers of the popular LiDAR processing software LAStools announce that the prototype for the “native LAS 1.4 extension” of their award-winning open source LASzip LiDAR compressor is ready for testing. An update to the compressed LAZ format had become necessary due to a core change in the ASPRS LAS 1.4 specification which had introduced several new point types.

A new feature of the updated LASzip compressor is the ability to selectively decompress of only those attributes of each point that really are needed by the application that is reading the LAZ file. Minimally this will be the x and y coordinate of each point and the return counts, which are sufficient to – for example – calculate the exact extend of the survey area. Most applications will also want to access z coordinate. However, the intensities, the GPS times, the RGB or NIR colors, and the new “Extra Bytes” are often not needed. As the updated LAZ format compresses these different attributes into separate layers, their decompression can then be skipped. Therefore sometimes only 40% of a compressed LAZ file needs to be decompressed to access the coordinates of points with many attributes.

percentage of bytes in a compressing LAZ file corresponding to different point attributes

The percentages of a compressed LAZ file used to encode different point attributes for two example LAS 1.4 files.

The new LASzip prototype is currently being crowd-tested. Interested parties who already have holdings of LAS 1.4 files with point types 6 to 10 may send an email to ‘lasproto@rapidlasso.com’ to participate in these tests.

The release of the new LASzip compressor comes more than a year late as development had been intentionally delayed to give ESRI an opportunity to contribute their needs and ideas to create a joint open format with the community and avoid LiDAR format fragmentation. Sadly, this effort ultimately failed.

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.