Integrating External Ground Points in Forests to Improve DTM from Dense-Matching Photogrammetry

The biggest problem of generating a Digital Terrain Model (DTM) from the photogrammetric point clouds that are produced from aerial imagery with dense-matching software such as SURE, Pix4D, or Photoscan is dense vegetation: when plants completely cover the terrain not a single point is generated on the ground. This is different for LiDAR point clouds as the laser can even penetrate dense multi-level tropical forests. The complete lack of ground points in larger vegetated areas such as closed forests or dense plantations means that the many processing workflows for vegetation analysis that have been developed for LiDAR cannot be used for photogrammetric point clouds  … unless … well unless we are getting those missing ground points some other way. In the following we see how to integrate external ground points to generate a reasonable DTM under a dense forest with LAStools. See this, this, this, this, and this article for further reading.

Here you can download the dense matching point cloud, the manually collected ground points, and the forest stand delineating polygon that we are using in the following example work flow:

We leave the usual inspection of the content with lasinfo and lasview that we always recommend on newly obtained data as an exercise to the reader. Using las2dem and lasgrid we created the Google Earth overlays shown above to visualize the extent of the dense matched point cloud and the distribution of the manually collected ground points:

las2dem -i DenseMatching.laz ^
        -thin_with_grid 1.0 ^
        -extra_pass ^
        -step 2.0 ^
        -hillshade ^
        -odix _hill_2m -opng

lasgrid -i ManualGround.laz ^
        -set_RGB 255 0 0 ^
        -step 10 -rgb ^
        -odix _grid_10m -opng

Attempts to ground-classify the dense matching point cloud directly are futile as there are no ground points under the canopy in the heavily forested area. Therefore 558 ground points were manually surveyed in the forest of interest that are around 50 to 120 meters apart from another. We show how to integrate these points into the dense matching point cloud such that we can successfully extract bare-earth information from the data.

In the first step we “densify” the manually collected ground points by interpolating them with triangles onto a raster of 2 meter resolution that we store as LAZ points with las2dem. You could consider other interpolation schemes to “densify” the ground points, here we use simple linear interpolation to prove the concept. Due to the varying distance between the manually surveyed ground points we allow interpolating triangles with edge lengths of up to 125 meters. These triangles then also cover narrow open areas next to the forest, so we clip the interpolated ground points against the forest stand delineating polygon with lasclip to classify those points that are really in the forest as “key points” (class 8) and all others as “noise” (class 7).

las2dem -i ManualGround.laz ^
        -step 2 ^
        -kill 125 ^
        -odix _2m -olaz

lasclip -i ManualGround_2m.laz ^
        -set_classification 7 ^ 
        -poly forest.shp ^
        -classify_as 8 -interior ^
        -odix _forest -olaz

Below we show the resulting densified ground points colored by elevation that survive the clipping against the forest stand delineating polygon and were classified as “key points” (class 8). The interpolated ground points in narrow open areas next to the forest that fall outside this polygon were classified as “noise” (class 7) and are shown in violet. They will be dropped in the next step.

We then merge the dense matching points with the densified manual ground points (while dropping all the violet points marked as noise) as input to lasthin and reclassify the lowest point per 1 meter by 1 meter with a temporary code (here we use class 9 that usually refers to “water”). Only the subset of lowest points that receives the temporary classification code 9 will be used for ground classification later.

lasthin -i DenseMatching.laz ^
        -i ManualGround_2m_forest.laz ^
        -drop_class 7 ^
        -merged ^
        -lowest -step 1 -classify_as 9 ^
        -o DenseMatchingAndDensifiedGround.laz

We use the GUI of lasview to pick several interesting areas for visual inspection. The selected points load much faster when the LAZ file is spatially indexed and therefore we first run lasindex. For better orientation we also load the forest stand delineating polygon as an overlay into the GUI.

lasindex -i DenseMatchingAndDensifiedGround.laz 

lasview -i DenseMatchingAndDensifiedGround.laz -gui

We pick the area shown below that contains the target forest with manually collected and densified ground points and a forested area with only dense matching points. The difference could not be more drastic as the visualizations show.

Now we run ground classification using lasground with option ‘-town’ using only the points with the temporary code 9 by ignoring all other classifications 0 and 8 in the file. We leave the temporary classification code 9 unchanged for all the points that were not classified with “ground” code 2 so we can visualize later which ones those are.

lasground -i DenseMatchingAndDensifiedGround.laz ^
          -ignore_class 0 8 ^
          -town ^
          -non_ground_unchanged ^
          -o GroundClassified.laz

We again use the GUI of lasview to pick several interesting areas after running lasindex and again load the forest stand delineating polygon as an overlay into the GUI.

lasindex -i GroundClassified.laz 

lasview -i GroundClassified.laz -gui

We pick the area shown below that contains all three scenarios: the target forest with manually collected and densified ground points, an open area with only dense matching points, and a forested area with only dense matching points. The result is as expected: in the target forest the manually collected ground points are used as ground and in the open area the dense-matching points are used as ground. But there is no useful ground in the other forested area.

Now we can compute the heights of the points above ground for our target forest with lasheight and either replace the z elevations in the file of store them separately as “extra bytes”. Then we can compute, for example, a Canopy Height Model (CHM) that color codes the height of the vegetation above the ground with lasgrid. Of course this will only be correct in the target forest where we have “good” ground but not in the other forested areas. We also compute a hillshaded DTM to be able to visually inspect the topography of the generated terrain model.

lasheight -i GroundClassified.laz ^
          -store_as_extra_bytes ^
          -o GroundClassifiedWithHeights.laz

lasgrid -i GroundClassifiedWithHeights.laz ^
        -step 2 ^
        -highest -attribute 0 ^
        -false -set_min_max 0 25 ^
        -o chm.png

las2dem -i GroundClassified.laz ^
        -keep_class 2 -extra_pass ^
        -step 2 ^ 
        -hillshade ^
        -o dtm.png

Here you can download the resulting color-coded CHM and the resulting hill-shaded DTM as Google Earth KMZ overlays. Clearly the resulting CHM is only meaningful in the target forest where we used the manually collected ground points to create a reasonable DTM. In the other forested areas the ground is only correct near the forest edges and gets worse with increasing distance from open areas. The resulting DTM exhibits some interesting looking  bumps in the middle of areas with manually collected ground point. Those are a result of using the dense-matching points as ground whenever their elevation is lower than that of the manually collected points (which is decided in the lasthin step). Whether those bumps represent true elevations of are artifacts of low erroneous elevation from dense-matching remains to be investigated.

For forests on complex and steep terrain the number of ground points that needs to be manually collected may make such an approach infeasible in practice. However, maybe you have another source of elevation, such as a low-resolution DTM of 10 or 25 meter provided by your local government. Or maybe even a high resolution DTM of 1 or 2 meter from a LiDAR survey you did several years ago. While the forest may have grown a lot in the past years, the ground under the forest will probably not have changed much …

LASmoons: Marzena Wicht

Marzena Wicht (recipient of three LASmoons)
Department of Photogrammetry, Remote Sensing and GIS
Warsaw University of Technology, Poland.

Background:
More than half of human population (Heilig 2012) suffers from many negative effects of living in cities: increased air pollution, limited access to the green areas, Urban Heat Island (UHI) and many more. To mitigate some of these effects, many ideas came up over the years: reducing the surface albedo, the idea of the Garden City, green belts, and so on. Increasing horizontal wind speed might actually improve both, the air pollution dispersion and the thermal comfort in urban areas (Gál & Unger 2009). Areas of low roughness promote air flow – discharging the city from warm, polluted air and supplying it with cool and fresh air – if they share specific parameters, are connected and penetrate the inner city with a country breeze. That is why mapping low roughness urban areas is important in better understanding urban climate.

Goal:
The goal of this study is to derive buildings (outlines and height) and high vegetation using LAStools and to use that data in mapping urban ventilation corridors for our case study area in Warsaw. There are many ways to map these; however using ALS data has certain advantages (Suder& Szymanowski 2014) in this case: DSMs can be easily derived, tree canopy (incl. height) can be joined to the analysis and buildings can be easily extracted. The outputs are then used as a basis for morphological analysis, like calculating frontal area index. LAStools has the considerable advantage of processing large quantities of data (~500 GB) efficiently.

Frontal area index calculation based on 3D building database

Data:
+ LiDAR provided by Central Documentation Center of Geodesy and Cartography
+ average pulse density 12 p/m^2
+ covers 517 km^2 (whole Warsaw)

LAStools processing:
1) quality checking of the data as described in several videos and blog posts [lasinfo, lasvalidate, lasoverlap, lasgrid, lasduplicate, lasreturnlas2dem]
2) reorganize data into sufficiently small tiles with buffers to avoid edge artifacts [lastile]
3) classify point clouds into vegetation and buildings [lasground, lasclassify]
4) normalize LiDAR heights [lasheight]
5) create triangulated, rasterized derivatives: DSM / DTM / nDSM / CHM [las2dem, blast2dem]
6) compute height-based metrics (e.g. ‘-avg’, ‘-std’, and ‘-p 50’) [lascanopy]
7) generate subsets during the workflow [lasclip]
8) generate building footprints [lasboundary]

References:
Heilig, G. K. (2012). World urbanization prospects: the 2011 revision. United Nations, Department of Economic and Social Affairs (DESA), Population Division, Population Estimates and Projections Section, New York.
Gal, T., & Unger, J. (2009). Detection of ventilation paths using high-resolution roughness parameter mapping in a large urban area. Building and Environment, 44(1), 198-206.
Suder, A., & Szymanowski, M. (2014). Determination of ventilation channels in urban area: A case study of Wroclaw (Poland). Pure and Applied Geophysics, 171(6), 965-975.

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

Leaked: “Classified LiDAR” of Pentagon in LAS 1.4 Format

LiDAR leaks have happened! Black helicopters are in the sky!  A few days ago a tiny tweet leaked the online location of “classified LiDAR” for Washington, DC. This LiDAR really is “classified” and includes an aerial scan of the Pentagon. For rogue scientists world-wide we offer a secret download link. It links to a file code-named ‘pentagon.laz‘ that contains the 8,044,789 “classified” returns of the Pentagon shown below. This “classified file” can be deciphered by any software with native LAZ support. It was encrypted with the “LAS 1.4 compatibility mode” of LASzip. The original LAS 1.4 content was encoded into a inconspicuous-looking LAZ file. New point attributes (such as the scanner channel) were hidden as “extra bytes” for fully lossless encryption. Use ‘laszip‘ to fully decode the original “classified” LAS 1.4 file … (-;

Seriously, a tiled LiDAR data set for the District of Columbia flown in 2015 is available for anyone to use on Amazon S3 with a very permissive open data license, namely the Creative Commons Attribution 3.0 License. The LiDAR coverage can be explored via this interactive map. The tiles are provided in LAS 1.4 format and use the new point type 6. We downloaded a few tiles near the White House, the Capitol, and the Pentagon to test the “native LAS 1.4 extension” of our LASzip compressor which will be released soon (a prototype for testing is already available). As these uncompressed LAS files are YUUUGE we use the command line utility ‘wget‘ for downloading. With option ‘-c’ the download continues where it left off in case the transfer gets interrupted.

LiDAR pulse density from 20 or less (blue) to 100 or more (red) pulses per square meter.

We use lasboundary to create labeled bounding boxes for display in Google Earth and lasgrid to a create false color visualization of pulse density with the command lines shown below. Pulse densities of 20 or below are mapped to blue. Pulse densities of 100 or above are mapped to red. We picked the min value 20 and the max value 100 for this false color mapping by running lasinfo with the ‘-cd’ option to compute an average pulse density and then refining the numbers experimentally. We also use lasoverlap to visualize how flightlines overlap and how well they align. Vertical differences of up to 20 cm are mapped to white and differences of 40 cm or more are mapped to saturated blue or red.

lasboundary -i *.las ^
            -use_bb ^
            -labels ^
            -odir quality -odix _bb -okml

lasgrid -i *.las ^
        -keep_last ^
        -point_density -step 2 ^
        -false -set_min_max 20 100 ^
        -odir quality -odix _d_20_100 -opng ^
        -cores 2

lasoverlap -i *.las ^
           -min_diff 0.2 -max_diff 0.4 ^
           -odir quality -opng ^
           -cores 2

The visualization of the pulse density and of the flightline overlap both show that there is no LiDAR for the White House or Capitol Hill. We will never know how tall the tomato and kale plants had grown in Michelle Obama’s organic garden on that day. Note that the White House and Capitol Hill were not simply “cut out”. Instead the flight plan of the survey plane was carefully designed to avoid these areas. Surprisingly, the Pentagon did not receive the same treatment and is (almost) fully included in the open LiDAR as mentioned in the dramatic first paragraph. Interesting is how the varying (tidal?) water level of the Potomac River shows up in the visualization of flightline miss-alignments.

There are a number of issues in these LiDAR files. The most serious ones are reported at the very end of this article. We will now scrutinize the partly-filled tile 2016.las close to the White House with only 11,060,334 returns. A lasvalidate check immediately reports three deviations from the LAS 1.4 specification:

lasvalidate -i 2016.las -o 2016_check.xml
  1. For proper LAS 1.4 files containing point type 6 through 10 all ‘legacy’ point counts in the LAS header should be set to 0. The following six fields in the LAS header should be zero for tile 2016.las (and all other tiles):
    + legacy number of point records
    + legacy number of points by return[0]
    + legacy number of points by return[1]
    + legacy number of points by return[2]
    + legacy number of points by return[3]
    + legacy number of points by return[4]
  2. There should not be any LiDAR return in a valid LAS file whose ‘number of returns of given pulse’ attribute is zero but there are 8 such points in tile 2016.las (and many more in various other tiles).
  3. There should not be any LiDAR return whose ‘return number’ attribute is larger than their ‘number of returns of given pulse’ attribute but there are 8 such points in tile 2016.las (and many more in various other tiles).

The first issue is trivial. There is an efficient in-place fix that does not require to rewrite the entire file using lasinfo with the following command line:

lasinfo -i 2016.las ^
        -nh -nv -nc ^
        -set_number_of_point_records 0 ^
        -set_number_of_points_by_return 0 0 0 0 0 ^

A quick check with las2txt shows us that the second and third issue are caused by the same eight points. Instead of writing an 8 for the ‘number of returns’ attribute the LAS file exporter must have written a 0 (marked in red for all eight returns) and instead of writing an 8 for the ‘return number’ attribute the LAS file exporter must have written a 1 (also marked in red). We can tell it from the true first return via its z coordinate (marked in blue) as the last return should be the lowest of all.

las2txt -i 2016.las ^
        -keep_number_of_returns 0 ^
        -parse xyzrnt ^
        -stdout
397372.70 136671.62 33.02 4 0 112813299.954811
397372.03 136671.64 28.50 5 0 112813299.954811
397371.28 136671.67 23.48 6 0 112813299.954811
397370.30 136671.68 16.86 7 0 112813299.954811
397369.65 136671.70 12.50 1 0 112813299.954811
397374.37 136671.58 44.17 3 0 112813299.954811
397375.46 136671.56 51.49 1 0 112813299.954811
397374.86 136671.57 47.45 2 0 112813299.954811

With las2las we can change the ‘number of returns’ from 0 to 8 using a ‘-filtered_transform’ as illustrated in the command line below. We suspect that higher number of returns such as 9 or 10 might have been mapped to 1 and 2. Fixing those as well as repairing the wrong return numbers will require a more complex tool. We would recommend to check all tiles with more scrutiny using the lasreturn tool. But wait … more return numbering issues are to come.

las2las -i 2016.las ^
        -keep_number_of_returns 0 ^
        -filtered_transform ^
        -set_extended_number_of_returns 8 ^
        -odix _fixed -olas

A closer look at the scan pattern reveals that the LiDAR survey was flown with a dual-beam system where two laser beams scan the terrain simultaneously. This is evident in the textual representation below as there are multiple “sets of returns” for the same GPS time stamp such as 112813952.110394. We group the returns from the two beams into an orange and a green group. Their coordinates show that the two laser beams point into different directions when they are simultaneously “shot” and therefore hit the terrain far apart from another.

las2txt -i 2016.las ^
        -keep_gps_time 112813952.110392 112813952.110396 ^
        -parse xyzlurntp ^
        -stdout
397271.40 136832.35 54.31 0 0 1 1 112813952.110394 117
397277.36 136793.35 38.68 0 1 1 4 112813952.110394 117
397277.35 136793.56 32.89 0 1 2 4 112813952.110394 117
397277.34 136793.88 24.13 0 1 3 4 112813952.110394 117
397277.32 136794.25 13.66 0 1 4 4 112813952.110394 117

The information about which point is from which beam is currently stored into the generic ‘user data’ attribute instead of into the dedicated ‘scanner channel’ attribute. This can be fixed with las2las as follows.

las2las -i 2016.las ^
        -copy_user_data_into_scanner_channel ^
        -set_user_data 0 ^
        -odix _fixed -olas

Unfortunately the LiDAR files have much more serious issues in the return numbering. It’s literally a “Total Disaster!” and “Sad!” as the US president will tweet shortly. After grouping all returns with the same GPS time stamp into an orange and a green group there is one more set of returns left unaccounted for.

las2txt -i 2016.las ^
        -keep_gps_time 112813951.416451 112813951.416455 ^
        -parse xyzlurntpi ^
        -stdout
397286.02 136790.60 45.90 0 0 1 4 112813951.416453 117 24
397286.06 136791.05 39.54 0 0 2 4 112813951.416453 117 35
397286.10 136791.51 33.34 0 0 3 4 112813951.416453 117 24
397286.18 136792.41 21.11 0 0 4 4 112813951.416453 117 0
397286.12 136791.75 30.07 0 0 1 1 112813951.416453 117 47
397291.74 136750.70 45.86 0 1 1 1 112813951.416453 117 105
las2txt -i 2016.las ^
        -keep_gps_time 112813951.408708 112813951.408712 ^
        -parse xyzlurntpi ^
        -stdout
397286.01 136790.06 45.84 0 0 1 4 112813951.408710 117 7
397286.05 136790.51 39.56 0 0 2 4 112813951.408710 117 15
397286.08 136790.96 33.33 0 0 3 4 112813951.408710 117 19
397286.18 136792.16 17.05 0 0 4 4 112813951.408710 117 0
397286.11 136791.20 30.03 0 0 1 2 112813951.408710 117 58
397286.14 136791.67 23.81 0 0 2 2 112813951.408710 117 42
397291.73 136750.16 45.88 0 1 1 1 112813951.408710 117 142

This can be visualized with lasview and the result is unmistakably clear: The return numbering is messed up. There should be one shot with five returns (not a group of four and a single return) in the first example. And there should be one shot with six returns (not a group of four and a group of two returns) in the second example. Such a broken return numbering results in extra first (or last) returns. These are serious issues that affect any algorithm that relies on the return numbering such as first-return DSM generation or canopy cover computation. Those extra returns will also make the pulse density appear higher and the pulse spacing appear tighter than they really are. The numbers from 20 (blue) to 100 (red) pulses per square meters in our earlier visualization are definitely inflated.

lasview -i 2016.las ^
        -keep_gps_time 112813951.416451 112813951.416455 ^
        -color_by_return

lasview -i 2016.las ^
        -keep_gps_time 112813951.408708 112813951.408712 ^
        -color_by_return

After all these troubles here something nice. Side-by-side a first-return TIN and a spike-free TIN (using a freeze of 0.8 m) of the center court cafe in the Pentagon. Especially given all these “fake first returns” in the Washington DC LiDAR we really need the spike-free algorithm to finally “Make a DSM great again!” … (-;

We would like to acknowledge the District of Columbia Office of the Chief Technology Officer (OCTO) for providing this data with a very permissive open data license, namely the Creative Commons Attribution 3.0 License.

 

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.