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

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.

LASmoons: Jesús García Sánchez

Jesús García Sánchez (recipient of three LASmoons)
Landscapes of Early Roman Colonization (LERC) project
Faculty of Archaeology, Leiden University, The Netherlands

Background:
Our project Landscapes of Early Roman Colonization (LERC) has been studying the hinterland of the Latin colony of Aesernia (Molise region, Italy) using several non-destructive techniques, chiefly artefactual survey, geophysics, and interpretation of aerial photographs. Nevertheless large areas of the territory are covered by the dense forests of the Matese mountains, a ridge belonging the Apennine chain, or covered by bushes due to the abandonment of the countryside. The project won’t be complete without integrating the marginal, remote and forested areas into our study of the Roman hinterland. Besides, it’s also relevant to discuss the feasibility of LiDAR data sets in the study of Mediterranean landscapes and its role within contemporary Landscape Archaeology.

some clever caption

LiDAR coverage in Molise region, Italy.

Goal:
+ to study in detail forested areas in the colonial hinterland of Aesernia.
+ to found the correct parameters of the classification algorithm to be able to locate possible archaeological structures or to document appropriately those we already known.
+ to document and create new visualization of hill-top fortified sites that belong to the indigenous population and are currently poorly studied due to inaccessibility and forest coverage (Monte San Paolo, Civitalla, Castelriporso, etc.)
+ to demonstrate the archaeological potential of LiDAR data in Italy and help other scholars to work with that kind of data, explaining basic information about data quality, where and how to acquire imagery and examples of application in archaeology. A paper entitled “Working with ALS – LiDAR data in Central South Italy. Tips and experiences”, will be presented in the International Mediterranean Survey Workshop by the end of February in Athens.

Civitella hillfort (Longano, IS) and its local context: ridges and forest belonging to the Materse mountains and the Appenines.

Data:
Recently the LERC project has acquired a large LiDAR dataset created by the Italian Geoportale Nazionale and the Minisstero dell’Ambiente e della Tutella del Territorio e del Mare. The data was produced originally to monitor land-slides and erosive risk.
The average point resolution is 1 meter.
+ The data sets were cropped originally in 1 sq km. tiles by the Geoportale Nazionale for distribution purposes.

LAStools processing:
1) data is provided in *.txt files thus the first step is to create appropriate LAS files to work with [txt2las]
2) combine areas of circa 16 sq km (still fewer than 20 million points to be processed in one piece with LAStools) in the surroundings of the colony of Aesernia and in the Matese mountains [lasmerge]
3) assign the correct projection to the data [lasmerge or las2las]
4) extract the care-earth with the best-fitting parameters [lasground or lasground_new]
5) create bare-earth terrain rasters as a first step to visualize and analyze the area [lasdem]

LASmoons: Rachel Opitz

Rachel Opitz (recipient of three LASmoons)
Center for Virtualization and Applied Spatial Technologies
Department of Anthropology, University of South Florida, USA

Background:
In Spring 2017 Rachel Opitz will be teaching a course on Remote Sensing for Human Ecology and Archaeology at the University of South Florida. The aim of the course is to provide students with the practical skills and knowledge needed to work with contemporary remote sensing data. The course focuses on airborne laser scanning and hyper-spectral data and their application in Human Ecology and Archaeology. Through the course students will be introduced to a number of software packages commonly used to process and interpret these data, with an emphasis on free and/or open source tools.

Classification parameters and the resolution at which the DTM is interpolated both have a significant impact on our ability to recognize anthropogenic features in the landscape. Here we see a small quarry. More aggressive filtering and a coarser DTM resolution (left) makes it difficult to recognize that this is a quarry. Less aggressive filtering and a higher resolution (right) leaves some vegetation behind, but makes the edges of the quarry and some in-situ blocks clearly visible.

Goal:
The students will develop practical skills in applied remote sensing through hands-on exercises. Learning to assess, manage and process large data sets is essential. In particular, the students in the course will learn to:
+ Identify the set of techniques needed to solve a problem in applied remote sensing
+ Find public imagery and specify acquisitions
+ Assess data quality
+ Process airborne LiDAR data
+ Combine complementary remote sensing data sources
+ Create effective data visualizations
+ Analyze digital topographic and spectral data to answer questions in human ecology and archaeology

Data:
The course will include case studies that draw on a variety of publicly available data sets that will all be used in the exercises:
+ the PNOA data from Spain
+ data held by NOAA
+ data collected using NASA’s GLiHT platform

LAStools processing:
LAStools will be used throughout the course, as students learn to assess the quality of LiDAR data, classify raw LiDAR point clouds, create raster terrain and canopy models, and produce visualizations. The online tutorials and videos available via the company website and the over 50 hours of video on YouTube as well as the LAStools user forum will be used as resources during the course.

NRW Open LiDAR: Download, Compression, Viewing

UPDATE: (March 6th): Second part merging Bonn into proper LAS files

This is the first part of a series on how to process the newly released open LiDAR data for the entire state of North Rhine-Westphalia that was announced a few days ago. Again, kudos to OpenNRW for being the most progressive open data state in Germany. You can follow this tutorial after downloading the latest version of LAStools as well as a pair of DGM and DOM files for your area of interest from these two download pages.

We have downloaded the pair of DGM and DOM files for the Federal City of Bonn. Bonn is the former capital of Germany and was host to the FOSS4G 2016 conference. As both files are larger than 10 GB, we use the wget command line tool with option ‘-c’ that will restart where it left off in case the transmission gets interrupted.

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 in ETRS89 / UTM 32 projection as simple ASCII text with centimeter resolution (i.e. two decimal digits).

>> more dgm1l-lpb_32360_5613_1_nw.xyz
360000.00 5613026.69 164.35
360000.00 5613057.67 164.20
360000.00 5613097.19 164.22
360000.00 5613117.89 164.08
360000.00 5613145.35 164.03
[...]

There is more than one tile for each square kilometer as the LiDAR points have been split into different files based on their classification and their return type. Furthermore there are also synthetic points that were used by the land survey department to replace certain LiDAR points in order to generate higher quality DTM and DSM raster products.

The zipped DGM archive is 10.5 GB in size and contains 956 *.xyz files totaling 43.5 GB after decompression. The zipped DOM archive is 11.5 GB in size and contains 244 *.xyz files totaling 47.8 GB. Repeatedly loading these 90 GB of text data and parsing these human-readable x, y, and z coordinates is inefficient with common LiDAR software. In the first step we convert the textual *.xyz files into binary *.laz files that can be stored, read and copied more efficiently. We do this with the open source LASzip compressor that is distributed with LAStools using these two command line calls:

laszip -i dgm1l_05314000_Bonn_EPSG5555_XYZ\*.xyz ^
       -epsg 25832 -vertical_dhhn92 ^
       -olaz ^
       -cores 2
laszip -i dom1l_05314000_Bonn_EPSG5555_XYZ\*.xyz ^
       -epsg 25832 -vertical_dhhn92 ^
       -olaz ^
       -cores 2

The point coordinates are is in EPSG 5555, which is a compound datum of horizontal EPSG 25832 aka ETRS89 / UTM zone 32N and vertical EPSG 5783 aka the “Deutsches Haupthoehennetz 1992” or DHHN92. We add this information to each *.laz file during the LASzip compression process with the command line options ‘-epsg 25832’ and ‘-vertical_dhhn92’.

LASzip reduces the file size by a factor of 10. The 956 *.laz DGM files compress down to 4.3 GB from 43.5 GB for the original *.xyz files and the 244 *.laz DOM files compress down to 4.8 GB from 47.8 GB. From here on out we continue to work with the 9 GB of slim *.laz files. But before we delete the 90 GB of bulky *.xyz files we make sure that there are no file corruptions (e.g. disk full, truncated files, interrupted processes, bit flips, …) in the *.laz files.

laszip -i dgm1l_05314000_Bonn_EPSG5555_XYZ\*.laz -check
laszip -i dom1l_05314000_Bonn_EPSG5555_XYZ\*.laz -check

One advantage of having the LiDAR in an industry standard such as the LAS format (or its lossless compressed twin, the LAZ format) is that the header of the file stores the number of points per file, the bounding box, as well as the projection information that we have added. This allows us to very quickly load an overview for example, into lasview.

lasview -i dgm1l_05314000_Bonn_EPSG5555_XYZ\*.laz -GUI
The bounding boxes of the DGM files quickly display a preview of the data in the GUI when the files are in LAS or LAZ format.

The bounding boxes of the DGM files quickly give us an overview in the GUI when the files are in LAS or LAZ format.

Now we want to find a particular site in Bonn such as the World Conference Center Bonn where FOSS4G 2016 was held. Which tile is it in? We need some geospatial context to find it, for example, by creating an overview in form of KML files that we can load into Google Earth. We use the files from the DOM folder with “fp” in the name as points on buildings are mostly “first returns”. See what our previous blog post writes about the different file names or look at the second part of this series. We can create the KML files with lasboundary either via the GUI or in the command line.

lasboundary -i dom1l_05314000_Bonn_EPSG5555_XYZ\dom1l-fp*.laz ^
            -gui
Only the "fp" tiles from the DOM folder loaded the GUI into lasboundary.

Only the “fp” tiles from the DOM folder loaded the GUI into lasboundary.

lasboundary -i dom1l_05314000_Bonn_EPSG5555_XYZ\dom1l-fp*.laz ^
            -use_bb -labels -okml

We zoom in and find the World Conference Center Bonn and load the identified tile into lasview. Well, we did not expect this to happen, but what we see below will make this series of tutorials even more worthwhile. There is a lot of “high noise” in the particular tile we picked. We should have noticed the unusually high z range of 406.42 meters in the Google Earth pop-up. Is this high electromagnetic radiation interfering with the sensors? There are a number of high-tech government buildings with all kind of antennas nearby (such as the United Nations Bonn Campus the mouse cursor points at).

Significant amounts of high noise are in the first returns of the DOM tile we picked.

Significant amounts of high noise are in the first returns of the DOM tile we picked.

But the intended area of interest was found. You can see the iconic “triangulated” roof of the building that is across from the World Conference Center Bonn.

The World Conference Center Bonn is across from the building with the "triangulated" roof.

The World Conference Center Bonn is across from the building with the “triangulated” roof.

Please don’t think it is the responsibility of OpenNRW to remove the noise and provide cleaner data. The land survey has already processed this data into whatever products they needed and that is where their job ended. Any additional services – other than sharing the raw data – are not in their job description. We’ll take care of that … (-:

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 … (-:

ultimate video hands-on for LiDAR with LAStools

An easy-to-follow video tutorial on LiDAR processing with LAStools recorded in two parts (1, 2) in Baja California on April 8th 2015 as part of a 3 day workshop at CICESE in Ensenada. It includes a presentation but is mostly a hands-on that you can follow after downloading LAStools. For the first part skip to the 45th minute (or use this direct link):

It introduces LiDAR processing with examples of different projects such as flood mapping in the Philippines, canopy modeling in the Canary Islands, conflict-archaeology in Polish forests, and other laser adventures. This is followed by a hands-on of the core steps of LiDAR processing using LAStools: (1) LiDAR quality checking, (2) LiDAR preparation (tiling, classifying, cleaning), (3) manual editing of LiDAR files, (4) LiDAR derivative creation (DTM/DSM/…). These steps are repeated in more depth in the second part:

The second part ends with a detailed exploration of full waveform data in PulseWaves format after 3 hours and 9 minutes (or use this direct link). Here a summary of the entire 3 day event provided by OpenTopography. We are very grateful to Dr. Alejandro Hinosa for organizing this amazing event and the sponsorship by CONACyT and CICESE.