LASmoons: Maria Kampouri

Maria Kampouri (recipient of three LASmoons)
Remote Sensing Laboratory, School of Rural & Surveying Engineering
National and Technical University of Athens, GREECE

The Aralar Natural Park, famous for its stunning landscapes, is located in the southeast of the province of Gipuzkoa, sharing a border with the neighboring province of Navarre. Inside the park there are nature reserves of exceptional importance, such as beech woods, large number of yew trees, very singular species of flora and fauna and areas of exceptional geological interest. Griffon vultures, Egyptian vultures, golden eagles and even bearded vultures (also known as lammergeier) can be seen flying over this area. European minks and Pyrenean desmans can be found in the streams and rivers that descend from the mountain tops.

The concept of biodiversity is based on inter- and intra-species genetic variation and has been evolving over the past 25 years. The importance of mapping biodiversity in order to plan its conservation, as well as identifying patterns in endemism and biodiversity hot-spots, have been pillars for EU and global environmental policy and legislation. The coupling of remote sensing and field data can increase reliability, periodicity and reproduce-ability of ecosystem process and biodiversity monitoring, leading to an increasing interest in environmental monitoring, using data for the same areas over time. Natural processes and complexity are best explored by observing ecosystems or landscapes through scale alteration, using spatial analysis tools, such as LAStools.

DTM generated with restricted version of las2dem above point limits

The aim of this study is to investigate the potential use of LiDAR data for the identification and determination of forest patches of particular interest, with respect to ecosystem dynamics and biodiversity and to produce a relevant biodiversity map, based on Simpson’s Diversity Index for Aralar Natural Park.

 approximately 123 km^2 of LiDAR in 1km x 1km LAS tiles
+ Average point density: 2 pts/m^2
+ Spatial referencing system: ETRS89 UTM zone 30N with elevations on the EGM08 geoid. Data from LiDAR flights are These files were obtained from the LiDAR flight carried out in 2008 by the Provincial Council of Gipuzkoa and the LiDAR flights of the Basque Government.

LAStools processing:
1) data quality checking [lasinfolasoverlaplasgridlasreturn]
2) classify ground and non-ground points [lasground]
3) remove low and high outliers [lasheight, lasnoise]
4) identify buildings within the study area [lasclassify]
5) create DTM tiles with 0.5 step in ‘.bil’ format [las2dem]
6) create DSM tiles with 0.5 step in ‘.bil’ format [las2dem]
7) create a normalized point cloud [lasheight]
8) create a highest-return canopy height model (CHM) [lasthin, las2dem]
9) create a pit-free (CHM) with the spike-free algorithm [las2dem]
10) create various rasters with forest metrics [lascanopy]

The generated elevation and forest metrics rasters are then combined with satellite data to create a biodiversity map, using Simpson’s Diversity Index.

Complete LiDAR Processing Pipeline: from raw Flightlines to final Products

This tutorial serves as an example for a complete end-to-end workflow that starts with raw LiDAR flightlines (as they may be delivered by a vendor) to final classified LiDAR tiles and derived products such as raster DTM, DSM, and SHP files with contours, building footprint and vegetation layers. The three example flightlines we are using here were flown in Ayutthaya, Thailand with a RIEGL LMS Q680i LiDAR scanner by Asian Aerospace Services who are based at the Don Mueang airport in Bangkok from where they are serving South-East-Asia and beyond. You can download them here:

Quality Checking

The minimal quality checks consist of generating textual reports (lasinfo & lasvalidate), inspecting the data visually (lasview), making sure alignment and overlap between flightlines fulfill expectations (lasoverlap), and measuring pulse density per square meter (lasgrid). Additional checks for points replication (lasduplicate), completeness of all returns per pulse (lasreturn), and validation against external ground control points (lascontrol) may also be performed.

lasinfo -i Ayutthaya\strips_raw\*.laz ^
        -cd ^
        -histo z 5 ^
        -histo intensity 64 ^
        -odir Ayutthaya\quality -odix _info -otxt ^
        -cores 3

lasvalidate -i Ayutthaya\strips_raw\*.laz ^
            -o Ayutthaya\quality\validate.xml

The lasinfo report generated with the command line shown computes the average density for each flightline and also generates two histograms, one for the z coordinate with a bin size of 5 meter and one for the intensity with a bin size of 64. The resulting textual descriptions are output into the specified quality folder with an appendix ‘_info’ added to the original file name. Perusing these reports tells you that there are up to 7 returns per pulse, that the average pulse density per flightline is between 7.1 to 7.9 shots per square meter, that the point source IDs of the points are already populated correctly, that there are isolated points far above and far below the scanned area, and that the intensity values range from 0 to 1023 with the majority being below 400. The warnings in the lasinfo and the lasvalidate reports about the presence of return numbers 6 and 7 have to do with the history of the LAS format and can safely be ignored.

lasoverlap -i Ayutthaya\strips_raw\*.laz ^
           -files_are_flightlines ^
           -min_diff 0.1 -max_diff 0.3 ^
           -odir Ayutthaya\quality -o overlap.png

This results in two color illustrations. One image shows the flightline overlap with blue indicating one flightline, turquoise indicating two, and yellow indicating three flightlines. Note that wet areas (rivers, lakes, rice paddies, …) without LiDAR returns affect this visualization. The other image shows how well overlapping flightlines align. Their vertical difference is color coded with while meaning less than 10 cm error while saturated red and blue indicate areas with more than 30 cm positive or negative difference.

One pixel wide red and blue along building edges and speckles of red and blue in vegetated areas are normal. We need to look-out for large systematic errors where terrain features or flightline outlines become visible. If you click yourself through this photo album you will eventually see typical examples (make sure to read the comments too). One area slightly below the center looks suspicious. We load the PNG into the GUI to pick this area for closer inspection with lasview.

lasview -i Ayutthaya\strips_raw\*.laz -gui

Why these flightline differences exist and whether they are detrimental to your purpose are questions that you will have to explore further. For out purpose this isolated difference was noted but will not prevent us from proceeding further. Next we want to investigate the pulse density. We do this with lasgrid. We know that the average last return density per flightline is between 7.1 to 7.9 shots per square meter. We set up the false color map for lasgrid such that it is blue when the last return density drops to 5 shots (or less) per square meter and such that it is red when the last return density reaches 10 shots (or more).

lasgrid -i Ayutthaya\strips_raw\*.laz -merged ^
        -keep_last ^
        -step 2 -density ^
        -false -set_min_max 4 8 ^
        -odir Ayutthaya\quality -o density_4ppm_8ppm.png

The last return density per square meter mapped to a color: blue is 5 or less, red is 10 or more.

The last return density image clearly shows how the density increases to over 10 pulses per square meter in all areas of flightline overlap. However, as there are large parts covered by only one flightline their density is the one that should be considered. We note great variations in density along the flightlines. Those have to do with aircraft movement and in particular with the pitch. When the nose of the plane goes up even slightly, the gigantic “fan of laser pulses” (that can be thought of as being rigidly attached at the bottom perpendicular to the aircraft flight direction) is moving faster forward on the ground far below and therefore covers it with fewer shots per square meter. Conversely when the nose of the plane goes down the spacing between scan lines far below the plane are condensed so that the density increases. Inclement weather and the resulting airplane pitch turbulence can have a big impact on how regular the laser pulse spacing is on the ground. Read this article for more on LiDAR pulse density and spacing.

LiDAR Preparation

When you have airborne LiDAR in flightlines the first step is to tile the data into square tiles that are typically 1000 by 1000 or – for higher density surveys – 500 by 500 meters in size. This can be done with lastile. We also add a buffer of 30 meters to each tile. Why buffers are important for tile-based processing is explained here. We choose 30 meters as this is larger than any building we expect in this area and slightly larger than the ‘-step’ size we use later when classifying the points into ground and non-ground points with lasground.

lastile -i Ayutthaya\strips_raw\*.laz ^
        -tile_size 500 -buffer 30 -flag_as_withheld ^
        -odir Ayutthaya\tiles_raw -o ayu.laz

NOTE: Usually you will have to add ‘-files_are_flightlines’ or ‘-apply_file_source_ID’ to the lastile command shown above in order to preserve the information which points is from which flightline. We do not have to do this here as evident from the lasinfo reports we generated earlier. Not only is the file source ID in the LAS header is correctly set to 2, 3, or 4 reflecting the intended flightline numbering evident from the file names. Also the point source ID of each point is already set to the correct value 2, 3, or 4. For more info see this or this discussion from the LAStools user forum.

Next we classify isolated points that are far from most other points with lasnoise into the (default) classification code 7. See the README file for the meaning of the parameters and play around with different setting to get a feel for how to make this process more or less aggressive.

lasnoise -i Ayutthaya\tiles_raw\ayu*.laz ^
         -step_xy 4 -step_z 2 -isolated 5 ^
         -odir Ayutthaya\tiles_denoised -olaz ^
         -cores 4

Especially for ground classification it is important that low noise points are excluded. You should inspect a number of tiles with lasview to make sure the low noise are all pink now if you color them by classification.

lasview -i Ayutthaya\tiles_denoised\ayu*.laz -gui

While the algorithms in lasground are designed to withstand a few noise points below the ground, you will find that it will include them into the ground model if there are too many of them. Hence, it is important to tell lasground to ignore these noise points. For the other parameters used see the README file of lasground.

lasground -i Ayutthaya\tiles_denoised\ayu*.laz ^
          -ignore_class 7 ^
          -city -ultra_fine ^
          -compute_height ^
          -odir Ayutthaya\tiles_ground -olaz ^
          -cores 4

You should visually check the resulting ground classification of each tile with lasview by selecting smaller subsets (press ‘x’, draw a rectangle, press ‘x’ again, use arrow keys to walk) and then switch back and forth between a triangulation of the ground points (press ‘g’ and then press ‘t’) and a triangulation of last returns (press ‘l’ and then press ‘t’). See the README of lasview for more information on those hotkeys.

lasview -i Ayutthaya\tiles_ground\ayu*.laz -gui

This way I found at least one tile that should be reclassified with ‘-metro’ instead of ‘-city’ because it still contained one large building in the ground classification. Alternatively you can correct miss-classifications manually using lasview as shown in the next few screen shots.

This is an optional step for advanced users who have a license. In case you managed to do such a manual edit and saved it as a LAY file using LASlayers (see the README file of lasview) you can overwrite the old file with:

laslayers -i Ayutthaya\tiles_ground\ayu_669500_1586500.laz -ilay ^
          -o Ayutthaya\tiles_ground\ayu_669500_1586500_edited.laz

move Ayutthaya\tiles_ground\ayu_669500_1586500_edit.laz ^

The next step classifies those points that are neither ground (2) nor noise (7) into building (or rather roof) points (class 6) and high vegetation points (class 5). For this we use lasclassify with the default parameters that only considers points that are at least 2 meters above the classified ground points (see the README for details on all available parameters).

lasclassify -i Ayutthaya\tiles_ground\ayu*.laz ^
            -ignore_class 7 ^
            -odir Ayutthaya\tiles_classified -olaz ^
            -cores 4

We  check the classification of each tile with lasview by selecting smaller subsets (press ‘x’, draw a rectangle, press ‘x’ again) and by traversing with the arrow keys though the point cloud. You will find a number of miss-classifications. Boats are classified as buildings, water towers or complex temple roofs as vegetation, … and so on. You could use lasview to manually correct any classifications that are really important.

lasview -i Ayutthaya\tiles_classified\ayu*.laz -gui

Before delivering the classified LiDAR tiles to a customer or another user it is imperative to remove the buffers that were carried through all computations to avoid artifacts along the tile boundary. This can also be done with lastile.

lastile -i Ayutthaya\tiles_classified\ayu*.laz ^
        -remove_buffer ^
        -odir Ayutthaya\tiles_final -olaz ^
        -cores 4

Together with the tiling you may want to deliver a tile overview file in KML format (or in SHP format) that you can easily generate with lasboundary using this command line:

lasboundary -i Ayutthaya\tiles_final\ayu*.laz ^
            -use_bb ^
            -overview -labels ^
            -o Ayutthaya\tiles_overview.kml

The small KML file generated b lasboundary provides a quick overview where tiles are located, their names, their bounding box, and the number of points they contain.

Derivative production

The most common derivative product produced from LiDAR data is a Digital Terrain Model (DTM) in form of an elevation raster. This can be obtained by interpolating the ground points with a triangulation (i.e. a Delaunay TIN) and by sampling the TIN at the center of each raster cell. The pulse density of well over 4 shots per square meter definitely supports a resolution of 0.5 meter for the raster DTM. From the ground-classified tiles with buffer we compute the DTM using las2dem as follows:

las2dem -i Ayutthaya\tiles_ground\ayu*.laz ^
        -keep_class 2 ^
        -step 0.5 -use_tile_bb ^
        -odir Ayutthaya\tiles_dtm -obil ^
        -cores 4

It’s important to add ‘-use_tile_bb’ to the command line which limits the raster generation to the original tile sizes of 500 by 500 meters in order not to rasterize the buffers that are extending the tiles 30 meters in each direction. We used the BIL format so that we inspect the resulting elevation rasters with lasview:

lasview -i Ayutthaya\tiles_dtm\ayu*.bil -gui

To create a hillshaded version of the DTM you can use your favorite raster processing package such as GDAL or GRASS but you could also use the BLAST extension of LAStools and create a large seamless image with blast2dem as follows:

blast2dem -i Ayutthaya\tiles_dtm\ayu*.bil -merged ^
          -step 0.5 -hillshade -epsg 32647 ^
          -o Ayutthaya\dtm_hillshade.png

Because blast2dem does not parse the PRJ files that accompany the BIL rasters we have to specify the EPSG code explicitly to also get a KML file that allows us to visualize the LiDAR in Google Earth.

A a hillshading of the merged DTM rasters produced with blast2dem.

Next we generate a Digital Surface Model (DSM) that includes the highest objects that the laser has hit. We use the spike-free algorithm that is implemented in las2dem that creates a triangulation of the highest returns as follows:

las2dem -i Ayutthaya\tiles_denoised\ayu*.laz ^
        -drop_class 7 ^
        -step 0.5 -spike_free 1.2 -use_tile_bb ^
        -odir Ayutthaya\tiles_dsm -obil ^
        -cores 4

We used 1.0 as the freeze value for the spike free algorithm because this is about three times the average last return spacing reported in the individual lasinfo reports generated during quality checking. Again we inspect the resulting rasters with lasview:

lasview -i Ayutthaya\tiles_dsm\ayu*.bil -gui

For reason of comparison we also generate the DSM rasters using a simple first-return interpolation again with las2dem as follows:

las2dem -i Ayutthaya\tiles_denoised\ayu*.laz ^
        -drop_class 7 -keep_first ^
        -step 0.5 -use_tile_bb ^
        -odir Ayutthaya\tiles_dsm -obil ^
        -cores 4

A few direct side-by-side comparison between a spike-free DSM and a first-return DSM shows the difference that are especially noticeable along building edges and in large trees.

Another product that we can easily create are building footprints from the automatically classified roofs using lasboundary. Because the tool is quite scalable we can simply on-the-fly merge the final tiles. This also avoids including duplicate points from the tile buffer whose classifications are also often less accurate.

lasboundary -i Ayutthaya\tiles_final\ayu*.laz -merged ^
            -keep_class 6 ^
            -disjoint -concavity 1.1 ^
            -o Ayutthaya\buildings.shp

Similarly we can use lasboundary to create a vegetation layer from those points that were automatically classified as high vegetation.

lasboundary -i Ayutthaya\tiles_final\ayu*.laz -merged ^
             -keep_class 5 ^
             -disjoint -concavity 3 ^
             -o Ayutthaya\vegetation.shp

We can also produce 1.0 meter contour lines from the ground classified points. However, for nicer contours it is beneficial to first generate a subset of the ground points with lasthin using option ‘-contours 1.0’ as follows:

lasthin -i Ayutthaya\tiles_final\ayu*.laz ^
        -keep_class 2 ^
        -step 1.0 -contours 1.0 ^
        -odir Ayutthaya\tiles_temp -olaz ^
        -cores 4

We then merge all subsets of ground points from those temporary tiles on-the-fly into one (using the ‘-merged’ option) and let blast2iso from the BLAST extension of LAStools generate smoothed and simplified 1 meter contours as follows:

blast2iso -i Ayutthaya\tiles_temp\ayu*.laz -merged ^
          -iso_every 1.0 ^
          -smooth 2 -simplify_length 0.5 -simplify_area 0.5 -clean 5.0 ^
          -o Ayutthaya\contours_1m.shp

Finally we composite all of our derived LiDAR products into one map using QGIS and then fuse it with data from OpenStreetMap that we’ve downloaded from BBBike. Here you can download the OSM data that we use.

It’s in particular interesting to compare the building footprints that were automatically derived from our LiDAR processing pipeline with those mapped by OpenStreetMap volunteers. We immediately see that there is a lot of volunteering work left to do and the LiDAR-derived data can assist us in directing those mapping efforts. A closer look reveals the (expected) quality difference between hand-mapped and auto-generated data.

The OSM buildings are simpler. These polygons are drawn and divided into logical units by a human. They are individually verified and correspond to actual buildings. However, they are less aligned with the Google Earth satellite image. The LiDAR-derived buildings footprints are complex because lasboundary has no logic to simplify the complicated polygonal chains that enclose the points that were automatically classified as roof into rectilinear shapes or to break directly adjacent roof points into separate logical units. However, most buildings are found (but also objects that are not buildings) and their geospatial alignment is as good as that of the LiDAR data.

LASmoons: Maeva Dang

Maeva Dang (recipient of three LASmoons)
Industrial Building and interdisciplinary Planning, Faculty of Civil Engineering
Vienna University of Technology, AUSTRIA

After centuries of urbanization and industrialization the green landscape of Rio de Janeiro in Brazil must be regenerated. The forests and other green areas, providers of ecosystem services, are fragmented and surrounded by dense urban occupation [1]. The loss of vegetation in the city reduces the amount of cooling and increases the urban heat islands effect. The metropolis also has a chronic problem with floods as a result of the lack of sustainable planning in urban areas of low permeability. A well-designed green infrastructure system is highly needed, since it would help the city to mitigate the negative effects of its urbanization and to be more resilient to environmental changes [2]. Intensive green roofs provide a large range of benefits from enhancing biodiversity in the city to reducing flood risks and mitigating the urban heat islands effect. The present research aims to quantitatively and accurately assess the intensive greening potential of the roof landscape of Rio de Janeiro based on LIDAR data.

A view of the roof landscape of the Urca district. Rio de Janeiro has high contrasts of forests and dense urban environments.

The LAStools software will be used to check the quality of the data and create a Digital Terrain Model (DTM) and Digital Surface Model (DSM) for the city of Rio de Janeiro. The goal of the study is to identify the existing flat roof surfaces suitable for intensive greening (i.e. that have a slope between 0 and 5 degrees). The results will be provided for free to the public.

 Airborne LiDAR data provided by the City hall of Rio de Janeiro, Instituto Municipal de Urbanismo Pereira Passos (IPP)
+ Average pulse density 2 pulses per square meter
+ Sensor System: Leica ALS60

LAStools processing:
1) check the quality of the LiDAR data [lasinfo, lasoverlap, lasgrid]
2) classify into ground and non-ground points using tile-based processing [lastilelasground]
3) remove low and high outliers [lasheight, lasnoise]
4) identify buildings within the study area [lasclassify]
5) normalize LiDAR heights [lasheight]
6) generate DTM and DSM [las2dem, lasgrid]

[1] Herzog C. (2012). Connecting the wonderful Landscapes of Rio de Janeiro. Available online . Accessed on 07/06/18.
[2] European Commission (2011). Communication from the Commission to the European Parliament, the Council, the
Economic and Social Committee and the Committee of the Regions: Our life insurance, our natural capital: an EU
biodiversity strategy to 2020. Available online. Accessed on 07/06/18.

LASmoons: Alex S. Olpenda

Alex S. Olpenda (recipient of three LASmoons)
Department of Geomatics and Spatial Planning, Faculty of Forestry
Warsaw University of Life Sciences, POLAND

The Bialowieza Forest is a trans-boundary property along the borders of Poland and Belarus consisting of diverse Central European lowland forest covering a total area of 141,885 hectares. Enlisted as one of the world’s biosphere reserves and a UNESCO World Heritage Site, the Bialowieza Forest conserves a complex ecosystem that supports vast wildlife including at least 250 species of birds and more than 50 mammals such as wolf, moose, lynx and the largest free-roaming population of the forest’s iconic species, the European bison [1]. The area is also significantly rich in dead wood which becomes a home for countless species of mushrooms, mold, bacteria and insects of which many of them are endangered of extinction [2]. Another factor, aside from soil type, that impacts the species of plant communities growing in the area is humidity [3] which can be considered as a function of solar radiation. Understanding the interactions and dynamics of these elements within the environment is vital for proper management and conservation practices. Sunlight below canopies is a driving force that affects the growth and survival of both fauna and flora directly and indirectly. Measurement and monitoring of this variable is crucial.

The European bison  (image credit to Frederic Demeuse).

Remote sensing technology can describe the light condition inside the forest with relatively high spatial and temporal resolutions at large scale. The goal of this research is to develop a predictive model to estimate sub-canopy light condition of Bialowieza Forest inside Poland’s territory using LiDAR data. Aside from common metrics based on heights and intensities, extraction of selected metrics known to infer transmitted light are also to be done. Returns that belong or are close to the ground are a good substitute for sun-rays that reach the forest floor while vegetation-classified returns could be assumed as the ones impeding the light. Relationships between these metrics and to both direct and diffuse sunlight derived from hemispherical photographs will be explored. Furthermore, multiple regression shall then be conducted between the parameters. Previous similar studies have been done successfully but mostly in homogeneous forest. The task might pose a challenge as Bialowieza Forest is a mixture of conifers and broad-leaved trees.

Location map of the study site with 100 random sample plots.

2015 ALS data set obtained using full waveform sensor (Riegl LMS-Q680i)
+ discrete point clouds (average pulse density: 6 points/m²)
+ 134 flightlines with 40% overlap
+ forest inventory data (100 circular plots, 12.62 m radius)
+ colored hemispherical photographs
All of this data is provided by the Forest Research Institute through the ForBioSensing project.

LAStools processing:
1) data quality checking [lasinfo, lasoverlap, lasgrid, lasreturn]
2) merge and clip the LAZ files [las2las]
3) classify ground and non-ground points [lasground]
4) remove low and high outliers [lasheight, lasnoise]
5) create a normalized point cloud [lasheight]
6) compute forestry metrics for each plot [lascanopy]

[1] UNESCO. World Heritage List. Available online (accessed on 2 October 2017).
[2] Polish Tourism Organization. Official Travel Website. Available online (accessed on 3 October 2017).
[3] Bialowieza National Park. Available online (accessed on 3 October 2017).

LASmoons: Martin Buchauer

Martin Buchauer (recipient of three LASmoons)
Cartography & Geomedia Technology
University of Applied Science Munich, GERMANY

Salt marsh areas provide numerous services such as natural flood defenses, carbon sequestration, agricultural services, and are a valuable coastal habitat for flora, fauna and humans. However, they are not only threatened by the constant rise of sea levels caused by global warming but also by human settlement in coastal areas. A sensible local coastal development is important as it may help to support the development and progression of stressed salt marshes.

Looking South you can see the salt marsh area next to a famous golf course with St Andrews in the background.


This research aims to visualize and extract vegetation metrics as well as the temporal analysis of four salt marsh data sets which are derived from terrestrial laser scanning. Located at the South and North shore of the Eden Estuary near St Andrews, Scotland, the scans were acquired in the summer and winter of 2016. Ground based laser scanning is an ideal method of fully reconstructing vegetation structures as well as having the ability to retrieve meaningful metrics such as height, area, and vegetation density. Although this technology has frequently been applied in the area of forestry, its application to salt marsh areas has not yet fully explored.

 TLS data acquired with a Leica HDS6100 (average density of 38000 points/m²)
+ ground control points (field data)

LAStools processing:
1) check the quality of the LiDAR data [lasinfo, lasoverlap, lasgrid]
2) merge and retile the original data with buffers [lastile]
3) classify point clouds into ground and non-ground [lasthin, lasground]
4) create digital terrain (DTM) and digital surface models (DSM) [lasthin, las2dem, blast2dem]

LASmoons: Sebastian Kasanmascheff

Sebastian Kasanmascheff (recipient of three LASmoons)
Forest Inventory and Remote Sensing
Georg-August-Universität Göttingen, GERMANY

Forest inventories are the backbone of forest management in Germany. In most federal forestry administrations in Germany, they are performed every ten years in order to assure that logging activities are sustainable. The process involves trained foresters who visit each stand (i.e. an area where the forest is similar in terms of age structure and tree species) and perform angle count sampling as developed by Walter Bitterlich in 1984. In a second step the annual growth is calculated using yield tables and finally a harvest volume is derived. There are three particular reasons to investigate how remote sensing can be integrated in the current inventory system:

  1. The current process does not involve random sampling of the sampling points and thus does not offer any measure of the accuracy of the data.
  2. Forest engineers hardly ever rely on the inventory data as a stand-alone basis for logging planning. Most often they rely on intuition alone and on the total volume count that they have to deliver for a wider area every year.
  3. In the last ten years, the collection of high-resolution LiDAR data has become more cost-effective and most federal agencies in Germany have access to it.

In order to be able to integrate the available remote-sensing data for forest inventories in Germany, it is important to tell apart different tree species as well as estimate their volumes.

Hesse is one of the most forested federal states in Germany.

The goal of this project is to perform an object-based classification of conifer trees in Northern Hesse based on high-resolution LiDAR and multi-spectral orthophotos. The first step is to delineate the tree crowns. The second step is to perform a semi-automated classification using the spectral signature of the different conifer species.

 DSM (1m), DTM (1m), DSM (0.2 m) of the study area
+ Stereo images with 0.2 m resolution
+ high-resolution LiDAR data (average 10 points/m²)
+ forest inventory data
+ vector files of the individual forest stands
+ ground control points (field data)
All of this data is provided by the Hessian Forest Agency (HessenForst).

LAStools processing:
1) merge and clip the LAZ files [las2las]
2) classify ground and non-ground points [lasground]
3) remove low and high outliers [lasheight, lasnoise]
4) identify buildings within the study area [lasclassify]
5) create a normalized point cloud [lasheight]
6) create a highest-return canopy height model (CHM) [lasthin, las2dem]
7) create a pit-free (CHM) with the spike-free algorithm [las2dem]

LASmoons: Manuel Jurado

Manuel Jurado (recipient of three LASmoons)
Departamento de Ingeniería Topográfica y Cartografía
Universidad Politécnica de Madrid, SPAIN

The availability of LiDAR data is creating a lot of innovative possibilities in different fields of science, education, and other field of interests. One of the areas that has been deeply impacted by LiDAR is cartography and in particular one highly specialized sub-field of cartography in the domain of recreational and professional orienteering running: the production of high-quality maps for orienteering races (Ditz et al., 2014). These are thematic maps with a lot of fine detail which demands many hours of field work for the map maker. In order to reduce the fieldwork, LiDAR data obtained from Airborne Research Australia (ARA) is going to be used in order to obtain DEM and to extract features that must be included in these maps. The data will be filtered and processed with the help of LAStools.

Final map with symbolism typical for use in orienteering running

The goal of this project is to extract either point (boulders, mounds), linear (contours, erosion gullies, cliffs) and area features (vegetation density) that should be drawn in a orienteering map derived from high-resolution LiDAR. Different LiDAR derived raster images are being created: 0.5m DTM, vegetation density (J. Ryyppo, 2013), slope, Sky-View factor (Ž. Kokalj et al., 2011), and shaded relief. The area used is in Renmark, South Australia and the produced map is going to be used for the Australian Orienteering Championships 2018.

Sky-View factor of DTM for same area as shown above.

4 square kilometers of airborne LiDAR data produced by Airborne Research Australia at 18 pulses per square meter using the full waveform scanning LiDAR Q680i-S laser scanner from RIEGL
+ 60 hours of check and validation work in the field

LAStools processing:
1) tile into 500 by 500 meter tiles with 20 meter buffer [lastile]
2) classify isolated points as noise [lasnoise]
3) classify point clouds into ground and non-ground [lasground]
4) create a Digital Terrain Model (DTM) [las2dem]
5) normalize height of points above the ground [lasheight]
6) compute vegetation density metrics [lascanopy]
7) create hillshades of the raster DTMs [blast2dem or GDAL]

Ditz, Robert, Franz Glaner, and Georg Gartner. (2014). “Laser Scanning and Orienteering Maps.” Scientific Journal of Orienteering 19.1.
JRyyppo, Jarkko. (2013). “Karttapullautin vegetation mapping guide”.
Kokalj, Žiga, Zaksek, Klemen, and Oštir, Krištof. (2011). Application of sky-view factor for the visualization of historic landscape features in lidar-derived relief models. Antiquity. 85. 263-273.