LASmoons: Sebastian Flachmeier

Sebastian Flachmeier (recipient of three LASmoons)
UniGIS Master of Science, University of Salzburg, AUSTRIA
Bavarian Forest National Park, administration, Grafenau, GERMANY

Background:
The Bavarian Forest National Park is located in South-Eastern Germany, along the border with the Czech Republic. It has a total area of 240 km² and its elevation ranges from 600 to 1453 m. In 2002 a project called “High-Tech-Offensive Bayern” was started and a few first/last return LiDAR transects were flown to compute some forest metrics. The results showed that LiDAR has an advantage over other methods, because the laser was able to get readings from below the canopy. New full waveform scanner were developed that produced many more returns in the lower canopy. The National Park experimented with this technology in several projects and improved their algorithms for single tree detection. In 2012 the whole park was flown with full waveform and strategies for LiDAR based forest inventory for the whole National Park were developed. This is the data that is used in the following workflow description.

The whole Bavarian Forest National Park (black line), 1000 meter tiles (black dotted lines), the coverage of the recovered flight lines (light blue). In the area marked yellow within the red frame there are gaps in some of the flightlines. The corresponding imagery in Google Earth shows that this area contains a water reservoir.

Goal:
Several versions of the LiDAR existed on the server of the administration that didn’t have the attributes we needed to reconstruct the original flight lines. The number of returns per pulse, the flight line IDs, and the GPS time stamps were missing. The goal was a workflow to create a LAStools workflow to convert the LiDAR from the original ASCII text files provided by the flight company into LAS or compressed LAZ files with all fields properly populated.

Data:
+
 ALS data flown in 2012 by Milan Geoservice GmbH 650 m above ground with overlap.
+ full waveform sensor (RIEGL 560 / Q680i S) with up to 7 returns per shot
+ total of 11.080.835.164 returns
+ in 1102 ASCII files with *.asc extension (changed to *.txt to avoid confusion with ASC raster)
+ covered area of 1.25 kilometers
+ last return density of 17.37 returns per square meter

This data is provided by the administration of Bavarian Forest National Park. The workflow was part of a Master’s thesis to get the academic degree UniGIS Master of Science at the University of Salzburg.

LAStools processing:

The LiDAR was provided as 1102 ASCII text files named ‘spur000001.txt’ to ‘spur001102.txt’ that looked like this:

more spur000001.txt
4589319.747 5436773.357 685.837 49 106 1 215248.851500
4589320.051 5436773.751 683.155 46 24 2 215248.851500
4589320.101 5436773.772 686.183 66 87 1 215248.851503
[…]

Positions 1 to 3 store the x, y, and z coordinate in meter [m]. Position 4 stores the “echo width” in 0.1 nanoseconds [ns], position 5 stores the intensity, position 6 stores the return number, position 7 stores the GPS time stamp in seconds [s] of the current GPS week. The “number of returns (of given pulse)” information is not explicitly stored and will need to be reconstructed in order, for example, to identify which returns are last returns. The conversion from ASCII text to LAZ was done with the txt2las command line shown below that incorporates these rationals:

  • Although the ASCII files list the three coordinates with millimeter resolution (three decimal digits), we store only centimeter resolution which is sufficient to capture all the precision in a typical airborne LiDAR survey.
  • After computing histograms of the “return number” and the “echo width” for all points with lasinfo and determining their maximal ranges it was decided to use point type 1 which can store up to 7 returns per shot and store the “echo width” as an additional attribute of type 3 (“unsigned short”) using “extra bytes”.
  • The conversion from GPS time stamp in GPS week time to Adjusted Standard time was done by finding out the exact week during which Milan Geoservice GmbH carried out the survey and looking up the corresponding GPS week 1698 using this online GPS time calculator.
  • Information about the Coordinate Reference System “DHDN / 3-degree Gauss-Kruger zone 4” as reported in the meta data is added in form of EPSG code 31468 to each LAS file.
txt2las -i ascii\spur*.txt ^
        -parse xyz0irt ^
        -set_scale 0.01 0.01 0.01 ^
        -week_to_adjusted 1698 ^
        -add_attribute 3 "echo width" "of returning waveform [ns]" 0.1 0 0.1 ^
        -epsg 31468 ^
        -odir spur_raw -olaz ^
        -cores 4

The 1102 ASCII files are now 1102 LAZ files. Because we switched from GPS week time to Adjusted Standard GPS time stamps we also need to set the “global encoding” flag in the LAS header from 0 to 1 (see ASPRS LAS specification). We can do this in-place (i.e. without creating another set of files) using the following lasinfo command:

lasinfo -i spur_raw\spur*.laz ^
        -nh -nv -nc ^
        -set_global_encoding 1

To reconstruct the missing flight line information we look for gaps in the sequence of GPS time stamps by computing GPS time histograms with lasinfo and bins of 10 seconds in size:

lasinfo -i spur_raw\spur*.laz -merged ^
        -histo gps_time 10 ^
        -o spur_raw_all.txt

The resulting histogram exhibits the expected gaps in the GPS time stamps that happen when the survey plane leaves the target area and turns around to approach the next flight line. The subsequent histogram entries marked in red show gaps of 120 and 90 seconds respectively.

more spur_raw_all.txt
[...]
bin [27165909.595196404,27165919.595196255) has 3878890
bin [27165919.595196255,27165929.595196106) has 4314401
bin [27165929.595196106,27165939.595195957) has 435788
bin [27166049.595194317,27166059.595194168) has 1317998
bin [27166059.595194168,27166069.595194019) has 4432534
bin [27166069.595194019,27166079.59519387) has 4261732
[...]
bin [27166239.595191486,27166249.595191337) has 3289819
bin [27166249.595191337,27166259.595191188) has 3865892
bin [27166259.595191188,27166269.595191039) has 1989794
bin [27166349.595189847,27166359.595189698) has 2539936
bin [27166359.595189698,27166369.595189549) has 3948358
bin [27166369.595189549,27166379.5951894) has 3955071
[...]

Now that we validated their existence, we use these gaps in the GPS time stamps to split the LiDAR back into the original flightlines it was collected in. Using lassplit we produce one file per flightline as follows:

lassplit -i spur_raw\spur*.laz -merged ^
         -recover_flightlines_interval 10 ^
         -odir strips_raw -o strip.laz

In the next step we repair the missing “number of returns (per pulse)” field that was not provided in the ASCII file. This can be done with lasreturn assuming that the point records in each file are sorted by increasing GPS time stamp. This happens to be true already in our case as the original ASCII files where storing the LiDAR returns in acquisition order and we have not changed this order. If the point records are not yet in this order it can be created with lassort as follows. As these strips can have many points per file it may be necessary to run the new 64 bit executables by adding ‘-cpu64’ to the command line in order to avoid running out of memory.

lassort -i strips_raw\strips*.laz ^
        -gpstime -return_number ^
        -odir strips_sorted -olaz ^
        -cores 4 -cpu64

An order sorted by GPS time stamp is necessary as lasreturn expects point records with the same GPS time stamp (i.e. returns generated by the same laser pulse) to be back to back in the input file. To ‘-repair_number_of_returns’ the tool will load all returns with the same GPS time stamp  and update the “number of returns (per pulse)” attribute of each return to the highest “return number” of the loaded set.

lasreturn -i strips_sorted\strips*.laz ^
          -repair_number_of_returns ^
          -odir strips_repaired -olaz ^
          -cores 4

In a final step we use las2las with the ‘-files_are_flightlines’ option (or short ‘-faf’) to set the “file source ID” field in the LAS header and the “point source ID” attribute of every point record in the file to the same unique value per strip. The first file in the folder will have all its field set to 1, the next file will have all its field set to 2, the next file to 3 and so on. Please do not run this on multiple cores for the time being.

las2las -i strips_repaired\strips*.laz ^
        -files_are_flightlines ^
        -odir strips_final -olaz

It’s always useful to run a final validation of the files using lasvalidate to reassure yourself and the people you will be sharing the data with that nothing funky has happened during any of these conversion steps.

lasvalidate -i strips_final\strip*.laz ^
            -o strips_final\report.xml

And it can also be useful to add an overview in SHP or KML format to the delivery that can be created with lasboundary as follows:

lasboundary -i strips_final\strip*.laz ^
            -overview -labels ^
            -o strips_final\overview.kml

The result was 89 LAZ files (each containing one complete flightline) totaling 54 GB compared to 1102 ASCII files (each containing a slice of a flightline) totaling 574 GB.

LASmoons: Maria Kampouri

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

Background:
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

Goal:
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.

Data:
+
 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.

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

Background:
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).

Goal:
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.

Data:
+
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]

References:
[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

Background:
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.

Goal:

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.

Data:
+
 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

Background:
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.

Goal:
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.

Data:
+
 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: Chris J. Chandler

Chris J. Chandler (recipient of three LASmoons)
School of Geography
University of Nottingham, UNITED KINGDOM

Background:
Wetlands provide a range of important ecosystem services: they store carbon, regulate greenhouse gas emissions, provide flood protection as well as water storage and purification. Preserving these services is critical to achieve sustainable environmental management. Currently, mangrove forests are protected in Mexico, however, fresh water wetland forests, which also have high capacity for storing carbon both in the trees and in the soil, are not protected under present legislation. As a result, coastal wetlands in Mexico are threatened by conversion to grazing areas, drainage for urban development and pollution. Given these threats, there is an urgent need to understand the current state and distribution of wetlands to inform policy and protect the ecosystem services provided by these wetlands.
In this project we will combine field data collection, satellite data (i.e. optical remote sensing, radar and LiDAR remote sensing) and modelling to provide an integrated technology for assessing the value of a range of ecosystem services, tested to proof of concept stage based on carbon storage. The outcome of the project will be a tool for mapping the value of a range of ecosystem services. These maps will be made directly available to local stakeholders including policy makers and land users to inform policy regarding forest protection/legislation and aid development of financial incentives for local communities to protect these services.

Wetland classification in the Chiapas region of Mexico

Goal:
At this stage of the project we have characterized wetlands for three priority areas in Mexico (Pantanos de Centla, La Encrucijada and La Mancha). Next stage is the up scaling of the field data at the three study sites using LiDAR data for producing high quality Canopy Height Model (CHM), which has been of great importance for biomass estimation (Ferraz et al., 2016). A high quality CHM will be achieved using LAStools software.

Data:
+
LiDAR provided by the Mexican National Institute of Statistics and Geography (INEGI)
+ average height: 5500 m, mirror angle: +/- 30 degrees, speed: 190 knots
+ collected with Cessna 441, Conquest II system at 1 pts/m².

LAStools processing:
1)
create 1000 meter tiles with 35 meter buffer to avoid edge artifacts [lastile]
2) classify point clouds into ground and non-ground [lasground]
3) normalize height of points above the ground [lasheight]
4) create a Digital Terrain and Surface Model (DTM and DSM) [las2dem]
5) generate a spike-free Canopy Height Model (CHM) as described here and here [las2dem]
6) compute various metrics for each plot and the normalized tiles [lascanopy]

References:
Ferraz, A., Saatchi, S., Mallet, C., Jacquemoud S., Gonçalves G., Silva C.A., Soares P., Tomé, M. and Pereira, L. (2016). Airborne Lidar Estimation of Aboveground Forest Biomass in the Absence of Field Inventory. Remote Sensing, 8(8), 653.

LASmoons: Huaibo Mu

Huaibo Mu (recipient of three LASmoons)
Environmental Mapping, Department of Geography
University College London (UCL), UK

Background:
This study is a part of the EU-funded Metrology for Earth Observation and Climate project (MetEOC-2). It aims to combine terrestrial and airborne LiDAR data to estimate biomass and allometry for woodland trees in the UK. Airborne LiDAR can capture large amounts of data over large areas, while terrestrial LiDAR can provide much more details of high quality in specific areas. The biomass and allometry for individual specific tree species in 1 ha of Wytham Woods located about 5km north west of the University of Oxford, UK are estimated by combining both airborne and terrestrial LiDAR. Then the bias will be evaluated when estimation are applied on different levels: terrestrial LiDAR level, tree level, and area level. The goal are better insights and a controllable error range in the bias of biomass and allometry estimates for woodland trees based on airborne LiDAR.

Goal:
The study aims to find the suitable parameters of allometric equations for different specific species and establish the relationship between the tree height and stem diameter and crown diameter to be able to estimate the above ground biomass using airborne LiDAR. The biomass estimates under different levels are then compared to evaluate the bias and for the total 6ha of Wytham Woods for calibration and validation. Finally the results are to be applied to other woodlands in the UK. The LiDAR processing tasks for which LAStools are used mainly center around the creation of suitable a Canopy Height Model (CHM) from the airborne LiDAR.

Data:
+ Airborne LiDAR data produced by Professor David Coomes (University of Cambridge) with Airborne Research and Survey Facility (ARSF) Project code of RG13_08 in June 2014. The average point density is about 5.886 per m^2.
+ Terrestrial LiDAR data collected by UCL’s team leader by Dr. Mat Disney and Dr. Kim Calders in order to develop very detailed 3D models of the trees.
+ Fieldwork from the project “Initial Results from Establishment of a Long-term Broadleaf Monitoring Plot at Wytham Woods, Oxford, UK” by Butt et al. (2009).

LAStools processing:
1) check LiDAR quality as described in these videos and articles [lasinfo, lasvalidate, lasoverlap, lasgrid, las2dem]
2) classify into ground and non-ground points using tile-based processing  [lastile, lasground]
3) generate a Digital Terrain Model (DTM) [las2dem]
4) compute height of points and delete points higher than maximum tree height obtained from terrestrial LiDAR [lasheight]
5) convert points into disks with 10 cm diameter to conservatively account for laser beam width [lasthin]
6) generate spike-free Digital Surface Model (DSM) based on algorithm by Khosravipour et al. (2016) [las2dem]
7) create Canopy Height Model (CHM) by subtracting DTM from spike-free DSM [lasheight].

References:
Butt, N., Campbell, G., Malhi, Y., Morecroft, M., Fenn, K., & Thomas, M. (2009). Initial results from establishment of a long-term broadleaf monitoring plot at Wytham Woods, Oxford, UK. University Oxford, Oxford, UK, Rep.
Khosravipour, A., Skidmore, A.K., Isenburg, M., Wang, T.J., Hussin, Y.A., (2014). Generating pit-free Canopy Height Models from Airborne LiDAR. PE&RS = Photogrammetric Engineering and Remote Sensing 80, 863-872.
Khosravipour, A., Skidmore, A.K., Isenburg, M. and Wang, T.J. (2015) Development of an algorithm to generate pit-free Digital Surface Models from LiDAR, Proceedings of SilviLaser 2015, pp. 247-249, September 2015.
Khosravipour, A., Skidmore, A.K., Isenburg, M (2016) Generating spike-free Digital Surface Models using raw LiDAR point clouds: a new approach for forestry applications, (journal manuscript under review).