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: Manuel Jurado

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

Background:
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

Goal:
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.

Data:
+
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]

References:
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.

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).

LASmoons: Marzena Wicht

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

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

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

Frontal area index calculation based on 3D building database

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

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

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

LASmoons: Gudrun Norstedt

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

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

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

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

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

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

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