LASmoons: Alen Berta

Alen Berta (recipient of three LASmoons)
Department of Terrestrial Ecosystems and Landscape, Faculty of Forestry
University of Zagreb and Oikon Ltd Institute for Applied Ecology, CROATIA

Background:
After becoming the EU member state, Croatia is obliged to fulfill the obligation risen from the Kyoto protocol: National Inventory Report (NIR) of the Green House Gasses according to UNFCCC. One of the most important things during the creation of the NIR is to know how many forested areas there are and their wood stock and increment. This is needed to calculate the size of the existing carbon pool and its potential for sequestration. Since in Croatia, according to legislative, it is not mandatory to calculate the wood stock and yield of the degraded forest areas (shrubbery and thickets) during the creation of the usual forest management plans, this data is missing. So far, only a rough approximation of the wood stock and increment is used during the creation of NIR. However, these areas are expanding every year due to depopulation of the rural areas and the cessation of traditional farming.

very diverse stand structure of degraded forest areas (shrubbery and thickets)

Goal:
This study will focus on two things: (1) Developing regression models for biomass volume estimation in continental shrubberies and thickets based on airborne LiDAR data. To correlate LiDAR data with biomass volume, over 70 field plots with a radius of 12 meters have been established in more than 550 ha of the hilly and lowland shrubberies in Central Croatia and all trees and shrubberies above 1 cm Diameter at Breast Height (DBH) were recorded with information about tree species, DBH and height. Precise locations of the field plots are measured with survey GNNS and biomass is calculated with parameters from literature. For regression modeling, various statistics from the point clouds matching the field plots will be used (i.e. height percentiles, standard deviation, skewness, kurtosis, …). 2) Testing the developed models for different laser pulse densities to find out if there is a significant deviation from results if the LiDAR point cloud is thinner. This will be helpful for planning of the later scanning for the change detection (increment or degradation).

Data:
+
641 square km of discrete returns LiDAR data around the City of Zagreb, the capitol of Croatia (but since it is highly populated area, only the outskirts of the area will be used)
+ raw geo-referenced LAS files with up to 3 returns and an average last return point density of 1 pts/m².

LAStools processing:
1)
extract area of interest [lasclip or las2las]
2) create differently dense versions (for goal no. 2) [lasthin]
3) remove isolated noise points [lasnoise]
4) classify point clouds into ground and non-ground [lasground]
5) create a Digital Terrain Model (DTM) [las2dem]
6) compute height of points above the ground [lasheight]
7) classify point clouds into vegetation and other [lasclassify]
8) normalize height of the vegetation points [lasheight]
9) extract the areas of the field plots [lasclip]
10) compute various metrics for each plot [lascanopy]
11) convert LAZ to TXT for regression modeling in R [las2txt]

LASmoons: Jane Meiforth

Jane Meiforth (recipient of three LASmoons)
Environmental Remote Sensing and Geoinformatics
University of Trier, GERMANY

Background:
The New Zealand Kauri trees (or Agathis australis) are under threat by the so called Kauri dieback disease. This disease is caused by a fungi like spore, which blocks the transport for nutrition and water in the trunk and finally kills the trees. Symptoms of the disease in the canopy like dropping of leaves and bare branches offer an opportunity for analysing the state of the disease by remote sensing. The study site covers three areas in the Waitakere Ranges, west of Auckland with Kauri trees in different growth and health classes.

Goal:

The main objective of this study is to identify Kauri trees and canopy symptoms of the disease by remote sensing, in order to support the monitoring of the disease. In the first step LAStools will be used to extract the tree crowns and describe their characteristics based on height metrics, shapes and intensity values from airborne LiDAR data. In the second step, the spectral characteristics of the tree crowns will be analyzed based on very high resolution satellite data (WV02 and WV03). Finally the best describing spatial and spectral parameters will be combined in an object based classification, in order to identify the Kauri trees and different states of the disease..

Data:
+
 high resolution airborne LiDAR data (15-35p/sqm, ground classified) taken in January 2016
+ 15cm RGB aerial images taken on the same flight as the LIDAR data
+ ground truth field data from 2100 canopy trees in the study areas, recorded January – March 2016
+ helicopter images taken in January – April 2016 from selected Kauri trees by Auckland Council
+ vector layers with infrastructure data like roads and hiking trackslasmoons_CHM_Jane_Meiforth_0

 

LAStools processing:
1)
create square tiles with edge length of 1000 m and a 25 m buffer to avoid edge artifacts [lastile]
2) generate DTMs and DSMs [las2dem]
3).produce height normalized tiles [lasheight]
4) generate a pit-free Canopy Height Model (CHM) using the method of Khosravipour et al. (2014) with the workflow described here [lasthin, las2dem, lasgrid]
5) extract crown polygons based on the pit-free CHM [inverse watershed method in GIS, las2iso]
6) normalize the points of each crown with constant ground elevation to avoid slope effects [lasclip, las2las with external source for the ground elevation]
7) derive height metrics for each crown on base of the normalized crown points [lascanopy]
8) derive intensity statistics for the crown points [lascanopy with ‘-int_avg’, ‘-int_std’ etc. on first returns]
9) derive metrics correlated with the dropping of leaves like canopy density, canopy cover and gap fraction for the crown points [lascanopy with ‘–cov’, ‘–dns’, ‘–gap’, ‘–fraction’]

Reference:
Hu B, Li J, Jing L, Judah A. Improving the efficiency and accuracy of individual tree crown delineation from high-density LiDAR data. International Journal of Applied Earth Observation and Geoinformation. 2014; 26: 145-55.
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.
Li J, Hu B, Noland TL. Classification of tree species based on structural features derived from high density LiDAR data. Agricultural and Forest Meteorology. 2013; 171-172: 104-14.
MPI New Zealand http://www.kauridieback.co.nz – website with information on the kauri dieback disease
Vauhkonen, J., Ene, L., Gupta, S., Heinzel, J., Holmgren, J., Pitkänen, J., Solberg, S., Wang, Y., Weinacker, H., Hauglin, K. M., Lien, V., Packalén, P., Gobakken, T., Koch, B., Næsset, E., Tokola, T. and Maltamo, M. (2012) Comparative testing of single-tree detection algorithms under different types of forest. Forestry, 85, 27-40.

LASmoons: Jakob Iglhaut

Jakob Iglhaut (recipient of three LASmoons)
Program for Geospatial Information Management
Carinthia University of Applied Sciences, Villach, AUSTRIA

Background:
As part of the EU LIFE programme two river stretches in Carinthia, Austria have recently been subject to restoration measures. The LIFE-project aims at protecting valuable riverine flora and fauna while improving flood protection. By remodelling the river beds, the construction of groynes and still water bodies the river environment was directed to more natural morphology and state. The joint R&D project “Remotely Piloted Aircraft Multi Sensor System (RPAMSS)” aims at capturing multi-dimensional environmental data in order to monitor the development of these rivers stretches in a holistic way. Flights with an RTK capable fixed wing UAV are carried out at a particular section of the rivers Gail and Drau respectively. The project site at the Upper-Drau is located in the area of Obergottesfeld, Austria (560m ASL), with an area currently remotely monitored by the RPAmSS of approximately 3.5km². The second study area is located close to Feistritz at the river Gail (550m ASL) with an area of approx. 0.9km². Apart from being addressed by the LIFE project both study areas are also defined as NATURA 2000 nature protection sites. In both areas frequent UAV flights are carried out collecting high-resolution multi-spectral imagery. Structure from Motion photogrammetry enables the creation of high-density multi-spectral point clouds.

lasmoons_jakob_iglhaut_0

Goal:
The aim of the project is to assess the morphology and related temporal changes of the described riverine environment based on SfM point clouds. A full processing chain will be developed to take full advantage of the high-density data. Particular interest lies in the extraction of ground points underneath vegetation in leaf-on/leaf-off. Ground points will be gridded to generate DTMs. The qualitative performance of the data will be held against an ALS acquired DTM. Furthermore forest metrics will be extracted for the riparian zone in order to quantify their current state and changes.

Data:
+
High-density multi-spectral (R,G,B,NIR) SfM derived point clouds (UAS imagery)
+ Variable point densities, GSD ~3cm.

LAStools processing:
1) 
fix SfM owing incoherence [lassort]
2) create 100m tiles (10m buffer) for parallel processing [lastile]
3) noise removal introduced by the SfM algorithm [lasnoise]
4).extract ground points [lasground_new]
5) generate normalized above heights [lasheight]
6) classify based on height-above-ground (low veg, high veg) [lasheight]
7) create DSM and DTM [blast2dem]
8) 
generate a Canopy Height Model (CHM) using the pit-free method of Khosravipour et al. (2014) with the workflow described here [lasthin, las2dem, lasgrid]
9) 
sub-sample the point clouds for other (spectral) analyses [lassplit, lasthin, lasmerge]

Reference:
Westoby, M. J., et al. “Structure-from-Motion photogrammetry: A low-cost, effective tool for geoscience applications.” Geomorphology 179 (2012): 300-314.
Fonstad, Mark A., et al. “Topographic structure from motion: a new development in photogrammetric measurement.” Earth Surface Processes and Landforms 38.4 (2013): 421-430.
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.
Javernick, L., J. Brasington, and B. Caruso. “Modeling the topography of shallow braided rivers using Structure-from-Motion photogrammetry.” Geomorphology 213 (2014): 166-182.

LASmoons: Asanga Ramanayake

Asanga Ramanayake (recipient of three LASmoons)
BGSU Remote Sensing Lab, School of Earth, Environment and Society
Bowling Green State University, Ohio, USA

Background:
Lake Erie is the Southern most of the Great Lakes and it is shared by 4 states and 2 countries. It is the shallowest, warmest, and most biologically productive of all the Great Lakes. At wetland habitats along the Western Lake Erie coast, more than 300 species of plants have been identified. To study land use and to classify vegetation cover it is important to consider the vertical distribution of the vegetation. LiDAR is an active data collection system for generating 3D spatial information of objects. High-resolution Digital Terrain Models (DTMs) and Digital Surface Models (DSMs) can be generated from the available LiDAR points that allow accurate estimates of canopy height.

Goal:

The main goal of this project is to derive Digital Terrain Models (DTMs) and Digital Surface Models (DSMs) for the coastal areas of Lake Erie using LIDAR data to estimate the height of the canopy. The derived products will be validated with in-situ measurements from other researchers and compared with ASTER Global Digital Elevation Model data.

coastal area LiDAR data coverage for Lake Erie

coastal area LiDAR data coverage for Lake Erie

Data:
+
The Ohio Geographically Referenced Information Program (OGRIP) has free downloadable LIDAR data in LAS format that was acquired by Ohio Statewide Imagery Program (OSIP) in 2006-2008.
+ In 2011-2012 NOAA’s mission was capturing coastal area LiDAR data. This data is served to the public and available in LAZ format.

LAStools processing:
1)
create square tiles to avoid edge artifacts [lastile]
2) classify point clouds into ground and non-ground [lasground]
3) generate DTMs and DSMs for the coastal areas of Lake Erie [las2dem]
4).produce height normalized tiles [lasheight]
5) generate a Canopy Height Model (CHM) using the pit-free method of Khosravipour et al. (2014) [lasthin, las2dem, lasgrid]

Reference:
Herdendorf, Charles E. The ecology of the coastal marshes of western Lake Erie: a community profile. OHIO STATE UNIV COLUMBUS, 1987.
Deems, Jeffrey S., Thomas H. Painter, and David C. Finnegan. “Lidar Measurement of Snow Depth: A Review.” Journal of Glaciology 59.215 (2013): 467–479. IngentaConnect. Web.
Jensen, John R. Remote Sensing of the Environment: An Earth Resource Perspective. 2nd ed. Upper Saddle River, NJ: Pearson Prentice Hall, 2007. Print. Prentice Hall Series in Geographic Information Science.
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.

Generating Spike-Free Digital Surface Models from LiDAR

A Digital Surface Model (DSM) represents the elevation of the landscape including all vegetation and man-made objects. An easy way to generate a DSM raster from LiDAR is to use the highest elevation value from all points falling into each grid cell. However, this “binning” approach only works when then the resolution of the LiDAR is higher than the resolution of the raster. Only then sufficiently many LiDAR points fall into each raster cell to prevent “empty pixels” and “data pits” from forming. For example, given LiDAR with an average pulse spacing of 0.5 meters one can easily generate a 2.5 meter DSM raster with simple “binning”. But to generate a 0.5 meter DSM raster we need to use an “interpolation” method.

Returns of four fightlines on two trees.

Laser pulses and discrete returns of four fightlines.

For the past twenty or so years, GIS textbooks and LiDAR tutorials have recommened to use only the first returns to construct the interpolating surface for DSM generation. The intuition is that the first return is the highest return for an airborne survey where the laser beams come (more or less) from above. Hence, an interpolating surface of all first returns is constructed – usually based on a 2D Delaunay triangulation – and the resulting Triangular Irregular Network (TIN) is rasterized onto a grid at a user-specified resolution to create the DSM raster. The same way a Canopy Height Model (CHM) is generated except that elevations are height-normalized either before or after the rasterization step. However, using a first-return interpolation for DSM/CHM generation has two critical drawbacks:

(1) Using only first returns means not all LiDAR information is used and some detail is missing. This is particularly the case for off-nadir scan angles in traditional airborne surveys. It becomes more pronounced with new scanning systems such as UAV or hand-held LiDAR where laser beams no longer come “from above”. Furthermore, in the event of clouds or high noise the first returns are often removed and the remaining returns are not renumbered. Hence, any laser shot whose first return reflects from a cloud or a bird does not contribute its highest landscape hit to the DSM or CHM.

(2) Using all first returns practically guarantees the formation of needle-shaped triangles in vegetated areas and along building roofs that appear as spikes in the TIN. This is because at off-nadir scan angles first returns are often generated far below other first returns as shown in the illustration above. The resulting spikes turn into “data pits” in the corresponding raster that not only look ugly but impact the utility of the DSM or CHM in subsequent analysis, for example, in forestry applications when attempting to extract individual trees.

In the following we present results and command-line examples for the new “spike-free” algorithm by (Khosravipour et. al, 2015, 2016) that is implemented (as a slow prototype) in the current LAStools release. This completely novel method for DSM generation triangulates all relevant LiDAR returns using Contrained Delaunay algorithm. This constructs a “spike-free” TIN that is in turn rasterized into “pit-free” DSM or CHM. This work is both a generalization and an improvement of our previous result of pit-free CHM generation.

We now compare our “spike-free” DSM to a “first-return” DSM on the two small urban data sets “france.laz” and “zurich.laz” distributed with LAStools. Using lasinfo with options ‘-last_only’ and ‘-cd’ we determine that the average pulse spacing is around 0.33 meter for “france.laz” and 0.15 meter for “zurich.laz”. We decide to create a hillshaded 0.25 meter DSM for “france.laz” and a 0.15 meter DSM for “zurich.laz” with the command-lines shown below.

las2dem -i ..\data\france.laz ^
        -keep_first ^
        -step 0.25 ^
        -hillshade ^
        -o france_fr.png
las2dem -i ..\data\france.laz ^
        -spike_free 0.9 ^
        -step 0.25 ^
        -hillshade ^
        -o france_sf.png
las2dem -i ..\data\zurich.laz ^
           -keep_first ^
           -step 0.15 ^
           -hillshade ^
           -o zurich_fr.png
las2dem -i ..\data\zurich.laz ^
        -spike_free 0.5 ^
        -step 0.15 ^
        -hillshade ^
        -o zurich_sf.png

The differences between a first-return DSM and a spike-free DSM are most drastic along building roofs and in vegetated areas. To inspect in more detail the differences between a first-return and our spike-free TIN we use lasview that allows to iteratively visualize the construction process of a spike-free TIN.

lasview -i ..\data\france.laz -spike_free 0.9

Pressing <f> and <t> constructs the first-return TIN. Pressing <SHIFT> + <t> destroys the first-return TIN. Pressing <SHIFT> + <y> constructs the spike-free TIN. Pressing <y> once destroys the spike-free TIN. Pressing <y> many times iteratively constructs the spike-free TIN.

One crucial piece of information is still missing. What value should you use as the freeze constraint of the spike-free algorithm that we set to 0.9 for “france.laz” and to 0.5 for “zurich.laz” as the argument to the command-line option ‘-spike_free’. The optimal value is related to the expected edge-length and we found the 99th percentile of a histogram of edge lengths of the last-return TIN to be useful. Or simpler … try a value that is about three times the average pulse spacing.

References:
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: Andreas Konring and Susanne Bjerg Petersen

Andreas Konring and Susanne Bjerg Petersen (recipients of three LASmoons)
Department of Environmental Engineering
Technical University of Denmark, Lyngby, DENMARK

Background:
Copenhagen has in the recent years experienced severe floodings due to cloudbursts which has increased the focus of climate adaption and the implementation of green infrastructure. The use of sustainable urban drainage system (SUDS) solutions to divert stormwater from the existing drainage system will be a central measure to increase the climate resilience while greenifying the city and Copenhagen municipality is investing 700 million euros in SUDS projects alone. Additionally, the city has decided to plant 100.000 new trees in the next 10 years as another measure to enhance natural amenities but also because of air cleansing and cooling effects. However, it has not been investigated what effect the current canopy cover has on the rainwater retention due to increased evaporation and soil infiltration and if planting more trees could help improve the pluvial flooding issues.

Example of a pit-free CHM in an urban environment.

Example of a pit-free CHM in an urban environment.

Goal:
This study aims to estimate the current number of trees and extract tree metrics such as volume, canopy cover and densities with the use of the national LIDAR dataset and NIR ortophotos from summer and spring. These canopy metrics will be used to inform a simple tree model which will be implemented in a 2-D overland flow model to assess the effect of trees on flood mitigation. The created CHM could also be used in further analysis of the urban heat island effect.

Data:
+
100 square kilometers of the Danish national LiDAR dataset collected in November 2014 covering the municipality of Copenhagen.
+  density of 4 – 5 last-returns per square meter
+  classified into surface (1), ground (2), vegetation (3,4,5), buildings (6), noise (7) and water (9).

LAStools processing:
1)
create square tiles with buffer to avoid edge artifacts [lastile]
2) generate DTMs and DSMs with only buildings and terrain [las2dem]
3).normalize height, remove outliers and keep classes 2, 5 and 6 [lasheight]
4) create rasters with forest metrics [lascanopy]
5) calculate the pit-free Canopy Height Model (CHM) proposed by Khosravipour et al. (2014) [lasthin, las2dem, lasgrid]

Reference:
Copenhagen Municipality, 2011. Copenhagen Climate Adaption Plan.
Geodatastyrelsen, 2014. Danmarks højdemodel, DHM/Punktsky – Dataversion 2.0 januar 2015. Product specification.
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.

LASmoons: Geoffrey Ower

Geoffrey Ower (recipient of three LASmoons)
School of Biological Sciences
Illinois State University, Normal, USA

Background:
The spatial distribution and abundance of mosquitoes is important because biting mosquitoes can acquire and transmit pathogens such as viruses that infect humans or domestic animals. There have been 1,263 confirmed human infections with mosquito-borne West Nile virus in Illinois since 2003. In 2004 there was an epidemic of mosquito-borne La Crosse encephalitis, which resulted in 9 human cases in Illinois (USGS, HHS & CDC 2015). Understanding the geographical factors that influence mosquito species’ distributions could help to predict at-risk areas, which could in turn help to prioritize public health efforts to control mosquitoes and the diseases that they transmit more effectively.

lasmoons_Marta_Grech_0

The spatial distribution of mosquitoes depends in a large part upon the availability of water sources because mosquito larval and pupal stages are aquatic. Some mosquito species are specialized to use water-filled natural (e.g., treeholes, rockholes) or artificial (e.g., buckets, discarded tires) containers. Mosquitoes also require access to blood meals, access to carbohydrates (e.g., nectar), and resting sites. Spatial factors thought to be important in determining the spatial distribution and abundance of container-dwelling mosquitoes include land cover and land use (Diuk-Wasser et al. 2006), temperature, precipitation (Ruiz et al. 2010), elevation (Sun et al. 2009), human population density (Higa et al. 2010), and socioeconomic status (Dowling et al. 2013).

Goal:

The objective of this project is to determine what spatial factors predict the distribution and abundance of mosquito species in Bloomington-Normal, Illinois. Species distribution maps will be produced for each species of mosquito that colonized oviposition traps (water-filled plastic cups lined with paper on which mosquito eggs are laid) placed on sampling transects during three sampling periods in August and September 2015. Poisson regression models will be used to produce maps predicting the occurrence of each mosquito species for the full 509 square kilometre study area.

Data:
+
509 square kilometres of LiDAR data including Bloomington-Normal, Illinois, U.S.A. and surrounding areas with an average point density of 3.12 points/square metre classified into LAS Specification v1.2 codes: 1 (unclassified), 2 (ground), 7 (noise/low points), 9 (water), 10 (ignored ground: breakline proximity).

LAStools processing:
1)
check the quality of the LiDAR data [lasoverlap, lascontrol, lasinfo, lasgrid]
2)
merge and retile the original data [lastile]
3) classify point clouds into ground and non-ground [lasground]
4) create digital terrain (DTM) and digital surface models (DSM) [las2dem, blast2dem]
5) classify building and vegeration points [lasclassify]
6) extract building footprints [lasboundary]
7)
.produce height normalized tiles [lasheight]
8) generate a Canopy Height Model (CHM) with the workflow described here using the pit-free algorithm of Khosravipour et al. (2014) [lasthin, las2dem, lasgrid]

References:
Diuk-Wasser, M. A., Brown, H. E., Andreadis, T. G., Fish, D. 2006. Modeling the spatial distribution of mosquito vectors for West Nile virus in Connecticut, USA. Vector-Borne and Zoonotic Diseases 6: 283-295.
Dowling, Z., Ladeau, S. L., Armbruster, P., Biehler, D., Leisnham, P. T. 2013. Socioeconomic status affects mosquito (Diptera: Culicidae) larval habitat type availability and infestation level. Journal of Medical Entomology 50: 764-772.
Higa, Y., Yen, N. T., Kawada, H., Son, T. H., Hoa, N. T., Takagi, M. 2010. Geographic distribution of Aedes aegypti and Aedes albopictus collected from used tires in Vietnam. Journal of the American Mosquito Control Association 26: 1-9.
Khosravipour, A., Skidmore, A. K., Isenburg, M., Wang, T. J., Hussin, Y. A. 2014. Generating pit-free Canopy Height Models from Airborne LiDAR. Photogrammetric Engineering and Remote Sensing 80: 863-872.
Ruiz, M. O., Chaves, L. F., Hamer, G. L., Sun, T., Brown, W. M., Walker, E. D., Haramis, L., Goldberg, T. L. Kitron, U. D. 2010. Local impact of temperature and precipitation on West Nile virus infection in Culex species mosquitoes in northeast Illinois, USA. Parasites & vectors 3: 19.
Sun, X., Fu, S., Gong, Z., Ge, J., Meng, W., Feng, Y., Wang, J., Zhai, Y., Wang, H. H., Nasci, R. S., Tang, Q., Liang, G. 2009. Distribution of arboviruses and mosquitoes in Northwestern Yunnan Province, China. Vector-Borne and Zoonotic Diseases 9: 623-630.
USGS, HHS & CDC. 2015. Disease maps. http://diseasemaps.usgs.gov/mapviewer