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: Stéphane Henriod

Stéphane Henriod (recipient of three LASmoons)
National Statistical Committee of the Kyrgyz Republic
Bishkek, Kyrgyzstan

This pilot study is part of the International Climate Initiative project called “Ecosystem based Adaptation to Climate change in the high mountainous regions of Central Asia” that is funded by the Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety (BMU) of Germany and implemented by the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH in Kyrgyzstan, Tajikistan and Kazakhstan.

lasmoons_Stephane_Henriod_1

Background:
The ecosystems in high mountainous regions of Central Asia are characterized by a unique diversity of flora and fauna. In addition, they are the foundation of the livelihoods of the local population. Specific benefits include clean water, pasture, forest products, protection against floods and landslides, maintenance of soil fertility, and ecotourism. However, the consequences of climate change such as melting glaciers, changing river runoff regimes, and weather anomalies including sharp temperature fluctuations and non-typical precipitation result in negative impacts on these ecosystems. Coupled with unwise land use, these events damage fragile mountain ecosystems and reduce their regeneration ability undermining the local population’s livelihoods. Therefore, people living in rural areas and directly depending on natural resources must adapt to adverse impacts of climate change. This can be done through a set of measures, known in the world practice as ecosystem-based adaptation (EbA) approach. It promotes the sustainable use of natural resources to sustain and enhance the livelihood of the population depending on those resources.

lasmoons_Stephane_Henriod_2 Goal:
In two selected pilot regions of Kyrgyzstan and Tajikistan, planned measures will concentrate on climate-informed management of ecosystems in order to maintain their services for the rural population. EbA always starts with identifying the vulnerability of the local population. Besides analyzing the socio-economic situation of the local population, this includes (1) assessing the ecological conditions of the ecosystems in the watershed and the related ecosystem services people benefit from, (2) identifying potential disaster risks, and (3) analyzing glacier dynamics with its response to water runoff. As a baseline to achieve this and to get spatially explicit, remote sensing based techniques and mapping activities need to be utilized.

A first UAV (unmanned aerial vehicle) mission has taken place in the Darjomj watershed of the Bartang valley in July 2016. RGB-NIR images as well as a high-resolution Digital Surface Model have been produced that now need to be segmented and analysed in order to produce comprehensive information. The main processing that will take advantage of LAStools is the generation of a DTM from the DSM that will then be used for identifying risk areas (flood zones, landslides and avalanches, etc.). The results of this approach will ultimately be compared with lower-cost satellite images (RapidEye, Planet, Sentinel).

Data:
+ High-resolution RGB and NIR image (10 cm) from a SenseFly Ebee
+ High-resolution DSM (10 cm) from a SenseFly Ebee

LAStools processing:
1)
classify DSM points obtained via dense-matching photogrammetry into a SenseFly Ebee imagery into ground and non-ground points via processing pipelines as described here and here [lastile, lassort, lasnoise, lasground]
2) create a DTM [las2dem, lasgrid, blast2dem]
3) produce 3D visualisations to facilitate the communication around adaptation to climate change [lasview]
lasmoons_Stephane_Henriod_0

Creating a Better DTM from Photogrammetic Points by Avoiding Shadows

At INTERGEO 2016 in Hamburg, the guys from Aerowest GmbH shared with us a small photogrammetric point cloud from the city of Soest that had been generated with the SURE dense-matching software from nFrames. We want to test whether – using LAStools – we can generate a decent DTM from these points that are essentially a gridded DSM. If this interest you also see this, this, this, and this story.

soest_00_google_earth

Here you can download the four original tiles (tile1, tile2, tile3, tile4) that we are using in these experiments. We first re-tile them into smaller 100 meter by 100 meter tiles with a 20 meter buffer using lastile. We use option ‘-flag_as_withheld’ that flags all the points falling into the buffer using the withheld flag so they can easily be removed on-the-fly later with the ‘-drop_withheld’ filter (see the README for more on this). We also add the missing projection with ‘-epsg 32632’.

lastile -i original\*.laz ^
        -tile_size 100 -buffer 20 ^
        -flag_as_withheld ^
        -epsg 32632 ^
        -odir tiles_raw -o soest.laz

Below is a screenshot from one of the resulting 100 meter by 100 meter tiles (with 20 meter buffer) that we will be focusing on in the following experiments.

The tiles 'soest_437900_5713800.laz'

The tile ‘soest_437900_5713800.laz’ used in all experiments.

Next we use lassort to reorder the points ordered along a coherent space-filling curve as the existing scan-line order has the potential to cause our triangulation engine to slow down. We do this on 4 cores.

lassort -i tiles_raw\*.laz ^
        -odir tiles_sorted -olaz ^
        -cores 4

We then use lasthin to lower the number of points that we consider as ground points (see the README for more info on this tool). We do this because the original 5 cm spacing of the dense matching points is a bit excessive for generating a DTM with a resolution of, for example, 50 cm. Instead we only use the lowest point in each 20 cm by 20 cm cell as a candidate for becoming a ground point, which reduces the number of considered points by a factor of 16. We achieve this by classifying these lowest point to a unique classification code (here: 9) and later tell lasground to ignore all other classifications.

lasthin -i tiles_sorted\*.laz ^
        -step 0.2 -lowest -classify_as 9 ^
        -odir tiles_thinned -olaz ^
        -cores 4
Then we run lasground on 4 cores to classify the ground points with options ‘-step 20’, ‘-bulge 0.5’, ‘-spike 0.5’ and ‘-fine’ that we selected after two trials on a single tile. There are several other options in lasground to play with that may achieve better results on other data sets (see README file for more on this). The ‘-ignore_class 0’ option instructs lasground to ignore all points that are not classified so that only those points that lasthin was classifying as 9 in the previous step are considered.
lasground -i tiles_thinned\*.laz ^
          -step 20 -bulge 0.5 -spike 0.5 -fine ^
          -ignore_class 0 ^
          -odir tiles_ground -olaz ^
          -cores 4
However, when we scrutinize the resulting ground classification notice that there are bumps in the corresponding ground TIN that seem to correspond to areas where the original imagery has dark shadows that in turn seem to significantly affect the geometric accuracy of the points generated by the dense-matching algorithm.
Looking a the bump from below we identify the RGB colors of the points have that form the bump that seem to be of much lower accuracy than the rest. This is an effect that we have noticed in the past for data generated with other dense-matching software and maybe an approach similar to the one we take here could also help with this low noise. It seems that points that are generated from shadowed areas in the input images can have a lot lower accuracy than those from well-lit areas. We use this correlation between RGB color and geometric accuracy to simply exclude all points whose RGB colors indicate that they might be from shadow areas from becoming ground points.
The RGB colors of low-accuracy points are mostly from very dark shadowed areas.

The RGB colors of low-accuracy points are mostly from very dark shadowed areas.

We use las2las with the relatively new ‘-filtered_transform’ option to reclassify all points whose RGB color is close to zero to yet classification code 7 (see README file for more on this). We do this for all points whose red value is between 0 and 30, whose green value is between 0 and 35, and whose blue value is between 0 and 40. Because the RGB values were stored with 16 bits in these files we have to multiply these values with 256 when applying the filter.
las2las -i tiles_thinned\*.laz ^
        -keep_RGB_red 0 7680 ^
        -keep_RGB_green 0 8960 ^
        -keep_RGB_blue 0 10240 ^
        -filtered_transform ^
        -set_classification 7 ^
        -odir tiles_rgb_filtered -olaz ^
        -cores 4
Below you see all points that will no longer be considered because their classification was set to 7 by the command above.
Points whose RGB values indicate they might lie in the shadows.

Points whose RGB values indicate they might lie in the shadows.

We then re-run lasground with the same options ‘-step 20’, ‘-bulge 0.5’, ‘-spike 0.5’ and ‘-fine’ as before but now we ignore all points that are still have classification 0 because they were not classified as 9 by lasthin earlier and we also ignore all points that have been assigned classification 7 by las2las in the previous step.
lasground -i tiles_thinned\*.laz ^
          -step 20 -bulge 0.5 -spike 0.5 -fine ^
          -ignore_class 0 7 ^
          -odir tiles_ground -olaz ^
          -cores 4
The situation has significantly improved for the bumb we saw before as you can see in the images below.

Finally we create a DTM with blast2dem (see README) and a DSM with lasgrid (see README) by merging all points into one file but dropping the buffer points that were marked as withheld by the initial run of lastile (see README).

blast2dem -i tiles_ground\*.laz -merged ^
          -drop_withheld -keep_class 2 ^
          -step 0.5 ^
          -o dtm.bil

lasgrid -i tiles_ground\*.laz -merged ^
        -drop_withheld ^
        -step 0.5 -average ^
        -o dsm.bil
 As our venerable lasview (see README) can directly read BIL rasters as points (just like all the other LAStools), so we can compare the DTM and the DTM by loading them as two flight lines (‘-faf’) and then switch back and forth between the two by pressing ‘0’ and ‘1’ in the viewer.
lasview -i dtm.bil dsm.bil -faf

Above you see the final DTM and the original DSM. So yes, LAStools can definitely create a DTM from point clouds that are the result of dense-matching photogrammetry. We used the correlation between shadowed areas in the source image and geometric errors to remove those points from consideration for ground points that are likely to be too low and result in bumps. For dense-matching algorithms that also output an uncertainty value for each point there is the potential for even better results as our range of eliminated RGB colors may not cover all geometrically uncertain points while at the same time may be too conservative and also remove correct ground points.

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: Patricia Andrade

Patricia Andrade (recipient of three LASmoons)
Earth Sciences Division
CICESE, MEXICO

Background:
The relief in the northwest coast of Baja California is subject to different processes. One process that has a major impact are landslides. The near-shore landslides have been a significant problem because this area coincides with the location of the Tijuana-Ensenada Scenic highway which is one of the main routes between Tijuana and Ensenada. On 28 December 2013 a rotational slip in the stretch of 93 km caused the closure of the Tijuana-Ensenada highway. Several measurements with emerging techniques such as photogrammetry by drones and terrestrial and airborne LiDAR surveys were taken since the landslide. From airborne LiDAR point clouds of different dates DTM are created and used to estimate differences (James, 2012). From terrestrial LiDAR point clouds the characteristics of planes and lines (i.e. striations) on the footwall are determined. An analysis of such geomorphological processes can facilitate a rapid response and help to reopen the highways faster.

Lasmoons_Patricia_Andrade_0

TLS point cloud of the landslide in the stretch km93 +50 (January 2014).

Goal:
The main goal of this project is to estimate the volume change on the landslide’s day and later years from digital terrain models (DTMs) of pre-event data (2006) and post-event (2013, 2014 and 2016). A second goal is to create a model of surface strain from TLS data and a point cloud (2013).

Data:
+
DTM of 2006 (pre-event) from the National Institute of Statistics and Geography (INEGI).
+ relief data of the day of landslide (2013) obtained by photogrammetry from 144 photos taken with a DJi S800 drone.
+ DTM from January 2014 aquired by satellite photogrammetry of images from GeoEye 1.
+ 11 TLS point clouds scanned and co-registered in February 2014  with a Faro Focus 3D x330.
+ NCALM aerial LiDAR captured In July 2014 of th landslide zone.
+ highway rehabilitation data taken in March 2016 from RGB / NIR photos of eBee drone flights.

LAStools processing:
1)
create square tiles with buffers [lastile]
2) classify isolated points as noise [lasnoise]
3) classify points clouds into ground and non-ground [lasground]
4).generate DTMs from ground-classified points [las2dem]
5) change the resolution of DEMs [lasgrid]
6) create hillshades of the DTMs [blast2dem]

References:
James, L. A., Hodgson, M. E., Ghoshal, S., Latiolais, M. M., 2012. Geomorphic change detection using historic maps and DEM differencing: The temporal dimension of geospatial analysis. Geomorphology 137, 181-198.

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: 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

LASmoons: Alejandro Hinojosa

Alejandro Hinojosa (recipient of three LASmoons)
Earth Sciences Division
CICESE, MEXICO

Background:
The Baja California peninsula in Mexico is a land feature drifting away from the continent due to tectonic plate movement leaving in its path scars of well-defined and studied faults system. An aerial LiDAR survey of the Agua Blanca fault corridor was collected by NCALM to primarily delineate its trace and locate offset features along its path to eventually estimate fault slip rates. Faults may act as barriers or conduits of water that may enable the development of vegetation patches. It is known the presence of water springs and native long-lived high vegetation patches along the Agua Blanca fault. As a secondary use of the aerial LiDAR survey, we intend to demonstrate that the spatial distribution of native long-lived high trees (like oaks) in the region is influenced by the Agua Blanca fault, indirectly by the persistent water resource from its springs.

lasmoons_Alejandro_Hinojosa_1
Goal:

The aim of this research is to assess through remote sensing the relation of the spatial distribution of native vegetation patches and the Agua Blanca Fault in Ensenada, Baja California, Mexico. We plan to use spatial analysis tools on passive (optical) and active sensors data to achieve our goal. A Canopy Height Model (CHM) will be calculated from the LiDAR data using the “pit-free” algorithm of (Khosravipour et.al., 2014) that can be implemented with LAStools. We will then investigae spatial correlation of the fault traces delineated from a Digital Terrain Model (DTM) and the vegetation patches obtained from the CHM. Hydrology models will be applied to the DTM in order to differentiate vegetation patches occurring in accumulation zones (like canyons) from those occurring along fault traces.

Data:
+ 75 square km of aerial LiDAR along Agua Blanca Fault corridor collected by NCALM on July 2014.
+ average point density: 5 pts/m2

LAStools processing:
1)
quality control of LiDAR [lasoverlap, lascontrol, lasinfo, lasgrid]
2) create a tiling with buffers [lastile]
3) classify points and create a DTM and DSM [lasgroundlas2dem, blast2dem]
4).normalized the LiDAR tiles [lasheight]
5) generate a Canopy Height Model (CHM) using the pit-free method of Khosravipour et al. (2014) with the workflow described here [lasthin, las2dem, lasgrid]

Reference:
Hooper, E. C. D. (1991). Fluid migration along growth faults in compacting sediments. Journal of Petroleum Geology, 14(2), 161-180.
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.
Carter, R. E., y Klinka, K. (1990). Relationships between growing-season soil water-deficit, mineralizable soil nitrogen and site index of coastal Douglas fir. Forest Ecology and Management, 30(1), 301-311.

LASmoons: Raja Ram Aryal

Raja Ram Aryal (recipient of three LASmoons)
Photogrammetry and Geoinformatics
University of Applied Sciences Stuttgart, GERMANY

Background:
Obtaining LiDAR-derived products like Digital Terrain Models (DTMs), Digital Surface Models (DSMs) and Canopy Height Models (CHMs) is a challenging task in steep forest areas. The Bavarian Forest National Park is an example of a steep terrain in central Europe.The national park mainly consists of alluvial spruce forest (700-900m altitude range), mixed mountain forest dominated by spruce, beech and fir (700-1150m) and high spruce forests (1150-1200m).
Leaf-on and leaf-off  LiDAR data acquisition affects the quality of the DTM needed for deriving a CHM. Different algorithms have been developed for separating ground points from non-ground points in steep forest terrains. The accuracy of such algorithms and their effect on derived forest attributes needs to be assessed. Furthermore, for various system-related or post processing reasons there are often “data pits” in the CHM. The “pit-free algorithm” developed by Khosravipour et al.(2014) that can be implemented with LAStools is currently the state-of-art for producing high-quality CHMs for better tree top detection. Further work is needed to investigate which other forest structure attributes can be derived with higher accuracy from a pit-free CHM than from a standard CHM.

Goal:

This study will focus on (1) evaluating the performance of a different ground classification algorithms across habitat types and topographical factors to assess their applicability for forest management in steep areas, and (2) comparing the accuracy of various forest parameter retrieved from pit-free versus standard CHMs incorporating the most accurate DTM derived from (1). To accomplish goal (1), DTMs will be produced by means of a set of commonly-used methods (REIN, MGF and TIN algorithms), which are then compared against precisely-recorded reference transect ground data, as well as across habitat types and topographical attributes. To accomplish goal (2) pit-free and standard CHMs will be derived and compared for various spatial plot-based models of forest structural attributes. The models will be cross-validated against the available forest inventory data.

conventional DSM from first-return Delaunay TIN

standard 0.5m CHM from first-return Delaunay TIN

Data:
+
Two acquisitions of small footprint discrete return LiDAR data and Full wave-form are conducted in the study area. The full wave form LiDAR data has been captured in 2012 at the leaf-on condition. A two pulse discrete returns LiDAR data was captured in 2009 by the “Bayrisches Landesvermessungsamt” at the  leaf-off with a lower point density about 4-5 points per m².
+ The ground data are transect- and systematically recorded plot designs. The transect data (ca. 300 sub plots) is constrained to ecological gradients in some parts of the park, whereas the systematic grid data (ca. 120 plots) is distributed throughout the entire national park.

pit-free DSM at same 0.5 m resolution with '-kill 2'

pit-free 0.5 m CHM with ‘-kill 2’

LAStools processing:
1)
create square tiles with edge length of 1000 m and a 25 m buffer to avoid edge artifacts [lastile]
2) classify point clouds into ground and non-ground [lasground]
3) generate DTMs and DSMs [las2dem]
4).produce height normalized tiles [lasheight]
5) compute plot metrics for forest structure from height normalized tiles [lascanopy]
6) generate a Canopy Height Model (CHM) using the pit-free method of Khosravipour et al. (2014) with the workflow described here [lasthin, las2dem, lasgrid]

Reference:
Heurich, M., Fischer, F., Knörzer, O., Krzystek, P. 2008. Assessment of Digital Terrain Models (DTM) from data gathered with airborne laser scanning in temperate European beech (Fagus sylvatica) and Norway spruce (Picea abies) forests. Photogrammetrie, Fernerkundung, Geoinformation 6/2008: 473-488.
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.
Kobler, A., Pfeifer, N., PeterOgrinc, Todorovski, L., Oštir, K. and Džeroski, S. 2007: Repetitive interpolation: A robust algorithm for DTM generation from aerial laser scanner data in forested terrain. Remote Sensing of Environment 108, 9-23.
Latifi, H., Heurich, M., Hartig, F., Müller, J., Krzystek, P., Jehl, H., Dech, S., 2015, Estimating over- and understorey canopy density of temperate mixed stands by airborne LiDAR data. Forestry (Article in Press). DOI. 10.1093/forestry/cpv032
Latifi, H., Fassnacht, F. E., Müller, J., Tharani, A., Dech, S., and Heurich, M. (2015) Forest inventories by LiDAR data: A comparison of single tree segmentation and metric-based methods for inventories of a heterogeneous temperate forest, International Journal of Applied Earth Observation and Geoinformation 42: 162-174.
Meng, X.; Wang, L.; Silván-Cárdenas, J.L.; Currit, N. A multi-directional ground filtering algorithm for airborne LiDAR. ISPRS J. Photogramm. Remote Sens. 2009, 64, 117-124.