Category Archives: CHM

LASmoons: Martin Romain

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Martin Romain (recipient of three LASmoons)
Marshall Islands Conservation Society
Majuro, Republic of the MARSHALL ISLANDS

Background:
As a low-lying coastal nation, the Republic of the Marshall Islands (RMI) is at the forefront of exposure to climate change impacts. RMI has a strong dependence on natural resources and biodiversity not only for food and income but also for culture and livelihood. However, these resources are threatened by rising sea levels and associated coastal hazards (king tides, storm surges, wave run-up, saltwater intrusion, erosion). This project aims at addressing the lack of technical capacity and available data to implement effective risk reduction and adaptation measures, with a particular focus on inundation mapping and local evacuation planning in population centers.

DCIM100MEDIADJI_0507.JPG

Typical low-lying coastal area of the Republic of the Marshall

Goal:
This project intends to use LAStools to generate a DEM of the inhabited sections of 3 remote atolls (Aur, Ebon, Likiep) and 1 island (Mejit). The resulting DEM will be used to produce an inundation exposure model (and map) under variable sea level rise projections for each site. The ultimate goal is to integrate the results into each site’s disaster risk reduction strategy (long-term outcome) and present it through community consultations in schools, community centers, and council houses.

Data:
+ Aerial imagery of 11.5 square kilometers of land (6.3% of total national landmass) using DJI Matrice 200 V2 & DJI Zenmuse X5S with a minimum overlap of 75/75 and maximum altitude of 120m.

LAStools processing:
1) tile large point cloud into tiles with buffer [lastile]
2) remove noise points [lasthin, lasnoise]
3) classify points into ground and non-ground [lasground]
4) create Digital Terrain Models and Digital Surface Models [lasthin, las2dem]

Potential LAStools pipelines:
1) Removing Excessive Low Noise from Dense-Matching Point Clouds
2) Digital Pothole Removal: Clean Road Surface from Noisy Pix4D Point Cloud
3) Creating DTMs from dense-matched points of UAV imagery from SenseFly’s eBee

LASmoons: Volga Lipwoni

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Volga Lipwoni (recipient of three LASmoons)
Department of Geography, School of Earth and Environment
University of Canterbury, NEW ZEALAND

Background:
Structure from motion (SfM) photogrammetry, has emerged as an effective tool to accurately extract three-dimensional (3D) structures from a series of overlapping two-dimensional (2D) Unmanned aerial vehicles (UAVs) images. The bid to switch from the current labour-intensive, and time consuming forestry inventory practices has seen a lot of interest geared towards understanding the use of SfM photogrammetry to derive forest metrics (Iglhaut et al., 2019). There are a range of commercial, free and open source SfM photogrammetric software packages that can be used to process UAV images into 3D point clouds. Selection of the most appropriate package has become an important issue for most projects (Turner, Lucieer, & Wallace, 2013). A comparison of software performance in terms of accuracy, processing times and related costs would help foresters in deciding the best tool for the job.

lasmoons_Volga_Lipwoni

Typical point cloud derived with SfM software from UAV imagery.

Goal:
The study will generate 3D point clouds of images of a young forest trial and LAStools will be used to derive canopy height models (CHM) for computing tree heights. Tree heights from LiDAR data will serve as a baseline for accuracy assessment of heights derived from the point clouds.

Data:
+ 422 UAV images processed into 3D point clouds using ten (10) different commercial and open source SfM software packages

LAStools processing:
1) tile large point cloud into tiles with buffer [lastile]
2) remove noise points [lasthin, lasnoise]
3) classify points into ground and non-ground [lasground]
4) create Digital Terrain Modelsand Digital Surface Models [lasthin, las2dem]
5) produce Canopy Height Models for computing tree heights [lasheight, las2dem]

References:
Iglhaut, J., Cabo, C., Puliti, S., Piermattei, L., O’Connor, J., & Rosette, J. (2019). Structure from motion photogrammetry in forestry: A review. Current Forestry Reports, 5(3), 155-168. doi:https://doi.org/10.1007/s40725-019-00094-3
Turner, D., Lucieer, A., & Wallace, L. (2013). Direct georeferencing of ultrahigh-resolution UAV imagery. EEE Transactions on Geoscience and Remote Sensing, 52(5), 2738-2745. doi:10.1109/TGRS.2013.2265295

LASmoons: David Bandrowski

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David Bandrowski (recipient of three LASmoons)
Yurok Tribe
Native American Indian Tribe in Northern California, USA

Background:
Wild spring-run Chinook salmon populations on the South Fork Trinity River in Northern California are near the brink of extinction. The South Fork Trinity River is the most remote and the largest un-dammed river in the State of California, federally designated as a wild and scenic river, and is a keystone watershed within the Klamath River basin supporting one of the last remaining populations of wild spring-run Chinook salmon. Ecosystem restoration is urgently needed to improve watershed health in the face of climate change, land use, and water diversions. This drastic decline of the wild salmon species motivated the Yurok Tribe and its partners to take action and implement this project as a last opportunity to save this species before extinction. Spring-run Chinook are extremely important for the Yurok people culturally, spiritually, and for a subsistence food source.

sample of the available photogrammetry data

Goal:
Due to budgetary constraints, airborne LiDAR is not available; therefore the Yurok Tribe has been using aerial drones and Structure for Motion (SfM) photogrammetry to develop DTM models that can be used in determining available salmon habitat and to develop prioritized locations for restoration. The watershed has extremely heavy vegetation, and obtaining bare-earth surfaces for hydraulic modeling is difficult without the proper tools. The goal is to use LAStools to further restoration science and create efficient workflows for DTM development.

Data:
+ length of river mapped: 8 Kilometers
+ number of points: 150,856,819
+ horizontal datum: North American Datum 83 – California State Plane – Zone 1 (usft)
+ vertical datum: North American Vertical Datum 88

LAStools processing:
1) data quality checking [lasinfo, lasview, lasgrid]
2) classify ground and non-ground points [lasground and lasground_new]
3) remove low and high outliers [lasheight, lasnoise]
4) create DTM tiles at appropriate resolution [las2dem]
5) create a normalized point cloud [lasheight]

LASmoons: Maria Kampouri

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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 [lasinfo, lasoverlap, lasgrid, lasreturn]
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: Sebastian Kasanmascheff

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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

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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

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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

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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, lasreturn, las2dem]
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: Muriel Lavy

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Muriel Lavy (recipient of three LASmoons)
RED (Risk Evaluation Dashboard) project
ISE-Net s.r.l, Aosta, ITALY.

Background:
The Aosta Valley Region is a mountainous area in the heart of the Alps. This region is regularly affected by hazard natural phenomena connected with the terrain geomorphometry and the climate change: snow avalanche, rockfalls and landslide.
In July 2016 a research program, funded by the European Program for the Regional Development, aims to create a cloud dashboard for the monitoring, the control and the analysis of several parameters and data derived from advanced sensors: multiparametrical probes, aerial and oblique photogrammetry and laser scanning. This tool will help the territory management agencies to improve the risk mitigation and management system.

The RIEGL VZ-4000 scanning the Aosta Valley Region in Italy.

Goal:
This study aims to classify the point clouds derived from aerial imagery integrated with laser scanning data in order to generate accurate DTM, DSM and Digital Snow Models. The photogrammetry data set was acquired with a Nikon D810 camera from an helicopter survey. The aim of further analysis is to detect changes of natural dynamic phenomena that have occurred via volume analysis and mass balance evaluation.

Data:
+ The photogrammetry data set was acquired with an RGB camera (Nikon D810) with a focal length equivalent of 50 mm from a helicopter survey: 1060 JPG images
+ The laser scanner data set was acquired using a Terrestrial Laser Scanner (RIEGL VZ-4000) combined with a Leica GNSS device (GS25) to georeference the project. The TLS dataset was then used as base reference to properly align and georeference the photogrammetry point cloud.

LAStools processing:
1) check the reference system and the point cloud density [lasinfo, lasvalidate]
2) remove isolated noise points [lasnoise]
3) classify point into ground and non-ground [lasground]
4) classify point clouds into vegetation and other [lasclassify]
5) create DTM and DSM  [las2dem, lasgrid, blast2dem]
6) produce 3D visualizations to facilitate the communication and the interaction [lasview]

Plots to Stands: Producing LiDAR Vegetation Metrics for Imputation Calculations

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Some professionals in remote sensing find LAStools a useful tool to extract statistical metrics from LiDAR that are used to make estimations about a larger area of land from a small set of sample plots. Common applications are prediction of the timber volume or the above-ground biomass for entire forests based on a number of representative plots where exact measurements were obtained with field work. The same technique can also be used to make estimations about animal habitat or coconut yield or to classify the type of vegetation that covers the land. In this tutorial we describe the typical workflow for computing common metrics for smaller plots and larger areas using LAStools.

Download these six LiDAR tiles (1, 2, 3, 4, 5, 6) from a Eucalyptus plantation in Brazil to follow along the step by step instructions of this tutorial. This data is courtesy of Suzano Pulp and Paper. Please also download the two shapefiles that delineate the plots where field measurements were taken and the stands for which predictions are to be made. You should download version 170327 (or higher) of LAStools due to some recent bug fixes.

Quality Checking

Before processing newly received LiDAR data we always perform a quality check first. This ranges from visual inspection with lasview, to printing textual content reports and attribute histograms with lasinfo, to flight-line alignment checks with lasoverlap, pulse density and pulse spacing checks with lasgrid and las2dem, and completeness-of-returns check with lassort followed by lasreturn.

lasinfo -i tiles_raw\CODL0003-C0006.laz ^
        -odir quality -odix _info -otxt

The lasinfo report tells us that there is no projection information. However, we remember that this Brazilian data was in the common SIRGAS 2000 projection and try for a few likely UTM zones whether the hillshaded DSM produced by las2dem falls onto the right spot in Google Earth.

las2dem -i tiles_raw\CODL0003-C0006.laz ^
        -keep_first -thin_with_grid 1 ^
        -hillshade -epsg 31983 ^
        -o epsg_check.png

Hillshaded DSM and Google Earth imagery align for EPSG code 31983

The lasinfo report also tells us that the xyz coordinates are stored with millimeter resolution which is a bit of an overkill. For higher and faster LASzip compression we will later lower this to a more appropriate centimeter resolution. It further tells us that the returns are stored using point type 0 and that is a bit unfortunate. This (older) point type does not have a GPS time stamp so that some quality checks (e.g. “completeness of returns” with lasreturn) and operations (e.g. “resorting of returns into acquisition order” with lassort) will not be possible. Fortunately the min-max range of the ‘point source ID’ suggests that this point attribute is correctly populated with flightline numbers so that we can do a check for overlap and alignment of the different flightlines that contribute to the LiDAR in each tile.

lasoverlap -i tiles_raw\*.laz ^
           -min_diff 0.2 -max_diff 0.4 ^
           -epsg 31983 ^
           -odir quality -opng ^
           -cores 3

We run lasoverlap to visualize the amount of overlap between flightlines and the vertical differences between them. The produced images (see below) color code the number of flightlines and the maximum vertical difference between any two flightlines as seen below. Most of the area is cyan (2 flightlines) except in the bottom left where the pilot was sloppy and left some gaps in the yellow seams (3 flightlines) so that some spots are only blue (1 flightline). We also see that two tiles in the upper left are partly covered by a diagonal flightline. We will drop that flightline later to create a more uniform density.across the tiles. The mostly blue areas in the difference are mostly aligned with features in the landscape and less with the flightline pattern. Closer inspection shows that these vertical difference occur mainly in the dense old growth forests with species of different heights that are much harder to penetrate by the laser than the uniform and short-lived Eucalyptus plantation that is more of a “dead forest” with little undergrowth or animal habitat.

Interesting observation: The vertical difference of the lowest return from different flightlines computed per 2 meter by 2 meter grid cell could maybe be used a new forestry metric to help distinguish mono cultures from natural forests.

flightline overlap: blue=1, cyan=2, yellow=3, orange=4, red=5
flightline differences: vertical up to 20 cm = white, above 40 cm = saturated red or blue 
lasgrid -i tiles_raw\*.laz ^
        -keep_last ^
        -step 2 -point_density ^
        -false -set_min_max 10 20 ^
        -epsg 31983 ^
        -odir quality -odix _d_2m_10_20 -opng ^
        -cores 3

lasgrid -i tiles_raw\*.laz ^
        -keep_last ^
        -step 5 -point_density ^
        -false -set_min_max 10 20 ^
        -epsg 31983 ^
        -odir quality -odix _d_5m_10_20 -opng ^
        -cores 3

We run lasgrid to visualize the pulse density per 2 by 2 meter cell and per 5 by 5 meter cell. The produced images (see below) color code the number of last return per square meter. The impact of the tall Eucalyptus trees on the density per cell computation is evident for the smaller 2 meter cell size in form of a noisy blue/red diagonal in the top right as well as a noisy blue/red area in the bottom left. Both of those turn to a more consistent yellow for the density per cell computation with larger 5 meter cells. Immediately evident is the higher density (red) for those parts or the two tiles in the upper left that are covered by the additional diagonal flightline. The blueish area left to the center of the image suggests a consistently lower pulse density whose cause remains to be investigated: Lower reflectivity? Missing last returns? Cloud cover?

pulse density per 2 by 2 meter: blue=20
pulse density per 5 by 5 meter: blue=20

The lasinfo report suggests that the tiles are already classified. We could either use the ground classification provided by the vendor or re-classify the data ourselves (using lastile, lasnoise, and lasground). We check the quality of the ground classification by visually inspecting a hillshaded DTM created with las2dem from the ground returns. We buffer the tiles on-the-fly for a seamless hillshade without artifacts along tile boundaries by adding ‘-buffered 25’ and ‘-use_orig_bb’ to the command-line. To speed up reading the 25 meter buffers from neighboring tiles we first create a spatial indexing with lasindex.

lasindex -i tiles_raw\*.laz ^
         -cores 3

las2dem -i tiles_raw\*.laz ^
        -buffered 25 ^
        -keep_class 2 -thin_with_grid 0.5 ^
        -use_orig_bb ^
        -hillshade -epsg 31983 ^
        -odir quality -odix _dtm -opng ^
        -cores 3

hillshaded DTM tiles generated with las2dem and on-the-fly buffering

The resulting hillshaded DTM shows a few minor issues in the ground classification but also a big bump (above the mouse cursor). Closer inspection of this area (you can cut it from the larger tile using the command-line below) shows that there is a group of miss-classified points about 1200 meters below the terrain. Hence, we will start from scratch to prepare the data for the extraction of forestry metrics.

las2las -i tiles_raw\CODL0004-C0006.laz ^
        -inside_tile 207900 7358350 100 ^
        -o bump.laz

lasview -i bump.laz

bump in hillshaded DTM caused by miss-classified low noise

Data Preparation

Using lastile we first tile the data into smaller 500 meter by 500 meter tiles with 25 meter buffer while flagging all points in the buffer as ‘withheld’. In the same step we lower the resolution to centimeter and put nicer a coordinate offset in the LAS header. We also remove the existing classification and classify all points that are much lower than the target terrain as class 7 (aka noise). We also add CRS information and give each tile the base name ‘suzana.laz’.

lastile -i tiles_raw\*.laz ^
        -rescale 0.01 0.01 0.01 ^
        -auto_reoffset ^
        -set_classification 0 ^
        -classify_z_below_as 500.0 7 ^
        -tile_size 500 ^
        -buffer 25 -flag_as_withheld ^
        -epsg 31983 ^
        -odir tiles_buffered -o suzana.laz

With lasnoise we mark the many isolated points that are high above or below the terrain as class 7 (aka noise). Using two tiles we played around with the ‘step’ parameters until we find good parameter settings. See the README of lasnoise for the exact meaning and the choice of parameters for noise classification. We look at one of the resulting tiles with lasview.

lasnoise -i tiles_buffered\*.laz ^
         -step_xy 4 -step_z 2 ^
         -odir tiles_denoised -olaz ^
         -cores 3

lasview -i tiles_denoised\suzana_206000_7357000.laz ^
        -color_by_classification ^
        -win 1024 192

noise points shown in pink: all points (top), only noise points (bottom)

Next we use lasground to classify the last returns into ground (2) and non-ground (1). It is important to ignore the noise points with classification 7 to avoid the kind of bump we saw in the vendor-delivered classification. We again check the quality of the computed ground classification by producing a hillshaded DTM with las2dem. Here the las2dem command-line is sightly different as instead of buffering on-the-fly we use the buffers stored with each tile.

lasground -i tiles_denoised\*.laz ^
          -ignore_class 7 ^
          -nature -extra_fine ^
          -odir tiles_ground -olaz ^
          -cores 3

las2dem -i tiles_ground\*.laz ^
        -keep_class 2 -thin_with_grid 0.5 ^
        -hillshade ^
        -use_tile_bb ^
        -odir quality -odix _dtm_new -opng ^
        -cores 3
vendor-delivered ground classification
new ground classification from LAStools

Finally, with lasheight we compute how high each return is above the triangulated surface of all ground returns and store this height value in place of the elevation value into the z coordinate using the ‘-replace_z’ switch. This height-normalizes the LiDAR in the sense that all ground returns are set to an elevation of 0 while all other returns get an elevation relative to the ground. The result are height-normalized LiDAR tiles that are ready for producing the desired forestry metrics.

lasheight -i tiles_ground\*.laz ^
          -replace_z ^
          -odir tiles_normalized -olaz ^
          -cores 3
Metric Production

The tool for computing the metrics for the entire area as well as for the individual field plots is lascanopy. Which metrics are well suited for your particular imputation calculation is your job to determine. Maybe first compute a large number of them and then eliminate the redundant ones. Do not use any point from the tile buffers for these calculations. We had flagged them as ‘withheld’ during the lastile operation, so they are easy to drop. We also want to drop the noise points that were classified as 7. And we were planning to drop that additional diagonal flightline we noticed during quality checking. We generated two lasinfo reports with the ‘-histo point_source 1’ option for the two tiles it was covering. From the two histograms it was easy to see that the diagonal flightline has the number 31.

First we run lascanopy on the 11 plots that you can download here. When running on plots it makes sense to first create a spatial indexing with lasindex for faster querying so that only those tiny parts of the LAZ file need to be loaded that actually cover the plots.

lasindex -i tiles_normalized\*.laz ^
         -cores 3

lascanopy -i tiles_normalized\*.laz -merged ^
          -drop_withheld ^
          -drop_class 7 ^
          -drop_point_source 31 ^
          -lop WKS_PLOTS.shp ^
          -cover_cutoff 3.0 ^
          -cov -dns ^
          -height_cutoff 2.0 ^
          -c 2.0 999.0 ^
          -max -avg -std -kur ^
          -p 25 50 75 95 ^
          -b 30 50 80 ^
          -d 2.0 5.0 10.0 50.0 ^
          -o plots.csv

The resulting ‘plots.csv’ file you can easily process in other software packages. It contains one line for each polygonal plot listed in the shapefile that lists its bounding box followed by all the requested metrics. But is why is there a zero maximum height (marked in orange) for plots 6 though 10? All height metrics are computed solely from returns that are higher than the ‘height_cutoff’ that was set to 2 meters. We added the ‘-c 2.0 999.0’ absolute count metric to keep track of the number of returns used in these calculations. Apparently in plots 6 though 10 there was not a single return above 2 meters as the count (also marked in orange) is zero for all these plots. Turns out this Eucalyptus stand had recently been harvested and the new seedlings are still shorter than 2 meters.

more plots.csv
index,min_x,min_y,max_x,max_y,max,avg,std,kur,p25,p50,p75,p95,b30,b50,b80,c00,d00,d01,d02,cov,dns
0,206260.500,7358289.909,206283.068,7358312.477,11.23,6.22,1.91,2.26,4.71,6.01,7.67,9.5,26.3,59.7,94.2,5359,18.9,41.3,1.5,76.3,60.0
1,206422.500,7357972.909,206445.068,7357995.477,13.54,7.5,2.54,1.97,5.32,7.34,9.65,11.62,26.9,54.6,92.2,7030,12.3,36.6,13.3,77.0,61.0
2,206579.501,7358125.909,206602.068,7358148.477,12.22,5.72,2.15,2.5,4.11,5.32,7.26,9.76,46.0,73.7,97.4,4901,24.8,28.7,2.0,66.8,51.2
3,206578.500,7358452.910,206601.068,7358475.477,12.21,5.68,2.23,2.64,4.01,5.14,7.18,10.04,48.3,74.1,95.5,4861,25.7,26.2,2.9,68.0,50.2
4,206734.501,7358604.910,206757.068,7358627.478,15.98,10.3,2.18,2.64,8.85,10.46,11.9,13.65,3.3,27.0,91.0,4946,0.6,32.5,44.5,91.0,77.5
5,207043.501,7358761.910,207066.068,7358784.478,15.76,10.78,2.32,3.43,9.27,11.03,12.49,14.11,3.2,20.7,83.3,4819,1.5,24.7,51.0,91.1,76.8
6,207677.192,7359630.526,207699.760,7359653.094,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0
7,207519.291,7359372.366,207541.859,7359394.934,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0
8,207786.742,7359255.850,207809.309,7359278.417,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0
9,208159.017,7358997.344,208181.584,7359019.911,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0
10,208370.909,7358602.565,208393.477,7358625.133,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0

Then we run lascanopy on the entire area and produce one raster per tile for each metric. Here we remove the buffered points with the ‘-use_tile_bb’ switch that also ensures that the produced rasters have exactly the extend of the tiles without buffers. Again, it is imperative that you download the version 170327 (or higher) of LAStools for this to work correctly.

lascanopy -version
LAStools (by martin@rapidlasso.com) version 170327 (academic)

lascanopy -i tiles_normalized\*.laz ^
          -use_tile_bb ^
          -drop_class 7 ^
          -drop_point_source 31 ^
          -step 10 ^
          -cover_cutoff 3.0 ^
          -cov -dns ^
          -height_cutoff 2.0 ^
          -c 2.0 999.0 ^
          -max -avg -std -kur ^
          -p 25 50 75 95 ^
          -b 30 50 80 ^
          -d 2.0 5.0 10.0 50.0 ^
          -odir tile_metrics -oasc ^
          -cores 3

The resulting rasters in ASC format can easily be previewed using lasview for some “sanity checking” that our metrics make sense and to get a quick overview about what these metrics look like.

lasview -i tile_metrics\suzana_*max.asc
lasview -i tile_metrics\suzana_*p95.asc
lasview -i tile_metrics\suzana_*p50.asc
lasview -i tile_metrics\suzana_*p25.asc
lasview -i tile_metrics\suzana_*cov.asc
lasview -i tile_metrics\suzana_*d00.asc
lasview -i tile_metrics\suzana_*d01.asc
lasview -i tile_metrics\suzana_*b30.asc
lasview -i tile_metrics\suzana_*b80.asc
maximum metric is sensitive to high noise
95th percentile is more robust against noise
50th percentile (of points with heights above cutoff)
25th percentile (of points with heights above cutoff)
percentage of points with heights between 2 and 10 meters (of those above cutoff)
percentage of points with heights between 10 and 100 meters (of those above cutoff)
30 percent bincentile
80 percent bincentile
canopy cover above 3 meters

The maximum height rasters are useful to inspect more closely as they will immediately tell us if there was any high noise point that slipped through the cracks. And indeed it happened as we see a maximum of 388.55 meters for of the 10 by 10 meter cells. As we know the expected height of the trees we could have added a ‘-drop_z_above 70’ to the lascanopy command line. Careful, however, when computing forestry metrics in strongly sloped terrains as the terrain slope can significantly lift up returns to heights much higher than that of the tree. This is guaranteed to happen for LiDAR returns from branches that are extending horizontally far over the down-sloped part of the terrain as shown in this paper here.

We did not use the shapefile for the stands in this exercise. We could have clipped the normalized LiDAR points to these stands using lasclip as shown in the GUI below before generating the raster metrics. This would have saved space and computation time as many of the LiDAR points lie outside of the stands. However, it might be better to do that clipping step on the rasters in whichever GIS software or statistics package you are using for the imputation computation to properly account for partly covered raster cells along the stand boundary. This could be subject of another blog article … (-:

not all LiDAR was needed to compute metrics for