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LiDAR for ecology and conservation - WWF Conservation Technology Series (3)


Abstract and Figures

A guide to using LiDAR data for conservation - best practices, data sources, methods and extensive literature collection.
Content may be subject to copyright.
Cover Image: Airborne LiDAR point cloud data collected in the Democratic Republic of the Congo in 2015
© WWF/BMUB/KFW/Southern Mapping Company
Special thanks for review and support to: Sverre Lundemo, Pablo Izquierdo, Evi Korakaki,
Naikoa Aguilar-Amuchastegui, and Lauri Korhonen.
Funding for this report was provided by WWF-UK.
WWF is one of the world’s largest and most experienced independent conservation
organizations, with over 5 million supporters and a global network active in more than 100
countries. WWF’s mission is to stop the degredation of the planets natural environment and
to build a future in which humans live in harmony with nature by conserving the worlds
biological diversity, ensuring that the use of renewable natural resources is sustainable, and
promoting the reduction of pollution and wasteful consumption.
Markus Melin, Aurélie C. Shapiro, and Paul Glover-Kapfer. 2017.
WWF Conservation Technology Series 1(3). WWF-UK, Woking, United Kingdom.
What is LiDAR?
LiDAR (light detection and ranging) is a remote sensing method that uses a laser to
measure distances. Pulses of light are emitted from a laser scanner, and when the
pulse hits a target, a portion of its photons are reected back to the scanner. Because
the location of the scanner, the directionality of the pulse, and the time between pulse
emission and return are known, the 3D location (XYZ coordinates) from which the pulse
reected is calculable. The laser emits millions of such pulses, and records from whence
they reect producing a highly precise 3D point cloud (model) which can be used to
estimate the 3D structure of the target area. See Chapters 2 and 3 for more details.
How are LiDAR data collected?
Most often, the scanning laser is mounted in an aircraft – typically a xed-wing airplane
although increasingly in drones – and scans the ground along its route or direction of
ight. Occasionally scanning lasers are mounted on a tripod or vehicle for terrestrial based
laser (TLS) scans. For additional information, see Chapter 2.
What can LiDAR data do?
LiDAR data provide a detailed 3D model of the target area, including its terrain,
topography, and vegetation. In turn the digital terrain model of the ground surface can be
used to derive a range of additional products including models of slope or visibility, and
the 3D data on vegetative structure can be used for a broad range of applications within
forestry and ecological contexts. See Chapter 5 for more information and additional
LiDAR applications.
Are LiDAR and Airborne Laser Scanning the same thing – terminology?
Airborne laser scanning, or ALS, is simply LiDAR whilst airborne, i.e. collecting LiDAR
data from an aircraft. Alternatively, if the LiDAR scanner is on a tripod, we would refer to
the method as Terrestrial Laser Scanning, or TLS.
What is the dierence between a pulse and an echo?
A pulse is what the scanning laser emits. The pulse can be thought of as a clump of
timestamped photons. As the pulse hits a target, some of its photons reect back and are
recognised by the LiDAR device: an echo was received. Therefore, the source of an echo
is a location that was hit by a pulse and from which photons were reected. One emitted
pulse can, and often does, yield multiple echoes. See Chapter 2 for additional details.
What is the resolution of LiDAR data?
Most often, the data that is freely distributed has a density between 0.8 – 3 pulses / m2.
This is termed the pulse density. Note that pulse density refers to emitted pulses / m2.
A common term is also echo- or return density, which refer to echoes / m2; one emitted
pulse can yield multiple echoes. If an airplane ies at a low altitude of 200-600 meters
the data may have a pulse density of 10 pulses / m2. Terrestrial Laser Scanning data easily
results in point densities of at least hundreds of pulses per square meter.
Do I need LiDAR or will other data suce?
Depending on your aim, photogrammetry, satellite or aerial imagery may suce.
However, if you require high precision 3D structural information about a target area (be it
forest or terrain), then LiDAR provides excellent precision, albeit at a relatively high cost.
See Chapter 5 for examples of LiDAR applications. In some cases, LiDAR data are used
in conjunction with other remote sensing data to produce insights neither data are able to
achieve alone.
How can I acquire LiDAR data?
Many countries have already collected vast amounts of LiDAR data for the purposes of
topographic mapping and forestry and in many cases, these data are freely available. See
Chapter 8 for a list of available datasets. If LiDAR data are not available for your area of
interest, you need to consider whether it is necessary to invest in its collection.
How much does it cost to collect LiDAR data?
With ALS or airborne LiDAR, the biggest expense is associated with the operation of the
aircraft, which is dependent on issues such as distance of the study site from the nearest
airport (which determines which types of aircraft can be used), fuel costs, altitude, pilot
time, and weather. As such, these prices vary as a function of geography and time as well as
the size of the target area, making general estimates dicult. The cost typically declines per
unit area as the size of the surveyed area increases and can be quite cost eective for large
scales. Terrestrial laser scanning systems are becoming more compact and aordable, and
the availability of drones are decreasing the cost of collecting small-scale LiDAR.
What do LiDAR data consist of?
The data contains information about each received echo. For example, in a text (.txt)
format the data is often structured so that one row holds information from one echo - its
XYZ coordinates, intensity, and order in the case of multiple echoes. See Chapter 3.2 for
more information.
How are LiDAR data dierent from aerial or satellite imagery?
Similar to a photograph without a ash, aerial and satellite imagery are considered passive
in that they record information from reected sunlight. In other words, the light source is
external because the sensor does not emit anything. In contrast, with its emitted pulses of
light, LiDAR is an active remote sensing method. Furthermore, whereas imagery contains
spectral data, LiDAR data do not; rather LiDAR data contain the structural information in
the form of 3D coordinates, and the intensity of each echo. See Chapter 3.2 for more on
LiDAR data and Chapter 9 for dierences between LiDAR and imagery.
Do I need to ground-truth LiDAR data?
Because LiDAR data are not interpreted in the same sense as imagery and is highly precise
(most commonly to within 5-15 cm) ground-truthing of the point cloud structure for 3D
mapping is generally not necessary. That being said, if you are studying something other
than physical terrain, such as whether a certain echo pattern came from a particular
species of tree, or are using LiDAR data in combination with other remotely sensed and
interpreted data, then ground-truthing may be necessary.
What format do the data come in and how large are the datasets?
Most often, LiDAR data come in compressed LAS/LAZ format. LAS format is a standard
format for LiDAR data storage, and LAZ format is a compressed format of LAS. For
example, the National Land Survey of Finland distributes their LiDAR data in LAZ les,
where a single le contains LiDAR data from a 3 x 3 km area. The compressed LAZ le can
be as small as 50 megabytes, but when uncompressed and converted into a text le, which
may have tens of millions of rows (one for each echo), could be on the order of many
gigabytes. See Chapter 3.2 for more information.
Can the LiDAR data format be changed?
The data can be converted to many other formats, including plain ASCII les and ESRI
shapeles. The only practical limitation is the size: a point shapele with 20 million points
would be very slow to load and process in GIS software. Many common programs for
processing LiDAR oer tools for conversion. See Chapter 7 for additional information.
What programs are available for processing LiDAR data?
There are many programs that have been tailored specically for processing and analysing
LiDAR data, and the most common GIS and remote sensing programs are able to process
LiDAR data in LAS/LAZ format as well. See Chapter 7 for information about common
programs for processing and analysing LiDAR data.
Can LiDAR data be used without any processing?
Generally no. In the core of the processing chain is the need to scale the heights
(Z-coordinates) of the LiDAR echoes to ground level so that each echo has a Z-coordinate
indicating its height above ground level, though often the service provider that collects the
data can process the data for a fee. This is achieved by rst separating the ground echoes
from the other echoes, then by creating a terrain model from the ground echoes and
nally, by subtracting this terrain model from the height of the echo in the LiDAR data.
This eectively scales all the echoes at above-ground-level scale. The basics of this process
are explained in Chapter 4.1.
How do I use LiDAR data in my analyses?
Typically, the rst step involves delineating your target area and extracting the relevant,
manageable data from the larger LiDAR data set. You will then frequently need to rescale
the data as described in the question above. Next, you calculate relevant metrics from the
available 3D point clouds, a common example being canopy height or density. Although
time consuming and relatively complex to work with, the adaptability of LiDAR data to suit
individual needs and derive myriad metrics of 3D structure is its beauty. See Chapter 5 for
examples of how the data have been used in dierent disciplines.
Can the point cloud data be converted into rasters like satellite images,
for instance?
There is no reason why LiDAR data cannot be converted into rasters, but more commonly
LiDAR data are used to derive habitat structural variables, which are then converted to
rasters. For example, in some forestry cases it is common to grid the study area into 16 x
16 meter grid cells and to extract LiDAR data within each cell. Variables of forest structure
are calculated from the point clouds within each cell, and these variables are then
incorporated into a raster with cells of the same dimensions. Common raster products
from LiDAR are digital terrain and canopy height models.
Image: © G.Asner/Carnegie Institution for Science
Light Detection and Ranging (LIDAR) provides
high precision data on the 3D structure of forests,
enabling landscape level mapping of forest change.
1 Preface 8
1.1 The aim of this guide 8
1.2 The structure of this guide 8
2 Introduction to LiDAR remote sensing 9
2.1 Terminology 9
2.2 History of LiDAR 10
2.3 The method – what is LiDAR? 10
3 Understanding the data 15
3.1 Types of LiDAR 15
3.2 Format and structure 15
4 Data processing and products 17
4.1 Aim of data processing 17
4.2 Common LiDAR data products 18
5 LiDAR Applications 22
5.1 Forestry, vegetation structure 22
5.2 Analysis of land cover, topography and hydrology 24
5.3 Ecological applications 24
5.3.1 Volant and canopy species 24
5.3.2 Terrestrial species 25
5.3.3 Mapping suitable habitat 25
5.3.4 Estimating canopy cover and leaf area index 25
5.3.5 Detecting forest change 25
5.4 Forest biomass mapping 26
5.5 Bathymetry 26
6 LiDAR horizons 28
6.1 Multi-wavelength (or multispectral) LiDAR 28
6.2 Spaceborne systems 28
7 LiDAR software 29
8 Data acquisition and availability 32
9 LiDAR strengths and weaknesses 34
9.1 LiDAR weaknesses in brief 34
9.2 LiDAR strengths in brief 34
9.3 Summary of strengths and weaknesses 35
10 Bibliography 36
1.1 The aim of this guide
Threats like climate change, habitat destruction and degradation, and pollution are ubiquitous,
requiring global-scale solutions. Remote sensing provides a means for us to not only map these
problems globally, but also plan countermeasures at the appropriate scale. Light Detection and
Ranging, or LiDAR, is a remote sensing method that uses lasers to record distances, which when
coupled with complementary data, can be used to model the 3D structure of a surface, be it land,
seabed, or forest canopy. LiDAR can even be used to model the internal 3D structure in some
cases, such as vegetation and the terrain below a forest canopy.
Despite its diverse utility, LiDAR’s relative newness and perceived technical complexity
serve as barriers to its widespread use. This guide is designed to bridge those barriers and
make LiDAR more accessible to conservation practitioners.
Importantly, this guide is not about ecological survey design, broad remote sensing
principles, or statistical inference, and users of this guide should either already possess
the requisite background in these topics or plan to seek additional advice from relevant
experts. This guide does require a basic understanding of ecology and the geographic
information systems because terms such as raster, grid, and canopy are used regularly
without reservation. Furthermore, although this guide does cover available LiDAR data
sets and software for processing and analysing those data, the availability of both is
growing rapidly. As such, it is likely that many other datasets and software are available,
or will be soon, and readers are advised to search broadly for additional options in both
regards. This guide does discuss the capabilities of some of the software available for
working with LiDAR, this discussion should not be construed as endorsement as the right
software for the job depends on the data, user, and analytical aims.
1.2 The structure of this guide
Although this guide is aimed at those with little to no knowledge of or experience with
LiDAR, seasoned users will also nd certain sections useful and are encouraged to skip to
those most relevant to their needs. Chapters 2 to 4 are primarily aimed at the former,
and provide a concise introduction to LiDAR, covering topics such as its history, basic
principles, terminology, and data, including a step-by-step walkthrough of how raw
LiDAR data are processed to produce ecologically-relevant datasets.
For those that want to better understand how LiDAR has been used in the past, Chapter
5 highlights examples of LiDAR applications, whereas Chapter 6 attempts to scan
LiDAR’s horizon and evolution.
The basic concepts of LiDAR, including practical advice on the application and
manipulation of data, are covered in Chapters 7 to 9. Chapter 7 briey summarizes
some of the available software programs that are commonly used to process and analyze
LiDAR data; Chapter 8 provides an incomplete yet helpful list of countries with available
LiDAR datasets and where to acquire them, and; Chapter 9 critically evaluates the
advantages and disadvantages (pros and cons) of LiDAR, with the aim of helping readers
to decide whether LiDAR provides a suitable tool for their work.
In general, this guide is written in a practical, easy-to-read manner. When this is not the
case because of the complexity of the material, the authors make every eort to balance
breadth, depth, and accessibility.
2.1 Terminology
Remote sensing technologies like aerial and satellite imagery are generally referred to as
passive technologies because they measure how radiation from an external source (i.e.
sunlight) reects o an object. Aerial and satellite images are widely used in the natural
sciences, and have been so since the launch of Landsat 1 in the 1970´s. The applications of
satellite and aerial imagery are extensive, and not covered in this guide.
Whereas satellite and aerial imaging are considered passive, techniques like LiDAR and
RADAR are considered active, as they are not dependent on sunlight but rather emit radiation
of their own and then measure how this radiation reects from a target. RADAR is an acronym
for Radio Detection and Ranging, and it is a method based on emitting radio waves and then
measuring their reection. Similarly, LiDAR is an acronym for Light Detection and Ranging.
Instead of emitting radio waves, LiDAR devices use light to detect and range. Before describing
the method in greater detail the terminology of the technique must be briey explained.
When learning about LiDAR, one is likely to encounter terms like airborne or terrestrial
LiDAR, or aerial or terrestrial laser scanning (ALS or TLS). Both ALS and TLS employ LiDAR.
A LiDAR device itself can be mounted on an airplane, helicopter, drone, ATV, car or tripod.
In the typical case, the LiDAR device is mounted on board an airplane and the system scans
the ground from the air with a laser – hence the term airborne laser scanning. There is also
a LiDAR dataset that is being collected from a device on board a satellite: GLAS (Geoscience
Laser Altimetry System). This guide, however, will focus solely on ALS, because this is
currently the most common method that governments and institutions use to collect their
data, and the most commonly available data. Henceforth, the term LiDAR is used to refer to
both the method and the data produced by the method.
2.2 History of LiDAR
The acronym LiDAR rst appeared in the literature in the 1960s and LiDAR’s rst
application in forestry occurred in the 1970s (Rempel & Parker 1964). The widespread
adoption of LiDAR for modelling forest structure began in the late 1990s, resulting in
LiDAR assisted forest inventories (Nilsson 1996; Næsset 1997; Næsset 2002; Lefsky
et al. 2001; Coops et al. 2004; Maltamo et al. 2006; Packalén et al. 2008). Recognition
of LiDAR’s ability to accurately measure forest and vegetation structure for ecological
purposes followed shortly, with the pioneering work of Hill et al. (2004) who linked
LiDAR-based estimates of canopy structure with avian habitat use. Subsequent research
has applied LiDAR to assess the habitat structure and use of numerous terrestrial and
avian species (Wehr & Lohr 1999). A more thorough review of the application of LIDAR in
ecology and conservation (with practical examples) is given in Chapter 5.
LiDAR was rst termed in the 1960s and saw its rst use in ecology in the 2000s
• Airborne laser scanning, terrestrial laser scanning, and LiDAR use lasers to
measure distances
• When emitted from a known location, the time between the emission and return of
laser pulses allows highly precise mapping of the 3D surface and in some cases the
structure of objects
Geoscience Laser Altimetry System:
2.3 The method – what is LiDAR?
At its most basic, LiDAR uses a laser to produce and emit pulses of light, and measures
the time it takes for a reection of this pulse to return. Most commonly, the LiDAR system
is carried by a xed-wing airplane, and in addition to the laser-emitter-receiver scanner,
LiDAR systems are also connected to a global positioning system (GPS), and an inertial
measurement unit (IMU). The GPS constantly measures the position of the laser scanner,
which is crucial for knowing the location where the light pulses are emitted from. The IMU
measures the tilting of the aircraft (roll, pitch, and yaw), which is crucial to calculating the
directionality of the aircraft and hence, the directionality of the emitted pulses of light.
The easiest way to understand how LiDAR works is to examine the life of a single emitted
pulse of light. The pulse of light is a clump of time-stamped photons emitted with
known directionality. As this pulse contacts a surface (e.g. a leaf in the forest canopy), a
portion of those photons are reected back towards the laser. The laser-emitting device
recognizes these time-stamped, reected photons and calculates the time between their
initial emission and their reected return, alternatively called their echo (i.e. an echo
was received from the emitted pulse). Next, the device calculates the location from where
the echo originated. This is possible because pulse speed (i.e. the speed of light), pulse
origin (location of emission), pulse directionality, and pulse travel time are known values,
making it a simple geometry problem to calculate the location of the echo.
Light pulses frequently yield multiple echoes because not all of the photons are reected
by the rst surface they contact. Instead, some continue through semi-transparent or
non-opaque surfaces before contacting something else and delivering another echo.
Therefore, whereas the rst echo comes from the highest surface encountered by the
pulse, for example the top of the canopy, the nal echo from a given pulse comes from the
last surface hit by the remaining photons. Most often this is the ground, but in a dense
forest, such as in the tropics, the last pulse can come from inside the canopy as well. In
fact, the echoes are typically categorized according to these details. The categories are
rst of many, last of many, only and intermediate. First of many means that the device
received many echoes from a pulse, from which the current echo was the rst one.
Correspondingly, last of many means that from the many received echoes, this was the last
one; intermediate echoes then come from between the two previous categories. Finally,
only means that from this pulse only one echo was detected.
In addition to the location, the device also records the intensity of the returning echo.
The intensity is higher if the pulse hits a solid surface, because more photons reect back
(ground -vs- forest canopy). Modern LiDAR systems can emit up to 800,000 pulses /
second. Each pulse can yield multiple echoes, and for each of these the echo location is
recorded. The result is generally referred to as point cloud data; a cloud of points, all of
which have an XYZ-location (coordinates). When the data are plotted, the structure of the
scanned target can be visualized (Figure 2.1).
Each echo (the points in Figure 2.1) are associated with the XYZ-coordinates from a
LiDAR pulse photon. Once processed, the data with the largest Z-coordinate value (Figure
2.1b) denotes the height of the top of the tree, and one could calculate how many echoes
come above or between certain heights to get estimates of, for instance, canopy density.
For more information on various derived metrics, refer to Chapter 4.
Note the dierences in the two visualized data sets (Figure 2.1). This is a result of a
dierence in the density of their point clouds (echo density). In Figure 2.1a, the density
of echoes is 0.8 pulses / m2, whereas in Figure 2.1b, it is 10 pulses / m2. The dierence
in echo density can depend on the LiDAR device, but most often is the result of ying
altitude. The LiDAR device emits the pulses in the form of a fan that is perpendicular to
the aircraft’s ight line (Figure 2.2).
Figure 1. Lidar echoes plotted from a 60 x 180 meter plot (a) and from the area of three
individual trees (b). Figure 1b originally appeared in Vauhkonen et al. (2009).
If the airplane ies at a lower altitude, the fan does not
spread out as widely, which results in denser data (more
pulses per square meter). The data depicted in Figure 2.1b
was collected from an altitude of 200 m above ground level
(agl). In comparison, the data depicted in Figure 2.1a was
collected from an altitude of 1500 – 2000 m agl; this is the
altitude at which most LiDAR data are collected. Therefore,
there is a tradeo between coverage and point density. At
higher altitudes, point density and cost per unit area are
lower, but coverage area larger. Because low density data are
sucient for most operational tasks, the added expense of
collecting higher density LiDAR data is rarely justied. For
example, terrain mapping and forest inventories can cover
tens of thousands of hectares and do not require high point
densities, so ying at higher altitudes reduces costs without
diminishing the utility of the data.
Figure 2.2 Illustration of the LiDAR or airborne laser scanning process. Image © Höe & Rutzinger (2011).
Figure 2.3 LiDAR echoes in point clouds categorized by target (a) or order of return of
the echoes (b).
The dierent echo categories as well as the categorization of ground and vegetation hits
are best understood visually (Figure 2.3). Whilst categorizing vegetation and ground
echoes is sucient in many cases (Figure 2.3a), the order of echo returns provides
additional information, such as vertical stratication of the canopy or other vegetative
layers, as represented by the rst of many and only echoes that occur in the canopy. This
may happen when the canopy is dense enough so that all the photons reect back at
once delivering an only echo. The dense canopy can also produce another phenomenon
where the last of many echoes occur within the canopy rather than the ground. This
phenomenon is depicted last of many echoes that occur in the canopy (Figure 2.3b);
the ground has both last of many and only echoes, whereas intermediate echoes occur
between the highest and the lowest surfaces.
The LIDAR sensor is positioned in an opening
created in the underbelly of the airplane.
Image: © E.S ombié/WW F
Image: © E.Sombié/WWF
LiDAR unit inside the aircraft generates the
powerful laser pulses which comprise the
LiDAR measurements for lidar unit image.
3.1 Types of LiDAR
LiDAR data can come in two forms, discrete return and full waveform data. Whereas
many of the principles covered in this guide apply to both types of data, this guide focuses
exclusively on discrete return data because they are the most common publicly available
datasets. In the collection phase of discrete return data, the system counts an echo only if
the incoming echo exceeds a pre-dened threshold of intensity. In practice, this has the
eect that echoes reected from certain objects, for example a single tree branch, may
not provide sucient intensity to be recorded in discrete return data – discrete return
data records information only from targets that yielded a strong enough return. This
intensity threshold can be adjusted. In contrast, in full waveform data the whole waveform
is practically digitized, regardless of intensity or strength. This guide focuses on discrete
return data. For more information about full waveform data, refer to Mallet & Bretar
(2009), Hollaus et al. (2014), Hovi (2015), or Hovi et al. (2016). Further, Sumnall et al.
(2015) provide examples of estimating forest variables with both discrete-return and full
waveform data.
3.2 Format and structure
The vast majority of publicly available LiDAR data is discrete return data and most
commonly it is provided in LAS or LAZ (.las/.laz) format. The LAS format is a format used
by the American Society of Photogrammetry and Remote Sensing (ASPRS). LiDAR data
contain information on the echoes of emitted pulses. The most important information
describing each pulse are categorized as X, Y, Z, I, N, R, C. X, Y and Z are the coordinates
of the echo locations (the location where this echo came from, typically the coordinates
are projected in the local coordinate system). I indicates the intensity or strength of the
echo. N represents the number of returns (echoes) received from a single pulse and R
indicates the order of these echoes, or the return number. For example, a combination of
N = 2 and R = 1 indicates that two echoes were recorded from this pulse (N = 2) of which
this record is the rst (R = 1). It is common for echoes in public data to be classied into
categories and the column C indicates these categories for this classication.
• LiDAR data can either be discrete or full waveform; discrete data represent the
majority of publicly available data
• LiDAR data consist of the 3D coordinates of the laser echo, the intensity of the echo,
the number and order of echoes from a single laser pulse, and frequently a category
of surface o which the echo reected
ASPRS full description:
Figure 3.1 LiDAR data in text format (.txt) from 9 echoes.
When LiDAR data are converted into text format (Figure 3.1) the rst row contains
column headings, and subsequent rows contain the X, Y, Z, I, N, R, C data for each
received echo. According to this dataset, the rst echo reected o vegetation (C=3)
whereas the last two echoes reected o the ground (C=2). The rst three echoes
provide an illustrative example of the N and R categories. The value of N is 3 for all
three echoes, indicating that they all originated from the same pulse. The values of R
varies from 1 to 3, indicating that the rst three rows hold information for the three
echoes that were received from the emitted pulse. The rows that have a value of 1 for N
and a value of 2 for C are ground hits, where the pulse hit ground without intersecting
any vegetation or other objects in its path, hence producing a single echo. The last and
second to last echoes also show a pattern. The third to last echo has a value of 2 for N,
1 for R, and 3 for C, indicating that two echoes were received from this pulse, of which
this echo is the rst one, and it reected o vegetation. After the pulse encountered the
vegetation and reected some of the its photons, the remaining photons continued the
journey before reecting o the ground and yielded the last echo as indicated by the
value of 2 for N, 2 for R, and 2 for C; of the two echoes received from this pulse, this, the
last of them, reected o the ground.
Close scrutiny of the data in Figure 4 suggests that some of the reections from
vegetation occurred at heights of more than 300 meters! Comparison of these Z values
to those from ground reections which are also greater than 300 meters provides an
explanation for this seemingly spurious phenomenon. The Z coordinate in the data does
not refer to the height above ground level, but needs to be scaled to reect this. This
core step is addressed in the next chapter.
597847.589 7336016.990 329.020 1 3 1 3
597847.290 7336017.230 325.050 1 3 2 3
597846.979 7336017.490 320.780 1 3 3 1
597845.609 7336017.429 319.820 14 1 1 1
597842.969 7336017.230 319.330 18 1 1 2
597840.359 7336017.009 319.060 17 1 1 2
597838.520 7336016.200 328.500 8 1 1 1
597836.849 7336016.370 323.710 5 2 1 3
597836.469 7336016.660 318.960 0 2 2 2
This chapter covers basic processing and analyses of LiDAR data without reference to any
particular program. Chapter 7 introduces software that are commonly used to process
and analyse LiDAR data. The data used here for illustrative purposes is publicly available
low-pulse density data (~ 1 pulses/ m2) that has been processed (Figure 3.1). The gures
in this section were produced from publicly available LiDAR data of the National Land
Survey of Finland.
4.1 Aim of data processing
In order to calculate, for example, meaningful metrics of vegetation structure, the
Z-coordinates in LiDAR point clouds need to be scaled to above ground level (agl). This
can be achieved by rst creating a terrain model from the ground echoes (Figure 3.1,
C=2) and then using this terrain model to re-scale the echo heights. In practice, this
is usually achieved by separating the ground echoes from the other echoes, and then
interpolating a digital terrain model (DTM) from them (Figure 4.1).
• Translating raw LiDAR data into ecologically relevant datasets requires some data
processing, including scaling the height of echoes relative to ground level
Common data products from LiDAR include digital terrain and canopy height models
Although LiDAR data usually require signicant processing before use, the raw
data contain an incredible wealth of information, allowing the derivation of myriad
relevant datasets
Figure 4.1 Vegetation echoes and ground echoes separated from a 25 meter wide circle,
and a digital elevation model interpolated from the ground echoes.
DTMs are often rasters (e.g. in a .ti format), where each cell´s (pixel of a raster image)
value indicates its elevation. The next step involves subtracting this DTM value from the
other (non-ground) echoes by identifying the DTM cell within which the LiDAR echo falls
and subtracting the DTM cell´s Z value from the LiDAR echo Z value. What remains is the
metric dierence in Z between the cell (ground level) and the LiDAR echo, and this value
represents the height of our LiDAR echo above ground level (Figure 4.2).
Figure 4.2 LiDAR echoes with their Z-coordinates scaled to above ground level height.
The grey circle has a 25 meter radius and an elevation of 0 meters (ground level). The
shading of the echoes symbolizes their height.
4.2 Common LiDAR data products
The DTM discussed in the previous section is a basic LiDAR product. DTMs are the
products that are most often of interest to National Land or Ordnance Surveys. The
National Land Survey of Finland for example is producing a LiDAR-based DTM with two
meter spatial resolution (one pixel is 2 x 2 meters) covering the entire country. This allows
for highly accurate models of the terrain and elevation (Figure 4.3).
Figure 4.3 Digital terrain model in 2D (a) and a 3D visualization of a 2D DTM (b).
The black shape in a is a river at an elevation of 230 meters above sea level. The whitest
(highest) points in a are at approximately 340 meters above sea level.
The DTM of Figure 4.3a can then be used to derive additional data such as slope
steepness, visibility from a certain location (viewshed), or aspect. For example, Figure
4.3b illustrates the steepness of the slope with green representing areas with low slopes (at
areas) and red representing areas with high slopes (steep areas). Although interesting and
relatively easy to create in GIS software, their creation falls outside the scope of this guide.
The DTM products described above were derived from ground hits only, and when used
with non-ground echoes can be used to produce surface models that show the highest
surfaces of the ground and whatever is above it, the surface we would see from the air
when viewing down. Common examples are Digital Surface Models (DSM), from which a
Canopy Height Model (CHM) is a very common example (Figure 4.4).
Figure 4.4 A LiDAR based Canopy Height Model showing the canopy height in detail.
Lighter colours indicate higher elevations, in this case higher trees. Darker colours
indicate lower elevations, and in this case black indicates locations of ground echoes,
which can be seen from the road curving on the bottom left or from the rectangular clear-
cut areas in the centre of the gure.
The important distinction between the DTM (Figure 4.3a), and the canopy height model
(Figure 4.4) is that the canopy height model summarizes the height of the target objects
above ground level, which is interpolated from the rst and only echoes (ground echoes),
as opposed to the DTMs which are created solely from ground echoes. As such, the data
displayed in Figure 4.4 depicts the rst surface intersected by the LiDAR pulse, whether
bare ground or the canopy, which is useful for delineating areas of uniformity (mature
forests, clear cuts etc.) or in estimating how fragmented the landscape is (patchiness of
e.g. mature forest cover).
The previous examples that illustrate how to assess the structure of the ground surface
and the terrain were relatively basic, and largely do not take full advantage of the potential
of LiDAR; data in 3D, which can be used for more complex analyses, such as estimation
of canopy cover. For analyses such as forest canopy cover estimation, the area of interest
can be delineated, for example with a polygon. Alternative delineations may be of a forest
stand, a buer around a GPS-collared animal´s location, a home range, or a portion of
the tree canopy with nesting birds. Figure 4.5 depicts an example where LiDAR data
were used to assess the structure of forests in areas preferred by moose (Alces alces), and
Figure 4.6 illustrates the use of LiDAR point clouds to delineate individual tree crowns.
Figure 4.5. Locations of GPS-collared moose (left) visualized inside LiDAR data. LiDAR
data extracted and visualized in more detail around one of the locations (right). Figure
adapted from Melin et al. 2014.
Figure 4.6. Tree crowns detected and delineated from LiDAR point cloud data. Figure
adapted from Vauhkonen et al. 2014, 2016.
The purpose of the analyses depicted in the two gures is to use the 3D information to
assess more complex structural features that are ecologically-relevant. In these cases, the
relevant structural features were 3D variables suitable for forest height and vegetation
structure modelling. As shown in these brief examples, LiDAR data are suitable for a
diversity of analyses aimed at understanding environmental 3D structure.
The examples above provide only a cursory examination of the myriad potential
applications of LiDAR, and the next chapter reviews in greater detail some additional
applications of LiDAR such as mapping vegetation and forest resources, land cover
analyses, bathymetry, wildlife habitat analysis, and discusses the types of variables useful
for deriving the desired information from LiDAR data.
Image: © G.Asner/Carnegie Institution for Science
The Carnegie Airborne Observatory combines imaging
spectroscopy with LiDAR which can be used to map
the chemical composition of trees. Scientist can use
this to measure differences in the relative rate of
growth among trees in a forest. This image shows the
fastest growing trees in the hottest red colours, whilst
slower growing trees are blue.
• LiDAR has seen numerous applications across multiple disciplines, including
geography, geology, ecology, pedology, hydrology, conservation biology, and forestry
• For many species, vegetative structure is a critical component of habitat suitability,
and LiDAR has been used to better understand wildlife-habitat relationships and
even animal behaviour across multiple species and systems
LiDAR can also be used to map land cover and land use, ood risk, bathymetry, and
carbon storage
This chapter provides select examples of LiDAR’s use in applied and research contexts.
Whereas this section seeks to provide a relatively broad image of how LiDAR data is used,
the rapid growth of LiDAR and its diversity of uses makes a comprehensive yet useful
summary nearly impossible. Interested readers should refer to the included references for
more detailed examples.
5.1 Forestry, vegetation structure
In many countries LiDAR is already in operational use in the forestry sector, where
it is used for forest inventory purposes. Variables that are commonly used in forestry
applications include height and density percentiles of the vertical distribution of the
LiDAR echoes calculated from the rst and only echoes or from last echoes. Commonly
these variables are named h5, h10,…, h100. However, if calculated with categorizing
echoes then the prex f_h80 or l_h80 is frequently used to refer to rst or last echoes,
respectively. For example, the variable h80 indicates the agl height below which 80% of
all the echoes were received. For example, and h80 value of 21.3 indicates that 80% of
the echoes received from this area came below the height of 21.3 meters. Other common
variables are the mean, maximum and standard deviation of the echo heights.
Calculating the variables in forestry typically entails gridding the study area (or forest
stands) and calculating the selected variable for each grid cell. For example, the echoes
coming from inside a single grid cell are analysed separately from the other echoes, so
the created variables will be cell-specic: e.g. the mean height of LiDAR echoes inside
this cell, and then the desired variables for those echoes are calculated. Extending this
to the entire study area produces maps of forest structure based on the LiDAR variables
(Figure 5.1).
For a wi de range of LIDA R
applications, refer to:
Figure 5.1 A common example where LiDAR variables have been calculated for a forest
stand that has been gridded into 16 x 16 meter grid cells. The nal LiDAR variables have
been then used to predict forest characteristics of interest within each grid cell, in this case
volume of timber.
The application of LiDAR in forestry is far more diverse and widespread than covered
here and outside the scope of this guide. Readers interested in learning more about the
history and other uses of LiDAR in forestry should refer to Hudak et al. (2009), White et
al. (2013), and Maltamo et al. (2014). As forests cover a broad range of dierent types,
the use of LiDAR is consequently very dierent between e.g. boreal coniferous forest or a
tropical rain forest. The main issue, no matter what the environment, is to reliably detect
the ground, without which the echo heights do not have a practical meaning. Nonetheless,
LiDAR has been applied in many dierent forest environments.
The practical use of LiDAR was rst pioneered in boreal forests due to their “easy” structure.
Unlike rain forests they are not composed of dense multiple layers so the pulses have
a higher probability of reaching the ground, which makes the data itself more accurate
because the ground surface can be reliably detected and modelled with high accuracy. By
contrast, the dense structure of tropical rain forests posed a major obstacle to LiDAR use.
However, as the devices improved it became possible to use LiDAR in more challenging
environments such as tropical forests. Examples of use of LiDAR in tropical forests are
provided by Asner et al. (2012) and Leitold et al. (2015), whilst Hansen et al. (2015) provide
examples of the challenges proposed by tropical forest structure and how to overcome them.
5.2 Analysis of land cover, topography and hydrology
In addition to its utility for mapping vegetative structure, LiDAR has also been employed to
assess landcover, topography and hydrology. Kiss et al. (2015, 2016) used LiDAR to assess
forest road conditions. In their studies, the analysis was conducted with LiDAR-based
DTMs, from which the topography of the road and the surrounding ditches and terrain was
assessed in great detail. The key determinant of the road quality was hydrology and the ow
of water, which they were able to assess based on the 3D characteristics of the road.
In general, LiDAR-based assessments of soil moisture content and topographic wetness or
ow of water have been studied in detail. For soil moisture content, see Gillin et al. (2015) or
Tenebaum et al. (2006). For examples of topographic wetness index (TWI) calculations based
on LiDAR, see Buchanan et al. (2014). For more applied cases, see the LiDAR-based mapping
of ood risk areas by Bales et al. (2007). Also, in a recent work, Pippuri et al. (2016) showed
that LiDAR data can be used to classify land cover and land use in boreal forests.
0.00 -
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5.3 Ecological applications
That vegetation structure is a primary determinant of habitat quality was rst noted in
the seminal work of MacArthur & MacArthur (1961). This concept is very practical; the
structure and arrangement of vegetation is what ultimately denes the distribution and
abundance of critical habitat components such as food, nesting sites (in the canopy or on the
ground), thermal shelters, cover against predation, and camouage. LiDAR provides data
that can be used to estimate all of these and other variables with unprecedented precision at
the landscape-level. A review of this concept is provided in Vierling et al. (2008).
The application of LiDAR to estimating habitat-related variables is summarized in the
following sections. For organizational purposes, this section has been divided into variable
estimation for volant and canopy species, terrestrial species, biodiversity mapping,
mapping of important habitats (hot-spots), and the estimation of attributes that are often
linked to global conditions of forests and vegetation such as Leaf Area Index (LAI) and
canopy cover. As with the other sections, it is not possible to cover all the studies or aspects
and readers are encouraged to reference publications cited therein for additional details.
5.3.1 Volant and canopy species
Birds were the species with which the connection between LiDAR and wildlife ecology
was rst published in the work of Hill et al. (2004). Since that initial work, multiple other
researchers have found LiDAR useful for assessing habitat of canopy-dependent, as well
as ground-nesting, birds (Graf et al. 2009; Martinuzzi et al. 2009; Goetz et al. 2010; Hill
& Hinsley 2015, Zellweiger et al. 2013, Zellweiger et al. 2016; and Melin et al. 2016).
Arboreal mammals living in trees have also been a topic of LiDAR-based studies that
have revealed patterns in habitat use preference in relation to canopy structure. Species
included in these analyses include bats (Froidevaux et al. 2016), primates (Palminteri et
al. 2012), and squirrels (Flaherty et al. 2014).
The methods applied to research questions have varied between the studies. Palminteri et
al. (2012) focused on assessing the home-ranges and thereby the occurrence of their target
species based on rst studying the canopy structure (from LiDAR) in the areas mostly
used by the target species. Melin et al. (2016) extracted LiDAR data from presence and
absence locations of boreal grouse broods and thereby estimated what are the structural
features of forests that mostly dene a good grouse brood habitat. In the latter study, the
LiDAR data was integrated with eld inventory data, which allowed to spatially dene the
areas of presence and absence.
5.3.2 Terrestrial species
Examination of forest structure relative to the habitat use of terrestrial species is linked
to the abundance of important attributes such as availability of food or shelter, which
presumably provides greater insights than do more typical metrics such as cover type.
Recent target species for these analyses have included ungulates (Coops et al. 2010; Melin
2015, Lone et al. 2014), bears (Wulder et al. 2008) and lions (Davies et al. 2016); as with
birds, the methods have varied between the studies. Lone et al. (2014) gridded the study
area into 50 x 50 meter cells and extracted LiDAR data from each of the cells. Next, they
created LiDAR metrics that were used to predict the availability of browse biomass for
moose in each of the cells, thus dening the suitability of this cell for moose. Davies et al.
(2016) rst identied the potential kill sites of lions and then extracted LiDAR data from
the area of these sites to assess the structure of their vegetation.
5.3.3 Mapping suitable habitat
Many of the studies listed above have integrated LiDAR with locations from tagged or
collared animals to examine habitat structure relative to use. LiDAR has also been used to
locate features of habitats or the canopy that are known to be required by a specic species,
which then allows the modelling of the suitability of a habitat patch or area in the landscape
and the distribution of suitable habitat. For example, coarse woody debris and snags are
important components of suitable habitat for many species and LiDAR has been used to
map their distribution. Pesonen (2010) used LiDAR data to successfully assess the presence
and abundance of coarse woody debris, the presence of which is a key determinant of boreal
forest biodiversity. Martinuzzi et al. (2009) improved habitat suitability estimates for four
bird species by the inclusion of LiDAR data, which was due to it being able to catch the
presence of snags and features of vegetation structure deemed important for the species
in question. Back in the boreal zone, Vehmas (2010) used LiDAR data to map old-growth
coniferous stands and stands with high herbaceous diversity.
5.3.4 Estimating canopy cover and leaf area index
Variables such as Canopy Cover (CC) and Leaf Area Index, (LAI) are frequently used as
proxies for classifying forests and assessing their condition and LiDAR has proven to be a
highly accurate tool for these tasks. Korhonen (2011) and Korhonen et al. (2011) provide
examples of assessing LAI and CC from LiDAR, and Vehmas et al. (2011) illustrate how to
detect canopy gaps and understory in boreal conditions whilst Hill & Broughton (2009)
mapped the understory of deciduous forests of England using leaf-on and leaf-o LiDAR data.
5.3.5 Detecting forest change
Forest and vegetation structural changes occur continuously for a host of reasons,
including natural succession, weather, damages or harvesting and logging. LiDAR data
has been successfully used to identify the severity of extent of these kinds of changes.
Examples include change resulting from snow damage (Vastaranta et al. 2012), insect
damage (Coops et al. 2009; Solberg et al. 2006; Vastaranta et al. 2013), moose browsing
damage (Melin et al. 2016), and natural succession (McRoberts et al. 2014). It is naturally
obvious that LiDAR is particularly well suited to detect man-induced changes such as
harvesting or clear-cutting (Figure 5.2).
Figure 5.2 Changes in the New Forest National Park (England, UK) visualized from LiDAR
data collected in 2009 (a) and 2015 (b). White colour indicates higher vegetation; taller trees.
As seen in Figure 5.2, the change is detectable in the CHM raster image as well. There is
no need for detailed point cloud analysis if the aim is to document that a change has taken
place. The change in the lower left corner is regrowth based on the appearance of small
grey trees growing on image b. In the top right corner, we see both the removal of trees
and the growth of those that remained (the trees appear whiter in the image b).
5.4 Forest biomass mapping
REDD+ (Reducing Emissions from Deforestation and Degradation) is a framework
designed to incentivise the protection and retention of carbon contained in forests. LiDAR
has been increasingly used for mapping above-ground forest biomass, as the ability to
penetrate the canopy and determine three-dimensional structure at scale is useful for
estimating forest carbon stock (Lefksy et al. 2002). To estimate biomass, LiDAR data
are calibrated with information from eld inventory plots, which may include data on
diameter at breast height (DBH), basal area, or wood density. These metrics, along with
precise measurements of forest canopy height and ground surface from LiDAR are used
to develop appropriate biomass models (Asner et al. 2012, Kotivuori et al. 2016) which
accurately estimate carbon stock nearly as well as eld plots (Mascaro et al. 2011), while
reducing the need for extensive ground inventories (Ferraz et al. 2016). Use of these
techniques enables national-scale mapping of biomass (Asner et al. 2013) and can be
used to monitor biomass over time (Meyer et al. 2013). For additional examples of LiDAR
being applied for REDD+ analyses refer to Joshi et al. (2014), Leitold et al. (2015), Tokola
(2015), Ene et al. (2016), and Kauranne et al. (2017).
5.4 Bathymetry
The purpose of bathymetric LiDAR is to provide detailed information on water depth
and underwater barriers for navigation of water bodies, and to date bathymetric scans
have been completed for the shores of Alaska and the Caribbean. Bathymetric LiDAR
diers from terrestrial LiDAR in one important way. Because bathymetric LiDAR aims
to map the structure of the terrain below the water surface it must employ a laser that
emits pulses that dier in wavelength to those emitted by terrestrial LiDAR devices;
the wavelengths of pulses emitted by terrestrial LiDAR devices are unable to penetrate
the surface of the water, and rapidly attenuate near the surface. Devices meant for
bathymetric purposes use lasers operating at shorter wavelengths, which penetrate the
water and thus give echoes from the lake or sea bottom, enabling one to map depth and
accurately depict the 3D terrain of the ocean/lake oor. This is very similar to the DTM
in Figure 4.3b. The depth the laser can reach is dependent on the clarity of the water;
the clearer the water, the deeper the pulse’s reach. Bathymetric LiDAR data, including its
application in coral reef conservation, can be found in Brock et al. (2004), Pittman et al.
(2009), Pe´eri et al. (2011),
For additional details about
biomass mapping, please refer to:
Image: © S. Popescu
Terrestrial lidar scan of oak trees during leaf-off season.
• Future LiDAR datasets will likely include multispectral properties and improved
spatial and temporal coverage and resolutions
• These advances will increasingly allow mapping of annual or more frequent changes
in the distribution and abundance of individual species at the global scale
This chapter briey discusses new and upcoming applications of LiDAR for surveying
terrestrial ecosystems. For a recent in-depth review of LIDAR horizons, see Eitel et al. (2016).
6.1 Multi-wavelength (or multispectral) LiDAR
Multispectral LiDAR is a technique that is currently receiving intense interest from many
researchers and it is rapidly developing, particularly in forestry. The core principle is
that the use of multi-wavelength LiDAR can discriminate more eciently among the
types of surfaces from which the echoes reect. For example, use of multispectral LiDAR
can distinguish among coniferous and deciduous trees, bare and vegetated ground, and
in some cases individual tree species. These discriminations are invaluable as they can
result in species-specic estimates of vegetative structure, which in turn yields more
accurate estimates of the species-specic variables of forest structure, stock, and volume.
Furthermore, the ability of multispectral LiDAR to identify individual species will assist
in locating endangered tree species. The potential application of this technique to wildlife
ecology is also promising, because of its complementarity to structure; having accurate
estimates of species-specic vegetative structure would improve models of habitat
suitability and identication of vital habitat patches for specialist species.
Multispectral LiDAR is able to distinguish among the dierent surfaces because it utilizes
lasers employing wavelengths that are able to dierentiate chlorophyll levels (Nevalainen
et al. 2014). Because plants possess chlorophyll and its concentration diers among
species, multispectral LiDAR is able to discriminate between the presence and absence of
chlorophyll (for example between bare ground and vegetation) and in some cases among
species that dier in chlorophyll content, for example between coniferous and deciduous
species. We are yet to see the full potential of this data to the wildlife research community.
6.2 Spaceborne systems
LiDAR sensors have also been placed on board satellites, and these provide global LiDAR
data, but at coarser resolutions. For instance, the NASAs GEDI LiDAR has a resolution
of 25 meters (the width of the pulse as it hits the ground and the pulses come between
~60 meters. These datasets are freely available and are useful for large-scale estimates of
ecosystem structure, but they cannot provide information about what is happening within
the 25 meter area of the pulse. The limitation of this coarse resolution was documented by
Vierling et al. (2013) who tested the utility of both airborne and spaceborne (GLAS) LiDAR
data in modelling woodpecker habitat occupancy. They concluded that the coarse resolution
of the spaceborne data was unable to capture habitat information at a suciently ne scale.
Despite these limitations, spaceborne LiDAR data remain valuable for large-scale, low
resolution analyses, such as for the GLAS LiDAR based 500 meter resolution canopy height
map of the Amazon (Sawada et al. 2015) and global maps of biomass (Baccini et al. 2012).
Examples of more research conducted with the spaceborne systems as well as comparisons
of spaceborne systems to ALS systems are provided in Bergen et al. (2009), Popescua et al.
(2011), Suna et al. (2008) and Tanga et al. (2014).
nsidc .org/data/d ocs/da ac/gla s_
icesat_l1_l2_ global_altimetry.
More information on the
spaceborne systems can
be found h ere:
global-ecosys tem-dynamics-
investigation-LiDA R
This section is not intended to be comprehensive or prescriptive, as the programs to process
LiDAR are numerous and increasing rapidly in accordance with the evolving eld and user
needs. In addition, those with programming skills and the right knowledge may prefer to
write their own programs for their own needs. Therefore, this section briey introduces a
few of the more commonly used software including GIS and remote sensing programs.
Detailed information on each tool is not given, but links (current at the time of writing) to
the software guidelines are provided. Some programs (e.g. LAS Tools, Fusion and Opals)
have been specically designed for processing and analysing LiDAR, whereas others (e.g.
QuantumGIS, ArcGIS, ENVI) are well-known GIS and remote sensing software which also
have LiDAR functionality.
Software Website Description
Software Website Description
LAS Tools tools The data in Figure 3.1 were converted from LA Z to text with LA2TXT software by Mar tin Isenburg.
LAS Tools oer every tool needed from raw data processing to analysis and variable ex traction.
LAS Tools runs on command line, but also oers a graphical user interface (GUI) for those not
comfor table with command lines and batch scripting. Every tool (e.g. las2DTM or las2txt) comes
with its own README le, which tells in principle what the tool is used for and how it is used
(inputs, outputs, parameters etc.). For an example, see the RE ADME on las2DTM: www.cs.unc.
Fusion r.washington.
Fusion is a free program designed also for ecient LiDAR data processing and analysis. Overviews
of the LiDAR processing tools and principles of Fusion as well as tutorials on these matters are
provided at:
Opals (Orientation and Processing of Airborne Laser Scanning data), made by researcher s at
Vienna University of Technology, provides a full chain of tools from raw data processing up
until variable extraction and dierent applications. The program as well as DTMonstrations on
how to get started using the software are available at the website.
Quantum GIS
(QGIS) Quantum GIS, or QGIS, is one of the most popular open-source GIS programs. QGIS processes
LiDAR data via the LAS Tools software that can be installed as its own toolbox. For instructions on
how to install the software, refer to:
ENVI www.
Software/EN VI.aspx
In addition to its utility for processing and analysing LiDAR datasets, EN VI has a range of
functionality for GIS and remote sensing analyses. Information about the use of ENVI to process
LiDAR data can be found at:
ENVI also has a designated LiDA R plug-in developed by Idaho State University. This plug-in (BCAL
LiDAR) is meant purely for LiDAR processing and is available at:
ArcGIS /
ESRI’s ArcGIS family is one of the most widely used GIS sof tware. It also has its own tools for
handling, processing, and analysing LiDAR datasets. Some guidelines on how to use the tools are
available at: dataset/a-quick-tour-of-LiDAR-in-arcgis.
htm dataset/using-LiDAR-in-arcgis.htmciently
ERDAS IMAGINE is a widely used remote sensing software that also oers tools for LiDAR.
Information and examples are available at:
FugroViewer Is a fast and easy-to-use tool for visualizing LiDAR data
Table 3. A brief description and download links to select software for processing LiDAR data.
Image: © G. Asner/Carnegie Institution for Science
This map of estimated above ground forest carbon
density in Peru was developed using airborne
LIDAR, satellite imagery, and field data in 2014 by
the Carnegie Institution for Science at Stanford
University, Wake Forest University, and Peru’s
Ministry of Environment.
LiDAR data are being collected by numerous national land survey agencies, research
institutions, and forestry agencies and companies. In many cases these data are freely
available, but not always, as some private companies collect the data in order to sell it to
end users. This section aims to provide a list of the countries that have collected LiDAR
data or are planning to do so, and links to the agencies or organizations through which
these data can be acquired.
The available data formats, dierent products (e.g. DTMs) are not listed in this section
because these details are provided by the data distributor. Given the ephemeral nature of
the internet some links may not remain functional but readers should be able to locate the
data sets based on the information contained herein.
When a country is not mentioned in the list it means that no information was available (or
was not found) from the status of their LiDAR collection plans. Searches were done from
the internet and through direct consultation with geospatial agencies and national land
surveys. The URL in the table leads either directly to the data download service or into a
web-page/document describing the data acquisition process. The list has both free LiDAR
data as well as data for sale.
Country Website
Amazonia a
Finland tiedostopalvelu.maanmit tauslaitos./tp/kartta?lang=en
Hungary ww
Malta ww
Mexico wwa/default.aspx
Netherlands ww
Netherlands ww _massive_point _cloud_data_LMTh_Swart.
New Zealand
New Zealand
Norway ww
Serbia Republic Geodetic Authority -
produktbeskrivningar/eng/LiDAR _data.pdf
United Kingdom
Table 3. Sources for LiDAR data or related products.
Country Website
LiDAR is not a remote sensing panacea, and in many cases will not be necessary or even
appropriate. Before deciding to put resources into LiDAR data acquisition, processing,
and analysis, it is critical to consider the advantages and disadvantages of the method.
One potential alternative to LiDAR is photogrammetry, which uses stereo imagery and a
technique called image matching to map and measure surfaces. Indeed, the high cost of
LiDAR will make it inaccessible in some cases, and photogrammetry data are frequently less
expensive to collect and depending on the need may be entirely suitable for mapping soil or
coastal erosion (Heng et al. 2010). However, whilst photogrammetry can be accurate, lidar
remains more accurate when it comes to 3D structure. This is because as photogrammetry
relies on reected sunlight, it cannot see beneath the top-most surface of the forest canopy
and therefore lacks data on subsurface/subcanopy structure (Tanhuanpää et al. 2016;
Melin et al. 2017), although there have been some promising results in forest inventories
(Bohlin et al. 2012; Kukkonen et al. 2017; Puliti et al. 2016; Puliti et al. 2017).
9.1 LiDAR weaknesses in brief
Limited spatial and temporal availability. Freely available LiDAR datasets are
available for a small fraction of the world, and are frequently limited to data collected at a
single point in time.
High cost of collection of LiDAR data. LiDAR costs decrease per unit area as the
total area surveyed increases, but can be substantial, and depend on the cost of fuel, pilots,
airplane rental, all of which depend on geography and weather. There are several large
international companies who do LiDAR data collection and will provide quotes1.
Technically complex processing, analysing and interpretations. LiDAR
data processing is time-consuming and technically challenging, particularly for the
unexperienced, and depending on the task may require expertise in GIS and remote
sensing to use and interpret properly.
Lack of multispectral information. That LiDAR currently largely lacks multispectral
data limits its utility to non-spectral analyses.
LiDAR cannot penetrate thick canopies. For instance, dense tropical forests are
problematic due to lack of ground hits.
9.2 LiDAR strengths in brief
Most accurate 3D information. LiDAR provides the most accurate data on 3D
structure of any remote sensing technique, particularly when it comes to dense vegetation,
with low-pulse density LiDAR typically exhibiting sub-meter accuracy.
Versatile data. LiDAR data can be used to produce digital models of terrain and canopy
and customized for myriad needs to suit the research questions at hand.
High complementarity with remotely sensed imagery. LiDAR provides highly
complementary data that can be used in conjunction with satellite and aerial imagery to gain
insights that neither imagery nor LiDAR can reach alone (Asner et al. 2008; Packalén 2009;
Cho et al. 2012; Melin et al. 2017).
services-and-technolog y/
airborne-imaging-L iDAR-
services/airborne- LiDAR
9.3 Summary of strengths and weaknesses
Multispectral information is not present in standard LiDAR data. Aerial and satellite images
provide information on the target´s reectance at dierent wavelengths and so they can be
used to detect dierent species or phenological stages of vegetation, but absent additional
information LiDAR can be used only to assess the structure (Figure 9.1):
Figure 9.1 LiDAR-based canopy height model (CHM) and a false-colour infrared aerial
image from the same location. Although the CHM provides information on tree height,
it does not contain information on tree species, whereas the aerial image does. Here
deciduous species are shown in red and coniferous as dark green.
The structural information from LiDAR and the spectral data from aerial or satellite
imagery can be integrated to model species-specic vegetative structure (Figure 9.1),
a method applied by Packalén (2009). Although classication of tree species from the
geometry of LiDAR point clouds has been done (Vauhkonen 2010), publicly available
data are generally not of sucient resolution to do so. As such, although suciently high
resolution LiDAR data are capable of identifying tree species under some circumstances,
LiDAR alone is not commonly used to identify species; this can be overcome by
integrating it with spectral data from another source such as aerial or satellite imagery.
A clear advantage of LIDAR is that it is stable and consistent - it can produce products
such as DTMs which are comparable and hence can be used across time and space (Hill &
Hinsley 2015; Vierling et al. 2014). Furthermore a stable and accurate DTM enables
detailed monitoring of the vegetation above the ground surface.
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Image: © WWF/BMUB/KFW/Southern Mapping Company
Airborne photo mosaic (10 cm resolution)
of forest and savanna collected during the
2014 LiDAR Democratic Republic of the
Congo campaign.
... On one hand, rooftop solar deployments are able to leverage unused space, and overcome the limitations of Q. Li et al. methods (Mohajeri et al., 2018;Lingfors et al., 2017) utilize light detection and ranging (LiDAR) data or stereo photos to acquire 3D building models, which are able to capture precise rooftop geometry and quantify the shadows cast by other buildings and vegetation canopies (Fogl and Moudrỳ, 2016). With the increasing availability of open LiDAR data (Melin et al., 2017), some studies (Margolis et al., 2017;Suomalainen et al., 2017) utilize these data for solar potential analysis. However, retrieving 3D building structures is highly expensive when there are no open LiDAR data or stereo photos. ...
Solar power is a clean and renewable energy source. Promoting solar technology can not only offer all people affordable, reliable, and modern energy, but also mitigate energy-related emissions and pollutants. This significantly contributes to sustainable development goals. Aerial imagery can provide a cost-effective way for large-scale rooftop solar potential analysis when compared to other data sources. Existing studies mainly utilize aerial imagery and convolutional neural networks to learn the roof segmentation mask or the rooftop geometry map, which are the preliminary input for rooftop solar potential estimation. However, these methods fail to achieve precise solar potential analysis results. To address this issue, we propose a framework, which is termed as SolarNet for rooftop solar potential estimation. A novel multi-task learning network is devised in SolarNet to learn our proposed novel representation for rooftop geometry that incorporates 6 roof segments and orientations. Specifically, this network first learns a roof segmentation map, and then together with the extracted multiscale and contextual features to learn a roof geometry map. Finally, the solar potential can be estimated from the learned roof geometry map. The effectiveness of SolarNet is validated on two datasets: DeepRoof and RID datasets. Experimental results demonstrate that SolarNet can improve not only rooftop geometry prediction accuracy but also solar potential estimation precision, which significantly outperforms other competitors.
... Currently, ALS datasets cover entire countries (Chapter 8 in Melin et al. 2017). In Finland, low-density ALS data are available for the entire country and the second round of data collection has already started. ...
The era of airborne laser scanning (ALS) and the development of new forest inventory methods has reduced the need for field visits and overall inventory costs over the last two decades. Although the development of inventory methods has been considerable, some systematic field visits are usually always required. For example, the most common ALS inventory method, the area-based approach (ABA), leans on field sample plot measurements. Likewise in the ALS inventory, the ABA method can also be used in drone-based inventories with image point cloud (IPC) data. Due to the small areal coverage of the drones, local sample plot measurements in drone image point cloud (DIPC) inventories are not usually profitable. The objective of this thesis was to examine the performance of ALS-based forest attribute models in ALS- and DIPC-based ABA inventories without new in-situ field measurements. In this study, nationwide ALS models for three forest attributes (stem volume, above ground biomass and dominant height) were fitted for the whole of Finland, and regional-level error rates of the nationwide model predictions were assessed. As the nationwide models tended to exhibit systematic region-wise under- and over-predictions, different calibration methods were examined. First, calibration of nationwide models with a small number of new field measurements from the target area was simulated. Second, the nationwide stem volume model or its regional predictions was calibrated without new in-situ field measurements by three test scenarios: a) using additional calibration variables in the models to account for geographical and environmental conditions throughout the country, b) refitting of the models by using existing sample plots from nearby regions, and c) matching the regional-level predictions with national forest inventory data. The DICP-based forest inventory without new in-situ field measurements was evaluated by replacing the ALS metrics from the ALS-based models with DIPC metrics when the models were applied. In the DIPC inventory, the metrics used in the ALS models were selected carefully so that they would be similar to the corresponding DIPC metrics. The results showed that forest attributes can be predicted without new in-situ field measurements using nationwide ALS-based models with moderate error rates. The systematic errors associated with the nationwide models decreased when the models were fitted with additional calibration variables, such as degree days, precipitation, and tree species proportions. However, the measurement of a carefully selected set of sample plots (e.g., 20 plots) from the target area for the calibration of the nationwide model is recommended, in instances where it is economically feasible. Prediction of forest attributes using ALS-based models with DIPC metrics is possible provided the predictor variables describe the upper canopy layer. The lowest error rates in DIPC-based inventories were obtained when the ALS-based model was fitted in a nearby region and the inventory units were disaggregated to coniferous and deciduous dominated areas before the prediction.
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L’inventaire et la cartographie des habitats sont des processus essentiels pour la mise en œuvre des politiques de conservation de la nature. Les méthodes actuelles, basées sur des prospections de terrain, sont difficilement applicables sur de vastes territoires et jugées inadaptées à un suivi régulier et harmonisé des habitats. L’objectif de cette thèse est d’explorer des approches innovantes afin de faciliter l’inventaire et la cartographie des habitats sur de grands sites naturels, en prenant comme cas d’étude le site Natura 2000 ‘Estuaire de la Loire’. Un système expert a été développé pour l’identification de relevés phytosociologiques afin d’établir la typologie des habitats du site. Cette démarche a permis de rattacher de manière formelle 1843 relevés de végétation à 89 habitats EUNIS et 17 habitats d’intérêt communautaire. Des images satellites Sentinel-2 et des données aéroportées hyperspectrales et LiDAR ont été exploitées pour spatialiser les habitats du site par télédétection. Ces différentes données, aux caractéristiques complémentaires (résolutions spatiales, résolutions spectrales, répétitivité, 3D), ont permis de cartographier avec une très grande précision la majorité des habitats des 24 000 ha de l’estuaire de la Loire. L’application de ces nouvelles approches démontre l’intérêt d’associer les systèmes experts et la télédétection pour typifier et cartographier des habitats de façon rentable et reproductible favorisant une gestion concertée du site Natura 2000.
Avian diversity has long been used as a surrogate for overall diversity. In forest ecosystems, it has been assumed that vegetation structure, composition and condition have a significant impact on avian diversity. Today, these features can be assessed via remote sensing. This study examined how structure metrics from lidar data and narrowband indices from hyperspectral data relate with avian diversity. This was assessed in four deciduous-dominated woods with differing age and structure set in an agricultural matrix in eastern England. The woods were delineated into cells within which metrics of avian diversity and remote sensing based predictors were calculated. Best subset regression was used to obtain best lidar models, hyperspectral models and finally, the best models combining variables from both datasets. The aims were not only to examine the drivers of avian diversity, but to assess the capabilities of the two remote sensing techniques for the task. The amount of understorey vegetation was the best single predictor, followed by Foliage Height Diversity, reflectance at 830 nm, Anthocyanin Reflectance Index 1 and Vogelmann Red Edge Index 2. This showed the significance of the full vertical profile of vegetation, the condition of the upper canopy, and potentially tree species composition. The results thus agree with the role that vegetation structure, condition and floristics are assumed to have for diversity. However, the inclusion of hyperspectral data resulted in such minor improvements to models that its collection for these purposes should be assessed critically
In the previous chapter, we described the location sensor under the CPS spectrum and the integration concept of GPS and INS sensors. It is said that the imagery data obtained by human eye accounts for 90% of total information acquired from various human sensory organs such as ear, nose and tongue. As the animals evolve from lower level (e.g. insects) to higher grade (e.g human), the utilization of visual data becomes higher. Likewise, as the substitution of natural intelligence by artificial intelligence advances, the dependence on imagery data increases. The underlying principle in data acquisition for CPS systems is to select imaging sensor suitable for operational application based on customer requirements and intended information such as training AI (Artificial Intelligence). An imaging sensor is a sensor converting the variable electromagnetic radiation delivered from the target into signals that convey the information. In order to identify the sensing requirements desired by the CPS instruments such as drones and self-driving cars, the pros and cons of various imaging sensors should be identified and utilized properly. Subsequently this chapter presented advantages and values of low-cost drone photography for hyper-localized targets (e.g. structural cracks in the skeleton of a concrete building and human hand gesture in the street crosswalk) in comparison to the existing methods.
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Reducing uncertainty of terrestrial carbon cycle depends strongly on the accurate estimation of changes of global forest carbon stock. However, this is a challenging problem from either ground surveys or remote sensing techniques in tropical forests. Here, we examine the feasibility of estimating changes of tropical forest biomass from two airborne lidar measurements of forest height acquired about 10 yr apart over Barro Colorado Island (BCI), Panama. We used the forest inventory data from the 50 ha Center for Tropical Forest Science (CTFS) plot collected every 5 yr during the study period to calibrate the estimation. We compared two approaches for detecting changes in forest aboveground biomass (AGB): (1) relating changes in lidar height metrics from two sensors directly to changes in ground-estimated biomass; and (2) estimating biomass from each lidar sensor and then computing changes in biomass from the difference of two biomass estimates, using two models, namely one model based on five relative height metrics and the other based only on mean canopy height (MCH). We performed the analysis at different spatial scales from 0.04 ha to 10 ha. Method (1) had large uncertainty in directly detecting biomass changes at scales smaller than 10 ha, but provided detailed information about changes of forest structure. The magnitude of error associated with both the mean biomass stock and mean biomass change declined with increasing spatial scales. Method (2) was accurate at the 1 ha scale to estimate AGB stocks (R2 = 0.7 and RMSEmean = 27.6 Mg ha−1). However, to predict biomass changes, errors became comparable to ground estimates only at a spatial scale of about 10 ha or more. Biomass changes were in the same direction at the spatial scale of 1 ha in 60 to 64% of the subplots, corresponding to p values of respectively 0.1 and 0.033. Large errors in estimating biomass changes from lidar data resulted from the uncertainty in detecting changes at 1 ha from ground census data, differences of approximately one year between the ground census and lidar measurements, and differences in sensor characteristics. Our results indicate that the 50 ha BCI plot lost a significant amount of biomass (−0.8 Mg ha−1 yr−1 ± 2.2(SD)) over the past decade (2000–2010). Over the entire island and during the same period, mean AGB change was 0.2 ± 2.4 Mg ha−1 yr−1 with old growth forests losing −0.7 Mg ha−1 yr−1 ± 2.2 (SD), and secondary forests gaining +1.8 Mg ha yr−1 ± 3.4 (SD) biomass. Our analysis suggests that repeated lidar surveys, despite taking measurement with different sensors, can estimate biomass changes in old-growth tropical forests at landscape scales (>10 ha).
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Accurately predicting soil moisture patterns in the landscape is a persistent challenge. In humid regions, topographic wetness indices (TWIs) are widely used to approximate relative soil moisture patterns. However, there are many ways to calculate TWIs and very few field studies have evaluated the different approaches – especially in the US. We calculated TWIs using over 400 unique formulations that considered different digital elevation model (DEM) resolutions (cell size), vertical precision of DEM, flow direction and slope algorithms, smoothing via low-pass filtering, and the inclusion of relevant soil properties. We correlated each TWI with observed patterns of soil moisture at five agricultural fields in central NY, USA, with each field visited five to eight times between August and November 2012. Using a mixed effects modeling approach, we were able to identify optimal TWI formulations applicable to moderate relief agricultural settings that may provide guidance for practitioners and future studies. Overall, TWIs were moderately well correlated with observed soil moisture patterns; in the best case the relationship between TWI and soil moisture had an average R2 and Spearman correlation value of 0.61 and 0.78, respectively. In all cases, fine-scale (3 m) lidar-derived DEMs worked better than USGS 10 m DEMs and, in general, including soil properties improved correlations.
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Good quality forest roads are important for forest management. Airborne laser scanning data can help create automatized road quality detection, thus avoiding field visits. Two different pulse density datasets have been used to assess road quality: high-density airborne laser scanning data from Kiihtelysvaara and low-density data from Tuusniemi, Finland. The field inventory mainly focused on the surface wear condition, structural condition, flatness, road side vegetation and drying of the road. Observations were divided into poor, satisfactory and good categories based on the current Finnish quality standards used for forest roads. Digital Elevation Models were derived from the laser point cloud, and indices were calculated to determine road quality. The calculated indices assessed the topographic differences on the road surface and road sides. The topographic position index works well in flat terrain only, while the standardized elevation index described the road surface better if the differences are bigger. Both indices require at least a 1 metre resolution. High-density data is necessary for analysis of the road surface, and the indices relate mostly to the surface wear and flatness. The classification was more precise (31–92%) than on low-density data (25–40%). However, ditch detection and classification can be carried out using the sparse dataset as well (with a success rate of 69%). The use of airborne laser scanning data can provide quality information on forest roads.
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The scientific community involved in the UN-REDD program is still reporting large uncertainties about the amount and spatial variability of CO 2 stored in forests. The main limitation has been the lack of field samplings over space and time needed to calibrate and convert remote sensing measurements into aboveground biomass (AGB). As an alternative to costly field inventories, we examine the reliability of state-of-the-art lidar methods to provide direct retrieval of many forest metrics that are commonly collected through field sampling techniques (e.g., tree density, individual tree height, crown cover). AGB is estimated using existing allometric equations that are fed by lidar-derived metrics at either the individual tree-or forest layer-level (for the overstory or underneath layers, respectively). Results over 40 plots of a multilayered forest located in northwest Portugal show that the lidar method provides AGB estimates with a relatively small random error (RMSE = of 17.1%) and bias (of 4.6%). It provides local AGB baselines that meet the requirements in terms of accuracy to calibrate satellite remote sensing measurements (e.g., the upcoming lidar GEDI (Global Ecosystem Dynamics Investigation), and the Synthetic Aperture Radar (SAR) missions NISAR (National Aeronautics and Space Administration and Indian Space Research Organization SAR) and BIOMASS from the European Space Agency, ESA) for AGB mapping purposes. The development of similar techniques over a variety of forest types would be a significant improvement in quantifying CO 2 stocks and changes to comply with the UN-REDD policies.
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Height models based on high-altitude aerial images provide a low-cost means of generating detailed 3D models of the forest canopy. In this study, the performance of these height models in the detection of individual trees was evaluated in a commercially managed boreal forest. Airborne digital stereo imagery (DSI) was captured from a flight altitude of 5 km with a ground sample distance of 50 cm and corresponds to regular national topographic airborne data capture programs operated in many countries. Tree tops were detected from smoothed canopy height models (CHM) using watershed segmentation. The relative amount of detected trees varied between 26% and 140%, and the RMSE of plot-level arithmetic mean height between 2.2 m and 3.1 m. Both the dominant tree species and the filter used for smoothing affected the results. Even though the spatial resolution of DSI-based CHM was sufficient, detecting individual trees from the data proved to be demanding because of the shading effect of the dominant trees and the limited amount of data from lower canopy levels and near the ground.
Canopy cover (CC) is a variable used to describe the status of forests and forested habitats, but also the variable used primarily to define what counts as a forest. The estimation of CC has relied heavily on remote sensing with past studies focusing on satellite imagery as well as Airborne Laser Scanning (ALS) using light detection and ranging (lidar). Of these, ALS has been proven highly accurate, because the fraction of pulses penetrating the canopy represents a direct measurement of canopy gap percentage. However, the methods of photogrammetry can be applied to produce point clouds fairly similar to airborne lidar data from aerial images. Currently there is little information about how well such point clouds measure canopy density and gaps. The aim of this study was to assess the suitability of aerial image point clouds for CC estimation and compare the results with those obtained using spectral data from aerial images and Landsat 5. First, we modeled CC for n=1149 lidar plots using field-measured CCs and lidar data. Next, this data was split into five subsets in north-south direction (y-coordinate). Finally, four CC models (AerialSpectral, AerialPointcloud, AerialCombi (spectral + pointcloud) and Landsat) were created and they were used to predict new CC values to the lidar plots, subset by subset, using five-fold cross validation. The Landsat and AerialSpectral models performed with RMSEs of 13.8% and 12.4%, respectively. AerialPointcloud model reached an RMSE of 10.3%, which was further improved by the inclusion of spectral data; RMSE of the AerialCombi model was 9.3%. We noticed that the aerial image point clouds managed to describe only the outermost layer of the canopy and missed the details in lower canopy, which was resulted in weak characterization of the total CC variation, especially in the tails of the data.
Image matching is emerging as a compelling alternative to airborne laser scanning (ALS) as a data source for forest inventory and management. There is currently an open discussion in the forest inventory community about whether, and to what extent, the new method can be applied to practical inventory campaigns. This paper aims to contribute to this discussion by comparing two different image matching algorithms (Semi-Global Matching [SGM] and Next-Generation Automatic Terrain Extraction [NGATE]) and ALS in a typical managed boreal forest environment in southern Finland. Spectral features from unrectified aerial images were included in the modeling and the potential of image matching in areas without a high resolution digital terrain model (DTM) was also explored. Plot level predictions for total volume, stem number, basal area, height of basal area median tree and diameter of basal area median tree were modeled using an area-based approach. Plot level dominant tree species were predicted using a random forest algorithm, also using an area-based approach. The statistical difference between the error rates from different datasets was evaluated using a bootstrap method. Results showed that ALS outperformed image matching with every forest attribute, even when a high resolution DTM was used for height normalization and spectral information from images was included. Dominant tree species classification with image matching achieved accuracy levels similar to ALS regardless of the resolution of the DTM when spectral metrics were used. Neither of the image matching algorithms consistently outperformed the other, but there were noticeably different error rates depending on the parameter configuration, spectral band, resolution of DTM, or response variable. This study showed that image matching provides reasonable point cloud data for forest inventory purposes, especially when a high resolution DTM is available and information from the understory is redundant.
The use of image-based three-dimensional data from unmanned aerial vehicles (UAV) has proven effective for forest inventories. However, limitations in the range of UAV operations hinder their use in large scale applications. Use of partial-coverage UAV data in combination with field plots may increase precision of field-based estimates of forest resource parameters and may offer a cost-effective alternative to wall-to-wall acquisition. In this study, data from UAV collected in systematically distributed blocks and combined with field plots in a two-phase design with hybrid inference (UAVHYB) were used to estimate mean volume and its precision (standard error) for a 7330 ha forest area in Norway. Because in UAVHYB the field data do not necessarily need to come from a probability sample, the approach offers great flexibility in field data collection. The estimate of precision using UAVHYB was compared to two alternative inventory approaches differing in terms of sampling design and mode of inference, i.e., (1) single-phase probabilistic sampling relying only on field samples for which the design-based approach to inference using simple random sampling estimators was adopted (FIELDDB) and (2) a model-based approach independent from design assumptions for which wall-to-wall airborne laser scanning data were applied (ALSMB). Relative efficiency (RE), calculated as the ratio between the estimated variances of two different inventory approaches, was used as measure for improvement in variance for one approach over the other. Comparison of UAVHYB against FIELDDB revealed that the use of the former was up to four times more efficient than the latter (RE = 4.4). This translates to a need for 4.4 times as many field plots under simple random sampling for a FIELDDB estimate to be equally precise as the UAVHYB estimate. For ALSMB the increase in efficiency compared to UAVHYB was limited (RE = 1.6). The study also demonstrated that the precision under UAVHYB can be improved when including additional field data from other inventories to enhance the model. Cost estimates for each inventory approach were compared, revealing that UAV may be a cost-effective tool for large scale forest resource assessments.
Capturing and quantifying the world in three dimensions (x,y,z) using light detection and ranging (lidar) technology drives fundamental advances in the Earth and Ecological Sciences (EES). However, additional lidar dimensions offer the possibility to transcend basic 3-D mapping capabilities, including i) the physical time (t) dimension from repeat lidar acquisition and ii) laser return intensity (LRIλ) data dimension based on the brightness of single- or multi-wavelength (λ) laser returns. The additional dimensions thus add to the x,y, and z dimensions to constitute the five dimensions of lidar (x,y,z, t, LRIλ1… λn). This broader spectrum of lidar dimensionality has already revealed new insights across multiple EES topics, and will enable a wide range of new research and applications. Here, we review recent advances based on repeat lidar collections and analysis of LRI data to highlight novel applications of lidar remote sensing beyond 3-D. Our review outlines the potential and current challenges of time and LRI information from lidar sensors to expand the scope of research applications and insights across the full range of EES applications.
Forest-dwelling grouse, and especially their broods, are highly dependent on forest and vegetation structure. In countries with intense forest management, it follows that the quality of their habitats is directly affected by forestry operations. Therefore, we must know which structural features of forests define a good grouse habitat and how the abundance of these features is affected by the forestry operations. Nowadays, airborne lidar (light detection and ranging) is being frequently used for forest inventories and terrain mapping. This data is becoming more and more publicly available and it holds detailed information about the forests and the vegetation structure; a key component of wildlife habitats. In this study, we integrated lidar data with grouse brood presence/absence data. Through GLMM modeling, we aimed 1.) to identify the structural features of forests that mostly determine grouse brood occurrence and 2.) to assess how they are affected by forest management. The three species assessed were capercaillie, hazel grouse and black grouse. Depending on species, the brood presence was positively (and significantly) affected by denser shrub layer cover, denser canopy cover, higher canopies, or all of these features. The results indicate that grouse broods are highly susceptible to changes in forest structure. In countries like Finland, game management is almost always implemented in managed forests, which in the light of our results creates a need to integrate habitat management into forest management. Our results suggest that when managing grouse brood habitats, attention should be given to maintain both, protective canopy cover and a good understorey cover. It is fair to assume that the removal of these components will significantly decrease brood occurrence. Further, the study showed that a dataset (lidar) collected more for the purposes of forestry can also be used to study wildlife habitats occurring often in the same forests.