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remote sensing
Review
The Use of LiDAR-Derived DEM in Flood
Applications: A Review
Nur Atirah Muhadi 1, *, Ahmad Fikri Abdullah 1,2, Siti Khairunniza Bejo 1,
Muhammad Razif Mahadi 1and Ana Mijic 3
1Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia,
Serdang 43400, Selangor, Malaysia; ahmadfikri@upm.edu.my (A.F.A.); skbejo@upm.edu.my (S.K.B.);
razifman@upm.edu.my (M.R.M.)
2International Institute of Aquaculture and Aquatic Sciences, Batu 7, Jalan Kemang 6, Teluk Kemang,
Si Rusa 71050, Port Dickson, Negeri Sembilan, Malaysia
3Department of Civil and Environmental Engineering, Skempton Building, Imperial College London,
South Kensington Campus, London SW7 2AZ, UK; ana.mijic@imperial.ac.uk
*Correspondence: gs53332@student.upm.edu.my; Tel.: +603-97694337
Received: 29 April 2020; Accepted: 21 June 2020; Published: 18 July 2020
Abstract:
Flood occurrence is increasing due to escalated urbanization and extreme climate change;
hence, various studies on this issue and methods of flood monitoring and mapping are also increasing
to reduce the severe impacts of flood disasters. The advancement of current technologies such
as light detection and ranging (LiDAR) systems facilitated and improved flood applications. In a
LiDAR system, a laser emits light that travels to the ground and reflects offobjects like buildings
and trees. The reflected light energy returns to the sensor, whereby the time interval is recorded.
Since the conventional methods cannot produce high-resolution digital elevation model (DEM) data,
which results in low accuracy of flood simulation results, LiDAR data are extensively used as an
alternative. This review aims to study the potential and the applications of LiDAR-derived DEM
in flood studies. It also provides insight into the operating principles of different LiDAR systems,
system components, and advantages and disadvantages of each system. This paper discusses several
topics relevant to flood studies from a LiDAR-derived DEM perspective. Furthermore, the challenges
and future perspectives regarding DEM LiDAR data for flood mapping and assessment are also
reviewed. This study demonstrates that LiDAR-derived data are useful in flood risk management,
especially in the future assessment of flood-related problems.
Keywords:
airborne LiDAR; DEM; flood inundation; flood map; flood model; LiDAR; terrestrial LiDAR
1. Introduction
Floods are a major severe natural catastrophe experienced in many countries around the world
including Malaysia. In Malaysia, flooding is the most frequent danger among all disasters, and it can
be considered as an annual disaster due to its consistent occurrence [
1
,
2
]. The flood issue is gaining
attention globally with significant efforts made to develop effective flood prevention and monitoring
solutions. Preparation of flood hazard and floodplain maps is one of the examples of the preparedness
phase in a disaster management cycle, which is widely used to reduce the impact of disasters, to react
during the event, and to take action to recover after a disaster occurs, including flood disasters [3].
Information on how far the floodwater inundates and how deep the area is flooded at what velocity
is required in floodplain management and flood damage estimation. To obtain such information,
elevation data that represent the earth’s surface represent one of the primary components for flood
studies. Accurate elevation information is crucial to both the input and the output of flood hydraulic
Remote Sens. 2020,12, 2308; doi:10.3390/rs12142308 www.mdpi.com/journal/remotesensing
Remote Sens. 2020,12, 2308 2 of 20
analysis, as well as to producing floodplain maps [
4
]. A flood hydraulic model requires several
input parameters that can be derived from the digital elevation data. The output of the flood model
simulation is then mapped onto a digital elevation surface to determine the flood hazard zone and
to further analyze the products to estimate probable flood damage in terms of flood inundation and
flood depth.
A digital elevation model (DEM) is a predominant source of elevation data due to its simplicity
and easy-to-use data [
4
,
5
]. A DEM provides gridded elevation data in a raster structure that represents
the terrain’s surface. It contains x-, y-, and z-values, which represent x- and y-coordinates and elevation
information, respectively. A DEM is commonly generated by extracting surface features from a
digital surface model (DSM). DEMs can be generated from many sources such as ground surveys,
digitizing existing hardcopy topographic maps, or remotely sensed technology. DEM sources range
from no cost to high-cost data sources depending on their accuracy. With the rapid development
of remote sensing technology, DEMs generated from this technology are a preferred choice using
photogrammetry, interferometric synthetic aperture radar (IfSAR), or light detection and ranging
(LiDAR). Photogrammetry is the science of measuring features without physical contact with the
features from photographs [
6
]. Aerial photographs are widely adopted due to their ability to provide
high-resolution and high-accuracy DEMs. However, this emerging technology can only be acquired
during cloud-free and low-haze conditions, which is not the finest condition during flood events.
On the other hand, IfSAR uses a microwave sensor to send signals to the earth’s surface and
records the scattered signal from the surface. It is an active microwave radar system that can obtain
imagery over a vast area at night or in cloud cover. Spaceborne IfSAR such as shuttle radar topography
mission (SRTM) is the most commonly used global DEM because it is an open-access DEM with
acceptable resolution and accuracy. In contrast to spaceborne IfSAR, airborne IfSAR systems have
more flexible system deployment and provide higher spatial resolution. However, both spaceborne
and airborne IfSAR have limitations in urban areas due to complex scattering environments [
7
].
Furthermore, using this technology in densely vegetated areas is challenging because the radar cannot
penetrate the ground surface beneath vegetation canopy.
Another emerging technology is LiDAR. A LiDAR sensor that is mounted on platforms such as
aircraft and helicopters is known as airborne LiDAR. Meanwhile, LiDAR systems that collect data
from the ground are referred to as ground-based LiDAR or terrestrial LiDAR. The generation of DEM
data using the LiDAR system has several advantages over other sources. LiDAR data can be acquired
during daylight or night time, as well as during cloudy conditions [
7
,
8
]. Moreover, it has the ability to
penetrate the ground surface in vegetated and urban areas more reliably than either photogrammetry
or IfSAR. Due to these reasons, it became a recent solution for flood-related problems. The ability of
LiDAR systems to provide higher-resolution and centimeter-accuracy outcomes diversified their use in
wide-ranging applications of flood studies.
In studies involving the application of remote sensing in floodplain and flood risk assessment,
DEMs are used to visualize the interface of floodwater with the elevation of the ground surface.
Moreover, a DEM is an important indicator in determining the flood inundation and flood depth [
4
,
9
,
10
].
The accuracy of a DEM is critical in hydrological modeling as it can affect the discharge values,
water depth, and the extent of flood inundation maps [
11
,
12
]. In a flat floodplain, a vertical error of
1 m in the DEM leads to an error of 100 km
2
in the estimated flood inundation [
10
]. Hence, accurate
and high-resolution DEM data are needed to produce reliable flood mapping, especially in the context
of flood simulation modeling.
In Malaysia, the Department of Irrigation and Drainage (DID) is responsible for providing flood
forecasts and flood hazard maps. A higher level of accuracy of the DEM such as LiDAR data will
improve the accuracy and reliability of the flood maps. Hence, the DID provides LiDAR-derived DEM
as the backbone of the hydrological model. Nevertheless, the existing LiDAR data coverage is minimal;
thus, IFSAR data are used to cover the rest of the potentially flooded area. Furthermore, LiDAR-derived
Remote Sens. 2020,12, 2308 3 of 20
DEM was also applied for flood risk assessments in some regions of the Philippines, as well as using
IFSAR and SAR DEM as other alternatives in the absence of LiDAR in certain areas [13,14].
In flood inundation modeling, DEM resolution and accuracy play an important role in terms of
modeling resolution and accuracy. For instance, a low-resolution DEM allows quick model simulations
but it simplifies the topographic information that may affect the flood propagation. High-resolution
DEM is needed, especially for urban areas, due to the presence of small features such as road curbs
and dykes; thus, it is likely that the accuracy of flood simulations can be affected by the resolution
of the DEM. Therefore, many researchers carried out studies to see whether coarser DEM resolution
decreases the accuracy of the predicted flood inundation extent.
Tamiru and Rientjes [
15
] investigated the effects of LiDAR-derived DEM resolution in flood
modeling. In this study, several DEMs with different resolutions were used as the input to the flood
model. The authors concluded that the DEM resolution has a significant effect on simulation results.
The affected flood simulation characteristics are inundation extent, flow depth, and flood velocity.
In short, a coarser-resolution DEM results in a larger loss of information, while a high-resolution DEM
results in excessive computational time. Furthermore, Casas et al. [
16
] conducted a study on different
topographic data sources and resolution on flood modeling. The differences between each DEM were
measured in terms of flood model outputs such as water discharge, water level, and flood inundation.
The authors emphasized that the flood modeling results are majorly dependent on the DEM accuracy,
whereby a LiDAR-derived DEM has the least root-mean-square error (RMSE) in terms of elevation
accuracy and estimated flood inundation.
Vaze et al. [
17
] carried out a study on the accuracy and resolution of LiDAR DEM to improve
the quality of hydrological features extracted from DEMs. The authors also investigated the effect of
re-sampling DEM data into coarser resolution. The results obtained demonstrate that the accuracy
and resolution of input DEM have a significant impact on the values of the hydrologically important
spatial indices derived from the DEM. Hsu et al. [
18
] conducted a case study on the influence of DEM
resolutions on the simulation of flood inundation in Tainan City, Taiwan. Five different grid sizes of
DEM from 1
×
1 m to 40
×
40 m were used as the input of the flood models. The results showed that
coarser DEM may cause losses of important small-scale features. Therefore, the inundation area may
increase with a coarser DEM, resulting in a reduction in the accuracy of flood inundation models.
Ozdemir et al. [
19
] investigated the impact of using different high-resolution terrestrial LiDAR
data on water depth, inundation extent, arrival time, and velocity predicted by the flood simulations.
It was found that increasing the terrain resolution significantly affected the flood simulation results.
The finding demonstrated that fine-resolution DEM can lead to significant differences in the dynamics
of flood inundation. On the other hand, a coarser resolution reduced the performance of flood
inundation prediction due to changes in flow paths at coarser resolution caused by losses of feature
representation [
20
]. Furthermore, de Almeida et al. [
21
] investigated the influence of fine-resolution
DEM on the flood inundation model over urban areas. The authors performed four different scenarios
with small-scale modifications to analyze the influence of the decametric-scale changes. The findings
from this study confirmed that flood hazard prediction was sensitive to decimetric-scale features,
and they had an impact on the dynamic and distribution of flooded areas.
In summary, the findings from previous studies confirmed that accurate terrain data had a big
impact on flood hazard prediction. Results of flood simulations varied in response to different DEM
resolutions, which could be associated with the degree of topography representation. It was found
that high-resolution DEM can provide relevant and reliable flood modeling results [
22
]. In contrast,
coarser resolutions deteriorate the performance of flood models [
20
]. The previous studies confirmed
that accurate terrain data have a big impact on flood hazard prediction and that the inundation
area evaluation increases with coarser DEMs. Hence, a finer model resolution is necessary if the
decision-maker is interested in local-scale inundation predictions [
23
]. Based on previous studies,
the researchers suggested that LiDAR data offer high-quality data as an essential input of flood modeling.
Remote Sens. 2020,12, 2308 4 of 20
Therefore, this review highlights the basic principles of LiDAR systems, system components,
and their applications in flood studies. It also presents a brief discussion on the number of papers
published in the Scopus database that focused on LiDAR data in flood studies over the past 10 years.
Moreover, this paper reviews the challenges and future directions of the technology when using LiDAR
data for flood modeling.
2. Principles of LiDAR Systems
LiDAR is an active remote sensing system, which allows the system to operate during the day
and at night. LiDAR systems are used in various applications, and their advantages are well noted by
researchers and practitioners all around the world. The development of laser scanning differs by the
position of a sensor, i.e., whether an airborne-based LiDAR system or a ground-based LiDAR system.
These two systems vary in terms of data acquisition modes, scanning mechanisms, and product
accuracy and resolution, with several similarities. One similarity is that both systems can capture point
cloud data and simultaneously acquire imagery [24].
2.1. Airborne LiDAR: Fundamentals and System Components
An airborne LiDAR is a multi-sensor system [
25
] that consists of several components which
are the platform, laser scanner, positioning hardware, photographic or video recording equipment,
computer, and data storage. For airborne LiDAR, the platform to mount the laser scanner can either be
a fixed-wing aircraft or a helicopter, which is used to fly the laser sensor over a region of interest.
A LiDAR sensor with a wavelength of 1000–1600 nm emits laser pulses toward the ground,
and the signal is backscattered by different objects, such as man-made structures, vegetation, and the
ground surface [
26
]. The reflected light energy returns to the sensor, whereby the sensor logs the
returning signal. The time of travel of the return pulse is used to measure the distance traveled.
The measurements of distance and orientation are done by utilizing positioning systems, including a
global positioning system (GPS) and inertial measurement unit (IMU).
LiDAR can produce high-resolution and high accuracy data by relying on the accuracy of GPS
and IMU components [
27
]. IMUs are used to measure the accurate position, trajectory, and orientation
of the aircraft. Meanwhile, the purpose of the GPS is to identify the X,Y, and Zlocation. The GPS
is responsible for providing the precise location of the sensor; hence, differential GPS is adopted by
setting up a ground GPS station to achieve a required position accuracy of better than 10 cm in the
airborne LiDAR [28,29].
The camera or video recording equipment flies along with the LiDAR sensor to provide color
information to represent the real-world color. The process is carried out by mapping red, green,
and blue values onto the georeferenced point location [
30
]. Other components in the airborne LiDAR
system are the control and data recording unit and onboard computer. The control and data recording
unit stores raw data collected by the scanner, IMU, and GPS. Laser scanners can produce about
20 gigabytes of ranging data per hour as compared to the summation of GPS and IMU data, which only
produce about 0.1 gigabytes per hour [31].
2.2. Terrestrial LiDAR: Fundamentals and System Components
Terrestrial LiDAR, also known as terrestrial laser scanning, is a ground-based version of the
airborne LiDAR, which is frequently used for terrain and topographic mapping. Terrestrial LiDAR
includes stationary laser scanning, whereby the sensor is mounted on a tripod for fixed positions and
mobile laser scanning, while the sensor is mounted on a mobile ground-based platform such as a
vehicle. The term terrestrial LiDAR usually refers to static laser scanning. Because static and mobile
laser scanning differ in terms of components and mechanisms, these two categories are discussed
separately in this section.
Nevertheless, both systems still have several similarities. For instance, the main component of
both terrestrial LiDAR systems is a laser scanner. Lasers with a wavelength of 500–600 nm are typically
Remote Sens. 2020,12, 2308 5 of 20
used in ground-based LiDAR systems [
26
]. Furthermore, terrestrial LiDAR, just like airborne LiDAR
systems, utilizes an integrated digital camera or video recording, which is responsible for colorizing
point clouds and three-dimensional (3D) models to represent color in the real world.
2.2.1. Static Laser Scanner
Static laser scanning is performed from the top of a fixed surveying tripod. Static terrestrial laser
scanning needs a two-dimensional (2D) scanning pattern to complete a scan survey, which is why static
terrestrial LiDAR integrates with one or two mirrors that can change the direction of laser pulses [
32
].
Therefore, this LiDAR system can scan and measure the distances of the surrounding objects. Terrestrial
LiDAR systems identify the range between the sensor and the targets by measuring the time required
for the laser pulse to travel to the target and return to the sensor. The basic components for this system
include the ranging unit, scanning mechanisms, laser controller, data recorder, and (optionally) a
digital camera.
Theoretically, the laser scanner operates by emitting an infrared laser beam to the center of a
rotating mirror, which deflects the laser beam around the scanning area. Once the scattered light hits
the objects, it reflects onto the scanner. The digital camera can be mounted on the scanner rotating axis
to provide images of the surroundings [
33
] The recorded time it takes is divided by two and multiplied
by the speed of light to get the distance. The coverage of terrestrial laser scanning usually ranges from
100 m to 300 m [
24
]. Because static terrestrial LiDAR scans are provided from a stable position and
orientation, point clouds with good geometric quality are obtained [31].
2.2.2. Mobile Laser Scanner
On the other hand, mobile laser scanning has similar data collection modes to airborne LiDAR.
Showing many similarities with airborne LiDAR, mobile laser scanning requires only one scanning (1D)
direction, whereas the other is performed by the moving platform. In mobile laser scanning systems,
the laser scanner is mounted on a moving vehicle such as a car or van. Due to the continuous motion of
the scanner, positioning systems based on GPS and IMU technologies are required to precisely measure
the respective positions and orientations. The systems perform as the vehicle moves around, while the
positioning systems track the trajectory and attitude of the vehicle for producing a 3D point cloud from
the range of data collected. Figure 1illustrates the operating principles of all types of laser scanning.
Remote Sens. 2020, 12, x FOR PEER REVIEW 5 of 20
Open
Nevertheless, both systems still have several similarities. For instance, the main component of
both terrestrial LiDAR systems is a laser scanner. Lasers with a wavelength of 500–600 nm are
typically used in ground-based LiDAR systems [26]. Furthermore, terrestrial LiDAR, just like
airborne LiDAR systems, utilizes an integrated digital camera or video recording, which is
responsible for colorizing point clouds and three-dimensional (3D) models to represent color in the
real world.
2.2.1. Static Laser Scanner
Static laser scanning is performed from the top of a fixed surveying tripod. Static terrestrial
laser scanning needs a two-dimensional (2D) scanning pattern to complete a scan survey, which is
why static terrestrial LiDAR integrates with one or two mirrors that can change the direction of laser
pulses [32]. Therefore, this LiDAR system can scan and measure the distances of the surrounding
objects. Terrestrial LiDAR systems identify the range between the sensor and the targets by
measuring the time required for the laser pulse to travel to the target and return to the sensor. The
basic components for this system include the ranging unit, scanning mechanisms, laser controller,
data recorder, and (optionally) a digital camera.
Theoretically, the laser scanner operates by emitting an infrared laser beam to the center of a
rotating mirror, which deflects the laser beam around the scanning area. Once the scattered light hits
the objects, it reflects onto the scanner. The digital camera can be mounted on the scanner rotating
axis to provide images of the surroundings [33] The recorded time it takes is divided by two and
multiplied by the speed of light to get the distance. The coverage of terrestrial laser scanning usually
ranges from 100 m to 300 m [24]. Because static terrestrial LiDAR scans are provided from a stable
position and orientation, point clouds with good geometric quality are obtained [31].
2.2.2. Mobile Laser Scanner
On the other hand, mobile laser scanning has similar data collection modes to airborne LiDAR.
Showing many similarities with airborne LiDAR, mobile laser scanning requires only one scanning
(1D) direction, whereas the other is performed by the moving platform. In mobile laser scanning
systems, the laser scanner is mounted on a moving vehicle such as a car or van. Due to the
continuous motion of the scanner, positioning systems based on GPS and IMU technologies are
required to precisely measure the respective positions and orientations. The systems perform as the
vehicle moves around, while the positioning systems track the trajectory and attitude of the vehicle
for producing a 3D point cloud from the range of data collected. Figure 1 illustrates the operating
principles of all types of laser scanning.
Figure 1. Light detection and ranging (LiDAR) survey data acquisition.
Figure 1. Light detection and ranging (LiDAR) survey data acquisition.
Remote Sens. 2020,12, 2308 6 of 20
2.3. Advantages and Disadvantages of Airborne and Terrestrial LiDAR
Both LiDAR systems have their advantages and drawbacks. Airborne LiDAR data offer rapid
data acquisition capability and a high degree of automation. Data are captured with a speed of up
to about 50 km
2
/h [
34
]. Due to this, it is considered to be a fast method of generating accurate DEM.
Furthermore, because airborne LiDAR captures data from above, it gives a direct and clearer view of
roads and rooftops of buildings as compared to terrestrial LiDAR.
In contrast, terrestrial LiDAR is preferable compared to airborne LiDAR in certain situations
because terrestrial LiDAR is more cost-effective for small-scale areas and can be portable, while it
produces high-resolution of terrain data. The main advantages of terrestrial LiDAR data are the
high measurement density and high data accuracy. It can collect higher point density, typically
100 points/m
2
[
35
]. It can also provide scan rates up to half a million points per second for 100 m to
300 m, depending on the distance range of the scanner [
31
]; thus, it provides detailed terrain description
and high-resolution surface roughness [
15
,
36
–
39
]. Terrestrial LiDAR yields high-resolution digital
elevation models (DEMs) with pixel sizes on the scale of centimeters rather than the 1–3-m-resolution
DEMs derived from airborne LiDAR [40].
Even though terrestrial LiDAR data can be used to provide information in small-scale areas,
it cannot provide data in certain areas such as private lands and steep slope areas. Therefore, several
researchers suggest fusing terrestrial LiDAR with airborne sources to cover topographic features in
inaccessible areas [38,41].
2.4. Overview
In summary, there are different ways of achieving data acquisition using LiDAR data, including
airborne LiDAR and terrestrial LiDAR. Previous studies demonstrated that terrestrial LiDAR data held
an advantage over other DEM sources, including airborne LiDAR, as they responded to small-scale
topographic features, which were important factors that influenced the flood prediction results.
Airborne LiDAR has difficulty in detecting small-scale features which are often not well represented
in DEMs, which is the reason why many researchers opted for terrestrial LiDAR to generate a
high-resolution DEM. Nevertheless, both LiDAR sensors proved to be able to maintain high accuracy
and produce high-resolution data due to their high scanning rates [
42
] compared to other DEM sources.
3. Applications of LiDAR System in Flood Monitoring
The application of LiDAR in supporting many science research activities such as geologic mapping,
landslide hazards, and flood risk management cannot be disputed [
43
]. The number of publications of
peer-reviewed research literature recorded in the Scopus database for the past 10 years, from 2010 to
2019, which discussed LiDAR data in flood studies, was determined. The related papers were searched
using the boolean “AND” to combine the words “LiDAR” and “flood”, and we sought these words
in the abstract, title, and keywords of the documents. This study decided to focus only on research
articles and conference proceedings of the related topic to be counted, as presented in Figure 2.
The graph shows that there was a rapid increase in the rate of publishing papers on LiDAR
and flood applications in early 2010, and this increasing trend remained until 2019. There may be
various reasons for the increase such as the availability and accessibility of LiDAR technology and the
occurrence of flood disasters in the world. For instance, Duan et al. [
44
] suggested that flood disasters
became more severe in China in recent years based on flood variations from 1950 to 2013. Furthermore,
the potential of LiDAR technology to provide high-quality data may receive attention from researchers
and practitioners, which leads to an increment in LiDAR data applications in multidisciplinary studies,
especially in flood applications.
Remote Sens. 2020,12, 2308 7 of 20
Remote Sens. 2020, 12, x FOR PEER REVIEW 7 of 20
Open
Figure 2. The number of papers that discussed on LiDAR and flood applications by year in the
Scopus database.
The graph shows that there was a rapid increase in the rate of publishing papers on LiDAR and
flood applications in early 2010, and this increasing trend remained until 2019. There may be various
reasons for the increase such as the availability and accessibility of LiDAR technology and the
occurrence of flood disasters in the world. For instance, Duan et al. [44] suggested that flood
disasters became more severe in China in recent years based on flood variations from 1950 to 2013.
Furthermore, the potential of LiDAR technology to provide high-quality data may receive attention
from researchers and practitioners, which leads to an increment in LiDAR data applications in
multidisciplinary studies, especially in flood applications.
LiDAR provides detailed information on the elevation of the ground surface for predicting
flood inundation from rivers. Detailed LiDAR measurements not only offer higher-resolution
elevation data for floodplain modeling, but they also provide a source of high-resolution surface
roughness information. High resolution and high accuracy of a topographic dataset are also
important in predicting the flood inundation [45].
Due to this, LiDAR technology is in demand for creating DEMs for flood-prone areas, especially
in urban areas. The application of high-resolution LiDAR data is increasing in developed countries
[46] such as in the United Kingdom, as well as in the United States. This section reviews the previous
studies on the application of the airborne and terrestrial LiDAR in flood mapping and monitoring
applications. This paper discusses several topics relevant to flood studies from a LiDAR data
perspective.
3.1. Development of Flood Models Using DEM LiDAR
One of the important factors in producing reliable flood inundation maps is the availability of
high-accuracy topographic data [47]. Detailed and accurate DEMs are needed, to represent specific
properties that may obstruct and conduct the flow of water in the real world. Inaccurate topographic
representation in a small-scale area would affect the simulation results [15], especially in urban
areas; hence, researchers tend to use high-resolution input data for flood simulation in urban areas
and floodplains to collect important small-scale features. Many studies were carried out to
demonstrate the effectiveness of using LiDAR-derived DEMs in developing flood models. Results
from the flood model simulations were compared with the observed water level during previous
flood events to validate the simulated results.
Figure 2.
The number of papers that discussed on LiDAR and flood applications by year in the
Scopus database.
LiDAR provides detailed information on the elevation of the ground surface for predicting flood
inundation from rivers. Detailed LiDAR measurements not only offer higher-resolution elevation
data for floodplain modeling, but they also provide a source of high-resolution surface roughness
information. High resolution and high accuracy of a topographic dataset are also important in
predicting the flood inundation [45].
Due to this, LiDAR technology is in demand for creating DEMs for flood-prone areas, especially
in urban areas. The application of high-resolution LiDAR data is increasing in developed countries [
46
]
such as in the United Kingdom, as well as in the United States. This section reviews the previous studies
on the application of the airborne and terrestrial LiDAR in flood mapping and monitoring applications.
This paper discusses several topics relevant to flood studies from a LiDAR data perspective.
3.1. Development of Flood Models Using DEM LiDAR
One of the important factors in producing reliable flood inundation maps is the availability of
high-accuracy topographic data [
47
]. Detailed and accurate DEMs are needed, to represent specific
properties that may obstruct and conduct the flow of water in the real world. Inaccurate topographic
representation in a small-scale area would affect the simulation results [
15
], especially in urban areas;
hence, researchers tend to use high-resolution input data for flood simulation in urban areas and
floodplains to collect important small-scale features. Many studies were carried out to demonstrate the
effectiveness of using LiDAR-derived DEMs in developing flood models. Results from the flood model
simulations were compared with the observed water level during previous flood events to validate the
simulated results.
Priestnell et al. [
36
] discussed the methods of extracting surface features from DSMs generated
by airborne LiDAR. The extraction of features could help in many applications, including flood
inundation modeling. This study explained the way in which the DEM and surface roughness layer
could be generated from the original DSM from LiDAR by using a simple filtering procedure and
an artificial neural network. The findings were illustrated in the case of flood inundation modeling.
Furthermore, Webster et al. [
48
] investigated the coastal impacts due to climate change and sea-level
rise in Charlottetown, Canada. Detailed topographic data were derived from airborne LiDAR for
flood risk mapping, and they were used to define flood risk hazards. This finding demonstrates
Remote Sens. 2020,12, 2308 8 of 20
the effectiveness of airborne LiDAR for identifying the impact of climate change and storm surge in
coastal areas.
Webster et al. [
49
] generated a flood risk map by using airborne LiDAR and geographical
information system (GIS) processing to study flood inundation in Southeast New Brunswick, Canada.
The flood inundation and flood depth of the proposed approach were validated by comparing the
results with water levels observed during the flood event in January 2000. It was found that the flood
extent and flood depth were accurate within 10–20 cm. Bales et al. [
50
] also carried out a study on
flood inundation maps derived from LiDAR data for real-time flood mapping applications in Tar River
Basin, North Carolina. This study used airborne LiDAR data with a vertical accuracy of about 20 cm
to produce topographic data for the inundation maps. The difference between the measured and
simulated water levels to high-water marks was less than 25 cm.
For terrestrial LiDAR, the first attempt at developing an urban hydraulic model using terrestrial
LiDAR was done by Fewtrell et al. [
51
]. The performance of the flood model was analyzed by comparing
the simulation results with the 50-cm-resolution model as a benchmark. This study found that errors
in coarse-scale topographic datasets were significantly high. Moreover, the authors concluded that
terrestrial LiDAR data can be used to provide information in small-scale flood risk management and
suggested fusing airborne and terrestrial LiDAR to cover topographic features in inaccessible areas.
Sampson et al. [
52
] investigated the capability of terrestrial LiDAR to provide high accuracy of DEMs
for improving flood inundation models in urban areas. The study found that small features such as
curbs and dykes, which had a significant impact on the flood propagation, could be represented from
the terrestrial LiDAR data. The authors concluded that terrestrial LiDAR could be employed when an
accurate representation of surface features is required, especially in urban inundation studies.
Furthermore, Poppenga and Worstell [
53
] demonstrated the need for hydrologic information
derived from airborne LiDAR elevation surfaces for flood inundation monitoring in coastal regions.
The study demonstrated how inland areas are hydrologically disconnected to ocean water due to
bridge decks or culverts. Next, Yin et al. [
54
] used LiDAR-derived DEM in a high-resolution 2D
hydraulic model to study the impact of land subsidence on urban pluvial flooding. The authors
concluded that land subsidence could lead to moderate impacts on flood extent and flood depth in
the urban areas. Chen et al. [
55
] assessed the accuracy of airborne LiDAR-derived flood extent by
evaluating the data during the 2008 Iowa flood in the United States (US) with field measurements
collected by the US Geological Survey (USGS) and Federal Emergency Management Agency (FEMA).
The root-mean-square error (RMSE) of the floodwater surface profile from LiDAR to field measurement
was 30 cm. The finding showed that LiDAR surveys could be used in measuring floodwater heights
with reliable quality.
After the devastating flood in 2008, the Iowa Flood Center (IFC) was established to improve the
availability of flood-relevant information to the community. Krajewski et al. [
56
] discussed several
projects conducted by the IFC that were related to flood disasters. One of the projects was flood
mapping that could be accessed online, working as flood inundation map libraries. Hydrodynamic
modeling was used to simulate river and floodplain flows by using the best approach to describe river
and floodplain topography, which was LiDAR data. In summary, these findings demonstrated the
effectiveness of airborne LiDAR for identifying the impact of flooding.
In the past few years, LiDAR technology was widely used in flood inundation research due to its
high potential of providing inundation models with detailed elevation data. Based on these studies,
it was found that LiDAR data produce high-resolution DEMs for flood simulation modeling, which can
be an efficient tool in floodplain inundation management.
3.2. Generation of Surface Roughness Maps Using LiDAR Data
One of the essential input parameters in a flood model is surface roughness, which is useful for
boundary conditions. Roughness maps can be derived from different sources such as orthophoto,
LiDAR data, and land-use data. The surface roughness has a significant effect on the output of
Remote Sens. 2020,12, 2308 9 of 20
hydrodynamic modeling. The roughness parameter is often defined through Manning’s formula [
57
].
Meanwhile, the roughness values are mostly derived from a look-up table based on land-use/land-cover
classification (LULC).
According to Straatsma and Baptist [
58
], roughness values have to be estimated accurately to
reduce the variation of input parameters during calibration. The authors carried out a study to derive
roughness parameterization using multispectral and airborne LiDAR data. After that, the results
of the proposed method were compared with a traditional roughness parameterization approach,
which was a manual interpretation of aerial photographs and a look-up table. This approach led to a
high-resolution roughness map.
Vetter et al. [
59
] used airborne LiDAR to derive hydraulic surface roughness estimations based
on geometry data by using vertical vegetation structure analysis. The effects of different roughness
coefficient values were quantified by calculating the inundated depth maps. The results showed that
the roughness values derived from airborne LiDAR represented the area in detail as compared to the
traditionally derived map.
High-resolution data are recommended as the best option for damage assessment applications.
Joyce et al. [
60
] recommended using airborne LiDAR data to generate DEMs and surface roughness
layers to be included in hazard models. Moreover, the high density of LiDAR data provides a
high-resolution surface roughness of floodplains. This information is very useful for boundary
conditions in flood simulations [61].
Dorn et al. [
57
] derived roughness maps based on several different datasets, including LiDAR.
This study aimed to analyze the effect of the roughness maps on flood simulations. LiDAR point
clouds were used to derive surface roughness by using a voxel structure, an approach developed by
Vetter et al. [
59
]. The results based on different roughness maps differed in terms of inundation area,
water depth, and flood intensity. The authors suggested using LiDAR data to derive a roughness map
for estimating the consequences of floods. Moreover, the authors mentioned that the use of the same
LiDAR data in producing the DEM data and the roughness maps is beneficial, as there is no issue of
temporal difference.
In short, in addition to topography, surface roughness has a great influence on hydrodynamic
models as it affects the flow regime [
62
–
64
]. Hence, appropriate roughness maps should be generated for
the use of hydrodynamic models for predicting the reliable consequences of flood disasters. Based on
previous research, it was concluded that LiDAR data provide high-resolution surface roughness,
which will increase the accuracy of the flood extent simulated by the hydrodynamic model. Hence,
laser scanning technology is able to produce a roughness map with a high level of spatial detail [58].
3.3. Comparisons of LiDAR-Derived DEMs with Other DEM Sources
Due to the significant impacts of DEM accuracy on the flood model outputs, it is important to
know which DEM sources could provide higher accuracy and spatial resolution before the selected
DEM is used for the assessment of flood hazard risk. Therefore, many comparative studies were carried
out using different DEM sources to understand the importance of the accuracy of DEM on the flood
model. The results were analyzed based on significant differences in the model output. This section
discusses the comparisons between LiDAR-derived DEM and other DEM sources, as well as their
significant characteristics in flood applications.
Casas et al. [
16
] evaluated the effects of DEM sources on the hydraulic modeling of floods in terms
of the hydraulic model outputs such as flood inundation and water surface elevation. The results of this
study demonstrated that the flood model output was highly dependent on the DEM quality with LiDAR
data, showing a high potential source for the parameterization of channel and floodplain topography.
Schumann et al. [
65
] carried out a comparison of DEMs generated from airborne LiDAR, contours,
and SRTM in terms of the effect on a flood inundation model. The results were compared with
inundation maps from a model calibrated with ground-surveyed maximum watermarks. As expected,
the authors found that LiDAR had the lowest RMSE, followed by contour DEM and SRTM. The estimated
Remote Sens. 2020,12, 2308 10 of 20
inundated area for LiDAR was the largest area and the nearest to the reference value. It was concluded
that LiDAR is the most reliable source of topographic data for flood hazard estimation.
Moreover, Wang and Zheng [
66
] compared LiDAR-derived DEM with United States Geologic
Survey (USGS) national elevation data (NED) on floodplains in North Carolina. Sanders [
67
] extended
the scope by evaluating the difference between LiDAR-derived DEM and NED with airborne IfSAR
and SRTM for flood inundation modeling. Sanders found that flood model predictions were highly
dependent on the DEM resolution. The author also concluded that LiDAR-derived DEM was more
accurate than other DEM sources which overestimated the flood extent. The need for LiDAR data is
now a fundamental input to hydrologic and hydraulic models, especially in flood inundation models.
Furthermore, Coveney and Fotheringham [
68
] examined the impact of DEM data sources on flood
risk prediction in the coastal areas. The authors used national-coverage DEM known as Ordnance
Survey Ireland, two GPS-derived DEMs generated at low and medium resolution, and terrestrial
LiDAR-derived DEM to model flood risk. The findings demonstrated that the DEM generated from
terrestrial LiDAR was more advantageous than other DEM data sources, especially in representing
small topographical features in a local flood.
Additionally, Papaioannou et al. [
69
] investigated the influence of different DEM sources used in a
hydraulic model for flood analysis. The results of the flood models were compared with historical flood
records. According to this study, DEMs derived from terrestrial LiDAR were best, as they generated
the closest values to the historical data. This finding indicated that the high accuracy of DEMs helped
improve the flood risk analysis task. This study concluded that the accuracy of DEMs is the major
factor that affects flood modeling results.
In addition, Li and Wong [
70
] studied the effects of different DEM sources on flood simulation
results. The authors concluded that different DEM sources have major impacts on inundation areas
from flood prediction results as compared to DEM spatial resolution. Based on the experimental
results, it was found that inundation areas from LIDAR-derived DEM were the closest to reality.
Furthermore, this study also highlighted that the reliability of the DEM source significantly affected
the flood simulation results.
Jakovljevic and Govedarica [
71
] simulated flood inundation by selecting the grid cell of a DEM
lower than the projected water level, connected to an adjacent flooded grid cell. In this study, the authors
used LiDAR-derived DEM and the Advanced Spaceborne Thermal Emission and Reflection Radiometer
Global Digital Elevation Model (ASTER GDEM) to study the difference in the estimated flood extents.
It was found that land elevation from ASTER GDEM was overestimated, which directly resulted in
an underestimation of flood inundation risk. The inundation map generated from ASTER GDEM
indicated that the inundation area was two times smaller than that generated from LiDAR-derived
DEM. Figure 3shows the visual comparison of the flood extent from LiDAR and ASTER GDEM.
Based on previous research, LiDAR proved to be an efficient method to provide terrain data
with high resolution as compared to other DEM sources [
72
]. According to Sampson et al. [
73
],
LiDAR-derived DEMs are considered the most reliable DEMs for flood modeling to date. In summary,
hydrological modeling studies showed that the vertical accuracy of DEMs does affect the accuracy of
hydrologic predictions [70].
Remote Sens. 2020,12, 2308 11 of 20
Remote Sens. 2020, 12, x FOR PEER REVIEW 11 of 20
Open
Figure 3. A comparison of flood extent derived from LiDAR and the Advanced Spaceborne Thermal
Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) according to
water level [71].
Based on previous research, LiDAR proved to be an efficient method to provide terrain data
with high resolution as compared to other DEM sources [72]. According to Sampson et al. [73],
LiDAR-derived DEMs are considered the most reliable DEMs for flood modeling to date. In
summary, hydrological modeling studies showed that the vertical accuracy of DEMs does affect the
accuracy of hydrologic predictions [70].
3.4. LIDAR as a Source of Information for Hydrodynamic Model Verification
Based on previous studies, it was found that LiDAR data are capable of producing
high-resolution DEMs for flood simulation modeling, which can be an efficient tool in floodplain
inundation management. Hence, they are commonly used for hydrodynamic model verification.
Courty et al. [74] mentioned that inundation areas from LIDAR-derived DEM were the closest to
reality as reported by Li and Wong [69]; therefore, they used LiDAR-derived DEM as a reference
when comparing DEMs generated from Advanced Land Observing Satellite (ALOS) World 3D-30m
(AW3D30), SRTM, and Advanced Spaceborne Thermal Emission and Reflection Radiometer
(ASTER) for flood modeling purposes. Based on the flood simulation results, AW3D30 performed
better than SRTM, while ASTER was the worst performer of all global DEMs.
Figure 3.
A comparison of flood extent derived from LiDAR and the Advanced Spaceborne Thermal
Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) according to
water level [71].
3.4. LIDAR as a Source of Information for Hydrodynamic Model Verification
Based on previous studies, it was found that LiDAR data are capable of producing high-resolution
DEMs for flood simulation modeling, which can be an efficient tool in floodplain inundation
management. Hence, they are commonly used for hydrodynamic model verification. Courty et al. [
74
]
mentioned that inundation areas from LIDAR-derived DEM were the closest to reality as reported
by Li and Wong [
69
]; therefore, they used LiDAR-derived DEM as a reference when comparing
DEMs generated from Advanced Land Observing Satellite (ALOS) World 3D-30m (AW3D30), SRTM,
and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) for flood modeling
purposes. Based on the flood simulation results, AW3D30 performed better than SRTM, while ASTER
was the worst performer of all global DEMs.
Remote Sens. 2020,12, 2308 12 of 20
Hashemi et al. [
75
] also used LiDAR-derived DEMs as a reference when investigating the quality
of DEMs generated from an unmanned aerial vehicle (UAV) used in flood modeling. These studies
concluded that the reliability of floodplain maps is dependent on the quality of DEM. Van de
Sande et al. [
76
] adopted LiDAR DEM data as ground truth referring to the terrain elevation. Hence,
the flood risk assessment of publicly available DEMs such as ASTER and SRTM DEM was compared
with flood risk based on LiDAR DEM. The inundation maps of these publicly available DEMs were
smaller than inundation maps produced using LiDAR DEM. The underestimations of the flood
risk influence the credibility when making appropriate decisions regarding flood risk management
and mitigation.
Furthermore, most small river basins in many countries are not characterized by high-quality
DEMs such as LiDAR data [
77
]. Hence, aerial photographs or globally available DEMs such as ASTER
and SRTM are commonly used, which leads to low accuracy of flood prediction due to the significant
effect of low-accuracy DEMs. Therefore, this study proposed using corrected DEMs generated from
aerial photographs as an option in flood modeling. The correction of DEM was performed based on
field measurements to determine vertical errors. Then, a reference DEM that was developed from
LiDAR data was used to validate the performance of the original and corrected DEM. The impact of
DEM accuracy was evaluated using the flood model. The results from the model indicated that the
flood prediction of corrected DEM was better than that of the original DEM when compared with the
simulated result of the reference DEM, as shown in Figure 4. However, the authors suggested that the
proposed method was not suitable for urban areas.
Remote Sens. 2020, 12, x FOR PEER REVIEW 12 of 20
Open
Hashemi et al. [75] also used LiDAR-derived DEMs as a reference when investigating the
quality of DEMs generated from an unmanned aerial vehicle (UAV) used in flood modeling. These
studies concluded that the reliability of floodplain maps is dependent on the quality of DEM. Van de
Sande et al. [76] adopted LiDAR DEM data as ground truth referring to the terrain elevation. Hence,
the flood risk assessment of publicly available DEMs such as ASTER and SRTM DEM was compared
with flood risk based on LiDAR DEM. The inundation maps of these publicly available DEMs were
smaller than inundation maps produced using LiDAR DEM. The underestimations of the flood risk
influence the credibility when making appropriate decisions regarding flood risk management and
mitigation.
Furthermore, most small river basins in many countries are not characterized by high-quality
DEMs such as LiDAR data [77]. Hence, aerial photographs or globally available DEMs such as
ASTER and SRTM are commonly used, which leads to low accuracy of flood prediction due to the
significant effect of low-accuracy DEMs. Therefore, this study proposed using corrected DEMs
generated from aerial photographs as an option in flood modeling. The correction of DEM was
performed based on field measurements to determine vertical errors. Then, a reference DEM that
was developed from LiDAR data was used to validate the performance of the original and corrected
DEM. The impact of DEM accuracy was evaluated using the flood model. The results from the
model indicated that the flood prediction of corrected DEM was better than that of the original DEM
when compared with the simulated result of the reference DEM, as shown in Figure 4. However, the
authors suggested that the proposed method was not suitable for urban areas.
Figure 4. Comparison of flood extent and flood depth obtained from flood model simulation: (a)
original DEM; (b) corrected DEM; (c) reference DEM [77].
3.5. LiDAR DEM for Flood Hazard and Flood Risk Mapping
Flood risk assessment and management rely on the accuracy of flood extent simulated using a
flood model. Most flood risk mapping is based on a conceptual risk approach that uses DEMs to
predict the flood hazard according to the projected water levels and to indicate the vulnerability of
areas to flood events with damage to properties and livelihood. Hazard mapping is an important
element in assessing risk and designing mitigation measures for flood-prone areas.
Flood hazard is usually generated based on the outcome of hydrological models that simulate
the water movement across the floodplain like flood extent, water velocity, or water depth [11,37,78].
In addition, flood hazards can also be produced using a statistical or machine-learning approach
integrated with GIS technology by using fluvial stage records and topographic data [79,80]. Flood
hazard and flood risk maps indicate the flood-prone area with possible destructive impact, which is
used for flood planning purposes.
For instance, existing digital mapping was not sufficient enough to provide a high accuracy of
flood risk maps for Annapolis Royal, Nova Scotia, Canada, an area that is vulnerable to coastal
flooding [81]. Hence, the need for a high-resolution DEM was studied to produce accurate
inundation maps based on sea level and climate change. As the sea level rises, water inundates the
nearby lands; thus, it is important to define the extent of the flood inundation. The predicted results
Figure 4.
Comparison of flood extent and flood depth obtained from flood model simulation: (
a
) original
DEM; (b) corrected DEM; (c) reference DEM [77].
3.5. LiDAR DEM for Flood Hazard and Flood Risk Mapping
Flood risk assessment and management rely on the accuracy of flood extent simulated using a
flood model. Most flood risk mapping is based on a conceptual risk approach that uses DEMs to
predict the flood hazard according to the projected water levels and to indicate the vulnerability of
areas to flood events with damage to properties and livelihood. Hazard mapping is an important
element in assessing risk and designing mitigation measures for flood-prone areas.
Flood hazard is usually generated based on the outcome of hydrological models that simulate
the water movement across the floodplain like flood extent, water velocity, or water depth [
11
,
37
,
78
].
In addition, flood hazards can also be produced using a statistical or machine-learning approach
integrated with GIS technology by using fluvial stage records and topographic data [
79
,
80
]. Flood hazard
and flood risk maps indicate the flood-prone area with possible destructive impact, which is used for
flood planning purposes.
Remote Sens. 2020,12, 2308 13 of 20
For instance, existing digital mapping was not sufficient enough to provide a high accuracy
of flood risk maps for Annapolis Royal, Nova Scotia, Canada, an area that is vulnerable to coastal
flooding [
81
]. Hence, the need for a high-resolution DEM was studied to produce accurate inundation
maps based on sea level and climate change. As the sea level rises, water inundates the nearby lands;
thus, it is important to define the extent of the flood inundation. The predicted results were compared
with the benchmark of a past storm event to test the model. Based on the prediction results, mitigation
structures such as dykes could be suggested if coastal development is planned to take place in any of
the risk areas.
Puno et al. [
13
] conducted flood simulations at different return periods with LiDAR-derived
DEMs as a primary source of elevation data in the hydrologic model. The model was calibrated by
comparing the predicted flood simulation with a real flood event in 2016. Flood hazard maps were
generated from the simulated flood events using GIS and LiDAR-derived DEM. The generated maps
were validated through an interview with the affected localities. The authors found that using LiDAR
data in the hydrologic model could produce high-resolution flood hazard maps that can offer more
accurate decisions and actions in disaster management and mitigation.
Ogania et al. [
14
] evaluated the effect of DEM resolutions on generating flood hazard maps using
hydraulic modeling software for disaster preparedness and mitigation. This study presented the
performance of three different DEM resolutions, which were LiDAR, IfSAR, and SAR DEMs in flood
modeling studies. The accuracy of each generated flood map was evaluated using a confusion matrix
approach by comparing the generated maps with the actual flood data. This paper revealed that
LiDAR-derived DEMs provide a more defined flood extent and clear distribution of flood hazards.
Furthermore, they offer more accurate flood maps compared to other DEM data sources, which aligned
with the findings from previous researchers such as Hailes and Rientjes [
82
] and Schumann et al. [
65
].
Mihu-Pintilie et al. [
83
] used high-density LiDAR data with 2D hydraulic modeling to improve
urban flood hazard maps. This study simulated four different multi-scenarios at different discharge
values. Because LiDAR data provide a precise representation of the hydraulic conditions such as
channels and roads, the combination of 2D hydraulic and LiDAR DEMs produced accurate information
regarding flood hazard vulnerability. Flood hazard maps were generated based on flood depth
classification according to the Japanese criteria of the Ministry of Land Infrastructure and Transport
(MLIT). The criteria suggested five hazard classes of very low, low, medium, high, and extreme classified
as H1, H2, H3, H4, and H5, respectively. Figure 5shows that all hazard classes were encountered
according to scenario 1 (s1). However, most of the affected areas were assigned with the very low or
low class of hazard (H1 and H2).
LiDAR datasets were implemented in a new procedure of flood hazard estimation proposed
by Guerriero et al. [
84
]. The authors developed algorithms of interpolation of multiple probability
models of hydrometric time-series data combined with topography derived from LiDAR data for the
production of flood hazard maps. Flood hazard maps produced from this method were compared
with a flood event observation in 2015 for validation. This suggested method can be considered as
another option for hydraulic simulations to provide flood hazard analysis.
In conclusion, high-resolution DEMs have great influence on producing accurate and reliable maps
in the field of flood simulations. Using these maps helps in disaster risk reduction and management,
especially in identifying specific areasthat need to be prioritized for providing appropriate flood
risk management measures to be taken to combat flood disaster. Previous studies implied that
LiDAR-derived DEMs improve the accuracy of flood parameters; hence, they can help in producing
high-quality flood hazard and flood risk mapping.
Remote Sens. 2020,12, 2308 14 of 20
1
Figure 5.
Flood hazard map based on flood depth classification according to the Ministry of Land
Infrastructure and Transport (MLIT) [83].
4. Challenges and Future Perspectives
The frequency of flood disasters all over the world is increasing due to climate change and
rapid urbanization. Future climate projections could provide an additional understanding of extreme
climate changes, including the risk of flood events [
85
]. Furthermore, studies on flood mapping
Remote Sens. 2020,12, 2308 15 of 20
and monitoring increased with the advancement of current technologies to reduce the impact of
flood disasters. LiDAR data acquisitions seem to be a promising approach to solve the problems
associated with the inadequate representation of topographic data. Both airborne and terrestrial
LiDAR systems are active imaging techniques operating with light that allow the systems to collect
data during daylight or nighttime. Previous studies revealed that LiDAR technology has many
advantages, which makes it suitable for flood modeling, particularly in flat areas and complex urban
environments. Depending on the spatial scale, LiDAR data offer different advantages for accurate
terrain mapping compared to other sources. Moreover, LiDAR could be advantageous to provide
information in small-scale flood risk management by having small important topographic features such
as dykes, ditches, and levees [
15
,
51
,
86
,
87
]. Furthermore, the integration of LiDAR technology with any
remotely sensed products may be used to increase the effectiveness of this technology, especially in
flood modeling.
However, there are some restrictions in using LiDAR-derived DEM in the context of flood
applications. The main drawback of both LiDAR systems is the process of classifying ground from
non-ground data for DEM generation, which is needed in simulations of the flood model. Ground
surface information is not easily extracted, especially in areas with complex terrain surface and features
such as buildings and vegetation [
88
]. The ground filtering process proves to be a challenging task as it
can affect the accuracy of the LiDAR products [
8
,
49
,
89
]. Several filtering algorithms were developed
by previous researchers to process LiDAR data. However, the LiDAR data must be correctly processed
because they could influence the outcomes of flood mapping [
69
]. The algorithms perform differently
depending on the specific surface conditions. This means that not all algorithms are competent in
producing high-quality LiDAR-derived DEM data [
90
]. A filtering algorithm should be selected based
on its ability to produce the desired result [
91
]. Common filtering algorithms used in LiDAR data
processing include elevation threshold with expand window (ETEW), maximum local slope, adaptive
triangulated irregular network (TIN), and progressive morphology [90,92,93]. Filtering problems are
expected to be better solved with the evolution of machine learning [88].
In addition, the sensitive response of flood inundation to small changes in topography
representation gives rise to several challenges [
21
]. Collecting small-scale features needs a high
resolution of DEM data, but the data are rarely available, especially for developing countries. Not all
countries can afford to use LiDAR data due to economic constraints. The high cost and the difficulty
of processing huge LiDAR datasets could be the main reason why LiDAR data are not used in some
developing countries. Even developed countries like the United States and the United Kingdom do not
have LiDAR data available for the entire country. Another challenge when using LiDAR data is the
need for huge data storage due to the high-point-density data. High-point-density data need a longer
computational time to process [
94
,
95
]. Between airborne and terrestrial LiDAR systems, the time
required for flood model simulations using terrestrial LiDAR is 10 times longer than that required for
airborne LiDAR [96].
Furthermore, even though high-resolution DEMs offer detailed information topography, they take
a longer time to process or analyze the data. Abucay and Tseng [
97
] carried out a visibility analysis
that could be used in identifying flood-prone areas using various DEM sources. The authors reported
that the LiDAR-derived DEM required 28 min to complete the visibility analysis, followed by the SAR
DEM that took 19 s, while ALOS and ASTER GDEM both required only 3 s to complete the process.
Nevertheless, the computational time problem may be solved with future advancements in computer
technology. Moreover, the LiDAR system cannot penetrate water bodies as its laser beam is absorbed
by the water. Therefore, the inaccurate elevation measurement of water-covered areas influences
cross-section attributes, leading to inaccuracies in hydrodynamic simulations [98].
5. Conclusions
Detailed topographic information is a crucial input parameter for flood modeling and monitoring.
The performance of flood modeling is highly dependent on the DEM accuracy [
10
], especially in
Remote Sens. 2020,12, 2308 16 of 20
small-scale flood modeling studies. Flood model simulation results show differences in water depth
and inundation when using detailed DEMs, proving that DEM accuracy has a significant impact on
flood hazard estimation [
21
,
41
]. Therefore, the need for high-resolution DEM explains the interest in
exploring new technology to generate detailed elevation data. In this review, the promising applications
in numerous flood studies demonstrate that the LiDAR system is capable of offering high-density and
high-resolution DEM data to improve the flood model input, thus resulting in a higher accuracy of
flood modeling results. However, LiDAR data also face several difficulties that need to be addressed
in the future regarding the filtering process for DEM generation and enormous point density data
that need huge data storage, resulting in a longer computational time to simulate flood models.
Additionally, integration between terrestrial and airborne LiDAR or any remotely sensed products
seems to be a promising approach to solve the problems associated with the inadequate representation
of topographic data in topographically complex areas [
99
]; hence, more investigation and research
work for the expansion of LiDAR systems can be foreseen in upcoming applications of flood detection
and monitoring.
Author Contributions:
N.A.M. executed the manuscript writing, coordinated the paper revisions, and contributed
to the workflow implementation. A.F.A. contributed to the workflow implementation, as well as the manuscript
compilation and revisions. S.K.B. proposed the research idea. M.R.M. and A.M. supervised the final manuscript.
All authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded by Universiti Putra Malaysia, grant number UPM/800-3/3/1/GPB/2019/9678700.
Acknowledgments:
The authors wish to acknowledge the assistance of the Department of Biological and
Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia for supplying the facilities for this
study. The authors would also like to appreciate the support for this study from the Institute of Aquaculture and
Aquatic Sciences.
Conflicts of Interest: The authors declare no conflict of interest.
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