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Rapid increase in population and growing concentration of capital in urban areas has escalated both the severity and longer-term impact of natural disasters. As a result, Disaster Risk Management (DRM) and reduction have been gaining increasing importance for urban areas. Remote sensing plays a key role in providing information for urban DRM analysis due to its agile data acquisition, synoptic perspective, growing range of data types, and instrument sophistication, as well as low cost. As a consequence numerous methods have been developed to extract information for various phases of DRM analysis. However, given the diverse information needs, only few of the parameters of interest are extracted directly, while the majority have to be elicited indirectly using proxies. This paper provides a comprehensive review of the proxies developed for two risk elements typically associated with pre-disaster situations (vulnerability and resilience), and two post-disaster elements (damage and recovery), while focusing on urban DRM. The proxies were reviewed in the context of four main environments and their corresponding sub-categories: built-up (buildings, transport, and others), economic (macro, regional and urban economics, and logistics), social (services and infrastructures, and socio-economic status), and natural. All environments and the corresponding proxies are discussed and analyzed in terms of their reliability and sufficiency in comprehensively addressing the selected DRM assessments. We highlight strength and identify gaps and limitations in current proxies, including inconsistencies in terminology for indirect measurements. We present a systematic overview for each group of the reviewed proxies that could simplify cross-fertilization across different DRM domains and may assist the further development of methods. While systemizing examples from the wider remote sensing domain and insights from social and economic sciences, we suggest a direction for developing new proxies, also potentially suitable for capturing functional recovery.
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remote sensing
Review
Remote Sensing-Based Proxies for Urban Disaster
Risk Management and Resilience: A Review
Saman Ghaffarian 1,*, Norman Kerle 1and Tatiana Filatova 2
1Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente,
Enschede 7500 AE, The Netherlands; n.kerle@utwente.nl
2Centre for Studies in Technology and Sustainable Development, University of Twente,
Enschede 7500 AE, The Netherlands; t.filatova@utwente.nl
*Correspondence: s.ghaffarian@utwente.nl; Tel.: +31-53-4894465
Received: 14 August 2018; Accepted: 1 November 2018; Published: 7 November 2018


Abstract:
Rapid increase in population and growing concentration of capital in urban areas has
escalated both the severity and longer-term impact of natural disasters. As a result, Disaster Risk
Management (DRM) and reduction have been gaining increasing importance for urban areas.
Remote sensing plays a key role in providing information for urban DRM analysis due to its agile
data acquisition, synoptic perspective, growing range of data types, and instrument sophistication,
as well as low cost. As a consequence numerous methods have been developed to extract information
for various phases of DRM analysis. However, given the diverse information needs, only few of
the parameters of interest are extracted directly, while the majority have to be elicited indirectly
using proxies. This paper provides a comprehensive review of the proxies developed for two risk
elements typically associated with pre-disaster situations (vulnerability and resilience), and two
post-disaster elements (damage and recovery), while focusing on urban DRM. The proxies were
reviewed in the context of four main environments and their corresponding sub-categories: built-up
(buildings, transport, and others), economic (macro, regional and urban economics, and logistics),
social (services and infrastructures, and socio-economic status), and natural. All environments and
the corresponding proxies are discussed and analyzed in terms of their reliability and sufficiency in
comprehensively addressing the selected DRM assessments. We highlight strength and identify gaps
and limitations in current proxies, including inconsistencies in terminology for indirect measurements.
We present a systematic overview for each group of the reviewed proxies that could simplify
cross-fertilization across different DRM domains and may assist the further development of methods.
While systemizing examples from the wider remote sensing domain and insights from social and
economic sciences, we suggest a direction for developing new proxies, also potentially suitable for
capturing functional recovery.
Keywords:
urban DRM; remote sensing; damage; recovery; vulnerability; resilience; economic; social;
proxy; indirect measurement
1. Introduction
By 2050, 80% of the world population will live in urban areas [
1
]. This unprecedented clustering
of infrastructure and people has been shifting the focus of Disaster Risk Management (DRM)
studies towards cities. Furthermore, the ability of a system to resist, absorb, accommodate to, and
recover from the effects of a hazard in a timely and efficient manner—known as resilience—has
become crucial to decrease the disaster risk [
2
]. Resilience also encompasses post-event processes
that allow for communities to reorganize, change, and learn in response to an event [
3
]. Hence,
enhancing community’s resilience to natural hazards implies improving its capacity to anticipate
Remote Sens. 2018,10, 1760; doi:10.3390/rs10111760 www.mdpi.com/journal/remotesensing
Remote Sens. 2018,10, 1760 2 of 30
threats, to reduce own overall vulnerability, and to allow the community to recover from adverse
impacts when they occur. Decades of disaster research offer extensive findings in this respect [
4
7
].
Remote sensing (RS)—as an effective and rapid tool for monitoring large areas—is essential for the
acquisition of geospatial data, which in turn constitutes the basis for risk assessment and management.
RS is widely used for various aspects of the DRM, ranging from vulnerability [
8
] to rapid damage
assessments [
9
], for diverse areas ranging from coastal ecosystems [
10
] to complex urban settings [
11
],
and for disasters as diverse as landslides [12,13] or cyclones [14].
Numerous methods have been developed to extract information from RS data to identify, characterize,
or quantify different phases of the disaster risk cycle: response, recovery, prevention/mitigation,
and preparedness [
15
]. However, early studies predominantly considered the physical side of
the assessments for both pre- and post-disaster phases and hazard assessment, using direct
observations. For example, scholars have assessed the number of buildings collapsed/damaged [
16
,
17
]
and reconstructed [
18
], or estimated their vulnerability [
19
]. However, the attention to the non-physical
side of DRM in both pre- and post-disaster situations was scare. Non-physical assessments usually
comprise social, economic, and natural aspects in addition to the built-up ones, which refer to physical
assessments, e.g., physical vulnerability assessment [
20
]. Recent studies started to include socio-economic
aspects when examining DRM phases such as vulnerability [
21
,
22
], resilience [
23
], damage [
24
,
25
], and
recovery [
26
] assessments. While several spatial and non-spatial parameters required for detecting and
quantifying of DRM-related elements can be extracted directly from RS imagery, many have to be elicited
indirectly. Similarly, Indirect measurements are fundamental tools in related research fields, such as
environmental science [
27
]. They have been also used in social and economic studies in the DRM context,
e.g., for social vulnerability [
28
] and business recovery [
29
] assessments. Proxies have become central
in RS-based DRM studies due to the inherent characteristics of several urban elements, such as social
and economic activities, which makes it very difficult, if not impossible, to directly observe them using
RS data. Proxies use observable features in RS data used to capture and extract information of interest
that is not directly visible or measurable, but is correlated with the former. In recent years, using proxies
has become the predominant approach to capture implicit information in urban DRM, for both pre- and
post-disaster situations [
30
]. For example, texture is used as a proxy to extract damage to buildings
and roads, with irregular texture indicating damaged area. For the purpose of this review, the most
relevant components of the disaster risk cycle for use of RS in urban areas are the post-disaster phase
proxies focusing on damage and recovery, and the pre-disaster phase proxies measuring vulnerability
and resilience.
This work was specifically motivated by a number of limitations in the published literature.
Firstly, there is inconsistency in terminology: indirect measurements labelled by various terms, such as
index [
31
], indicator [
32
], and proxy [
33
]. In this paper, the term proxy is overarching for all such
indirect measurements. Secondly, duplications in efforts: in certain cases methodologically the same
proxies have been developed independently in more than one domain. For instance, the presence of
vegetation in urban areas has been used as a proxy for both social vulnerability [
33
] and post-disaster
recovery [
34
] assessments. Thirdly, the current DRM literature suggests several proxies that are, at times,
unreliable. For example, the presence of vehicles has been used to evaluate the accessibility of the
roads [
34
]. However, the metric is highly dependent on the image acquisition date and time, and other
legal or environmental parameters that affect the presence of vehicles. Finally, need to go beyond the
physical side towards functional assessments is prominent. Yet, few proxies have been developed for
social, economic, and functional assessments in urban DRM. This paper conducts critical analyses
on current RS-based proxies and borrows insights from other research fields (e.g., the economy
and social sciences). To address these limitations, we conduct a systematical review following a
number of steps. We start by splitting reviewed proxies into four groups that characterize built-up,
economic, social, and natural environments. Then, a proxy catalog for each group is generated based
on the reviewed studies that were used by researchers and governments in the different DRM phases.
Finally, a comprehensive analysis is done on the reviewed proxies, and the most suitable proxies for
Remote Sens. 2018,10, 1760 3 of 30
each DRM situation are discussed. These analyses demonstrate current limitations, including lack
of proxies to evaluate urban functions for DRM and suggests future directions in developing new
proxies. Furthermore, proxies originating from the wider RS domain and other disciplines—urban
form studies, structural engineering, and natural sciences—were identified and systemized in this
paper. This structured analysis may provide guidance for developing reliable RS-based proxies.
The review is structured as follows. Section 2defines the terminology used for indirect
measurements, i.e., proxy, in this article. In Section 3, we explain the methodology employed for this
review. Section 4presents a comprehensive literature review of RS-based proxies for each of the four
selected environments, as well as a corresponding table of proxies. In addition, new insights towards
improving current or developing new proxies are provided based on the state of the art analysis from
different research fields, including urban form studies, structural engineering, economics, and natural
sciences. The final section provides conclusions and outlines directions for future development of
the field.
2. Defining A Proxy in Remote Sensing
Indirect estimation using proxies allows researchers to deduce the condition of an element or
process based on their relations and links with directly identifiable and observable features in the
surrounding/neighboring areas. According to the Oxford Dictionary, a proxy is a figure that can be
used to represent the value of a non-directly measurable object in a calculation. In statistics, it is
a measurable variable that is used in place of a variable that cannot be measured [
35
]. Moreover,
there are good examples of using proxies in economics, such as per capita Gross Domestic Product
(GDP) being a proxy for growth in wealth and potentially quality of life [36].
There is currently no clear definition for proxy in RS-based studies, though proxies play a critical
role in this field. When considering the growing role of proxies in RS, similarities and differences can
be found when compared with the traditional use of proxies, particularly in the wider geosciences
(including climatology). In all disciplines, proxies have been used as indirect variables to determine
or to constrain unobservable or immeasurable variables or as physical variables to go back in time
and measure the immeasurable or unobservable parameters because of lack of data from that time.
However, passive RS is by definition an indirect form of measurement and an information source
that acquires data at a certain time. By that logic, any image derivative or index measure is a proxy.
Therefore, the traditional definition of the proxy cannot be used in this field. In this paper, the term
proxy is defined as use of observable physical features or directly measurable variables to understand
and extract what actually exists on the ground, but what is not directly observable or measurable
from RS data. For example, the proportion of built-up and vegetated area has been measured and
used as a proxy to determine a settlement type to determine the vulnerability of such settlements to
disasters [33]. Proxies can also be separated into two groups based on their use in the RS field:
(i)
not-directly observable physical parameters, e.g., use of shadow to determine the height of an
element at risk (building) [37,38],
(ii)
and measures to assess processes and functions, e.g., the building morphology and use to
determine functionality of buildings and urban areas to assess the post-disaster recovery processes
and vulnerability [39].
3. Methods
We systemize the published literature based on classification by (i) DRM stage (focus on pre-
and post-disaster) zooming into vulnerability, resilience, damage, and recovery assessments practices,
and by (ii) the type of environment changes in which a proxy tries to capture. Each is common in
DRM literature and fits well the main purpose of this article. Following [
20
], we differentiate between
4 types of environment: (1) the built-up environment divided into ‘buildings’, ‘transport’ and ‘others’;
(2) the economic environment with ‘macro, regional and urban economics’ and ‘logistics’ subcategories;
Remote Sens. 2018,10, 1760 4 of 30
(3) the social environment covering ‘services and infrastructure’ and ‘socio-economic status’; and (4)
the natural environment.
This review is based on a systematic literature search, performed in summer 2018, using several
databases (Web of Science, ScienceDirect, SpringerLinkjournals, Taylor&Francis, and Scopus). We select
the articles for this review, using the following keywords: indirect, proxy, proxies, index, indices,
indicator, and remote sensing, GIS, vector, raster, map, satellite image, aerial image, lidar, UAV,
UAS, drone, crowdsourced, ground image, ground photo, and resilience, resilient, adaptive capacity,
coping capacity, preparation, prevention, vulnerability, damage, response, impact, rescue, recovery,
reconstruction, rehabilitation, and relief. These keywords are selected to address the goal of the review:
to provide a systematic categorization of proxies for DRM and resilience, supporting them with
some key citations and relevant state-of-the-art examples. Finding relevant papers for this review was
difficult, because not necessarily all of the papers use keywords to describe their indirect measurements
(proxies). Indeed, with nearly every type of passive RS being per se indirect, every form of processing of
such data inherently uses proxies. Furthermore, there are many commonly used proxies (e.g., NDVI for
vegetation cover extraction, the number or configuration of buildings to extract information about
urban sprawl and presence of slums, nightlights to detect electrification) that researchers accept as
natural RS-based measurements, without labeling them as proxies. Hence, there may be additional
studies using RS-based proxies that are not included in this review due to different terminology.
Moreover, there might be uncertainties in the used terms for the selected pre-and post-disaster
situations, which also made it hard to find relevant studies. For example, for post-disaster damage
assessment, similar key phrases have been used such as response to a disaster [
40
], and impacts of a
disaster [
41
]. Naturally, to the best of our knowledge, we have carried the search as comprehensively
as possible. Since the purpose of this article is to provide a structured overview of existing approaches
with exemplary citations on key studies rather than perform a full coverage of the literature, the core
conclusions are not affected. The review covered journal publications, book sections and conference
publications that can be retrieved either via the research engines employed or the websites of the main
RS conferences. Only English language papers were considered.
4. Remote Sensing-Based Proxies for DRM in Urban Areas
In total, we identify 114 key publications. Out of these, 52 papers address damage assessment
and 21, 40, and seven articles focus on recovery, vulnerability, and resilience assessments, respectively.
Papers with a focus on two DRM areas, e.g., both damage and recovery assessments, are counted
on both of them separately. A chronological overview of the publications shows an increasing
trend in studies for RS-based proxy literature for urban DRM (Figure 1). Continuous progress in
RS technology and sensors, which supplies ever more diverse and detailed image data to a growing
number of communities, is one of the reasons behind this increase. Furthermore, an understanding
that DRM assessments have to go beyond capturing the physical impact, calls for a need for indirect
measurements, which also contributed to this increase.
Remote Sens. 2018,10, 1760 5 of 30
Remote Sens. 2018, 10, x FOR PEER REVIEW 5 of 34
Remote Sens. 2018, 10, x; doi: FOR PEER REVIEW www.mdpi.com/journal/remotesensing
Figure 1. Number of annual publications on remote sensing-based proxies for Disaster Risk Management
(DRM). Papers with a focus on two DRM areas, e.g., both damage and recovery assessments, are counted
on both of them separately.
Figure 2 shows the number of RS-based proxies that have been developed for each component of
DRM. The built-up environment is the most frequently studied one with 35 RS-based proxies developed.
Natural environment accounts for the lowest number of developed RS-based proxies, offering a choice of
13. Furthermore, the Economic and Social environments both have 21 developed RS-based proxies. Some
proxies that are used interchangeably in more than one area, e.g., damage and recovery assessments, are
counted on all of the used categories separately.
Figure 2. Number of developed remote sensing-based proxies for DRM in each environment (Built-up,
Economic, Social, Natural). The proxies that are used interchangeably in more than one area, e.g., damage
and recovery assessments, are counted on all of the used categories separately. However, the green colored
bar shows the total number of unique RS-based proxies for each environment.
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Total
Damage 10020203616410 6 9 50
Recovery 00000104212313421
Vulnerability 0100014312445510 40
Resilience 0000000010120217
Total 110204410 10 413 13 16 16 24
0
10
20
30
40
50
60
Number of publications
0
5
10
15
20
25
30
35
40
0
5
10
15
20
25
30
35
40
Built-up Economic Social Natural
Number of proxies
Recovery Resilience Damage Vulnerability Total
Figure 1.
Number of annual publications on remote sensing-based proxies for Disaster Risk
Management (DRM). Papers with a focus on two DRM areas, e.g., both damage and recovery
assessments, are counted on both of them separately.
Figure 2shows the number of RS-based proxies that have been developed for each component
of DRM. The built-up environment is the most frequently studied one with 35 RS-based proxies
developed. Natural environment accounts for the lowest number of developed RS-based proxies,
offering a choice of 13. Furthermore, the Economic and Social environments both have 21 developed
RS-based proxies. Some proxies that are used interchangeably in more than one area, e.g., damage and
recovery assessments, are counted on all of the used categories separately.
Remote Sens. 2018, 10, x FOR PEER REVIEW 5 of 34
Remote Sens. 2018, 10, x; doi: FOR PEER REVIEW www.mdpi.com/journal/remotesensing
Figure 1. Number of annual publications on remote sensing-based proxies for Disaster Risk Management
(DRM). Papers with a focus on two DRM areas, e.g., both damage and recovery assessments, are counted
on both of them separately.
Figure 2 shows the number of RS-based proxies that have been developed for each component of
DRM. The built-up environment is the most frequently studied one with 35 RS-based proxies developed.
Natural environment accounts for the lowest number of developed RS-based proxies, offering a choice of
13. Furthermore, the Economic and Social environments both have 21 developed RS-based proxies. Some
proxies that are used interchangeably in more than one area, e.g., damage and recovery assessments, are
counted on all of the used categories separately.
Figure 2. Number of developed remote sensing-based proxies for DRM in each environment (Built-up,
Economic, Social, Natural). The proxies that are used interchangeably in more than one area, e.g., damage
and recovery assessments, are counted on all of the used categories separately. However, the green colored
bar shows the total number of unique RS-based proxies for each environment.
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Total
Damage 10020203616410 6 9 50
Recovery 00000104212313421
Vulnerability 0100014312445510 40
Resilience 0000000010120217
Total 110204410 10 413 13 16 16 24
0
10
20
30
40
50
60
Number of publications
0
5
10
15
20
25
30
35
40
0
5
10
15
20
25
30
35
40
Built-up Economic Social Natural
Number of proxies
Recovery Resilience Damage Vulnerability Total
Figure 2.
Number of developed remote sensing-based proxies for DRM in each environment (Built-up,
Economic, Social, Natural). The proxies that are used interchangeably in more than one area, e.g.,
damage and recovery assessments, are counted on all of the used categories separately. However,
the green colored bar shows the total number of unique RS-based proxies for each environment.
4.1. Built-Up RS-Based Proxies
The build-up environment in urban areas tends to be significantly impacted during a disaster and
these adverse impacts are relatively easy to detect. It makes this category the most studied in the RS
literature (Figure 1), with the majority of RS-based proxies being developed here as compared to other
environments within urban DRM (Tables 13). To discuss and explain the proxies in detail the built-up
component is separated into three main categories: Buildings, Transport, Others.
4.1.1. Buildings Category
The predominant land cover in urban areas is the buildings class. This is why one of the mostly
used proxies for damage [
37
] and recovery [
42
] assessments in urban DRM is extracting the status
Remote Sens. 2018,10, 1760 6 of 30
of the buildings. For example collapsed/damaged or reconstructed buildings are detected either
using multi-temporal RS data by comparing the pre-event and post-event situations [
43
], or only
using mono-temporal RS data acquired after the disaster [
44
]. For example, Bevington, et al. [
25
]
identify the damage level in urban areas after the Haiti earthquake by extracting buildings damage
while using qualitative analysis from satellite imagery along with the integration of visual field data.
Similarly, Chen, et al. [
45
] use aerial photography to identify collapsed and damaged buildings
(residential houses), and develop a damage pattern mining framework to detect them. Besides,
the physical changes in the recovery processes are assessed mostly using a pathway from rubble of
collapsed buildings to standing (reconstructed) buildings. Hence, Platt, et al. [
32
] use the reconstruction
of buildings as a proxy for recovery assessment after an earthquake. Several studies also employ this
proxy for physical recovery assessments [
32
,
34
,
46
49
]. A reconstruction of some specific buildings
(e.g., commercial buildings) is also used as a proxy for socio-economic recovery assessments [
49
].
However, all of them disregard the functional recovery assessment of buildings, which indicates
whether a building is operating and/or the building is being used for its intended type of activity
(e.g., commercial).
In addition, the value of point height change has been used as a proxy to extract information for
grading the damage for each building using pre- and post-earthquake DEMs [
50
]. Although this proxy
provides a good source of information to assess per building damages, it cannot detect damages that
do not necessarily change the value of the height of the buildings, such as cracks and holes in facades.
Some proxies are considered as higher level ones due to their indirect observability in
addition to indirect interpretations to indicate something meaningful for computation. One of
them is shadow, which has been used in the DRM context to extract information about
collapsed/damaged/reconstructed structures from RS images [
44
,
51
53
]. Kerle and Hoffman [
37
]
provide a comprehensive discussion of pros and cons of using shadow as a proxy to detect
building-based changes. They demonstrate that, although missing or smaller shadows compared to
pre-event situation may indicate a damaged or collapsed buildings (e.g., pancake collapse), the sun
direction differences between the acquired images resulting in shadow changes, can lead to confusion.
Image texture has frequently been used as a proxy to identify damaged/collapsed buildings [
54
57
],
damaged roads [
58
,
59
], and damaged areas [
9
,
60
] from RS imagery. Damaged buildings/areas have
more irregular texture than intact ones [
55
]; therefore, the damaged/collapsed ones can be extracted
by comparing their textures. However, this proxy may not be reliable in detecting damages to informal
settlement (slums) since they normally have irregular textures. Moreover, texture has been shown
as useful additional information for urban structure discriminations, and has been used for building
seismic vulnerability analysis [61,62].
In addition, building roof offsets between adjacent buildings have been used as a proxy to
detect pancake collapse, in which the building is characterized by an intact roof but collapsed
floors [37]. Building material has been used for vulnerability [62,63] and resilience assessments [6466].
Building material detection from RS data is typically based on the interpretation of rooftop colors from
aerial or satellite images without considering other materials of building (e.g., building wall materials).
Data by drones/UAV on facades status have a potential to offer more reliable proxies for building
material analysis/assessment due to providing information about the walls and other elements of the
buildings in the facades in addition to their rooftops [
67
]. Energy loss by buildings has been used as a
proxy for recovery [
49
], where a decrease in energy loss of a structure is considered as a sign of positive
recovery, as it indicates an improved building standard. This proxy is extracted based on the heat level
of structures using hyperspectral imagery. A lower heat level of a house shows a lower energy loss level.
The rate of energy loss reduction of houses demonstrates their insulation level and is related to building
materials and overall construction quality. However, a non-functioning building does not produce
energy (heat), and thus, when compared to others, shows a low energy loss, which can lead to inaccurate
results in the extraction of the insulation level of buildings. Meanwhile, this point also demonstrates that
the energy loss of buildings can be used as a proxy for functional assessment of buildings.
Remote Sens. 2018,10, 1760 7 of 30
Table 1.
Remote sensing-based built-up proxies for urban DRM, Buildings category. Mono and Multi refer to mono-temporal and multi-temporal remote sensing (RS)
data that are used for the extraction, respectively.
Proxy Essence Used in Disaster Phase Used RS Data to Extract Mono Multi Key References
Structural/building
damages/collapsed
Number of damaged/collapsed buildings shows the
damaged area Damage VHR satellite images,
Aerial images, UAV data X X [25,45,47]
Shadow
To extract collapsed/damaged buildings
(collapsed/damaged buildings do not produce regular
shadow pattern)
Damage VHR satellite images,
Aerial images X[37,44,5153]
Texture
To extracted damaged buildings, roads, and urban areas
(irregular texture over buildings/roads indicates
damaged areas)
Damage, Vulnerability HR and VHR aerial and
satellite images X X [9,5462]
Building offset To extract building pancake collapse Damage VHR satellite images X[37]
Roofing tile displacements or
collapsed
To extract building damage ratio (displaced/collapsed
roofs indicates damage to buildings) Damage Radar images, VHR
satellite images, UAV data X[6871]
Building deformation
Damaged buildings have deformations in geometries
including inclined building, discontinuous surface
structure (roof)
Damage
Radar images, HR-VHR
satellite images, UAV data,
Aerial video
X[68,70,7274]
Spalling building Spalling building indicates damages to buildings Damage UAV data, VHR satellite
images X[71,75]
Ruble piles and debris To extract building damage ratio Damage UAV data, VHR satellite
images X[71,73,7581]
Value of point height change Change in building heights to detect collapsed buildings
(collapsed building has lower height value than intact one)
Damage
VHR remote sensing height
data (e.g., UAV data) X[50]
(Walls/facades with) Cracks To extract building damage ratio Damage UAV data, Radar data,
Aerial images X[68,70,82,83]
Holes/gaps on roof and facade
of the structures To extract building damage ratio Damage UAV data X[6971,82,84,85]
Interaction of cracks with
structural element To extract building damage ratio Damage UAV data X[69,70]
Local symmetry pattern of
facade windows
Change in the windows pattern from its original to
irregular one shows damage Damage Oblique aerial images X X [86]
Building removal and
reconstruction
Number of reconstructed buildings shows a progress in
recovery process Recovery VHR satellite images,
Aerial images X[32,38,4649,87]
[34,49,88]
Building morphology
Morphology of a building is a proxy for building use/land
use extraction, and change in this proxy from pre- to
post-disaster shows land use changes in recovery process
Recovery, Vulnerability VHR satellite images X[32,34,48,49,87,89]
Energy loss Lower energy loss value shows better insulation of house Recovery VHR images X X [49]
Position of building in relation to
the street level
The difference between elevation of building to street
(buildings in lower elevation in relation to street level are
more vulnerable)
Vulnerability VHR satellite images,
DEM data X[90,91]
Building materials
To determine the structural vulnerability and resilience
(e.g., concrete-based buildings are more resilient than
wooden ones to water-related disasters)
Vulnerability, Resilience VHR images, Urban map X[6166]
Remote Sens. 2018,10, 1760 8 of 30
Table 2.
Remote sensing-based built-up proxies for urban DRM, Transport category. Mono and Multi refer to mono-temporal and multi-temporal RS data, respectively,
used for the extraction, respectively.
Proxy Essence Used in Disaster Phase Used RS Data to Extract Mono Multi Key References
Blow-out debris
To detect blocked roads (During a disaster blow-out debris
block partially/completely the roads) Damage UAV data, HR and VHR
satellite images X X [37,71,72,75]
Accessibility analysis/Road
accesses/road condition
Accessibility analysis shows the transportation capacity of
the network Damage, Recovery VHR satellite images X X [32,34,4649,92,93]
Reconstruction of bridges and
public transport facilities
Bridges and public transport facilities are essential parts of
providing accessibility in a transport network Damage, Recovery VHR satellite images X X [32,34,4749]
Presence of vehicles Presence of vehicles on the roads provides information
about transportation condition and functioning of roads Recovery VHR satellite images X[32,34,48,49]
Length of roads Length of the roads shows the capacity of the
transport network. Recovery, Vulnerability Oblique aerial images,
VHR satellite images X[48]
[39]
Width of roads Wider roads are less vulnerable to be completely blocked
during a disaster Vulnerability VHR satellite images X[94]
Road network density
Higher ratio of road network density shows less vulnerable
transport network Vulnerability HR satellite images,
Radar data X[95]
The proportion of
low-grade highway
Low-grade highways (e.g., county road) are
more vulnerable Vulnerability HR satellite images X[96]
Table 3.
Remote sensing-based built-up proxies for urban DRM, Others category. Mono and Multi refer to mono-temporal and multi-temporal RS data that used for
extraction, respectively.
Proxy Essence Used in Disaster Phase Used RS Data to Extract Mono Multi Key References
Mean flood water height Higher mean flood water cause more damages Damage VHR satellite imagery X[97]
Sea water penetration on land Low level sea water penetration on land increases the
tsunami inundation zone Damage ALOS images,
PALSAR data X X [98]
Surface water areas/Level of
flood water coverage
Increase in the level of flood water coverage in land
increase the damage in the area Damage
VHR satellite images, DEM
data, ASTER images X[47,99,100]
Debris line To identify how far water reached inland and extract
debris zone Damage Landsat TM, VHR aerial
and satellite imagery X[81,101,102]
Impervious surface classification
To extract permeability of the surface Recovery VHR satellite images X[49]
Ratio of permanent residential
buildings and temporary
accommodation
To extract movement to reconstructed/recovered areas Recovery VHR satellite images X X [32,34,46,48]
Drainage network density
Lower drainage network density in case of a water-related
disaster increase the vulnerability Vulnerability Sentinel-2 imagery, DEM
data, VHR satellite images X[103106]
River network density Higher river network density in case of a water-related
disaster increase the vulnerability Vulnerability DEM data,
Landsat 8 images X[95]
Impervious surface
To extract permeability of the surface (impervious surface
has low level of permeability, which increases
the vulnerability)
Vulnerability Landsat images X[95]
Remote Sens. 2018,10, 1760 9 of 30
Several proxies rely on geometric and morphological characteristics of built-up components to
extract detailed information about structural deformations of buildings. They are used to compute the
damage ratio of buildings. Gerke and Kerle [
73
] use the discontinuity of surface of building rooftops
and vertical walls as a proxy for damage detection. They show that continuous roofs indicate intact
buildings. In addition, Galarreta, et al. [
70
] identify cracks and holes in the building roof or facades,
intersections of cracks with load-carrying elements, and dislocated tiles (Figure 3A–C) as proxies to
be utilized to assess per-building damages after a disaster. Since these details can only be extracted
using VHR images and height data, UAV images were used to produce three-dimensional (3D) point
clouds to extract them. The results illustrate the efficiency of using those proxies for building-based
damage assessment and classification when UAV data are available. Tamkuan and Nagai [
68
] used
similar proxies: cracks, displaced and collapsed roofing tiles, wall mortar that is somewhat peeled off,
or inclined buildings (Figure 3D) to evaluate and classify the building-based damages, e.g., roof tile
displacements and inclined buildings are used as proxies for detecting slightly and heavily damaged
buildings, respectively. Rubble piles and debris (Figure 3E) are the first features to draw attention and
they have been frequently used as proxies for damage detection [
76
81
], while spalling of buildings
(Figure 3E) also is one of the features for heavy damages which has been used for damage detection
with UAV [
71
,
82
] and satellite images [
75
]. Furthermore, geometric deformation of entire buildings,
such as changes in building shape and size, is employed as a proxy to extract the partly damaged
buildings using satellite images [
72
]. Change in the symmetry of windows on the facade of a building
serves as a proxy for damage assessment from oblique aerial imagery [
86
]. To do so, local symmetry
points are detected in a sliding window. Then, histogram bins of those points in the vertical and
horizontal directions are generated to distinguish damaged and undamaged building facades using
the Gini Index. Using changes to different components of the buildings, e.g., facade and wall, as a
proxy to evaluate damages to the buildings in detail remains a challenge and continues to be actively
studied (e.g., Duarte, et al. [83]).
Figure 3.
Examples of remote sensing-based built-up proxies. (
A
) Roof with dislocated tiles, (
B
) cracks
in concrete facade, (
C
) cracks and hole in brick facade [
70
], (
D
) inclined building [
68
,
70
], (
E
) debris,
rubble piles, spalling [82], and (F) facade windows symmetry [86].
Further improvements are required for robust and accurate damage assessment per building.
This can be done by extracting structural damage patterns from RS data, for example by making use of
the characteristics of facade wall geometry, similar to using facades windows symmetry (Figure 1f) [
86
],
wall slenderness, area of wall, or use of misalignment of wall openings [107] as RS-based proxies.
Remote Sens. 2018,10, 1760 10 of 30
Building morphology acts as a proxy to extract building-based land use [
32
,
48
,
87
,
89
] and to
measure living conditions [
34
,
42
,
49
]. It has been used for post-disaster recovery and vulnerability [
89
]
evaluations, but hardly in other DRM situations. In a different approach, Müller, et al. [
91
] apply
the position of buildings in relation to the street level as a proxy to determine the likelihood of
constructions to suffer damage in the case of a flood event and assess the building vulnerability.
Accordingly, buildings with lower elevations than the street are more vulnerable.
Building-based functional assessment has not been sufficiently addressed through the developed
proxies that only consider the land use information. For example, functionality of the buildings may
change during the post-disaster recovery phase, which cannot be identified only by extracting the
reconstructed buildings. Nevertheless, facades are good sources of information for building-based
functional assessment, and new proxies could make better use for example of the presence of cars
in a driveway, flower pots in front of windows of residential buildings, or the presence of panel and
banners on top of shops for extracting the functional use of buildings.
4.1.2. Transport Category
Transportation is also a vital component for DRM, as it provides accessibility to different locations
within urban areas. One of the first emergency activities in post-disaster situations is to reopen blocked
roads to reach damaged areas for rescue operations. Road network connectivity and condition, i.e.,
accessibility [
92
] has been used as a proxy for damage assessment. In addition, monitoring this proxy
over a long period of time after a disaster also contributes to measuring recovery of the area [
34
,
49
].
In terms of more detailed proxies, debris on the road (blocked road) [
47
], and condition of bridges
and transport facility [
32
] have been proposed to evaluate the transportation condition for urban
DRM. Presence of vehicles has also been used as a proxy to evaluate the accessibility of the roads
after a disaster [
34
]. However, the presence of vehicles on a road does not necessarily signal the good
condition and usability of the roads. On the other hand, reconstruction of bridges and roads is a
reliable proxy for road accessibility analysis after a disaster.
In another study, Liu, et al. [
39
] utilize length of roads as a proxy to evaluate the storm
surge vulnerability of urban area, and Brown, et al. [
48
] use it for recovery assessment. However,
a non-functioning road does not demonstrate the successful recovery processes. In a different study,
Kumagai [
94
] estimate width of roads as a proxy to assess the vulnerability of the roads to blockades.
The results show that, in a dense urban area, in which high-rise buildings exist and their blow-out
debris or in case of collapsing during a disaster event can block the road, the width of roads can be used
as a reliable proxy to assess the vulnerability of the transport network. In a similar study, Hu, et al. [
95
]
rely on road network density as a proxy for disaster vulnerability assessment. Although denser road
networks and wider roads can increase the chance of having access to damaged areas after a disaster,
occupying urban areas to reach denser road ratios and wider roads is a controversial. Similar problems
exist in using proportion of low-grade highways, such as county roads [
96
], as a proxy for vulnerability
assessment of a city. High proportion of low-grade highways indicates a less developed transportation
infrastructure, which leads to higher level of vulnerability of urban areas.
4.1.3. Others
One of the unique features of tsunami inundation zones important during and after an event
is the level of sea water penetration on land. Thus, RS studies have focused on extracting water
bodies from the post-event images as the first step to use it as a proxy for damage assessment [
98
].
Similarly, surface water areas (level of flood water coverage) serves for damage [
99
,
100
] and recovery
assessments [
46
]. Furthermore, debris line acts as a proxy to identify the impact of water related
disasters, such as tsunami [
102
] and hurricane [
101
]. The debris line demonstrates how far water
reached inland after a disaster, and it has also been used to identify debris zone [81].
In a different study, impervious surface ratio is estimated as a proxy to compute the vulnerability
of urban areas because the increase of impervious surface can lead to an increase of volume, intensity,
Remote Sens. 2018,10, 1760 11 of 30
and duration of urban water run-off, i.e., the duration that water remains on the surface after
a flood [
95
]. In addition, Costa Viera and Kerle [
49
] used impervious surface as a measure for
recovery assessment.
As a unique recovery situation, after a disaster people that are located in a temporary shelter
during the reconstruction processes move back to their permanent houses when the reconstructions is
completed. Consequently, change in the ratio of permanent buildings to temporary accommodations
serves as a proxy for movement of the population from temporary accommodation to permanent
houses and as a successful recovery process [32,46,48].
After a flood event, drainage plays an important role to evacuate flood waters, with an increase
in drainage density reducing the vulnerability from the affected area; therefore, it has been used as a
proxy for urban flood vulnerability assessment [
103
106
]. In contrast, a higher river network density
ratio indicates a high vulnerability due to the relatively larger amount of flood-prone regions during a
flood event [95].
4.2. Economic RS-based Proxies
The nature of economic and business flows associated with functions and processes in urban areas
is not directly observable and measurable through RS data. Hence, the disruptions to the functioning of
the economic environment are one of the least studied topic in urban DRM. To discuss and explain the
proxies for the economic component in details, we differentiate between two main categories—(macro,
regional, and urban) economics and logistics—that characterize the state of an economy and assure
physical flows of economic activities correspondingly.
4.2.1. Macro, Regional, and Urban Economics
The macro, regional, and urban economics category includes proxies that are associated with
the performance of the economy as a whole and its associated changes (Table 4). Land use data
provides information about locations and types of economic activities. In the urban context it is
one of the most popular proxies used to identify disaster-related economic damage for different
economic sectors [
47
], as well as recovery [
49
] and vulnerability [
108
]. For example, Brown, et al. [
47
]
applied the building-based land use proxy to identify damages to commercial and industrial buildings
when assessing economic damages from the Wenchuan earthquake in China. Similarly, it has been
also used for recovery assessment by extracting reconstructed buildings that are associated with
economic activities [34,49]. The land use proxy and corresponding economic activities, their location,
public infrastructure, economic assets and economic capacity, has also been used for economic
vulnerability evaluations of urban areas [
109
]. Although land use is a crucial source of information
with respect to direct impact on economic activities, it is salient on the detailed economic information,
such as type of businesses and operationally of the impacted buildings. Moreover, while land use
proxies can capture direct economic damages to physical assets, it provides little information about
direct damage to building content (e.g., equipment and furniture) and about indirect damage due to
business interruption.
Nightlight satellite imagery has been utilized to assess the economic impacts of hurricanes [
110
],
typhoons [
111
], and tropical cyclone [
41
]. Nightlight images give an opportunity to compare changes
in night light intensity, which relates to electricity availability, for before and after a disaster. It has been
shown that a strong correlation exists between night light intensity and GDP [
112
,
113
]. Furthermore,
it has also been suggested as a potential proxy for economic resilience assessment [114].
Remote Sens. 2018,10, 1760 12 of 30
Table 4.
Remote sensing-based economic proxies for urban DRM, Macro, regional, and urban economics category. Mono and Multi refer to mono-temporal and
multi-temporal RS data that used for extraction, respectively.
Proxy Essence Used in Disaster Phase Used RS Data to Extract Mono Multi Key References
Land use To extract economic activity types/location and economic
focal spaces
Damage, Recovery,
Vulnerability,
VH-H resolution satellite
images, Landsat images, X X [30,34,104,108,109,
115120]
Nightlight intensity To estimate GDP and amount of economic activities Damage, Recovery,
Resilience
VIIRS nightlight
satellite imagery X X [41,110,111,114]
Pier length Increase in pier length shows stronger fishery industry,
which is a vital source of livelihood Damage, Recovery VHR images X X [34,48]
Presence of boats High number of boats indicates stronger fishery industry,
which is avital source of livelihood Damage, Recovery VHR images X X [34,48,49]
Presence of shrimp hatcheries
and ponds
High number of shrimp hatcheries and ponds indicates
strong fishery industry, which is a vital source of livelihood
Damage, Recovery VHR images X X [34,46,48]
Crops Agriculture industry for livelihood recovery Damage, Recovery VHR images X X [32]
Arable land Agriculture industry for livelihood recovery Recovery VHR images X X [34,46,48,49]
Presence of heavy vehicles To extract industrial buildings Recovery VHR images X X [34,49]
Chimneys To extract industrial buildings Recovery VHR images X X [34,49]
Warehouses To extract industrial buildings Recovery VHR images X X [34,49]
Transportation to move raw
materials around site
e.g., conveyors, pipelines, Railroads to extract
industrial buildings Recovery VHR images X X [34,49]
Roof color and material
To extract industrial buildings (such buildings have
different roof color and materials than other buildings such
as residential)
Recovery VHR images X X [34,49]
Change in building morphology
and use
Morphology of a building is a proxy for building use/land
use extraction, and change in this proxy from pre- to
post-disaster shows land use changes in recovery process
Recovery VHR images X[32,34,48,87]
Change in urban morphology To extract changes in business, economic activity types
and locations Recovery VHR images X[49,121]
Land surface temperature
Industrial buildings produce more temperature. Used for
urban land use change detection (also change in
settlement locations)
Recovery Landsat ETM+ X X [122]
Building geometry and heights
Usually in an urban area commercial buildings are higher
than other buildings, also industrial buildings have bigger
sizes and irregular shapes. Used for building use and
density calculations and then economic value
Vulnerability Oblique imagery X[39]
Farmland ratio Used for detecting human/economic activities. High
farmland ratio shows higher economic activities Vulnerability VHR-HR images X[103]
Remote Sens. 2018,10, 1760 13 of 30
Disruption of livelihood options for local population is a key component of disaster risk and
resilience assessments. A number of case-based proxies assess a short-term economic recovery
processes after a disaster, in particular, for the fishery and agricultural livelihood activities.
These proxies include pier length, presence of boats [
34
], shrimp hatcheries and ponds [
46
], and area
of arable land [
49
]. Although livelihood sources across study regions, generic proxies can address it in
a more reliable manner than case-based ones, for example, as with the case of crop recovery [32].
Industry is one of the main pillars of an economy. Therefore, urban proxies to assess industrial
recovery include presence of heavy vehicles, chimneys, warehouses, transportation to move raw
materials around site (e.g., conveyors, pipelines, railroads), and roof color and materials [
34
,
49
].
These proxies for detecting industrial buildings and industry-related activities are especially useful for
assessing recovery that is associated with rebuilding and reconstruction. However, they are not always
reliable when used in isolation. For example, the effectiveness of using the presence of heavy vehicles
to detect an industry highly depends on the acquisition time of RS data, not to mention that not all
industries use heavy vehicles. Hence, using multiple complementary proxies promises to deliver more
robust assessments. Furthermore, building facade materials, in addition to rooftop materials, can also
contribute to extract building use/land use information [67].
Building morphology is used as a proxy for extracting building use/land use information
in urban areas [
32
,
34
]. Characteristics of buildings such as size and shape have been used to
extract morphological information per building and then to classify building use [
87
]. For example,
an industrial building usually has a complex shape and a large size [
123
]. Similarly, and from a wider
point of view, change in urban morphology has been used for economic recovery assessment by
detecting businesses movements and types in large scales [121].
Land surface temperature was used to extract evolution of settlement locations and land use
information after the earthquake in L’Aquila city, Italy [
122
]. This proxy is robust when used on coarse
resolution data to extract large size settlement changes. However, it cannot identify any detailed land
use information for those settlements.
There are several RS-based proxies developed for economic vulnerability assessment, in contrast
to other DRM components. For instance, building geometry and heights have been employed as
proxies to assess the number of floors of buildings with economic activities. These two proxies may
indicate the density of such activities, which is essential for computing the economic value of an urban
area [
39
]. Building heights, when integrated with local construction regulations, provide reliable data
to compute the number of floors. Moreover, a calculation of building density based on the number
of floors is reliable [
124
]. However, it requires additional RS data, such as oblique imagery or nDSM
data [125].
Farmland ratio to other types of land uses is strongly linked to economic activities and can be
regarded as comprehensive characteristics of socio-economic systems [
103
]. Accordingly, it has been
used as a proxy to detect economic activity, with higher scores signaling lower economic vulnerability
in e.g., a flood scenario [
103
]. However, in large regions, which offer multiple livelihood options,
the farmland ratio alone is not a reliable proxy for the identification of economic vulnerability.
Moreover, a level of diversification of economic activities is an important factor in assessing the
resilience [
29
,
126
]. Hence, eliciting spatial proxies for a variety of economic in an ensemble is a
promising approach as compared to computing the farmland ratio alone.
4.2.2. Logistics
Logistics based on transport networks and facilities are vital aspects of a strong economy in urban
areas. Consequently, their state and performance are important for urban DRM economic assessment
(Table 5). Gil and Steinbach [
40
] compute the impact of a simulated disaster on the performance of
the street networks that are not directly affected by a flooding scenario in London. The potential of
street network analysis is to provide objective indicators of indirect impacts of flooding on urban
street networks in unaffected areas. The indirect effects of business interruption, particularly in a
Remote Sens. 2018,10, 1760 14 of 30
city center, play a key role in assessing an economic performance after an adverse event. In a similar
study, Contreras, et al. [
127
] propose spatial connectivity as a proxy for a post-earthquake recovery
assessment. Variables, such as distance, travel time, and quality of public transportation are used
to compute this metric. These authors conclude that an efficient spatial connectivity of areas to the
central business district of L’Aquila after an earthquake can decrease the recovery time due to an agile
return of economic functioning. Cox, et al. [
128
] indicate that an accessibility and commuting intensity
within an urban area—computed based on resources availability, vulnerability, and flexibility of the
transportation system—significantly boost economic activities. Accordingly, Hsieh and Feng [
129
]
propose a model for accessibility analysis as a proxy to compute an economic vulnerability of an
urban are. This proxy is mostly based on streets connectivity in an urban road network. However,
the proposed connectivity-based approaches need traffic data in addition to RS data to compute street
network performance. In a different study, Liu and Shi [
109
] show that a higher ratio of transportation
lands (e.g., roads) and facilities reduces the economic vulnerability of urban areas. However, this proxy
also disregards the functional analysis of the roads and transportation facilities focusing on the physical
infrastructure side only.
Table 5.
Remote sensing-based economic proxies for urban DRM, Logistics category. Mono and Multi
refer to mono-temporal and multi-temporal RS data that used for extraction, respectively.
Proxy Essence Used in
Disaster Phase
Used RS Data
to Extract Mono Multi Key References
Indirect street network performance
Impacts of an event not
only on the effected roads
but also on the other streets
Damage VHR images X X [40,130]
Spatial connectivity Spatial connectivity to
central business district Recovery VHR images X X [127]
Transportation land and facilities
High level of transportation
facilities reduces
economic vulnerability
Vulnerability VHR images X[109]
Accessible vulnerability/connectivity
Fragile accessibility
increases the
economic vulnerability
Vulnerability VHR images X[129]
4.3. Social RS-Based Proxies
The social dimension dealing with the status of people in a community is one of the most important
components in DRM. Yet, few reliable RS-based proxies exist to address it. This is mostly because
the social dimension of disasters embraces an individual status of community members, diversity of
livelihoods (economic activities they may engage in), level of inequality, community cohesion, and level
of services. Hardly any of these are directly captured through physical infrastructure or other spatial
data that can be extracted using RS data. Tables 6and 7show existing RS-based proxies in the social
environment for urban DRM. To discuss and explain the proxies in detail we separate them into two
categories: services and infrastructures, and socio-economic status.
4.3.1. Services and Infrastructures
Social services and infrastructures are one of the most important tools to assess the social features
and characteristics of a community in an urban area. Accordingly, several proxies relying on RS
data can be used in the urban DRM context. Urban primary social facilities and services, such as
administrative services, schools, healthcare facilities, and religious buildings, which are crucial sources
for networking and building social ties and cohesion, provide a proxy information on social damage
and recovery assessments [
32
,
34
,
48
]. In addition, local facilities in use, such as car parking, highways,
city gardens, children playgrounds, and sport playgrounds have been used to assess the social
condition of the people living in the shelters and temporary accommodation in recovery processes [
34
,
49
,
131
]. Meanwhile, the number of urban facilities, such as hospitals and schools, serve as a proxy to
assess the social resilience of a community to disaster. An increase in the number of facilities indicates
an increase in social resilience [131].
Remote Sens. 2018,10, 1760 15 of 30
To prevent a disease outbreak during the disaster recovery stage it is crucial to monitor and avoid
overcrowding in campus/temporary settlement sites [
132
]. Here, the minimum covered living space
can contribute to measuring overcrowding in the site using RS [34,49].
During a recovery process, moving population that has been located in temporary accommodation
back to their permanent houses is one of the positive processes, because it increases people’s ability to
return to normal lives and offers stability. Therefore, temporary accommodation size—as a proxy to
estimate the total population living in temporary accommodation—can shed light on the quality of
life [
34
,
49
]. However, in cases where the temporary accommodation is not removed, the movement
of population cannot be monitored using this proxy. Moreover, the destination of the population
movement cannot be extracted using this proxy. For example, people may migrate to other cities
rather than moving back to their former houses, which happened on a large scale in New Orleans after
hurricane Katrina [133].
Pedestrian access/mobility serves as a proxy for evaluation of the recovery processes that is
linked to urban transportation facilities [
134
]. It indicates inequalities in mobility and access of
people to social facilities, which depends on the size of block parcels in the urban area, number of
street network intersections and distance to those facilities [
49
]. Building height, similar to its use
in the economy environment, indicates social recovery. For example, commercial and industrial
developments indicate employment facilities and new job opportunities [
49
]. However, often buildings
are in a mixed use, i.e., half commercial and half residential, limiting the application of this proxy.
In this case, using UAV/oblique images, which contain information about the facades of the buildings,
can be useful. For example, one can extract signs/banners of shops located on buildings’ facades
to identify a functional use of these buildings. Number of inhabitants per settlement indicates the
recovery processes, serving as a proxy for a contentment level of new settlements. Low occupancy
numbers in new developments demonstrate the dissatisfaction in new settlements, which is usually
due to lack of facilities, infrastructure, and job opportunities [
135
]. RS data is used to detect new
settlements and demographic data to extract number of inhabitants [
131
]. Transportation facilities are
a vital infrastructure. Hence, their availability define the better life condition for the people living in
the region and they form the basis for assessing social vulnerability of an urban area [
39
]. In addition,
a distance between buildings and lifelines (e.g., hospitals) is important in emergency cases: the longer
this distance the higher is the social vulnerability of urban areas [33,93,136,137].
Potential to evacuate based on the density of available roads (road/km
2
) has been used as
a proxy to assess social resilience of an urban area during floods [
31
]. Although the availability of
evacuation points is important, distance to them and early warning systems are also crucial. As another
disaster resilience proxy, the presence of open spaces, including free and green areas and the street
networks, has been used for providing services, gathering and social interactions after/during a
disaster (e.g., an earthquake [
138
]). In addition, open spaces, such as hills and street networks,
are significant for evacuation purposes [138,139].
Remote Sens. 2018,10, 1760 16 of 30
Table 6.
Remote sensing-based social proxies for urban DRM, Services and infrastructures category. Mono and Multi refer to mono-temporal and multi-temporal RS
data that used for extraction, respectively.
Proxy Essence Used in Disaster Phase Used RS Data to Extract Mono Multi Key References
Administration, education,
healthcare and religious facilities
Number buildings such as schools, prisons, libraries, the
emergency services and places of worship (e.g., churches,
mosques or temples) shows higher rate of social
infrastructure and services
Damage, Recovery VHR images X X [32,34,48]
Temporary accommodation
Multi-temporal monitoring of size of temporary
accommodation (decrease in size of temporary
accommodation shows the movement towards permanent
ones with higher qualities)
Recovery VHR images X X [32,34,4749]
Monitoring overcrowding Through covered living space extraction Recovery VHR images X[34,49]
Pedestrian access/mobility Number of street network intersections, block parcel size Recovery VHR images X X [49]
Building heights To extract commercial and industrial buildings Recovery VHR images, DEM data X X [49]
Number of inhabitants
per settlement
Confirm that if the attachment level to the new settlements
is low, it hinders the recovery process and hence the
resilience of the city.
Recovery VHR images X X [131]
Local facility in use/number of
urban facilities
Such as car parking, main high street, garden, playground,
swimming pool Recovery, Resilience VHR images X X [34,49,131]
Transport facilities High level transport facilities shows less vulnerable
urban areas Vulnerability VHR images X[39]
Distance to lifeline
Decrease in distance to health systems, fire stations and etc.
decreases the vulnerability Vulnerability VHR images X[33,93,136,137]
Potential to evacuation
High level of covered road area shows resilient urban area
which is calculated as Road/km2Resilience Road map/road vector
data X[31]
Presence open spaces
Open spaces such as green spaces, street networks, hills
provide potential safe zones for disaster and
increase resilience
Resilience Urban map X[138,139]
Remote Sens. 2018,10, 1760 17 of 30
4.3.2. Socio-Economic Status
The socio-economic status of households is estimated based on the location information of
the houses in the city and topographic area characteristics for social recovery and vulnerability
assessments [
33
,
49
]. Urban areas with poor road quality, little available infrastructure and economic
development, little vegetation, and situated in hazard-prone areas are the most socially vulnerable
areas. However, these proxies are not necessarily correct for all cases in urban areas. For example, not all
people located in hazard-prone areas (e.g., steep slopes or lower elevations) have a low socio-economic
status; in contrast, in some cases, there are luxury buildings located in areas with steeper slopes [
33
,
140
]
or close to a waterfront [
141
,
142
]. Therefore, an effective way of using those proxies is to use them as
an ensemble to describe the socio-economic status of an urban area. In addition, image-based texture
serves as a proxy to extract homogeneity of an area [
33
,
143
,
144
]. It is a good complementary source
of information for a settlement type extraction from images, e.g., slum areas, even using medium
resolution images [
145
]. Similar to texture analysis, de Almeida, et al. [
146
] used irregular clusters of
roofs in an urban area as a proxy to extract socially vulnerable areas, based on the fact that buildings
poorer area are often not regularly constructed. A share of slum areas in a city compared to regular or
high-income housing may serve as an indicator of social vulnerability. Müller [
90
] also used the amount
of green spaces around each building block to estimate the socio-economic status of householders
based on the findings of Stow, et al. [
147
]. Accordingly, the occupants of buildings that are surrounded
by more green spaces—estimated using land cover and land use information to extract socio-economic
status—are less socially vulnerable to hazards.
Remote Sens. 2018,10, 1760 18 of 30
Table 7.
Remote sensing-based social proxies for urban DRM, Socio-economic status category. Mono and Multi refer to mono-temporal and multi-temporal RS data that
used for extraction, respectively.
Proxy Essence Used in Disaster Phase Used RS Data to Extract Mono Multi Key References
Slope position People living in a steep sloped position are with poor
economy/income Recovery, Vulnerability VHR images X[33,49,140]
Proportion of built-up and
vegetated area
High proportion of vegetation in an urban (built-up) area
shows building with reach householders. Recovery, Vulnerability VHR images X[33,49]
Available infrastructure More infrastructures are available in wealthy urban areas. Recovery, Vulnerability VHR images X[33,49]
Road conditions Road conditions are better in wealthy urban areas. Recovery, Vulnerability VHR images X X [33,49,148]
Roof type Roofs of the buildings with high income has better
type/materials Recovery, Vulnerability VHR images X[33,49]
Texture To extract settlement type (e.g., irregular textures shows
slum areas) Vulnerability VHR images X[33,143,144]
Proportion of green spaces per
building block
Higher proportion of green spaces per building block
shows high social status of building holders, which are less
vulnerable
Vulnerability VHR images X[90,91]
Share of population in
irregular clusters
Irregular clusters as roof types refer to people with poor
economy/income (Slum area) Vulnerability Urban map X[146]
Night time light
High night light intensity in image for an urban area shows
more resilient areas due to presence more facilities Resilience VIIRS nightlight
satellite imagery X[114]
Remote Sens. 2018,10, 1760 19 of 30
4.4. Natural RS-Based Proxies
Table 8lists existing RS-based proxies capturing the natural environment for urban DRM. As one
of the most important sources of information for assessing urban DRM, the natural environment relies
on the vegetation cover information. Vegetation spatial heterogeneity serves as a proxy to extract
changes in vegetation pattern, and to monitor ecosystem changes before and after a disaster at the
landscape scale for natural damage assessment [
149
]. Spatial heterogeneity may affect functions and
processes in ecological systems, which is a key to assess ecosystem changes [
150
]. Vegetation cover has
been used as a proxy to extract the damages to the natural and ecological environment of an urban
area by comparing the vegetation cover before and immediately after a disaster [
99
,
151
]. Decrease in
vegetation cover ratio of an area demonstrates the ecological damage ratio. In a different study,
Li, et al. [
152
] use vegetation cover change to assess the long-term effects of a hurricane in the urban
natural environment recovery in New Orleans after Hurricane Katrina. Specifically, while using
MODIS and Landsat images, Li and colleagues assess the period from immediately after the event
to ten years later to discover that a decade later the vegetation cover is still at a lower level than
before Katrina, indicating that the area has not yet fully recovered. Similarly, Brown, et al. [
47
] and
Brown, et al. [
46
] use vegetation cover maps to assess the changes in vegetation/natural environment
patterns in the recovery processes using NDVI between year1-year2. In addition, the cover ratio for
various vegetation classes can provide detailed and reliable information for damage and recovery
assessments. Fractional vegetation cover [
153
,
154
], which represents the vegetation ecological function,
and vegetation cover type [
154
], have also been used to assess the vegetation recovery after a disaster.
Fractional vegetation index can be computed using NDVI values of vegetation and soil, and is
a powerful tool for vegetation cover classification and extraction [
155
]. A change analysis allows
for computing the vegetation recovery rate. In addition, vegetation offers an important ecosystem
service in flood-prone areas by providing a low drainage density and high permeable surfaces, thus
contributing to preventing floods [
156
]. Accordingly, vegetation cover ratio has also been used as a
proxy for vulnerability assessment of urban areas, particularly for water-related hazards [
39
,
103
,
106
].
Furthermore, vegetation cover ratio has been used as a proxy to evaluate the biophysical [
106
]
and ecological [
157
] vulnerability, as well as resilience. A higher ratio of vegetation cover signified and
increase in resilience (the vegetation coverage is one of the most important indicators for measuring
ecological capacity for self-restoration). In a similar study, Mainali and Pricope [
158
] use the Standard
Deviation of NDVI as a proxy to extract diversity of the plants in vegetated areas for vulnerability
assessment. Debris and floodwater removal are also important for environmental recovery after a
disaster, as it provides space for the vegetated area to regrow [32,46,49,88,97].
The environmental role of public spaces and urban green areas, in addition to providing clean air,
is to provide habitats for biodiversity and helps to regulate temperature. Therefore, it has been used as
a proxy to assess natural recovery processes [34,48].
Presence of debris, mud, and salt in water as an indicator of water contamination [
34
,
49
]
and the availability of land as a provision of access to recreation has been employed as proxies
for natural recovery assessment. Costa Viera and Kerle [
49
] used the permeability of surfaces to
measure environmental quality as a proxy for recovery assessment. For example, the permeability
of impervious surfaces is very low in urban areas, which leads to an increase in flood frequency and
storm flows, and consequently influences urban climate and pollution levels [
159
]. Similarly, low levels
of permeability of surfaces increase the vulnerability of the area to water-related hazards [160].
Disasters destroy the natural environment directly through landslide and flooding, or indirectly
by human activities, such as intense construction work after a disaster in the recovery stage [
34
].
Consequently, monitoring land cover provides a proxy information on the natural environment
recovery, e.g., land cover change from tree/forest to building as a result of constructions during
the recovery processes [
49
,
161
]. Land cover change assessments also contribute to monitoring land
degradation, erosion, and deforestation in the recovery processes [162,163].
Remote Sens. 2018,10, 1760 20 of 30
Land cover and land use types are also used to indicate the vulnerability of the natural
environment [
8
,
106
,
108
,
117
,
164
]. For example, expansion of urbanized areas, cultivation development,
and decreasing of natural forest lands increase the eco-environmental vulnerability to hazards [
116
].
In another example, Mainali and Pricope [
158
] associate built-up and bare land with high vulnerability
ranks and forest land—with low natural vulnerability rank [
158
]. In addition, a high proportion of
managed natural land cover indicates greater vulnerability because vegetation and crop yield are
negatively influenced by environmental degradation [165].
Evapotranspiration is a combined process of evaporation and transpiration and is an important
factor in the hydrological cycle. Consequently, it plays a role of a proxy to evaluate flood vulnerability
through extracting vegetation cover and type [166].
Frazier, et al. [
167
] use gross primary production (GPP) as a proxy for ecosystem-wellness derived
from RS data, and assess the effect of resilience of ecological capital in DRM, particularly in recovery
processes. GPP represents the photosynthetic capacity of vegetation, which can be used to estimate the
vegetation productive capability. Liu, et al. [
168
] employ it also for damage and recovery assessments.
Ecological RS studies offer robust proxies that are applicable for urban DRM. For example,
high plants productivity is directly related to good soil moisture and nutrient retention. The amount of
soil organic carbon is a proxy to assess them and can be extracted using RS data [
169
,
170
]. Therefore,
areas with higher soil organic carbon are more suitable for agriculture and consequently more resilient
to disasters, particularly those that are linked to climate change [171].
Remote Sens. 2018,10, 1760 21 of 30
Table 8.
Remote sensing-based proxies for the natural environment of urban DRM. Mono and Multi refer to mono-temporal and multi-temporal RS data that used for
extraction, respectively.
Proxy Essence Used in Disaster Phase Used RS Data to Extract Mono Multi Key References
Vegetation spatial heterogeneity To examine the structure of ecological system Damage Landsat images X[149]
Vegetation cover
Vegetation cover ratio and its changes (using near-infrared
band of satellite images and NDVI index) shows
damages/recovery to environment
Damage, Recovery,
Vulnerability
MODIS images, Landsat
images, VHR images X X [39,46,47,99,103,106,
151,152,157]
Fractional vegetation cover An index for ecological assessment/change Damage, Recovery Landsat images,
MODIS images X[153]
Vegetation cover type Change in vegetation cover type shows damage/recovery
of ecology Damage, Recovery Landsat images, MODIS
images X X [153,154]
Debris and floodwater removal Removing effects of a disaster (debris and floodwater)
gives space for growth of vegetation Recovery Ground-based
photography, VHR images X X [32,46,49,88,97]
Urban green space cover/public
spaces
High ratio of urban green/public space covers provide
space for different biological/ecological types (Biodiversity
assessment)
Recovery VHR images X X [34,48]
Presence of debris, mud and salt
in water For water contamination assessment Recovery VHR images X[34,49]
Access to recreation Availability of land Recovery VHR images X X [49]
Permeability of surfaces
Impact on urban water runoff (high permeability of surface
decreases the water runoff) Recovery, Vulnerability VHR images X X [49,160]
Land cover To monitor environmental erosion, degradation,
deforestation. Recovery, Vulnerability
ASTER images, Landsat
images, MR images,
VHR images
X X [34,49,106,116,117,
158,161,164,165]
Land use To monitor environmental erosion,
degradation, deforestation. Recovery, Vulnerability
ALOS images, ASTER
images, Landsat images,
MR images, VHR images
X X [8,104,108,116118,
120,164]
Gross primary production (GPP)
To compute vegetation productivity Recovery, Resilience MODIS images X X [167,168]
Evapotranspiration To evaluate flood vulnerability through extracting
vegetation cover and type Vulnerability MODIS images X[166]
Remote Sens. 2018,10, 1760 22 of 30
5. Conclusions and Discussion
This article surveyed recent advances in the development of RS-based proxies for urban DRM,
focusing on the literature for both pre- and post-disaster situation assessments. Specifically, damage,
recovery, vulnerability, and resilience RS-based proxies were identified along the four dimensions
of built-up, economic, social, and natural environments. The structured review of 109 published
articles shows that a comprehensive toolkit of RS-proxies has been developed and it is widely used
in practice. We observed an increasing use in the number of proxy-based studies in urban DRM.
This is due to the availability of diverse and detailed image data and rapidly increasing computational
power to a growing number of communities. This also shows an increasing need for and interest in
proxy-based measurements.
Despite significant progress, there are gaps evident in the field that demand further research to
focus on a number of directions:
(1) A rich set of RS-based proxies currently focuses on the physical side of urban DRM. Yet,
urban DRM assessments rarely go beyond aggregated proxies for the socio-economic environment
and the offered proxies are very limited to assess functional aspects. For example, although a good
number of proxies has been developed for building-based damage detection, none of them addresses
the functionality of the buildings that is also a critical information source for all aspects of urban DRM.
Assessing the extent of damage to socio-economic activities and their functional recovery is essential
and will likely be possible via a complimentary set of proxies rather than a single index.
(2) RS-based proxies have been developed in several different fields that can be interchangeably
used for other DRM phases with little modification in estimation methodology. There are relatively
few detailed proxies for recovery assessment when compared to damage assessment in built-up
environments (Figure 2), whereas most of the damage assessment proxies can also be used for recovery
assessment. The opposite is also true, and some of the RS-based proxies that have been developed
for other environments, e.g., in the economics environment, can also be used for damage assessment.
Furthermore, this statement is also correct with respect to the use of proxies for other risk elements in
urban DRM: e.g., GPP that has been used for economic recovery and resilience assessments, could also
be used for damage and vulnerability assessments. As a result, developing proxies that are focused on
a particular risk element may overlook similar proxies, which have been developed to address the
same point but under different names. Our review integrates these different streams of literature and
offers a structured overview of the state-of-the-art RS methods across various DRM phases. More work
on aligning methodological advancements and use of similar RS tools, data, and analysis to address
DRM at various stages is vital to advance the field without reinventing the wheel.
(3) Using insights from other disciplines in developing RS-based proxies offers possibilities to
intelligently connect various proxies that allow to comprehensively assess vulnerability, resilience,
damage, and recovery for urban DRM beyond the physical impact alone. As suggested in this review,
a structured analysis of specific proxy-based examples and finding important proxies from structural
engineering, economic, social and natural sciences for urban DRM will be instrumental in creating
a public interdisciplinary library of methods for urban DRM. If maintained and updated by an
interdisciplinary scholarly and practitioners community, such an open library, will always rely on the
state-of-the-art RS data and processing methods to extract them. It will help the RS community to fill
the gap in comprehensive assessments of the different DRM phases in urban settings and beyond.
(4) Disaster resilience is one of the significant components of DRM, which has been gaining
increasing importance in this field. However, only seven of the reviewed articles focus on resilience
(Figure 1), and only seven RS-based proxies have been developed for its assessment (Figure 2).
These numbers indicate a significant need for further studies in resilience assessments. Modifying and
using RS-based proxies form other disciplines, such as vulnerability, which is comprehensively
discussed in this paper, can help the RS community to develop proxies for resilience assessments.
Remote Sens. 2018,10, 1760 23 of 30
Author Contributions:
S.G. analyzed the reviewed publications and wrote the majority of the paper. N.K. helped
in analyzing the reviewed publications. N.K., T.F. supported the developing the structure of the paper and revised
the paper.
Funding: This research received no external funding.
Acknowledgments:
We thank the anonymous reviewers for their insights and constructive comments,
which helped to improve the paper.
Conflicts of Interest: The authors declare no conflict of interest.
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... A detailed overview of the type of application of different wavebands is provided in Satellite data are especially of interest when assessing post-disaster recovery (Brown et al., 2015(Brown et al., , 2008Dash et al., 2004;Ghaffarian, Kerle, & Filatova, 2018 Thermal infrared 3.0-14mm disaster risk management cycle, post-disaster recovery represents an ideal one for addressing longterm risk reduction (Becker et al., 2011). Remote sensing applications for recovery, however, appear to be the least developed, despite the recognition that there is great potential for the remote sensing community to work on time series analysis of remotely sensed data to identify disaster recovery indicators that can be measured and monitored from earth observation (Ghaffarian et al., 2018;Joyce et al., 2009). ...
... A detailed overview of the type of application of different wavebands is provided in Satellite data are especially of interest when assessing post-disaster recovery (Brown et al., 2015(Brown et al., , 2008Dash et al., 2004;Ghaffarian, Kerle, & Filatova, 2018 Thermal infrared 3.0-14mm disaster risk management cycle, post-disaster recovery represents an ideal one for addressing longterm risk reduction (Becker et al., 2011). Remote sensing applications for recovery, however, appear to be the least developed, despite the recognition that there is great potential for the remote sensing community to work on time series analysis of remotely sensed data to identify disaster recovery indicators that can be measured and monitored from earth observation (Ghaffarian et al., 2018;Joyce et al., 2009). ...
... Evidence of recovery from other sources can also help in identifying significant interval of times over which to acquire imagery to look for changes (Brown et al., 2008). Similarly, the acquisition of satellite images following multiple disasters caused by natural hazards can help in monitoring and assessing post-disaster recovery (Brown et al., 2008;Ghaffarian et al., 2018). ...
Thesis
Disasters and development are closely intertwined. This is because development changes can increase or decrease vulnerabilities and capacities in the face of disasters, just like disasters can destroy development efforts or create development opportunities. These links become particularly evident in the post-disaster recovery phase, when rehabilitation and reconstruction can shape future development. Recovery, however, is one of the least studied phases of the disaster risk reduction cycle and the question of which attributes lead to quicker or slower recovery remains uncertain. The Indian state of Odisha is highly prone to tropical cyclones, with the most intense recorded event being the 1999 Odisha Super Cyclonic Storm. Twenty years later, there is still no comprehensive documentation of the losses caused by the cyclone or evaluation of the extent or speed of recovery from the event. This research contributes to enhancing the understanding of how socio-economic, environmental and infrastructural development changes can result in differential post-disaster recovery rates and how different associates of development interact and contribute to speed of recovery in local communities. An innovative mixed methods approach is used (including a systematic review, statistical analysis, remote sensing techniques, focus group discussions and semi-structured interviews) to assess socio-economic, infrastructural and environmental pre-disaster conditions and their relation to speed of recovery. This thesis provides: a comprehensive assessment of documented losses caused by the 1999 Odisha Super Cyclonic Storm; an evaluation of differential recovery over space and time; an assessment of developmental ‘hotspots’ (where recovery exceeded expectation) and ‘coldspots’ (where there was delayed recovery) for the Kendrapara District of Odisha.
... Emergency rescuers can use high-resolution images to closely monitor ongoing natural disasters and coordinate disaster relief. However, it is almost impossible to extract nearreal-time human dynamics over the evolution of a disaster from satellite images, and such information is very important in disaster mitigation and reduction (Liu et al., 2015;Ghaffarian et al., 2018). ...
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Disaster-relevant authorities could make uninformed decisions due to the lack of a clear picture of urban resilience to adverse natural events. Previous studies have seldom examined the near-real-time human dynamics, which are critical to disaster emergency response and mitigation, in response to the development and evolution of mild and frequent rainfall events. In this study, we used the aggregated Tencent location request (TLR) data to examine the variations in collective human activities in response to rainfall in 346 cities in China. Then two resilience metrics, rainfall threshold and response sensitivity, were introduced to report a comprehensive study of the urban resilience to rainfall across mainland China. Our results show that, on average, a 1 mm increase in rainfall intensity is associated with a 0.49 % increase in human activity anomalies. In the cities of northwestern and southeastern China, human activity anomalies are affected more by rainfall intensity and rainfall duration, respectively. Our results highlight the unequal urban resilience to rainfall across China, showing current heavy-rain-warning standards underestimate the impacts of heavy rains on residents in the northwestern arid region and the central underdeveloped areas and overestimate impacts on residents in the southeastern coastal area. An overhaul of current heavy-rain-alert standards is therefore needed to better serve the residents in our study area.
... This information is crucial to the country's socioeconomic development and aids in successful decision-making (Saxena, Jain, and RadhaKrishna 2020). RS is one of the most effective approaches for studying the Earth's surface, and it is used in a variety of applications, such as climate change (Yang et al. 2013), agriculture (Ghaffarian and Turker 2019;Weiss, Jacob, and Duveiller 2020;Valente et al. 2020;, urban planning (Ghaffarian and Ghaffarian 2014;Nielsen 2015;Kadhim, Mourshed, and Bray 2016), land and resource surveys (Zhou et al. 2014), disaster monitoring, and assessment (Rahman and Liping 2017;Ghaffarian, Kerle, and Filatova 2018;Ghaffarian and Emtehani 2021;Ghaffarian, Rezaie Farhadabad, and Kerle 2020), crop growth monitoring (Zhou et al. 2019), urbanization (Zhang and Huang 2018), and land-use/cover changes (Halefom et al. 2018), coastline extractions (Yasir et al. 2020;Hossain et al. 2021), modality translation ) and have been portrayed as a useful and vital method of disseminating knowledge. The information acquired from satellites has a variety of advantages due to its vast spatial and temporal coverage (Saxena, Jain, and RadhaKrishna 2020). ...
Article
This study is conducted in accordance with a systematic literature review (SLR) protocol. SLR is tasked with finding publications, publishers, deep learning types, enhanced and adapted deep learning techniques, impacts, proactive approaches, key parameters, and applications in the field of remote sensing. It is also expected to identify current research directions, gaps, and unsolved issues in order to provide understanding and recommendations for future studies. The data is collected from important research papers published in recognized journals between the years 2015 and 2021, however, conference/seminar proceedings and other online resources are excluded to minimize unnecessary complications. Based on previously established exclusion, inclusion, and quality parameter criteria, a total of 122 primary studies are considered. The literature review overcomes a number of significant problems, including key variables taken into account by researchers in the remote sensing (RS) domain, various deep learning (DL) solutions proposed for RS analysis, various proactive strategies recommended in the literature to reduce risks linked to the RS domain, and various DL applications reported in the remote sensing domain. The results show that there is still a lack of structured information that enables DL to be employed for crucial applications in the field of remote sensing, despite substantial research and development of numerous DL algorithms. Furthermore, it is evident that DL approaches in the remote sensing domain have not been thoroughly exploited, thus demanding further research. The findings suggest that deep learning techniques need further investigations and the development of an authentic mechanism is essential for accurate results retrieved from remote sensing data. The proposed study would let scientists examine previous investigations into deep learning methods, which can then be utilized as support for further investigations.
... Remote sensing is a popular tool for extracting various information on damage situations in urban and rural areas resulting from natural hazards [1,2]. Optical and SAR satellite sensors have frequently been used, because pre-and post-event images covering affected areas are often available, and various change detection or classification techniques have been applied for these images [3,4]. ...
Article
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This study demonstrates the use of multi-temporal LiDAR data to extract collapsed buildings and to monitor their removal process in Minami-Aso village, Kumamoto prefecture, Japan, after the April 2016 Kumamoto earthquake. By taking the difference in digital surface models (DSMs) acquired at pre- and post-event times, collapsed buildings were extracted and the results were compared with damage survey data by the municipal government and aerial optical images. Approximately 40% of severely damaged buildings showed a reduction in the average height within a reduced building footprint between the pre- and post-event DSMs. Comparing the removal process of buildings in the post-event periods with the damage classification result from the municipal government, the damage level was found to affect judgements by the owners regarding demolition and removal.
... Earth Observation (EO) satellite capabilities increasingly support progress towards sustainable development using programs of global data collection, such as Landsat, that span over 50 years [7]. EO data are now embedded in the processes of understanding natural hazards and their interactions [8], measuring and monitoring urban growth and intersection with hazards [9], informing disaster risk reduction strategies [10,11], and responding to disaster events [12,13]. ...
Article
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Earth observation (EO) data can provide large scale, high-resolution, and transferable methodologies to quantify the sprawl and vertical development of cities and are required to inform disaster risk reduction strategies for current and future populations. We synthesize the evolution of Bishkek, Kyrgyzstan, which experiences high seismic hazard, and derive new datasets relevant for seismic risk modeling. First, the urban sprawl of Bishkek (1979–2021) was quantified using built-up area land cover classifications. Second, a change detection methodology was applied to a declassified KeyHole Hexagon (KH-9) and Sentinel-2 satellite image to detect areas of redevelopment within Bishkek. Finally, vertical development was quantified using multi-temporal high-resolution stereo and tri-stereo satellite imagery, which were used in a deep learning workflow to extract buildings footprints and assign building heights. Our results revealed urban growth of 139 km2 (92%) and redevelopment of ~26% (59 km2) of the city (1979–2021). The trends of urban growth were not reflected in all the open access global settlement footprint products that were evaluated. Building polygons that were extracted using a deep learning workflow applied to high-resolution tri-stereo (Pleiades) satellite imagery were most accurate (F1 score = 0.70) compared to stereo (WorldView-2) imagery (F1 score = 0.61). Similarly, building heights extracted using a Pleiades-derived digital elevation model were most comparable to independent measurements obtained using ICESat-2 altimetry data and field-measurements (normalized absolute median deviation < 1 m). Across different areas of the city, our analysis suggested rates of building growth in the region of 2000–10,700 buildings per year, which when combined with a trend of urban growth towards active faults highlights the importance of up-to-date building stock exposure data in areas of seismic hazard. Deep learning methodologies applied to high-resolution imagery are a valuable monitoring tool for building stock, especially where country-level or open-source datasets are lacking or incomplete.
... Many articles have reviewed the applications of remote sensing technology in urban flooding [31][32][33]. These research works comprehensively interpreted specific applications related to urban flooding: detection, monitoring, risk assessment and modeling, impact assessment, protection structure inspection, and reconstruction monitoring. ...
Article
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As a result of urbanization and climate change, urban areas are increasingly vulnerable to flooding, which can have devastating effects on the loss of life and property. Remote sensing technology can provide practical help for urban flood disaster management. This research presents a review of urban flood-related remote sensing to identify research trends and gaps, and reveal new research opportunities. Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), the systematic literature search resulted in 347 documents classified as geography, disaster management application, and remote sensing data utilization. The main results include 1. most of the studies are located in high-income countries and territories and inland areas; 2. remote sensing for observing the environment was more popular than observing the building; 3. the most often applied disaster management activities were vulnerability assessment and risk modeling (mitigation) and rapid damage assessment (response); 4. DEM is often applied to simulate urban floods as software inputs. We suggest that future research directions include 1. coastal urban study areas in non-high-income countries/territories to help vulnerable populations; 2. understudied disaster management activities, which often need to observe the buildings in more urban areas; 3. data standardization will facilitate integration with international standard methods for assessing urban floods.
... There are also many scholars who conduct management evaluation on certain aspects, such as transportation infrastructure management [13][14][15], household management [16], insurance pricing [17] and so on. The evaluation methods of urban flood disaster generally fall into four types, i.e., those based on historical disaster data [18], those based on remote sensing [19] and GIS technology [20], those based on index systems [21] and those based on scenario simulations [22]. ...
Article
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In the context of climate change and urbanization, increasing flood disasters leads to severe losses and impacts on urban inhabitants. In order to enhance urban capacity to cope with floods and reduce losses, the comprehensive emergency-response capacity to flood disaster (CERCF) was studied in Zhengzhou City, which is seriously affected by floods. Firstly, the evaluation index system of flood emergency capacity was constructed from three aspects, including pre-disaster prevention capacity, during-disaster disposal capacity and post-disaster recovery capacity. Secondly, the weight of each index was calculated by the combination of the entropy weight method and the coefficient of variation method, and the evaluation model was established by the comprehensive index method. Thirdly, the CERCF of Zhengzhou City was classified into three grades by the Jenks natural-breakpoint classification method. Finally, the contribution model was used to reveal the contribution factors of flood emergency capacity in Zhengzhou city. The following beneficial conclusions were drawn: (1) The overall CERCF of Zhengzhou City was on a low level. The proportions of the study area at low, medium and high levels were 58.33%, 33.33% and 8.34%, respectively. Spatially, the CERCF was high in central regions and low in in the west and east parts of Zhengzhou City. (2) It was found that PDPC and PDRC made the greatest contribution, while DDDC has a relatively low contribution degree.
... Its presence also serves as a proxy for various plant functions and characteristics [28]. The fractional cover can inform various analyses involving a wide range of ecological processes from flora to fauna dynamics [29], forest growth [30], food web structure [31], vegetation studies [32], land management practices [33], hydrology [34], dynamics of soil carbon [33], disaster risk [35] and drought monitoring [36]. Additionally, these quantities capture the biophysical consequences of several disturbances caused by natural and human drivers [37,38]. ...
Article
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Forests are threatened globally by deforestation. Forest restoration at the landscape scale can reduce these threats. Ground-based and remote sensing inventories are needed to assess restoration success. Fractional canopy cover estimated from forest algorithms can be used to monitor forest loss, growth, and health via remote sensing. Various studies on the fractional cover of forest have been published. However, none has yet conducted a bibliometric analysis. Bibliometrics provide a detailed examination of a topic, pointing academics to new research possibilities. To the best of the authors’ knowledge, this is the first bibliometric study screening publications to assess the incidence of studies of the fractional cover of forests in Web of Science (WoS) and Scopus databases. This research analyses WoS and Scopus publications on the fractional cover of forest dating from 1984 to 2021. The current study uses the Bibliometrix R-package for citation metrics and analysis. The first paper on the fractional cover of forest was published in 1984 and annual publication numbers have risen since 2002. USA and China were the most active countries in the study of fractional cover of forests. A total of 955 documents from 69 countries with multiple languages were retrieved. Vegetation, forestry, and remote sensing were the most discussed topics. Findings suggest more studies on the fractional cover of forests algorithms should be conducted in tropical forest from developing countries.
Article
Rapid and cost-effective economic loss estimation for buildings in non-life insurance is an important issue for insurance industries in order to provide immediate financial supports to residents affected by natural disasters. This study introduces an empirical approach for economic loss estimation of typhoon-induced building damage from post-disaster remote sensing (RS) images based on insurance records obtained in Osaka and Chiba, Japan affected by the 2018 Typhoon Jebi and the 2019 Typhoon Faxai, respectively. From the insurance records and the analysis of the RS images, we found that area-based loss rates (ALRs) defined as ratio of amount of loss to amount of insured values within a mesh were proportional to building damage ratios (BDRs) identified from number of damaged buildings in the RS images and existing building inventory data, whereas it was still challenging to accurately estimate loss rate for building-by-building even from very high-resolution images. A linear regression function was developed from the relationship between the ALRs and BDRs obtained in this study. We confirmed that the regression function provided a good approximation of the insured losses from the typhoon disasters. The result indicates that typhoon-induced insured losses can be rapidly estimated from the insurance inventory and the analysis of post-disaster RS images without field investigations.
Article
Although deep learning has made semantic segmentation of very-high-resolution remote sensing images practical and efficient, its large-scale application is still limited. Given the diversity of imaging sensors, acquisition conditions, and regional styles, a deep learning network well-trained on one source domain dataset often suffers from drastic performance drops when applied to other target domain datasets. Thus, we propose a novel end-to-end Mutual Information Domain Adaptation Network (MIDANet) that can shift between semantic segmentation domains by integrating multitask learning in the convolutional neural networks within an entropy adversarial learning framework. Through the joint learning of semantic segmentation and elevation estimation, the features extracted by MIDANet can concentrate more on the elevation clues while dropping the domain-variant information ( i.e ., texture, spectral information). Firstly, one encoder is applied to excavate general semantic features. Two decoders that share the same architecture are used to perform pixel-level classification and digital surface model (DSM) regression. Secondly, feature interaction modules (FIMs) and a mutual information attention unit (MIAU) are designed to mine the latent relationships between the two tasks and enhance their feature representations. Finally, a final MIDANet is obtained for semantic segmentation that does not require any semantic segmentation labels in the target domain after the adversarial learning of the classification entropy at the output level. Extensive comparative experiments and ablation studies were conducted on the ISPRS Potsdam and Vaihingen test datasets. The results show that MIDANet outperforms other state-of-the-art DA methods in both evaluation metrics and visual assessment.