Download Remote sensing imagery for damage assessment of buildings after

Remote sensing imagery for
damage assessment of buildings after
destructive seismic events
G. Bitelli1, R. Camassi2, L. Gusella1 & A. Mognol1
1
2
DISTART Dept., Bologna University, Italy
INGV, Bologna, Italy
Abstract
The adoption of a macroseismic scale to describe the effect of an earthquake
requires the collection in a short time of a comprehensive documentation about
damage on buildings and structures. In the case of large destructive earthquakes,
however, the traditional field surveying methods are time consuming and costly.
This is true though new methods and technologies can offer today more effective
and rigorous solutions. Satellite imagery can play an important role for
monitoring of earthquake effects on the builing estate, representing an extensive
and quickly available information on the pre- and post-event situations. Where a
cartographic base is not available, this could be the only reachable solution.
Furthermore, the new generation of very high resolution imagery, with a ground
pixel size on the order of one meter, may permit one to obtain a detailed
description of the damage referred to each building, with very significant savings
in time and costs in respect to other techniques. The paper presents the results of
a research carried out on the Marmara (1999) and Boumerdes (2003) highly
destructive earthquakes. Pre- and post-event images acquired by different
platforms, from medium to very high resolution, panchromatic and multispectral,
were evaluated and compared, and specific considerations made about their
characteristics; some damage classifications are presented, where different
approaches were applied.
Keywords: remote sensing, VHR imagery, earthquake, macroseismic, disaster,
multitemporal, change detection, classification.
Risk Analysis IV, C. A. Brebbia (Editor)
© 2004 WIT Press, www.witpress.com, ISBN 1-85312-736-1
726 Risk Analysis IV
1
Introduction
Macroseismic data, describing the effect of a seismic event, play an important
role both for social and scientific purposes. These data permit to examine the
interaction between shaking and buildings, supplying information on engineering
aspects, allowing to estimate the risk for future earthquakes, to operate for an
effective damage reduction and to provide adequate resource allocation.
Moreover, these data contribute to improve seismological knowledge, supplying
information about propagation, dimension and orientation of seismogenetic
structures, and allow calibrating historical seismological data, which constitute
the main component on hazard estimates.
The adoption of a macroseismic scale, i.e. the European Macroseismic Scale,
requires to collect in a short time a comprehensive documentation about damages
on buildings and structures; this task was performed till now by field surveying
methods, but this is clearly inadequate in case of large damaged areas or when
problems exist in accessing the site. Furthermore, in some situations (e.g.
destructive events in developing countries) a cartographic base is not available or
is largely obsolete. Satellite imagery can constitute an effective solution to obtain
a map of the damaged area, either supporting the rescue operations and providing
an extensive evaluation of earthquake effects on the built estate. This assessment
can be reinforced by the availability of pre-event images and the application of
change detection techniques. Besides, the new generation of very high resolution
imagery, with a ground pixel size on the order of one meter, represents the first
possibility to have a detailed description of the damage referred to each building,
with very significant saving in time and costs in respect to other field-based
traditional techniques.
2
Damage classification and macroseismic survey techniques
After a severe earthquake the data about aftershocks are collected by local and
temporary instrumental networks. Collection of macroseismic data derives
instead principally from two sources: questionnaire surveys and field
investigations. Questionnaire surveys are commonly used for assessing
macroseismic intensities in the range of 2 to 5, while for 6 (the damage
threshold) and above field investigations are necessary.
The engineers are mainly interested in severe failures and damage assessment
to decide if a building should be used, restored or demolished; the
macroseismologist is interested instead in the overall damage distribution,
including the borderline between slight damage and no damage, and the spatial
patterns of variation in intensity caused by local conditions. These data can be
the core of the possibility of establish a link between past and present events.
Sometime, an isolated small damage to an old structure could be much more
important to this aim rather that an extensive, uniform collapse of RC buildings.
Distinctions between different construction types, usage, height and age and
quality of construction should always be made wherever possible.
Risk Analysis IV, C. A. Brebbia (Editor)
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Strong earthquakes require extensive and immediate field investigation to
record damage patterns. The revised version of the European Macroseismic
Scale (EMS-98) based on the MSK scale, provided by Grünthal [8], tries to
define accurately some key terms, like building type, damage grade and quantity.
The EMS-98 scale needs a lot of detailed information on buildings types and
vulnerability classes, on damage grades and its percentage distribution among
the total number of structures interested by the earthquakes. A modern
macroseismic survey should then include techniques that may give a more
precise scientific meaning to the observation carried out, and ideally to do this
they should provide the possibility of being repeatable and investigate
neighbouring conditions that may have led to a certain damage level.
The first proposition is the hardest to satisfy: few days (or even hours) after
an earthquake the state of buildings is forever changed by restoration or rescue
operations. The simple photographic or movie survey is giving only a limited
view of damage, extrapolating a single case from the context and thus providing
a subjective interpretation by the surveyor. The Quick Time Virtual Reality
(QTVR) technique, based on photographic images stitched together by software
providing 360° panoramas subsequently linked in complex scenes, was then
recently adopted by the authors: it has the advantage of providing not-interpreted
data, allowing each user to perform a virtual navigation inside a damaged
neighbourhood (Mucciarelli et al [10]).
Nevertheless, immediate direct intervention on the damaged areas are often
not possible, and other information unavailable. Scope of this work is to
investigate the possibility to undertake some damage evaluation from satellite
imagery, even without ground data, in support of macroseismic classification.
3
High resolution satellite imagery after earthquakes
Modern Very High Resolution (VHR) sensors aboard remote sensing platforms
can provide imagery with a ground resolution up to 70cm, involving in this way
the possibility to obtain precise information about objects like buildings,
infrastructures, trees, etc.
For these reasons, interpretation and classification of VHR imagery could
provide useful detailed data about ground situation and intervened modifications,
especially for urban scenarios. In the case of natural disasters, like catastrophic
earthquakes, VHR imagery could become essential, particularly when a
cartographic base of affected areas is absent or obsolete (this is a typical situation
for developing countries), or when damaged zones are hardly accessible (in this
case the images could also be helpful to identify the quickest way to reach them
and go through). Moreover, VHR images are very important in the emergency
phase because rescue teams can use them as base maps in handheld GPS-GIS
systems. They could be very useful in a pre-event phase in order to update
existing databases related to exposure and vulnerability to earthquake of
buildings and lifelines, and their possibility to reduce both the cost and the
number of field surveys could be of great interest also for the insurance and
reinsurance industry.
Risk Analysis IV, C. A. Brebbia (Editor)
© 2004 WIT Press, www.witpress.com, ISBN 1-85312-736-1
728 Risk Analysis IV
In the following paragraphs different classification approaches are described;
they were applied also for medium-high resolution imagery.
Table 1:
Specifications of the scenes acquired.
sample
in fig.1
• MARMARA
ETM+ 08.10.1999
TM5 08.18.1999
IRS 08.08.1999
IRS 09.27.1999
• BOUMERDES
IRS 08.12.2002
IRS 06.08.2003
QuickBird 4.22.2002
QuickBird 6.13.2003
(a)
Figure 1:
resolution
product
level
63.2, 152.4
47.0, 167.0
30 m
30 m
5m
5m
syscorr.
syscorr.
1D
1D
63.5, 141.0
69.8, 129.3
61.4, 144.2
67.2, 119.9
5m
5m
0.70 m
0.70 m
1D
1D
2A
2A
sun elevation,
off nadir,
azimut
target azimuth
(a)
(b)
(c)
(d)
11.2,176
15.7,278
(b)
(c)
(d)
Post-event imagery used for the study: from left to right, the
increase in resolution for different satellite products (Marmara
and Boumerdes earthquakes).
The case studies are related to two recent highly destructive earthquakes
occurred in the Mediterranean area: the Marmara earthquake, Turkey, occurred
in 1999, 17 August (Magnitude Richter 7.4, about 17,100 victims and 25,000
injured people), and the Boumerdes earthquake, Algeria, occurred in 2003, May
21 (Magnitude Richter 6.8, about 2,300 victims and 11,000 injured people).
Table 1 shows the images used in the tests till now carried out. The visual
effect of the different image resolutions is shown in fig. 1.
The study conducted by the authors in relation to Marmara earthquake is
presented in Bitelli et al [2] [3], the present paper is mainly referred to the
Boumerdes area, where VHR imagery is available.
4
Macroseismic image classification
In EMS98 specifics, the object of interest is the single building, which has to be
classified in relation to typology and grade of damage: building typologies and
material (masonry, reinforced concrete, steel…) are quite impossible to extract
Risk Analysis IV, C. A. Brebbia (Editor)
© 2004 WIT Press, www.witpress.com, ISBN 1-85312-736-1
Risk Analysis IV
729
from VHR images, and also grade of damage is not clearly visible as by a ground
survey. The use of satellite imagery brings nevertheless some advantages, in
terms of rapidity in saving resources and human life. However, a macroseismic
classification of VHR imagery suffers some limitation, due to image resolution,
not adequate to achieve a classification of soft damage (< 3 on the EMS98 scale)
and due to the point of view: for a damage assessment by satellite or aerial
imagery, an off-nadir view is preferable, since is possible to see building façades,
but satellite imagery usually is almost nadiral and, if the post-event image is
intentionally oblique, considerable problems arise for automatic change detection
algorithms.
Furthermore a change detection algorithm have to deal not only with
earthquake related damage but also with differences on shadow, seasonal
changes, presence of new housing units, etc.
In Table 2, macroseismic damage assessment for the city of Boumerdes is
reported, showing also the ratio between panchromatic bands and the sum of
absolute difference in each multispectral band (eqns (1) and (2)).
Ratio = Mean( JunPAN ) / Mean( AprPAN )
AD =
(1)
∑ abs(Mean( Jun ) − Mean( Apr ))
i = R ,G , B , IR
i
i
(2)
It is also to notice that the images used for the test aren’t orthorectified and
have the standard 2A geocoding accuracy; no cartographic support is available
for this study. The correspondence between object shapes on two images was
carried out by visual interpretation and an assisted matching procedure between
features: this punctual information of displacement is also used for the automatic
change detection as input for post-event registration process, producing a
virtually precise registered image, fig. 2.
Figure 2:
Objects are manually registered between pre- and post-event
images: a partial collapse of the structure is clearly visible both
observing shadows and comparing object shape in the two images.
Table 2 shows the summary of visual image interpretation of the damage
carried out on the two images; areas characterized by new constructions and by
Risk Analysis IV, C. A. Brebbia (Editor)
© 2004 WIT Press, www.witpress.com, ISBN 1-85312-736-1
730 Risk Analysis IV
the presence of debris are also considered as changes. The average values
reported for the indexes are referred to the eqns (1) and (2).
Table 2:
Damage Class
Class0
Class3
Class4
Class5
Built new
Debris
5
Image classification and damage indexes.
Buildings
2328
12
54
100
Ratio
1.13
1.21
1.099
1.13
1.3
1.51
AD
82.5
118
129
144
155
214
Automatic image classification
The first phase before applying an image classification algorithm is to perform
an accurate image co-registration (as outlined, orthorectification with absolute
georeferencing was not possible in this case due to lack of ground control data).
For the Boumerdes Earthquake there are problems in registering VHR
images, because of buildings geometry and the presence of shadows caused by
different acquisition parameters and geometry. A rubber sheeting model,
implemented in Erdas Imagine, was applied using 116 CPs (Control Points) and
2512 CPs, obtained from a manual classification of Boumerdes urban area. In the
first case the results weren’t so satisfactory, instead in the second case the shift in
buildings position between the registered and the master image was reduced to
minimum. It’s obvious that this is a long work and is generally not applicable,
especially when the aim is a quick post-event damage assessment.
5.1 Pixel based classification
After registering the images, the work focuses on the identification of damaged
areas. Erdas 8.6 Knowledge Engineer module, by Leica, was used to conduct a
pixel based classification of pre- and post-event QuickBird pan images in order
to extract a class representative of damaged buildings. This classifier uses
hypotheses, rules and variables to create classes of interest, and makes a rulebased classification through their implementation in a decision tree. The target is
to produce a semi-automatic procedure, applicable not only in the cases studied:
for this reason it’s necessary to define, where possible, standard values for
variables.
Areas featuring damaged buildings present high values of brightness due to
the spread of debris after the building collapse (Estrada et al [7]). Through a
user-specified threshold on the brightness change, the model permits to identify
large damaged areas, but lots of bright zones not damaged are also included,
which can be called “false alarms”. In order to improve the result and remove
these false alarms the model was implemented in the decision tree and combined
with other variables. These come from ISODATA unsupervised classification of
pre-event image (extraction of the urban areas from QuickBird multispectral
registered images), the evaluation of NDVI index, and finally the calculation of
Risk Analysis IV, C. A. Brebbia (Editor)
© 2004 WIT Press, www.witpress.com, ISBN 1-85312-736-1
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the distances between the bright areas and the zones classified as shadows in the
post-event image; it must be however noticed that not all the false alarms are
removed.
5.2 Object oriented classification
The object oriented classification was performed using eCognition 3.0, by
eDefiniens, using the QuickBird images above described and IRS images. In this
case, the scope of an object oriented classification is to simulate human
perception of image features, using a segmentation procedure (Baatz & Shape
[1]) to recognize the objects, applying classification rules to describe the
perception of changes.
In eCognition, it is necessary to perform the segmentation before the
classification processes: segments extracted represent objects of interest. The
post-event image is thus processed.
The particularity of eCognition is the possibility to perform a multiscale
segmentation and hierarchy; it is then possible to use a higher scale value to
separate big-scale objects, such as urban areas, and a lower scale value to
separate the objects of interest (in this case damaged buildings and debris).
The hierarchy is therefore composed by at least two levels, the texture (level
3, scale 60-120) and the urban built (level 2, scale 15-30). In the urban level, it is
necessary to define how an object is perceived as changed. Human eye is able to
recognize reflectance changes without considering shadows; contrariwise, using
image differencing techniques, an increasing in reflectance could happens when
a building fall across a shadow in the other image. Thus, a sublevel of urban
level (level 1) is created, to recognize reflectance only on not saturated and not
shadow zones. The final level hierarchy structure is resumed in fig. 3.
5.3 Classification evaluation
The accuracy assessment of automatic change detection for VHR imagery suffers
from problems derived by geometric issues and methods of interpretation of the
results, and it is quite difficult to consider separately each question. Geometric
problems arise from image registration, image resolution and off-nadir effects.
Evaluation of results is related to how the percentage of hit areas is considered,
and how to take into account false alarms. The percentage can be calculated in
terms of damaged edified area detected in relation to the total built area, or in
terms of buildings exactly detected as damaged, as outlined in fig. 4.
Accuracy evaluation shows quite similar results in object-oriented
classification (o.o.c., software eCognition) and in pixel-based classification
(p.b.c., software ERDAS Imagine). From registered to precise registered image,
damaged buildings detection (grade 4-5) increases from 58% to 74% using o.o.c.
and from 60% to 82% using p.b.c. It is also to notice that p.b.c. is less accurate in
extracting undamaged objects (75% for o.o.c., 55% for p.b.c.) and in general
shows less stable results without a strict co-registration. With IRS imagery,
results are quite similar as using registered QuickBird imagery, indicatively 51%
Risk Analysis IV, C. A. Brebbia (Editor)
© 2004 WIT Press, www.witpress.com, ISBN 1-85312-736-1
732 Risk Analysis IV
for o.o.c. and 65% for p.b.c. of buildings correctly classified as damaged
(respectively 83% and 77% of buildings correctly classified as no damaged).
(a)
Data Set
• QuickBird
Scale:
Color:
Smoothness
• IRS
Scale:
Color:
Smoothness
(b)
(c)
Level 1
Level 2
Level 3
1
1
30
0.1
0.9
60
0.1
0.9
1
1
5
0.7
0.9
30
0.7
0.9
(d)
Object hierarchy
Figure 3:
For each object, in the pre-event (a) and post-event (b) image,
portions to exclude as shadow and as saturated are detected (c), then
the image is classified (d) using a damage index. Class hierarchy and
segmentation parameter are reported.
Figure 4:
Accuracy assessment explanation. On the left: object oriented
classification, on the right: pixel based classification.
In terms of false alarms, calculated as the fraction of the damaged detected
area that doesn’t fall in a change area corresponding to buildings, using
registered image the percentage is 77% for p.b.c. and 69% for o.o.c.; this
percentage is reduced using precise registered imagery (respectively 56% and
54%). This widespread phenomenon is primarily due to the large areas covered
by debris after a strong earthquake and its entity could be clearly reduced by
different procedures for building classification or by the availability of a large
Risk Analysis IV, C. A. Brebbia (Editor)
© 2004 WIT Press, www.witpress.com, ISBN 1-85312-736-1
Risk Analysis IV
733
scale vector cartographic database. In fig. 5, results of classification are visually
presented for a sample area.
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
Figure 5: (a) pre-event image, (b) post-event image. Results from classification
Erdas with (c) 116 GCP, (d) 2512 GCP, (e) IRS imagery; eCognition
with (f) 116 GCP, (g) 2512 GCP, (h) IRS imagery.
6
Conclusion
Some problems related to the macroseismic damage assessment on urban areas
have been introduced and the results provided by remote sensing techniques, in
high and very high resolution, presented. The use of these data is in general not
much sensitive to soft damage and suffers from geometric image co-registration
problems, permitting an acceptable quantitative assessment of damage for grades
higher than 3. The overall results are promising and deserve further
investigation. The object-oriented classification technique, in particular, shows
Risk Analysis IV, C. A. Brebbia (Editor)
© 2004 WIT Press, www.witpress.com, ISBN 1-85312-736-1
734 Risk Analysis IV
in this case interesting capabilities (e.g. behaviour regarding co-registration
problems) in comparison with the more diffused pixel-based methods, even
though they provide comparable results.
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Risk Analysis IV, C. A. Brebbia (Editor)
© 2004 WIT Press, www.witpress.com, ISBN 1-85312-736-1