IEEE Geoscience and Remote Sensing Magazine - March 2015 - 22
tablE 1. ovErviEw of tyPES of loSSES vErSuS tHE main vulnErability comPonEntS [4].
Human-Social
PHySical
Economic
cultural EnvironmEntal
Direct Losses
* Fatalities
* Injuries
* Loss of income or
employment
* Homelessness
* Structural damage or
collapse to buildings
* Non-structural damage and
damage to contents
* Structural damage
infrastructure
* Interruption of business due
to damage to buildings and
infrastructure
* Loss of productive workforce
through fatalities, injuries and
relief efforts
* Capital costs of response and
relief
* Sedimentation
* Pollution
* Endangered species
* Destruction of ecological
zones
* Destruction of cultural
heritage
Indirect Losses
* Diseases
* Permanent disability
* Psychological impact
* Loss of social cohesion
due to disruption of
community
* Political unrest
* Progressive deterioration
of damaged buildings and
infrastructure which are not
repaired
* Economic losses due to short
term disruption of activities
* Long term economic losses
* Insurance losses weakening the
insurance market
* Less investments
* Capital costs of repair
* Reduction in tourism
* Loss of biodiversity
* Loss of cultural diversity
high-resolution spaceborne sensors (e.g. QuickBird and
COSMO/SkyMed) larger sets of geo-information indicators were made available with significantly higher information content. For some of these indicators, however,
crucial improvements on the result accuracy can only be
achieved by integrating GIS-based data.
The state-of-art on methods for extracting physical vulnerability indicators from remote sensing has been collected considering sensors with different capabilities and
collecting different types of data (multi-spectral, panchromatic, hyper-spectral, Light Detection And Ranging (LIDAR), and RAdio Detection And Ranging (RADAR)). In
this study, carried out in the framework of a specific project, we concentrated on the extraction of such indicators
from multispectral optical sensors with a very brief mention on the usage of LIDAR. This latter was considered to
be too expensive for the large-scale risk mapping purposes
of the project.
3. Proxies extraction from
multi-sPectral satellite images
3.1 Data selection
User needs were collected by pooling perspective users'
opinion and summarizing them into a document. This
process defined the main outlines of the most desirable
remote sensing data sources considering sensor type and
scale. The geographical scale, as a product of the image
resolution, determines the level of the spatial information of the classification product. Data details are meant
in four different ways: spatial (objects detectability and
separability), radiometric (intensity differences), spectral
(spectral distinction), and temporal (acquisition repeatability) resolution.
In literature, several efforts have been made to investigate the properties of the main data types of different
resolutions [35], as well as the effect of selecting a suitable
sensor that best serve the imagery objective [36]. Others
22
investigated the importance of relationship between scale
and spatial resolution on producing the thematic maps
[37]. Weather conditions would add a constraint on optical data acquisition, and proper usage of complementary
technologies like radar-based acquisition would offer a
back-up data source in such situations. However, the impact of adverse weather in extracting vulnerability indicators is quite limited as the task of vulnerability estimation
concerns the preparedness phase of a disaster cycle and
typically entails long-term monitoring, for which unavailability of data at a single date or even on an interval of
dates has limited effect.
Analyzing the dynamics of physical vulnerability imposes understanding the relationship between the geometric characteristics of urban features, data resolution and a
set of representative indicators.
Image classifications of remotely sensed products have
been carried out on global, regional scales since the seventies. On the global scale, coarse spatial resolution satellites like the Advanced Very High Resolution Radiometer
(AVHRR), and the Moderate Resolution Imaging Spectroradiometer (MODIS) are preferable. Gamba and Herold in
[38] assessed eight major research efforts in global urban
mapping and found that most maps were produced at the
spatial resolution of 1-2 km. On the regional scale, from
medium spatial resolution (10-100 m) the Thematic Mapper (TM) sensors on board the Landsat satellites represent
one of the most reliable and accessible data sources, which
improved greatly the extraction of thematic information
[39, 40]. Moreover, high-resolution satellite imagery offered a local scale classification which improved significantly the extraction of the geo-information in general and
in particular of cartographic features (buildings and roads)
and has been used largely in urban mapping [41, 42].
3.2 Pre-Processing
Data pre-processing has a crucial importance in easing access to the geographical information on small objects that
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