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Geoinformation for Disaster and Risk Management - ISPRS

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Map making<br />

All the map products were st<strong>and</strong>ardized mapping<br />

based on a template. Once the in<strong>for</strong>mation was<br />

extracted or assessment results determined, the map<br />

could be produced rapidly by loading the<br />

in<strong>for</strong>mation into the template according to specific<br />

st<strong>and</strong>ards.<br />

Product dissemination <strong>and</strong> service<br />

There were a total of more than a hundred map<br />

products produced during the earthquake<br />

emergency relief stage. They were distributed to four<br />

different types of agencies in different <strong>for</strong>ms, by fax,<br />

email, hardcopy, website or news conference. The<br />

first <strong>and</strong> primary agencies were the central<br />

government emergency management agencies to<br />

support the emergency rescue, relief, recovery <strong>and</strong><br />

reconstruction decision making. The second<br />

agencies were local government <strong>and</strong> field teams to<br />

support their field deployment. The third agencies<br />

were the public <strong>and</strong> international societies, to<br />

support their awareness raising <strong>and</strong> assistance. The<br />

fourth agencies were the professional institutions in<br />

support of their scientific research.<br />

Experiences <strong>and</strong> lessons learned.<br />

Space technology provided effective <strong>and</strong> timely<br />

in<strong>for</strong>mation <strong>for</strong> Wenchuan Earthquake emergency<br />

response management support. At the same time,<br />

some lessons were also learned <strong>and</strong> should be<br />

carefully considered <strong>for</strong> future events.<br />

44<br />

Experiences<br />

(1) An inventory of the space resources should be<br />

compiled <strong>and</strong> made a component of the national<br />

emergency response working procedures. Full<br />

advantage should be taken of international <strong>and</strong><br />

domestic space data acquisition scheduling to ensure<br />

timely acquisitions. Large quantities <strong>and</strong> multiple<br />

types of space borne EO images should be acquired.<br />

(2) Scientifically based effective emergency<br />

operational procedures should be developed<br />

according to the disaster response requirements <strong>and</strong><br />

space resources. Forming different working teams<br />

which were assigned distinct responsibilities<br />

according to the working procedures proved<br />

effective.<br />

(3) Social resources must be mobilizing <strong>and</strong> made<br />

best use of. For major disasters, a single agency<br />

cannot cope with all the problems. More than 40<br />

professional volunteers from research institutes <strong>and</strong><br />

universities were mobilization to participate in<br />

processing the data <strong>and</strong> mapping.<br />

(4) Remote sensing cannot provide all the<br />

in<strong>for</strong>mation <strong>for</strong> disaster management support.<br />

Integration of images with social in<strong>for</strong>mation,<br />

statistical disaster in<strong>for</strong>mation <strong>and</strong> field<br />

investigation were essential.<br />

lessons learned<br />

(1) The observing time <strong>and</strong> location of the requested<br />

EO data should be dynamically planned to ensure<br />

they cover all disaster affected area <strong>and</strong> at different<br />

stages. Because of the lack of an observation plan,<br />

many acquired images were redundant around the<br />

area of the epicentre but there was a shortage of<br />

images on the edges of the affected area.<br />

(2) Capabilities to process the massive amount of EO<br />

data <strong>and</strong> extract useful in<strong>for</strong>mation <strong>for</strong> emergency<br />

response should be improved. It is important to<br />

develop automatic or semi-automatic methods <strong>for</strong><br />

data processing in real-time or near real-time, <strong>for</strong><br />

example, within 1-2 hours after acquisition (Deren<br />

Li, 2009).

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