Recommended Practice: Disaster Preparedness Using Free Software Extensions

Remote sensing technologies can support all stages of the disaster management cycle. In the prevention and preparedness phases, they often find their application in risk assessments, scenario modelling and early warning. This UN-SPIDER Recommended Practice explains how remote sensing data about recurring floods, information about infrastructure and socio-economic data can be integrated using free and open source software to support prevention and preparedness efforts. It makes use, among others, of the InaSAFE plug-in for the desktop GIS software QGIS to estimate the number of people and infrastructure potentially affected by a 100-year flood. The resulting insights can be used to contingency planning and related efforts before a disaster strikes.


The objective of this step-by-step procedure is to identify potentially damaged buildings and streets, as well the number of potentially displaced persons for two different flood events in Africa. This information can be used for future contingency planning and to improve the design of preventive measures.

Disaster type: 

Disaster Cycle Phase: 

  • Preparedness

Test Site: 

This Recommended Practice has been applied to flood scenarios in Mozambique and Ghana.


The presented QGIS plug-in was developed jointly by the Indonesian Disaster Management Organization (BNPB), the Australian Government and the World Bank (GFDRR). It was created as free and open source software (FOSS) and is available in the QGIS plug-in library. The use cases provided on the website of InaSAFAE are primarily related to disasters in Indonesia such as floods in the city of Jakarta. For this Recommended Practice, two use cases in Africa have been chosen. The first one focuses on a past flood event in Beira, Mozambique, whereas the second one addresses the larger area of Accra, Ghana, and makes use of a 100-year returning flood layer from the Global Flood Awareness System (GloFAS) of the Joint Research Center (JRC) of the European Commission (EC).


Both case studies provided show several options to adapt the calculations to other case studies. However, the plugIn does not give the exact number of people as the calculations are rather basic. This allows for faster processing, but also means that values are only an estimate of the expected damage and never an exact number. This practice should raise awareness about risk prevention and provide incentives to improve preparation and planning processes.

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