Inundación

In order to encourage the targeted retrieval of space-based information and data by disaster risk reduction practitioners, content on the Portal is systematically enriched with metadata. For instance, data sources are marked up with data about their file type, satellite/sensor and spatial coverage and whether they relate to the disaster risk management or emergency response phase. The tool features a range of filters that draw on the metadata, thereby allowing users to narrow down their search, for example, filtering available GIS software by hazard type. This ensures that users efficiently find the content that is most relevant to them. At the same time, the Portal encourages the discovery of resources related to those accessed by the user, by providing links to them on the same page. This allows for contextualisation and highlights links between resources...

Based on optical and radar images of different resolutions, UNOSAT products are also enriched with the available baseline GIS datasets and crowdsourcing data. Satellite-derived analysis performed by UNOSAT is delivered in the form of GIS Data, Static Maps, Live Web Maps, and Reports, and is then shared with a wide range of end-users such as UN Agencies, International Organizations, and Governments.

The Global Human Settlement (GHS) framework produces global open source spatial information about the human presence on the planet over time. This is in the form of built up maps, population density maps and settlement maps. This information is generated with evidence-based analytics and knowledge using new spatial data mining technologies. The framework uses heterogeneous data including global archives of fine-scale satellite imagery, census data, and volunteered geographic information. The data is processed fully automatically and generates analytics and knowledge reporting objectively and systematically about the presence of population and built-up infrastructures. 

The guidelines will be reviewed and updated periodically, in order to integrate new best practices and to be responsive to evolutions in technology and end-user needs. The IWG-SEM chair has the responsibility to initiate the review, by agreement of the Working Group. 

The production and the maintenance of the guidelines are based on a joint effort by the members of the International Working Group on Satellite-based Emergency Mapping (IWG- SEM), a voluntary group of organizations involved in satellite-based emergency mapping. It was founded to improve cooperation, communication and professional standards among the global network of satellite-based emergency mapping providers. The chairperson of the group is nominated for a term of one year and is responsible for organizing the monthly telecons and bi-annual meetings. The current chair is from the Department of Geoinformatics, Z_GIS University of Salzburg / Spatial Services Ltd, Austria...

Intensive capacity development sessions for Pacific island countries  (Cook Islands, Fiji, Kiribati, Micronesia (the Federated States of), Samoa, Solomon Islands, Tonga, and Vanuatu).

The project aims to enhance institutional and technical capacity for using geospatial data and technology applications and promote regional cooperation for sharing geospatial data for disaster management in Pacific island countries.

Developed for the needs of the ASEAN sub-region in Asia and the Pacific, the handbooks can also be adapted for use in other regions.

The handbooks have been developed through expert working groups, in collaboration with United Nations partners including UNOOSA/UN-SPIDER, UNITAR-UNOSAT, and OCHA. As well as extensive consultation with space agencies, national disaster management authorities and regional institutions, including GISTDA, LAPAN, ASEAN Coordinating Centre for Humanitarian Assistance on Disaster Management and Asian Institute of Technology.

Vegetation Condition Index according to Kogan et al. (1990). Two-weeks normalized Differenced Vegetation Index (NDVI) composites based on 250m MODIS data are freely available for download from the MODIS/NDVI Time Series Database from the Global Agriculture Monitoring (GLAM) Project provided via the website of Geographic Department of Maryland University.

The automatically derived flood masks are based on Sentinel-1 and TerraSAR-X radar data. TerraSAR-X data can be accessed free of cost via scientific data proposals or are provided by DLR during activations of the International Charter ‘Space and Major Disasters’. Data from Sentinel-1 is accessible free of cost via ESA’s Copernicus Open Access Hub.