Sequía

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...

IDSI integrates multi-source remote sensing data from moderate resolution imaging spectroradiometer (MODIS) and tropical rainfall measuring mission (TRMM), ESA Soil Moisture (ASCAT) Products and it synthesizes precipitation deficits, soil thermal stress and vegetation growth status in drought process. Therefore, this method is favourable to monitor the comprehensive drought over South Asia.

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.

Persistent and prolonged droughts in Cambodia are severely affecting agricultural production, specifically for rice. Cambodia is moving towards improving drought management through ESCAP’s Regional Drought Mechanism. Supported by China, a tailored drought monitoring system is being established through DroughtWatch at different scales using different earth observation data. The Government of Australia is working towards building a water accounting system for Cambodia, using its innovative tool Source. In addition, Australia, with support from Thailand, is also working towards supporting both of these tools by establishing a pilot DataCube for Cambodia to improve storage of pre-processed Earth observation data. Working with institutes such as the Regional Integrated Multi-Hazard Early Warning System for Africa and Asia (RIMES), information can be shared before the start of the growing season through their Monsoon Forums.  Additional partners, such as UNOSAT, are supporting...

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.

Mapping of built-up height and share of built-up area for the example of the city of Munich. The estimated average built-up height features a mean absolute error (MAE) of 2.7 m (i.e., less than one floor) and the share of built-up area could be estimated with an MAE of 13.3 %, when compared to a reference data set.

For a comprehensive and objective analysis of the settlement patterns, the DLR additionally developed an approach to display the spatial networks between the mapped settlements. It enables the computation of various form and centrality measures to characterize settlement patterns, at different spatial units, ranging from global to local scale.