Humanitarian action has rapidly adopted Earth observation (EO) and geospatial technologies shaping them according to their needs. Protracted crises and large-scale population displacements require up-to-date information in many facets of humanitarian action support, from mission planning, resource deployment and monitoring, to nutrition and vaccination campaigns, camp plotting, damage assessment, etc. Even though nearly all assets of remote sensing apply in such demanding scenarios, it remains a challenge to fully implement and sustain a trustful and reliable information service. This paper discusses achievements and open issues in the use and uptake of EO technology, from a technical and organisational point of view, motivated by an information service for Médecins Sans Frontières (MSF) and its extension to other NGO's information needs in the humanitarian sector. With a focus on EO-based population estimation based on (semi-)automated dwelling counting from very high-resolution optical satellite imagery as well as the exploitation of data integration (including radar sensors), the paper also covers potential service elements with respect to environmental and ground-or surface water monitoring. It investigates workflow elements in relation to information extraction and delivery by illustrating a broad range of application scenarios, and discusses first operational solutions of a customized service portfolio.

For effective management of refugee camps or camps for internally displaced persons (IDPs) relief organizations need up-to-date information on the camp situation. In cases where detailed field assessments are not available, Earth observation (EO) data can provide important information to get a better overview about the general situation on the ground. In this study, different approaches for dwelling detection were tested using the example of a highly complex camp site in Somalia. On the basis of GeoEye-1 imagery, semi-automatic object-based and manual image analysis approaches were applied, compared and evaluated regarding their analysis results (absolute numbers, population estimation, spatial pattern), statistical correlations and production time. Although even the results of the visual image interpretation vary considerably between the interpreters, there is a similar pattern resulting from all methods, which shows same tendencies for dense and sparse populated areas. The statistical analyses revealed that all approaches have problems in the more complex areas, whereas there is a higher variance in manual interpretations with increasing complexity. The application of advanced rule sets in an object-based environment OPEN ACCESS Remote Sens. 2014, 6 9278 allowed a more consistent feature extraction in the area under investigation that can be obtained at a fraction of the time compared to visual image interpretation if large areas have to be observed.

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This study describes the development of a semi-automatic object-based image analysis approach for the detection and quantification of deforestation in Zalingei, Darfur, in consequence of the increasing concentration of refugees or internally displaced persons (IDPs) in the region. The classification workflow is based on a multi-scale approach, ranging from the analysis of high resolution SPOT-4 to very high resolution IKONOS and QuickBird satellite imagery between 2003 and 2008. The overall accuracy rates for the classification of the SPOT 4 data ranged from 92% up to 95%, while those for the QuickBird and IKONOS classification have shown values of 88 and 87%, respectively. The resulting trends in woody vegetation cover were compared with the development of the local population and the variability of precipitation. The results show that the strong increase in human population in the Zalingei IDP camps can be associated with considerable decrease in woody vegetation in the camp vicinity.