Identification d'indicateurs de risque des populations victimes de conflits par imagerie satellitaire études de cas : le nord de l'Irak
Mubareka, Sarah Betoul
Remote sensing and security, terms which are not usually associated, have found a common platform this decade with the conjuring of the GMOSS network (Global Monitoring for Security and Stability ), whose mandate is to discover new applications for satellite-derived imagery to security issues. This study focuses on human security, concentrating on the characterisation of vulnerable areas to conflict. A time-series of satellite imagery taken from Landsat sensors from 1987 to 2001 and the SRTM mission imagery are used for this purpose over a site in northern Iraq. Human security issues include the exposure to any type of hazard. The region of study is first characterised in order to understand which hazards are and were present in the past for the region of study. The principal hazard for the region of study is armed conflict and the relative field data was analysed to determine the links between geographical indicators and vulnerable areas. This is done through historical research and the study of open-sourced information about disease outbreaks; the movements of refugees and the internally displaced; and humanitarian aid and security issues. These open sources offer information which are not always consistent, objective, or normalized and are therefore difficult to quantify. A method for the rapid mapping and graphing and subsequent analysis of the situation in a region where limited information is available is developed. This information is coupled with population numbers to create a"risk map": A disaggregated matrix of areas most at risk during conflict situations. The results show that describing the risk factor for a population to the hazard conflict depends on three complex indicators: Population density, remoteness and economic diversity. Each of these complex indicators is then derived from Landsat and SRTM imagery and a satellite-driven model is formulated. This model based on satellite imagery is applied to the study site for a temporal study. The output are three 90 m × 90 m resolution grids which describe, at a pixel level, the risk level within the region for each of the dates studies, and the changes which occur in northern Iraq as the result of the Anfal Campaigns. Results show that satellite imagery, with a minimum of processing, can yield indicators for characterising risk in a region. Although by no means a replacement for field data, this technological source, in the absence of local knowledge, can provide users with a starting point in understanding which areas are most at risk within a region. If this data is coupled with open sourced information such as political and cultural discrimination, economy and agricultural practices, a fairly accurate risk map can be generated in the absence of field data.