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dc.contributor.authorAung, Thiri Shwesin
dc.contributor.authorØverland, Indra
dc.contributor.authorVakulchuk, Roman
dc.contributor.authorXie, Yanhua
dc.date.accessioned2021-06-08T08:00:47Z
dc.date.available2021-06-08T08:00:47Z
dc.date.created2021-04-16T10:51:32Z
dc.date.issued2021
dc.identifier.citationEnvironmental Research Communications (ERC). 2021, 3 (2)en_US
dc.identifier.issn2515-7620
dc.identifier.urihttps://hdl.handle.net/11250/2758433
dc.description.abstractThis paper studies socioeconomic and environmental changes in the neighboring areas Bangladesh-Myanmar border from 2012 to 2019, thus covering the period before and after the 2017 Rakhine conflict in Myanmar and outflux of refugees across the border to Bangladesh. Given the scarcity and costliness of traditional data collection methods in such conflict areas, the paper uses a novel methodological model based on very-high-resolution satellite imagery, nighttime satellite imagery, and machine-learning algorithms to generate reliable and reusable data for comparative assessment of the impacts of the Rakhine conflict. Assessments of welfare and environmental risks using this approach can be accurate and scalable across different regions and times when other data are unavailable. Key findings are: the general livelihood situation has worsened and income sources shrunk in Rakhine; forced migration damaged the ecologically fragile regions in the two countries; the destruction of aquaculture wetland ecosystems is observed in Rakhine; the deforestation rate reached 20% in Rakhine and 13% on the Bangladeshi side of the border. The results can provide guidance to policymakers and international actors as they work to repatriate the victims of the conflict in Rakhine and minimize the conflict’s security and environmental consequences. The methodology can be applied to other data-poor conflict and refugee areas in the world.
dc.language.isoengen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.subjectKonflikt
dc.subjectConflict resolution
dc.subjectSikkerhetspolitikk
dc.subjectSecurity policies
dc.subjectAsia
dc.subjectAsia
dc.subjectUtviklingspolitikk
dc.subjectDevelopment policy
dc.titleUsing satellite data and machine learning to study conflict-induced environmental and socioeconomic destruction in data-poor conflict areas: The case of the Rakhine conflicten_US
dc.typeOthersen_US
dc.description.versionpublishedVersion
dc.subject.nsiVDP::Internasjonal politikk: 243
dc.subject.nsiVDP::International politics: 243
dc.source.pagenumber19en_US
dc.source.volume3en_US
dc.source.journalEnvironmental Research Communications (ERC)en_US
dc.source.issue2en_US
dc.identifier.doi10.1088/2515-7620/abedd9
dc.identifier.cristin1904525
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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