Satellite analysis of the environmental impacts of armed-conflict in Rakhine, Myanmar
Creators
- 1. Asia Center, Faculty of Arts and Sciences, Harvard University, Cgis South, 1730 Cambridge St., Cambridge, MA, 02138 (United States)
Description
Highlights: • Rohingya conflict is one of the worst humanitarian crises of the 21st Century. • Satellite-based machine-learning estimates LULCC in conflict-zone. • Investigated the environmental conditions pre-, during, and post-conflict. • Google Earth Engine (GEE) cloud-based computing platform was used. • Found demolition of inhabited region, increase in burning areas, and deforestation. The impacts of armed conflict on the environment are extremely complex and difficult to investigate, given the impossibility of accessing the affected area and reliable data limitation. Very-high-resolution satellite imageries and highly reliable machine learning algorithms become very useful in studying direct and indirect impacts of war on the ecosystem, in addition to connected effects on human lives. The Rohingya conflict is described as one of the worst humanitarian crises and human-made disasters of the 21st Century. Quantification of damage due to the conflict and the suitability of human resettlement has been lacking despite the ongoing agreements to repatriate refugees and the importance of ecosystem services for the communities' survival. In this work, the investigation of environmental conditions pre-, during, and post-conflict in the conflict zone was carried out using satellite data. The Google Earth Engine (GEE) cloud-based computing platform with a widely applied algorithm, the Random Forest (RF) classifier was implemented and experienced. Striking near-complete demolition of inhabited regions, dramatic and highly significant increase in burning areas, and substantial deforestation were found. The study discusses the reasons behind such findings from the Rakhine case and debates some general conservation lessons applicable to other countries undergoing post-conflict transitions.
Availability note (English)
Available from http://dx.doi.org/10.1016/j.scitotenv.2021.146758Additional details
Identifiers
- DOI
- 10.1016/j.scitotenv.2021.146758;
- PII
- S004896972101826X;
Publishing Information
- Journal Title
- Science of the Total Environment
- Journal Volume
- 781
- Journal Page Range
- vp.
- ISSN
- 0048-9697
- CODEN
- STENDL
INIS
- Country of Publication
- Netherlands
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 54057698
- Subject category
- S54: ENVIRONMENTAL SCIENCES; S47: OTHER INSTRUMENTATION;
- Descriptors DEI
- DEFORESTATION; ECOSYSTEMS; ENVIRONMENTAL IMPACTS; FORESTS; MACHINE LEARNING; NATURAL DISASTERS; RANDOMNESS; REMOTE SENSING; RESOLUTION
- Descriptors DEC
- ALGORITHMS; ARTIFICIAL INTELLIGENCE; LEARNING; MATHEMATICAL LOGIC
Optional Information
- Copyright
- Copyright (c) 2021 Elsevier B.V. All rights reserved.