Environmental impacts of war’s social consequences. Case Study: Aleppo Governorate Syria
DOI:
https://doi.org/10.14806/ej.26.1.965Keywords:
Environmental impacts, armed conflicts, remote sensing, geographic information system, Syria, Aleppo GovernorateAbstract
War is an anthropogenic phenomenon with devastating effects, which cause loss of human life, alongside the disastrous effects on the natural environment. The environmental impacts of armed conflicts can be either direct, arising from the act of war itself, or indirect due to the massive population displacement, infrastructure breakdown, and militarised zones. Impacts may be irreversible, affecting areas even far away from the territory of the conflict where they manifest. The environmental impacts of war create social and economic consequences that lead to greater environmental degradation by showing the interdependent relationship between the environment, society, and economy. It is imperative to address the subject in a multidisciplinary approach and implement stricter international legislation on environmental disasters during wars. This study aims to identify environmental impacts by using satellite-derived images at Aleppo Governorate, analyse them via statistics supplemented with the available information for the research region, and demonstrate the subsequent social and economic consequences by creating indices, such as the Normalised Difference Vegetation Index (NDVI), digital image composites and classified images, which record the extent of reduction of healthy vegetation and the extent of destruction at the city of Aleppo. Data from international organisations corroborated the findings, and hereinafter societal and economic effects were analysed. Using remote sensing alongside with geographic information systems can be a useful tool as it offers access to war zones where physical observations are usually impossible.
Additional Files
- Figure 1 Histogram NDVI 2013
- Figure 2 Histogram NDVI 2017
- Figure 3 NDVI 2013
- Figure 4 NDVI 2017
- Figure 5 Histogram NBR 2013
- Figure 6 Histogram NBR 2017
- Figure 7 NBR 2013
- Figure 8 NBR 2017
- Figure 9 ?NBR
- Figure 10 Histogram ?NBR
- Figure 11 Histogram image composite 2013
- Figure 12 Image-composite 2013
- Figure 13 Histogram image composite 2017
- Figure 14 Image-composite 2017
- Figure 15 Pseudo-coloured composite 2013 (after algorithm)
- Figure 15 Pseudo-coloured composite 2013 (before algorithm)
- Figure 16 Pseudo-coloured composite 2017 (after algorithm)
- Figure 16 Pseudo-coloured composite 2017 (before algorithm)
- Figure 17 Image-composite 6,5,2 (2013)
- Figure 18 Image-composite 6,5,2 (2017)
- Figure 19 Classified image 2013
- Figure 20 Classified image 2017
- Table 1. Details of the satellite images for 2013 and 2017
- Table 2. Reference points to evaluate accuracy of classification
- Table 3. Integrated classification accuracy scoreboard -2013
- Table 4. Integrated classification accuracy scoreboard -2017
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