ArticleData in brief2024
A dataset to model Levantine landcover and land-use change connected to climate change, the Arab Spring and COVID-19.
Article in Data in brief, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed, 2 citations in OpenAlex.
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1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Levant is highly vulnerable to climate change and experiences prolonged heat waves that have led to societal crises and population displacement. In addition, the region has been impacted by further socio-political turmoil at least since 2010, including the Syrian civil war and currently the escalation of the so-called Israeli-Palestinian Conflict, which strained neighbouring countries like Jordan due to the influx of Syrian refugees and increases population vulnerability to governmental decision-making. Jordan, in particular, has seen rapid population growth and significant changes in land-use and infrastructure, leading to over-exploitation of the landscape through irrigation and unregulated construction activity. This article uses climate data, satellite imagery, and land cover information in a multicomponent trend analysis to illustrate the substantial increase in construction activity and to highlight the intricate relationship between climate change predictions and current socio-political development in the Levant. The analyses were performed using annual and seasonal composites of MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI (Normalized Difference Vegetation Index) datasets with a spatial resolution of 250 m compared to climate indices of the GLDAS (Global Land Data Assimilation System) Noah Land Surface Model L4 dataset for the period 2001-2023. Surface reflectance and climatic parameters were then evaluated on the basis of socio-cultural factors, such as population dynamics, governmental decision-making, water withdrawal regulations, and built-up change as a result of large-scale migration processes. All analyses were conducted using R-software and can be reproduced and replicated using the code and the data provided in this article and the repository.
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