Dataset for “There are no alternative routes”: Performing afforestation in the Aral Seabed in Uzbekistan
Shields, Kate
Shields, Kate
Citations
Altmetric:
Contributor
Photographer
Author
Artist
Editor
Advisor
Keywords
Afforestation—Environmental aspects, Desert reclamation—Uzbekistan, Aral Sea Region—Environmental conditions, Remote sensing—Environmental monitoring, Environmental policy—Uzbekistan
Local ID
Collections
Abstract
Afforestation initiatives have been and remain a key tool to “improve” the desert and to allay state anxieties around arid lands, including in the world’s newest desert, the Aralkum, which has emerged as the Aral Sea has dried and shrunk. The Uzbek state regularly announces the latest statistics on their large-scale high modernist afforestation of the Aral Seabed with the native salt-tolerant plant saxaul. Their stated goal: to mitigate the Aral “catastrophe” by stabilizing the soils of the seabed and restoring the landscape. In this paper, I present results of a critical remote sensing analysis of actual growth of afforested saxaul on the seabed. Thinking with and against different technologies of seeing (e.g. human eye, satellite sensor), I compare these results to reported afforestation activities, embedded observations from participant observation, and insights from key stakeholder interviews. I show how the planting of trees on the Aral Seabed is not simply an effort to create a functioning more-than-human infrastructural forest, but to perform global environmental stewardship to the world. I argue that we should see the Aral Seabed as an extractive landscape where restoring functioning landscapes is less important than narrating hectares planted and showing tractors moving in unison to produce spectacle value. These statistics and images in turn shape ongoing mitigation of the Aral “catastrophe,” as high modernist afforestation becomes seen as the only solution. I suggest ecosystem restoration instead requires slowing down, accepting non-scalability, and appreciating the quiet complexity of drylands, without countable trees, or forest aesthetics.
Description
This dataset was uploaded to DLynx during fall 2025.
