Paper short abstract:

GEDI – a satellite LIDAR designed for measuring forest structure – is said to revolutionise remote sensing and monitoring. This paper considers the political economy of GEDI data products across scientific units and its applicability to small, postcolonial island nations with inaccessible forests

Paper long abstract:

In 2018, GEDI – The Global Ecosystem Dynamics Investigation – was deployed on the International Space Station for a two-year mission (since renewed for a further five). GEDI is the first satellite LIDAR designed specifically for measuring the canopy height, vertical structure, and surface elevation of tropical and temperate forests, with a cluster of high-powered, full-bean lasers designed to penetrate the densest of canopy cover and return reliable reflected waveform data from which vegetative structure can be deduced. Its applications include largescale, long-term monitoring of deforestation and forest composition and structure. The training data used to create waveform algorithms is almost exclusively from North America and Europe, where relative ease of access has supported a wealth of fieldwork and associated ground data. GEDI’s use meanwhile is largely targeted at dense tropical forests in the global south where remote sensing can compensate for the geographical and political inaccessibility that precludes many data collection efforts.

The aim of this paper is twofold. First it explores the political economy of the launch of GEDI, in which Python programmers are courted and seduced by novel libraries and visualisations and through a series of webinars and tutorials of its use in familiar IDEs and environments, thus securing end users for the GEDI data products. Second, it takes as an example small island developing states (SIDS) to show how inaccuracies are exacerbated and relative spatial coverage reduced in the hundreds of SIDS protected forests to which GEDI data products might likely be applied.