Paper short abstract:

This paper explores an interdisciplinary research project linking Satellite Remote Sensing, environmental modelling and others to monitor and predict the mowing of grasslands. It argues that the project's way of meeting in data is in incongruent scales, or dealing with matters of scale.

Paper long abstract:

This paper explores the meetings of heterogeneously scaled data between Satellite Remote Sensing (SRS) and other sciences dealing with biodiversity and agriculture. It is based on an ethnographic inquiry in the making - part of my PhD-research - into an interdisciplinary research project in its first year. Its aim is the SRS-based monitoring and modelling of grasslands in Germany in order to better grasp different "mowing regimes" and predict them both for economic purposes and in relation to issues of biodiversity.

The task of making different data meet is a core concern for the project's success. With six different research organisations and at least three major approaches to doing science assembled - SRS, modelling and field experiments - the research questions easily break up into a series of diverging data that need to be balanced carefully. Knowledge of varying species compositions in grasslands is confronted with interests in model validation or spectral information.

These approaches and data come with different spatial scales, including 1m² plots, models with 1km² resolutions or SRS data with pixels of 20m². I argue that it is precisely in such meetings that the research project becomes vivid, as apparent in the search for research locations that meet criteria of visibility from outer space, sufficiently equipped technology for in situ measurements and access to farmers willing to collaborate: How is this BigData-approach collaboratively evolving or failing to address the variety of concerns it is meant to handle, e.g. monitoring the whole of Germany and site-specific consultation?