There is a common theme at play in our talk of data generally, of digital earth data more specifically, and of environmental monitoring most specifically: more data leads to more action and, ultimately, to societal good. This data-to-action framework is troubled. Its taken-for-grantedness prevents us from attending to the processes between data and action. It also dampens our drive to investigate the contexts of that data, that action, and that envisioned societal good. In this dissertation, I deconstruct this data-to-action model in the context of Landsat, the United States' first natural resource management satellite. First, I talk about the ways in which Landsat's data and instrumentation hold conflicting narratives and values within them. Therefore, Landsat data does not automatically or easily yield action toward environmental preservation, or toward any unified societal good. Furthermore, I point out a parallel dynamic in STS, where critique is somewhat analogous to data. We want our critiques to yield action, and to guide us toward a more just technoscience. However, critiques—like data—require intentional, reconstructive interventions toward change. Here is an opportunity for a diffractive intervention: one in which we read STS and remote sensing through each other, to create space for interdisciplinary dialogue around environmental preservation. A focus on this shared goal, I argue, is imperative. At stake are issues of environmental degradation, dwindling resources, and climate change. I conclude with beginnings rather than endings: with suggestions for how we might begin to create infrastructure that attends to that forgotten space between data, critique, action, and change. / Doctor of Philosophy / I have identified a problem I call the data-to-action paradigm. When we scroll around on Facebook and find articles –– citing pages and pages of statistics –– on our rapidly melting glaciers and increasingly unpredictable weather patterns, we are existing within this paradigm. We have been offered evidence of looming, catastrophic change, but no suggestions on what to do about it. This is not only happening with climatological data and large-scale environmental systems modelling. Rather, this is a general problem across the field of Earth Remote Sensing. The origins of this data-to-action paradigm, I argue, can be found in old and new rhetoric about Landsat, the United States’ first natural resource management satellite. This rhetoric often says that Landsat — and other natural resource management satellites’ — data is a way toward societal good. The more data we have, the more good will proliferate in the world. However, we haven’t been specific about what that good might look like, and what kinds of actions we might take toward that good using this data. This is because, I argue, Earth systems science is politically complicated, with many different conceptions of societal good. In order to be more specific about how we might use this data toward some kind of good we must (1) explore the history of environmental data, and figure out where this rhetoric comes from (which I I do in this dissertation), and (2) encourage interdisciplinary collaborations between Earth Remote Sensing scientists, social scientists, and humanists, to more specifically flesh out connections between digital Earth data, its analyses, and subsequent civic action on such data.

Nous avons développé un système intégré permettant l'acquisition simultanée de la forme 3D ainsi que de la réflectance des surfaces des objets scannés. Nous appelons ce système un scanner 3D multispectral du fait qu’il combine, dans un couple stéréoscopique, une caméra multispectrale et un système projecteur de lumière structurée. Nous voyons plusieurs possibilités d’application pour un tel système mais nous mettons en avant des applications dans le domaine de l’archivage et la diffusion numériques des objets du patrimoine. Dans le manuscrit, nous présentons d’abord ce système ainsi que tous les calibrages et traitements nécessaires à sa mise en oeuvre. Ensuite, une fois que le système est fonctionnel, les données qui en sont générées sont riches d’informations, hétérogènes (maillage + réflectances, etc.) et surtout occupent beaucoup de place. Ce fait rend problématiques le stockage et la transmission, notamment pour des applications en ligne de type musée virtuel. Pour cette raison, nous étudions les différentes possibilités de représentation et de codage des données acquises par ce système pour en adopter la plus pertinente. Puis nous examinons les stratégies les plus appropriées à la compression de telles données, sans toutefois perdre la généralité sur d’autres données (type satellitaire). Nous réalisons un benchmark des stratégies de compression en proposant un cadre d’évaluation et des améliorations sur les stratégies classiques existantes. Cette première étude nous permettra de proposer une approche adaptative qui se révélera plus efficace pour la compression et notamment dans le cadre de la stratégie que nous appelons Full-3D. / We have developed an integrated system permitting the simultaneous acquisition of the 3D shape and the spectral spectral reflectance of scanned object surfaces. We call this system a 3D multispectral scanner because it combines within a stereopair, a multispectral video camera and a structured light projector. We see several application possibilities for a such acquisition system but we want to highlight applications in the field of digital archiving and broadcasting for heritage objects. In the manuscript we first introduce the acquisition system and its necessary calibrations and treatments needed for his use. Then, once the acquisition system is functional, data that are generated are rich in information, heterogeneous (mesh + reflectance, etc.) and in particular require lots of memory space. This fact makes data storage and transmission problematic, especially for applications like on line virtual museum. For this reason we study the different possibilities of representation and coding of data acquired by this system to adopt the most appropriate one. Then we examinate the most appropriate strategies to compress such data, without lost the generality on other data (satellite type). We perform a benchmark of compression strategies by providing an evaluation framework and improvements on existing conventional strategies. This first study will allow us to propose an adaptive approach that will be most effective for compression and particularly in the context of the compression strategy that we call Full-3D.