scholarly journals Interrupting the Anthropo-obScene: Immuno-biopolitics and Depoliticizing Ontologies in the Anthropocene

2018 ◽  
Vol 35 (6) ◽  
pp. 3-30 ◽  
Author(s):  
Erik Swyngedouw ◽  
Henrik Ernstson
Keyword(s):  
Object Oriented ◽  
The Political ◽  
System Management ◽  
Earth System ◽  
Ecological Situation ◽  
Earth System Science ◽  
System Science ◽  
Political Perspective ◽  
The Creation

This paper argues that ‘the Anthropocene’ is a deeply depoliticizing notion. This de-politicization unfolds through the creation of a set of narratives, what we refer to as ‘AnthropoScenes’, which broadly share the effect of off-staging certain voices and forms of acting. Our notion of the Anthropo-obScene is our tactic to both attest to and undermine the depoliticizing stories of ‘the Anthropocene’. We first examine how various AnthropoScenes, while internally fractured and heterogeneous, ranging from geo-engineering and earth system science to more-than-human and object-oriented ontologies, place things and beings, human and non-human, within a particular relational straitjacket that does not allow for a remainder or constitutive outside. This risks deepening an immunological biopolitical fantasy that promises adaptive and resilient terraforming, an earth system management of sorts that permits life as we know it to continue for some, while turning into a necropolitics for others. Second, we develop a post-foundational political perspective in relation to our dramatically changing socio-ecological situation. This perspective understands the political in terms of performance and, in an Arendtian manner, re-opens the political as forms of public-acting in common that subtracts from or exceeds what is gestured to hold socio-ecological constellations together. We conclude that what is off-staged and rendered obscene in ‘the AnthropoScenes’ carries precisely the possibility of a return of the political.

scholarly journals Chlorophyll a fluorescence illuminates a path connecting plant molecular biology to Earth-system science

Nature Plants ◽  
2021 ◽  
Author(s):  
Albert Porcar-Castell ◽  
Zbyněk Malenovský ◽  
Troy Magney ◽  
Shari Van Wittenberghe ◽  
Beatriz Fernández-Marín ◽  
...  
Keyword(s):  
Molecular Biology ◽  
Chlorophyll A ◽  
Chlorophyll A Fluorescence ◽  
Plant Molecular Biology ◽  
Earth System ◽  
Earth System Science ◽  
System Science

Earth system science and remote sensing

1985 ◽  
Vol 73 (6) ◽  
pp. 1118-1127 ◽  
Author(s):  
F.P. Bretherton
Keyword(s):  
Remote Sensing ◽  
Earth System ◽  
Earth System Science ◽  
System Science

scholarly journals Multivariate anomaly detection for Earth observations: a comparison of algorithms and feature extraction techniques

2017 ◽  
Vol 8 (3) ◽  
pp. 677-696 ◽  
Author(s):  
Milan Flach ◽  
Fabian Gans ◽  
Alexander Brenning ◽  
Joachim Denzler ◽  
Markus Reichstein ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Anomaly Detection ◽  
Data Streams ◽  
Multivariate Data ◽  
Earth System ◽  
Earth System Science ◽  
Sample Mean ◽  
System Science ◽  
Detection Algorithms ◽  
Earth Observations

Abstract. Today, many processes at the Earth's surface are constantly monitored by multiple data streams. These observations have become central to advancing our understanding of vegetation dynamics in response to climate or land use change. Another set of important applications is monitoring effects of extreme climatic events, other disturbances such as fires, or abrupt land transitions. One important methodological question is how to reliably detect anomalies in an automated and generic way within multivariate data streams, which typically vary seasonally and are interconnected across variables. Although many algorithms have been proposed for detecting anomalies in multivariate data, only a few have been investigated in the context of Earth system science applications. In this study, we systematically combine and compare feature extraction and anomaly detection algorithms for detecting anomalous events. Our aim is to identify suitable workflows for automatically detecting anomalous patterns in multivariate Earth system data streams. We rely on artificial data that mimic typical properties and anomalies in multivariate spatiotemporal Earth observations like sudden changes in basic characteristics of time series such as the sample mean, the variance, changes in the cycle amplitude, and trends. This artificial experiment is needed as there is no gold standard for the identification of anomalies in real Earth observations. Our results show that a well-chosen feature extraction step (e.g., subtracting seasonal cycles, or dimensionality reduction) is more important than the choice of a particular anomaly detection algorithm. Nevertheless, we identify three detection algorithms (k-nearest neighbors mean distance, kernel density estimation, a recurrence approach) and their combinations (ensembles) that outperform other multivariate approaches as well as univariate extreme-event detection methods. Our results therefore provide an effective workflow to automatically detect anomalies in Earth system science data.

scholarly journals Multivariate Anomaly Detection for Earth Observations: A Comparison of Algorithms and Feature Extraction Techniques

2016 ◽  
Author(s):  
Milan Flach ◽  
Fabian Gans ◽  
Alexander Brenning ◽  
Joachim Denzler ◽  
Markus Reichstein ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Anomaly Detection ◽  
Data Streams ◽  
Multivariate Data ◽  
Detection Methods ◽  
Earth System ◽  
Earth System Science ◽  
System Science ◽  
Detection Algorithms ◽  
Earth Observations

Abstract. Today, many processes at the Earth's surface are constantly monitored by multiple data streams. These observations have become central to advance our understanding of e.g. vegetation dynamics in response to climate or land use change. Another set of important applications is monitoring effects of climatic extreme events, other disturbances such as fires, or abrupt land transitions. One important methodological question is how to reliably detect anomalies in an automated and generic way within multivariate data streams, which typically vary seasonally and are interconnected across variables. Although many algorithms have been proposed for detecting anomalies in multivariate data, only few have been investigated in the context of Earth system science applications. In this study, we systematically combine and compare feature extraction and anomaly detection algorithms for detecting anomalous events. Our aim is to identify suitable workflows for automatically detecting anomalous patterns in multivariate Earth system data streams. We rely on artificial data that mimic typical properties and anomalies in multivariate spatiotemporal Earth observations. This artificial experiment is needed as there is no 'gold standard' for the identification of anomalies in real Earth observations. Our results show that a well chosen feature extraction step (e.g. subtracting seasonal cycles, or dimensionality reduction) is more important than the choice of a particular anomaly detection algorithm. Nevertheless, we identify 3 detection algorithms (k-nearest neighbours mean distance, kernel density estimation, a recurrence approach) and their combinations (ensembles) that outperform other multivariate approaches as well as univariate extreme event detection methods. Our results therefore provide an effective workflow to automatically detect anomalies in Earth system science data.

Earth system science and the western worldview

1999 ◽  
Vol 161 (1-3) ◽  
pp. 365-371 ◽  
Author(s):  
James C.G Walker
Keyword(s):  
Earth System ◽  
Earth System Science ◽  
System Science
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