From Atmospheric Chemistry to Earth System Science. Contributions to the recent history of the Max Planck Institute for Chemistry (Otto Hahn Institute), 1959–2000.

10.47261/1112 ◽  
2018 ◽  
Author(s):  
Gregor Lax ◽  
Keyword(s):  
Atmospheric Chemistry ◽  
Recent History ◽  
Earth System ◽  
Max Planck ◽  
Earth System Science ◽  
System Science ◽  
Otto Hahn ◽  
History Of

scholarly journals Semi-Centennial of Landsat Observations & Pending Landsat 9 Launch

2021 ◽  
Vol 87 (8) ◽  
pp. 533-539
Author(s):  
Samuel N. Goward ◽  
Jeffrey G. Masek ◽  
Thomas R. Loveland ◽  
John L. Dwyer ◽  
Darrel L. Williams ◽  
...  
Keyword(s):  
Natural Resources ◽  
Physical System ◽  
Major Element ◽  
Landsat Data ◽  
Earth System ◽  
Earth System Science ◽  
System Science ◽  
Half Century ◽  
History Of ◽  
Nearly Continuous

The first Landsat was placed in orbit on 23 July 1972, followed by a series of missions that have provided nearly continuous, two-satellite 8-day repeat image coverage of the Earth's land areas for the last half-century. These observations have substantially enhanced our understanding of the Earth's terrestrial dynamics, both as a major element of the Earth's physical system, the primary home of humans, and the major source of resources that support them. The history of Landsat is complex, reflective of the human systems that sustain it. Despite the conflicted perspectives surrounding the continuation of the program, Landsat has survived based on worldwide recognition of its critical contributions to understanding land dynamics, management of natural resources and Earth system science. Launch of Landsat 9 is anticipated in Fall 2021, and current planning for the next generation, Landsat Next is well underway. The community of Landsat data users is looking forward to another 50 years of the Landsat program.

scholarly journals Geomorphology and Earth System Science

2021 ◽  
pp. M58-2021-9
Author(s):  
Simon J. Dadson
Keyword(s):  
Plate Tectonics ◽  
Global Environmental Change ◽  
Earth System ◽  
Earth System Science ◽  
System Science ◽  
Mountain Ranges ◽  
Global Environmental ◽  
Research Programmes ◽  
History Of ◽  
19Th And 20Th Century

AbstractThis chapter surveys the history of geomorphology and Earth system science 1965-2000. With roots in Enlightenment thought from Hutton, Somerville, Humboldt and Darwin we see a preoccupation with a holistic form of Earth system science develop through the reductionist, mechanistic ideas of the 19th and 20th century to be re-awoken in the 1960 and 1970s environmental movements and the space age, culminating in the major research programmes set by NASA and others subsequently. At the same time the chapter charts the evolution in geomorphology to consider plate tectonics and the origins of mountain ranges, geochemistry and its links between surfaces systems and the atmosphere, to later ideas emphasising the interplay between landforms and life. This chapter surveys changing interconnected ideas within this field and draws parallels and contrasts between the holistic depictions of Earth system science in the early part of the subject's history and the fundamental challenges facing us today as we grapple to find science-led solutions to global environmental change.

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.

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