Climate Crisis and Creation Care: Historical Perspectives, Ecological Integrity and Justice

0548471 - ÚVGZ 2022 RIV GB eng M - Část monografie knihy
Stehlík, M. - Kiselák, J. - Dušek, Jiří - Brandmayr, A. - Grubauer, B. - Haider, J. - Pfeiler, R. - Raidl, S. - Schindlauer, A. - Stadler, A.
Recurrence Analysis of Methane Emissions from Wetland Ecosystem.
Climate Crisis and Creation Care: Historical Perspectives, Ecological Integrity and Justice. Newcastle upon Tyne: Cambridge Scholars Publishing, 2021 - (Nellist, C.), s. 308-328. ISBN 978-1-5275-7420-5
Výzkumná infrastruktura: CzeCOS III - 90123
Institucionální podpora: RVO:86652079
Klíčová slova: emission * methane * carbon dioxide * wetland * statistical analysis * complex data * complex processes
Obor OECD: Environmental sciences (social aspects to be 5.7)
https://www.cambridgescholars.com/product/978-1-5275-7420-5

The growing possibility of continuous monitoring of complex natural processes including meteorological and component flux measurements creates problems with evaluation and usage of discrete and/or continuous complex data, also known as big data. Big data is usually defined as a huge amount of structured or unstructured data (e.g., Lynch 2008, Schadt et al. 2010, Pal et al. 2020). We think that big data is not just about its quantity and thus its unprocessability, but especially about its complexity (Stehlík et al. 2016). Complexity means diversity of the data itself but also its scale. We can understand scale not only as a unit in the physical sense but also in the sense of the size scale and time scale. There is an evident difference between measuring processes inside the cell, in its neighbourhood and on the continental scale. Analogously to the size scale, the time scale is very important. The difference between measurements of very fast (picoseconds to microseconds) processes of electron transport in the Z scheme of the photosynthesis (Nobel 2005) and measurements of very slow processes in hundreds or thousands of years (e.g., peat accumulation in wetlands, Belyea & Malmer 2004) is clear at first glance. Data measured at different scales creates complex big data sets. The functioning of complex processes involves various behaviors which can be characterized as deterministic, stochastic and chaotic. To sufficiently understand the studied process, we need to know how the process behaves. Knowledge of the processes behaviour facilitates data analysis and interpretation. Analysis of recurrence is a novel approach on how to analyse data including its behaviour. This method has the potential to quantify individual types of behaviour. Determining the chaotic behaviour of the studied process can be very beneficial for the interpretation process itself and in a wider context for the different ecosystems.
Trvalý link: http://hdl.handle.net/11104/0324493