Berücksichtigung von Komplexität in Umweltinformationssystemen
| dc.contributor.author | Knoflacher, Markus | |
| dc.contributor.author | Resetarits, Andreas | |
| dc.contributor.editor | Hilty, Lorenz M. | |
| dc.contributor.editor | Gilgen, Paul W. | |
| dc.date.accessioned | 2019-09-16T09:31:53Z | |
| dc.date.available | 2019-09-16T09:31:53Z | |
| dc.date.issued | 2001 | |
| dc.description.abstract | Environmental systems are the most complex systems concerning the number of participating elements as also the number and different qualities of relationships between the elements. Along mass and energy gradients a hierarchical chain of subsystems, beginning from abiotic subsystems down to biotic subsystems can be identified. During the evolution several strategies to overcome the energetical and conditional constraints were developed in biotic systems. Consequently processes in environmental systems have characteristics which are far beyond causal reaction chains. The most obvious phenomena is non-linearity in the relationships between impact and effects. Less obvious, but also very important for understanding of reactions in environmental systems is irreversibility, adaptability, resilience and emergence. How important are this phenomena in environmental information? From the theoretical point of view it is evident that observations without consideration of basic system characteristics will produce misinformation. A big problem to accept this fact in reality is, that decoding of complex signals (e.g. from environmental systems) by any code will produce specific information to the observer. To solve the problem a structured data analysis, based on conceptual modelling can be applied. Conceptual modelling is a successful tool in identification of complex system properties because of its knowledge orientation and the possibility to integrate qualitative and quantitative factors. With a step by step modelling the relevant subsystems can be zoomed out at the relevant scales in space and time. On this basis the structured data analysis can be carried out, comprising analysis of observation method (= decoding analysis) and data analysis for information processing. To illustrate potential methodological approaches one example in information analysis of forest remote sensing data, and one example of information analysis in running water quality assessment will be presented. | de |
| dc.description.uri | http://enviroinfo.eu/sites/default/files/pdfs/vol104/0777.pdf | de |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/26656 | |
| dc.publisher | Metropolis | |
| dc.relation.ispartof | Sustainability in the Information Society | |
| dc.relation.ispartofseries | EnviroInfo | |
| dc.title | Berücksichtigung von Komplexität in Umweltinformationssystemen | de |
| dc.type | Text/Conference Paper | |
| gi.citation.publisherPlace | Marburg | |
| gi.conference.date | 2001 | |
| gi.conference.location | Zürich | |
| gi.conference.sessiontitle | Workshop: Simulation in den Umwelt- und Geowissenschaften |