• Treffer 2 von 12
Zurück zur Trefferliste

Potential and challenges of harmonizing 40 years of AVHRR data: the TIMELINE experience

Zitieren Sie bitte immer diese URN: urn:nbn:de:bvb:20-opus-246134
  • Earth Observation satellite data allows for the monitoring of the surface of our planet at predefined intervals covering large areas. However, there is only one medium resolution sensor family in orbit that enables an observation time span of 40 and more years at a daily repeat interval. This is the AVHRR sensor family. If we want to investigate the long-term impacts of climate change on our environment, we can only do so based on data that remains available for several decades. If we then want to investigate processes with respect to climateEarth Observation satellite data allows for the monitoring of the surface of our planet at predefined intervals covering large areas. However, there is only one medium resolution sensor family in orbit that enables an observation time span of 40 and more years at a daily repeat interval. This is the AVHRR sensor family. If we want to investigate the long-term impacts of climate change on our environment, we can only do so based on data that remains available for several decades. If we then want to investigate processes with respect to climate change, we need very high temporal resolution enabling the generation of long-term time series and the derivation of related statistical parameters such as mean, variability, anomalies, and trends. The challenges to generating a well calibrated and harmonized 40-year-long time series based on AVHRR sensor data flown on 14 different platforms are enormous. However, only extremely thorough pre-processing and harmonization ensures that trends found in the data are real trends and not sensor-related (or other) artefacts. The generation of European-wide time series as a basis for the derivation of a multitude of parameters is therefore an extremely challenging task, the details of which are presented in this paper.zeige mehrzeige weniger

Volltext Dateien herunterladen

Metadaten exportieren

Weitere Dienste

Teilen auf Twitter Suche bei Google Scholar Statistik - Anzahl der Zugriffe auf das Dokument
Metadaten
Autor(en): Stefan Dech, Stefanie Holzwarth, Sarah Asam, Thorsten Andresen, Martin Bachmann, Martin Boettcher, Andreas Dietz, Christina Eisfelder, Corinne Frey, Gerhard Gesell, Ursula Gessner, Andreas Hirner, Matthias Hofmann, Grit Kirches, Doris Klein, Igor Klein, Tanja Kraus, Detmar Krause, Simon Plank, Thomas Popp, Sophie Reinermann, Philipp Reiners, Sebastian Roessler, Thomas Ruppert, Alexander Scherbachenko, Ranjitha Vignesh, Meinhard Wolfmueller, Hendrik Zwenzner, Claudia Kuenzer
URN:urn:nbn:de:bvb:20-opus-246134
Dokumentart:Artikel / Aufsatz in einer Zeitschrift
Institute der Universität:Philosophische Fakultät (Histor., philolog., Kultur- und geograph. Wissensch.) / Institut für Geographie und Geologie
Sprache der Veröffentlichung:Englisch
Titel des übergeordneten Werkes / der Zeitschrift (Englisch):Remote Sensing
ISSN:2072-4292
Erscheinungsjahr:2021
Band / Jahrgang:13
Heft / Ausgabe:18
Aufsatznummer:3618
Originalveröffentlichung / Quelle:Remote Sensing (2021) 13:18, 3618. https://doi.org/10.3390/rs13183618
DOI:https://doi.org/10.3390/rs13183618
Allgemeine fachliche Zuordnung (DDC-Klassifikation):5 Naturwissenschaften und Mathematik / 52 Astronomie / 526 Mathematische Geografie
5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften
Freie Schlagwort(e):AVHRR; Earth Observation; Europe; TIMELINE; automatic processing; climate related trends; harmonization; time series analysis
Datum der Freischaltung:25.05.2023
Datum der Erstveröffentlichung:10.09.2021
Lizenz (Deutsch):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International