TY  - JOUR
A1  - Dech, Stefan
A1  - Holzwarth, Stefanie
A1  - Asam, Sarah
A1  - Andresen, Thorsten
A1  - Bachmann, Martin
A1  - Boettcher, Martin
A1  - Dietz, Andreas
A1  - Eisfelder, Christina
A1  - Frey, Corinne
A1  - Gesell, Gerhard
A1  - Gessner, Ursula
A1  - Hirner, Andreas
A1  - Hofmann, Matthias
A1  - Kirches, Grit
A1  - Klein, Doris
A1  - Klein, Igor
A1  - Kraus, Tanja
A1  - Krause, Detmar
A1  - Plank, Simon
A1  - Popp, Thomas
A1  - Reinermann, Sophie
A1  - Reiners, Philipp
A1  - Roessler, Sebastian
A1  - Ruppert, Thomas
A1  - Scherbachenko, Alexander
A1  - Vignesh, Ranjitha
A1  - Wolfmueller, Meinhard
A1  - Zwenzner, Hendrik
A1  - Kuenzer, Claudia
T1  - Potential and challenges of harmonizing 40 years of AVHRR data: the TIMELINE experience
JF  - Remote Sensing
N2  - 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 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.
KW  - AVHRR
KW  - Earth Observation
KW  - harmonization
KW  - time series analysis
KW  - climate related trends
KW  - automatic processing
KW  - Europe
KW  - TIMELINE
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-246134
SN  - 2072-4292
VL  - 13
IS  - 18
ER  - 
TY  - JOUR
A1  - Uereyen, Soner
A1  - Bachofer, Felix
A1  - Kuenzer, Claudia
T1  - A framework for multivariate analysis of land surface dynamics and driving variables — a case study for Indo-Gangetic river basins
JF  - Remote Sensing
N2  - The analysis of the Earth system and interactions among its spheres is increasingly important to improve the understanding of global environmental change. In this regard, Earth observation (EO) is a valuable tool for monitoring of long term changes over the land surface and its features. Although investigations commonly study environmental change by means of a single EO-based land surface variable, a joint exploitation of multivariate land surface variables covering several spheres is still rarely performed. In this regard, we present a novel methodological framework for both, the automated processing of multisource time series to generate a unified multivariate feature space, as well as the application of statistical time series analysis techniques to quantify land surface change and driving variables. In particular, we unify multivariate time series over the last two decades including vegetation greenness, surface water area, snow cover area, and climatic, as well as hydrological variables. Furthermore, the statistical time series analyses include quantification of trends, changes in seasonality, and evaluation of drivers using the recently proposed causal discovery algorithm Peter and Clark Momentary Conditional Independence (PCMCI). We demonstrate the functionality of our methodological framework using Indo-Gangetic river basins in South Asia as a case study. The time series analyses reveal increasing trends in vegetation greenness being largely dependent on water availability, decreasing trends in snow cover area being mostly negatively coupled to temperature, and trends of surface water area to be spatially heterogeneous and linked to various driving variables. Overall, the obtained results highlight the value and suitability of this methodological framework with respect to global climate change research, enabling multivariate time series preparation, derivation of detailed information on significant trends and seasonality, as well as detection of causal links with minimal user intervention. This study is the first to use multivariate time series including several EO-based variables to analyze land surface dynamics over the last two decades using the causal discovery algorithm PCMCI.
KW  - time series analysis
KW  - trends
KW  - seasonality
KW  - partial correlation
KW  - causal networks
KW  - NDVI
KW  - snow cover area
KW  - surface water area
KW  - Indus-Ganges-Brahmaputra-Meghna
KW  - Himalaya Karakoram
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-255295
SN  - 2072-4292
VL  - 14
IS  - 1
ER  -