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A Framework for Multivariate Analysis of Land Surface Dynamics and Driving Variables-A Case Study for Indo-Gangetic River Basins

Uereyen, Soner and Bachofer, Felix and Kuenzer, Claudia (2022) A Framework for Multivariate Analysis of Land Surface Dynamics and Driving Variables-A Case Study for Indo-Gangetic River Basins. Remote Sensing, 14 (1), pp. 1-24. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/rs14010197. ISSN 2072-4292.

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Official URL: https://www.mdpi.com/2072-4292/14/1/197

Abstract

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.

Item URL in elib:https://elib.dlr.de/147966/
Document Type:Article
Title:A Framework for Multivariate Analysis of Land Surface Dynamics and Driving Variables-A Case Study for Indo-Gangetic River Basins
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Uereyen, Sonersoner.uereyen (at) dlr.dehttps://orcid.org/0000-0003-3733-0049
Bachofer, FelixFelix.Bachofer (at) dlr.dehttps://orcid.org/0000-0001-6181-0187
Kuenzer, Claudiaclaudia.kuenzer (at) dlr.deUNSPECIFIED
Date:2 January 2022
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:14
DOI :10.3390/rs14010197
Page Range:pp. 1-24
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2072-4292
Status:Published
Keywords:time series analysis; trends; seasonality; partial correlation; causal networks; NDVI; snow cover area; surface water area; Indus-Ganges-Brahmaputra-Meghna; Himalaya Karakoram
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Remote Sensing and Geo Research
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center
Deposited By: Üreyen, Soner
Deposited On:10 Jan 2022 10:43
Last Modified:10 Jan 2022 10:43

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