Statistical inference for trends in spatiotemporal data

Global change analyses are facilitated by the growing number of remote-sensing datasets that have both broad spatial extent and repeated observations over decades. These datasets provide unprecedented power to detect patterns of time trends involving information from all pixels on a map. However, ri...

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Bibliographic Details
Published in:	Remote sensing of environment Vol. 266; p. 112678
Main Authors:	Ives, Anthony R., Zhu, Likai, Wang, Fangfang, Zhu, Jun, Morrow, Clay J., Radeloff, Volker C.
Format:	Journal Article
Language:	English
Published:	New York Elsevier Inc 01.12.2021 Elsevier BV
Subjects:	Asia Autocorrelation Data analysis data collection Datasets environment Europe global change Global patterns in temporal trends Hypotheses Independent variables Land cover Large datasets Latitude Pixels Remote sensing Spatial autocorrelation Spatiotemporal analysis Spatiotemporal data Statistical analysis Statistical analysis of data Statistical hypothesis testing Statistical inference Statistical methods Statistics Temporal autocorrelation time series analysis Trends Asia Europe Temporal autocorrelation Statistical hypothesis testing Spatial autocorrelation Large datasets Global patterns in temporal trends Spatiotemporal analysis
ISSN:	0034-4257, 1879-0704
Online Access:	Get full text
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