High spatiotemporal resolution of river planform dynamics from landsat: The rivMAP toolbox and results from the Ucayali river

Jon Schwenk, Ankush Khandelwal, Mulu Fratkin, Vipin Kumar, Efi Foufoula-Georgiou

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

Quantifying planform changes of large and actively migrating rivers such as those in the tropical Amazon at multidecadal time scales, over large spatial domains, and with high spatiotemporal frequency is essential for advancing river morphodynamic theory, identifying controls on migration, and understanding the roles of climate and human influences on planform adjustments. This paper addresses the challenges of quantifying river planform changes from annual channel masks derived from Landsat imagery and introduces a set of efficient methods to map and measure changes in channel widths, the locations and rates of migration, accretion and erosion, and the space-time characteristics of cutoff dynamics. The techniques are assembled in a comprehensive MATLAB toolbox called RivMAP (River Morphodynamics from Analysis of Planforms), which is applied to over 1500 km of the actively migrating and predominately meandering Ucayali River in Peru from 1985 to 2015. We find multiscale spatial and temporal variability around multidecadal trends in migration rates, erosion and accretion, and channel widths revealing a river dynamically adjusting to sediment and water fluxes. Confounding factors controlling planform morphodynamics including local inputs of sediment, cutoffs, and climate are parsed through the high temporal analysis.

Original languageEnglish (US)
Pages (from-to)46-75
Number of pages30
JournalEarth and Space Science
Volume4
Issue number2
DOIs
StatePublished - 2017

Bibliographical note

Funding Information:
This research benefitted from colla borations made possible through the NSF grant EAR-1242458 under Science Across Virtual Institutes (SAVI): LIFE (Linked Institutions for Future Earth). Funding was also provided by NSF grant EAR-1209402 under the Water Sustainability and Climate Program (WSC): REACH (REsilience under Accelerated CHange). J.S. acknowledges support provided by an NSF Graduate Research Fellowship. Eric McCaleb, Mace Blank, and Anuj Karpatne of the University of Minnesota’s Computer Science Department provided assis tance obtaining and classifying Landsat imagery of the Ucayali River. We also appreciate thoughtful reviews from Burch Fischer, Tamlin Pavelsky, and Joel Rowland. We thank Christian Abizaid at the University of Toronto, St. George, for providing stage data at Pucallpa, Peru. Landsat imagery was acquired from the Google Earth Engine API. RivMAP files, including a demo, walkthrough, and sample data, are available at the Mathworks File Exchange: https://www. mathworks.com/matlabcentral/fileex-change/58264-rivmap-river-morphody-namics-from-analysis-of-planforms.

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