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    Rights statement: This is the author’s version of a work that was accepted for publication in International Journal of Applied Earth Observation and Geoinformation. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Applied Earth Observation and Geoinformation, 83, 2020 DOI: 10.1016/j.jag.2019.101910

    Accepted author manuscript, 2.12 MB, PDF document

    Embargo ends: 19/07/20

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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River network delineation from Sentinel 1 SAR data

Research output: Contribution to journalJournal article

Published
Article number101910
<mark>Journal publication date</mark>1/11/2019
<mark>Journal</mark>International Journal of Applied Earth Observation and Geoinformation
Volume83
Number of pages12
Publication statusPublished
Original languageEnglish

Abstract

In many regions of the world, especially in developing countries, river network data are outdated or completely absent, yet such information is critical for supporting important functions such as flood mitigation efforts, land use and transportation planning, and the management of water resources. In this study a new method was developed for delineating river networks using Sentinel-1 imagery. Unsupervised classification was applied to multi-temporal Sentinel-1 data to discriminate water bodies from other land cover types then the outputs were combined to generate a single persistent water bodies product. A thinning algorithm was then used to delineate river centre lines which were converted into vector features and built into a topologically structured geometric network. The complex river system of the Niger Delta was used to compare the performance of the Sentinel-based method against alternative freely available waterbody products from USGS, ESA and OpenStreetMap and a river network derived from a SRTM DEM. From both raster-based and vector-based accuracy assessments it was found that the Sentinel-based river network products were superior to the comparator data sets by a substantial margin. The resulting geometric river network was used to perform flow routing analysis which is important for a variety of environmental management and planning applications. The approach developed in this study holds considerable potential for generating up to date, detailed river network data for the many countries globally where such data are deficient.