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Extraction of connected river networks from multi-temporal remote sensing imagery using a path tracking technique

journal contribution
posted on 10.07.2020 by Huili Chen, Qiuhua Liang, Zhongyao Liang, Yong Liu, Tingyu Ren
Precise delineation of river networks is important for accurate hydrological and flood modelling. Whilst remote sensing (RS) has showed great potential in monitoring hydrological changes over space and time, the existing RS-based methods extract river networks based on local morphologies and seldom take into account the overall hydrological connectivity of the rivers. The existing methods also commonly neglect the effect of seasonal variation of water surfaces and the existence of temporary water bodies, which deteriorate the precision of positioning river networks. To address these challenges, a new two-stage method is developed to Extract spatiotemporal variation of water surfaces based on Multi-temporal remote sensing Imagery and Delineate connected river networks with improved accuracy (EMID method for short) using a path tracking technique. The EMID method delineates connected river networks using (a) multi-temporal imagery and a Random Forest model to synoptically map the location and extent of water surfaces under different hydrological conditions, and (b) an optimization algorithm to find the best river paths based on water-occurrence frequency. Four drainage basins with various river morphologies are considered to validate EMID. Comparing with alternative methods, the EMID method consistently produces river network results with improved accuracy in terms of stream location, river coverage and network connectivity.

Funding

WeACT project (NE/S005919/1) funded by the UK Natural Environment Research Council (NERC)

National Science Foundation of China (51721006)

River Basins as ‘Living Laboratories’ project (NE/S012427/1) funded by the UK Natural Environment Research Council (NERC) and High-performance Computing Platform of Peking University

History

School

  • Architecture, Building and Civil Engineering

Published in

Remote Sensing of Environment

Volume

246

Publisher

Elsevier BV

Version

AM (Accepted Manuscript)

Rights holder

© Elsevier Inc.

Publisher statement

This paper was accepted for publication in the journal Remote Sensing of Environment and the definitive published version is available at https://doi.org/10.1016/j.rse.2020.111868.

Publication date

2020-05-29

Copyright date

2020

ISSN

0034-4257

eISSN

1879-0704

Language

en

Depositor

Prof Qiuhua Liang. Deposit date: 8 July 2020

Article number

111868

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