Orinoco: Retrieving a River Delta Network with the Fast Marching Method and Python
We present Orinoco, an open-source Python toolkit that applies the fast-marching method to derive a river delta channel network from a water mask and ocean delineation. We are able to estimate flow direction, along-channel distance, channel width, and network-related metrics for deltaic analyses inc...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/9/11/658 |
| _version_ | 1797549152889995264 |
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| author | Charlie Marshak Marc Simard Michael Denbina Johan Nilsson Tom Van der Stocken |
| author_facet | Charlie Marshak Marc Simard Michael Denbina Johan Nilsson Tom Van der Stocken |
| author_sort | Charlie Marshak |
| collection | DOAJ |
| description | We present Orinoco, an open-source Python toolkit that applies the fast-marching method to derive a river delta channel network from a water mask and ocean delineation. We are able to estimate flow direction, along-channel distance, channel width, and network-related metrics for deltaic analyses including the steady-state fluxes. To demonstrate the capabilities of the toolkit, we apply our software to the Wax Lake and Atchafalaya River Deltas using water masks derived from Open Street Map (OSM) and Google Maps. We validate our width estimates using the Global River Width from Landsat (GRWL) database over the Mackenzie Delta as well as in situ width measurements from the National Water Information System (NWIS) in the southeastern United States. We also compare the stream flow direction estimates using products from RivGraph, a related Python package with similar functionality. With the exciting opportunities afforded with forthcoming surface water and topography (SWOT) data, we envision Orinoco as a tool to support the characterization of the complex structure of river deltas worldwide and to make such analyses easily accessible within a Python remote sensing workflow. To support that end, all the data, analyses, and figures in this paper can be found within Jupyter notebooks at Orinoco’s GitHub repository. |
| first_indexed | 2024-03-10T15:10:38Z |
| format | Article |
| id | doaj.art-1108fa5a5cd8470ab2ef8d7af1cb5ad2 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T15:10:38Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-1108fa5a5cd8470ab2ef8d7af1cb5ad22023-11-20T19:21:48ZengMDPI AGISPRS International Journal of Geo-Information2220-99642020-10-0191165810.3390/ijgi9110658Orinoco: Retrieving a River Delta Network with the Fast Marching Method and PythonCharlie Marshak0Marc Simard1Michael Denbina2Johan Nilsson3Tom Van der Stocken4Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USAJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USAJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USAJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USAJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USAWe present Orinoco, an open-source Python toolkit that applies the fast-marching method to derive a river delta channel network from a water mask and ocean delineation. We are able to estimate flow direction, along-channel distance, channel width, and network-related metrics for deltaic analyses including the steady-state fluxes. To demonstrate the capabilities of the toolkit, we apply our software to the Wax Lake and Atchafalaya River Deltas using water masks derived from Open Street Map (OSM) and Google Maps. We validate our width estimates using the Global River Width from Landsat (GRWL) database over the Mackenzie Delta as well as in situ width measurements from the National Water Information System (NWIS) in the southeastern United States. We also compare the stream flow direction estimates using products from RivGraph, a related Python package with similar functionality. With the exciting opportunities afforded with forthcoming surface water and topography (SWOT) data, we envision Orinoco as a tool to support the characterization of the complex structure of river deltas worldwide and to make such analyses easily accessible within a Python remote sensing workflow. To support that end, all the data, analyses, and figures in this paper can be found within Jupyter notebooks at Orinoco’s GitHub repository.https://www.mdpi.com/2220-9964/9/11/658SWOTdeltasgeomorphologyPython |
| spellingShingle | Charlie Marshak Marc Simard Michael Denbina Johan Nilsson Tom Van der Stocken Orinoco: Retrieving a River Delta Network with the Fast Marching Method and Python ISPRS International Journal of Geo-Information SWOT deltas geomorphology Python |
| title | Orinoco: Retrieving a River Delta Network with the Fast Marching Method and Python |
| title_full | Orinoco: Retrieving a River Delta Network with the Fast Marching Method and Python |
| title_fullStr | Orinoco: Retrieving a River Delta Network with the Fast Marching Method and Python |
| title_full_unstemmed | Orinoco: Retrieving a River Delta Network with the Fast Marching Method and Python |
| title_short | Orinoco: Retrieving a River Delta Network with the Fast Marching Method and Python |
| title_sort | orinoco retrieving a river delta network with the fast marching method and python |
| topic | SWOT deltas geomorphology Python |
| url | https://www.mdpi.com/2220-9964/9/11/658 |
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