Stream network conflation with topographic DEMs
This paper presents DEM-Stream-Conflation (DSC) algorithm – a scale-independent robust technique of aligning vector streams with flowpaths dictated by raster DEMs. Designed as an alternative to both stream-burning and threshold-dependent stream segmentation techniques, DSC utilizes the existing vect...
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Veröffentlicht in: | Environmental modelling & software : with environment data news 2018-04, Vol.102, p.241-249 |
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creator | Yadav, Bidhyananda Hatfield, Kirk |
description | This paper presents DEM-Stream-Conflation (DSC) algorithm – a scale-independent robust technique of aligning vector streams with flowpaths dictated by raster DEMs. Designed as an alternative to both stream-burning and threshold-dependent stream segmentation techniques, DSC utilizes the existing vector flowlines to identify the channel heads and a sink filled hydrologically conditioned DEM to resolve the flowpaths. The algorithm conceptually initiates the movement of water on a DEM at the starting node of channel heads, from which it traces the path of water to its ultimate watershed outlet. Each trace represents a stream, which is in perfect alignment with the direction dictated by the raster DEM. The algorithm is tested with different DEMs, and its efficacy is demonstrated through the replication of the original vector drainage pattern, derivation of geomorphic attributes that are independent of tested DEM scale, and the visualization of monotonically decreasing longitudinal stream profiles.
•An algorithm for aligning vector stream network with topographic DEM is presented.•Avoids ambiguity of channel initiation threshold, and the pitfalls of stream-burning.•Does not require any user-defined parameters for stream segmentation.•Developed in Python using ArcPy and Numpy libraries.•Fits seamlessly into existing catchment modeling framework. |
doi_str_mv | 10.1016/j.envsoft.2018.01.009 |
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•An algorithm for aligning vector stream network with topographic DEM is presented.•Avoids ambiguity of channel initiation threshold, and the pitfalls of stream-burning.•Does not require any user-defined parameters for stream segmentation.•Developed in Python using ArcPy and Numpy libraries.•Fits seamlessly into existing catchment modeling framework.</description><identifier>ISSN: 1364-8152</identifier><identifier>EISSN: 1873-6726</identifier><identifier>DOI: 10.1016/j.envsoft.2018.01.009</identifier><language>eng</language><publisher>Oxford: Elsevier Ltd</publisher><subject>Algorithms ; Burning ; Conditioning ; Creeks & streams ; Geomorphology ; Hydrology ; Raster ; Segmentation ; Stream profiles ; Streams ; Watersheds</subject><ispartof>Environmental modelling & software : with environment data news, 2018-04, Vol.102, p.241-249</ispartof><rights>2018 Elsevier Ltd</rights><rights>Copyright Elsevier Science Ltd. Apr 2018</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c337t-e45d6d21960f0257bb251cb9139d3c6bac4b3fb054b8f9732f750857306c5e893</citedby><cites>FETCH-LOGICAL-c337t-e45d6d21960f0257bb251cb9139d3c6bac4b3fb054b8f9732f750857306c5e893</cites><orcidid>0000-0002-9481-4894</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.envsoft.2018.01.009$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Yadav, Bidhyananda</creatorcontrib><creatorcontrib>Hatfield, Kirk</creatorcontrib><title>Stream network conflation with topographic DEMs</title><title>Environmental modelling & software : with environment data news</title><description>This paper presents DEM-Stream-Conflation (DSC) algorithm – a scale-independent robust technique of aligning vector streams with flowpaths dictated by raster DEMs. Designed as an alternative to both stream-burning and threshold-dependent stream segmentation techniques, DSC utilizes the existing vector flowlines to identify the channel heads and a sink filled hydrologically conditioned DEM to resolve the flowpaths. The algorithm conceptually initiates the movement of water on a DEM at the starting node of channel heads, from which it traces the path of water to its ultimate watershed outlet. Each trace represents a stream, which is in perfect alignment with the direction dictated by the raster DEM. The algorithm is tested with different DEMs, and its efficacy is demonstrated through the replication of the original vector drainage pattern, derivation of geomorphic attributes that are independent of tested DEM scale, and the visualization of monotonically decreasing longitudinal stream profiles.
•An algorithm for aligning vector stream network with topographic DEM is presented.•Avoids ambiguity of channel initiation threshold, and the pitfalls of stream-burning.•Does not require any user-defined parameters for stream segmentation.•Developed in Python using ArcPy and Numpy libraries.•Fits seamlessly into existing catchment modeling framework.</description><subject>Algorithms</subject><subject>Burning</subject><subject>Conditioning</subject><subject>Creeks & streams</subject><subject>Geomorphology</subject><subject>Hydrology</subject><subject>Raster</subject><subject>Segmentation</subject><subject>Stream profiles</subject><subject>Streams</subject><subject>Watersheds</subject><issn>1364-8152</issn><issn>1873-6726</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNqFkLtOwzAUhi0EEqXwCEiRmJMe27FjTwiVq1TEAMxW4tjUoY2D7bbi7UnV7kznH_6LzofQNYYCA-azrjD9NnqbCgJYFIALAHmCJlhUNOcV4aejprzMBWbkHF3E2AHAqMsJmr2nYOp11pu08-E70763qzo532c7l5ZZ8oP_CvWwdDq7f3iNl-jM1qtoro53ij4fHz7mz_ni7ellfrfINaVVyk3JWt4SLDlYIKxqGsKwbiSmsqWaN7UuG2obYGUjrKwosRUDwSoKXDMjJJ2im0PvEPzPxsSkOr8J_TipCHDJgAkhRhc7uHTwMQZj1RDcug6_CoPas1GdOrJRezYKsBrZjLnbQ86ML2ydCSpqZ3ptWheMTqr17p-GPx_Xbok</recordid><startdate>201804</startdate><enddate>201804</enddate><creator>Yadav, Bidhyananda</creator><creator>Hatfield, Kirk</creator><general>Elsevier Ltd</general><general>Elsevier Science Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7SC</scope><scope>7ST</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0002-9481-4894</orcidid></search><sort><creationdate>201804</creationdate><title>Stream network conflation with topographic DEMs</title><author>Yadav, Bidhyananda ; Hatfield, Kirk</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c337t-e45d6d21960f0257bb251cb9139d3c6bac4b3fb054b8f9732f750857306c5e893</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Algorithms</topic><topic>Burning</topic><topic>Conditioning</topic><topic>Creeks & streams</topic><topic>Geomorphology</topic><topic>Hydrology</topic><topic>Raster</topic><topic>Segmentation</topic><topic>Stream profiles</topic><topic>Streams</topic><topic>Watersheds</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yadav, Bidhyananda</creatorcontrib><creatorcontrib>Hatfield, Kirk</creatorcontrib><collection>CrossRef</collection><collection>Aqualine</collection><collection>Computer and Information Systems Abstracts</collection><collection>Environment Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Environment Abstracts</collection><jtitle>Environmental modelling & software : with environment data news</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yadav, Bidhyananda</au><au>Hatfield, Kirk</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Stream network conflation with topographic DEMs</atitle><jtitle>Environmental modelling & software : with environment data news</jtitle><date>2018-04</date><risdate>2018</risdate><volume>102</volume><spage>241</spage><epage>249</epage><pages>241-249</pages><issn>1364-8152</issn><eissn>1873-6726</eissn><abstract>This paper presents DEM-Stream-Conflation (DSC) algorithm – a scale-independent robust technique of aligning vector streams with flowpaths dictated by raster DEMs. Designed as an alternative to both stream-burning and threshold-dependent stream segmentation techniques, DSC utilizes the existing vector flowlines to identify the channel heads and a sink filled hydrologically conditioned DEM to resolve the flowpaths. The algorithm conceptually initiates the movement of water on a DEM at the starting node of channel heads, from which it traces the path of water to its ultimate watershed outlet. Each trace represents a stream, which is in perfect alignment with the direction dictated by the raster DEM. The algorithm is tested with different DEMs, and its efficacy is demonstrated through the replication of the original vector drainage pattern, derivation of geomorphic attributes that are independent of tested DEM scale, and the visualization of monotonically decreasing longitudinal stream profiles.
•An algorithm for aligning vector stream network with topographic DEM is presented.•Avoids ambiguity of channel initiation threshold, and the pitfalls of stream-burning.•Does not require any user-defined parameters for stream segmentation.•Developed in Python using ArcPy and Numpy libraries.•Fits seamlessly into existing catchment modeling framework.</abstract><cop>Oxford</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.envsoft.2018.01.009</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-9481-4894</orcidid></addata></record> |
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subjects | Algorithms Burning Conditioning Creeks & streams Geomorphology Hydrology Raster Segmentation Stream profiles Streams Watersheds |
title | Stream network conflation with topographic DEMs |
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