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The Development of a GIS Methodology to Identify Oxbows and Former Stream Meanders from LiDAR-Derived Digital Elevation Models

1
Department of Natural Resource Ecology and Management, Iowa State University, Ames, IA 50011, USA
2
Iowa Flood Center, The University of Iowa, Iowa City, IA 52242, USA
3
U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University, Ames, IA 50011, USA
4
Department of Ecology, Evolution, and Organismal Biology (EEOB), Iowa State University, Ames, IA 50011, USA
5
Iowa Geological Survey, Iowa City, IA 52242, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(1), 12; https://doi.org/10.3390/rs11010012
Received: 10 November 2018 / Revised: 8 December 2018 / Accepted: 18 December 2018 / Published: 21 December 2018
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Abstract

Anthropogenic development of floodplains and alteration to natural hydrological regimes have resulted in extensive loss of off-channel habitat. Interest has grown in restoring these habitats as an effective conservation strategy for numerous aquatic species. This study developed a process to reproducibly identify areas of former stream meanders to assist future off-channel restoration site selections. Three watersheds in Iowa and Minnesota where off-channel restorations are currently being conducted to aid the conservation of the Topeka Shiner (Notropis topeka) were selected as the study area. Floodplain depressions were identified with LiDAR-derived digital elevation models, and their morphologic and topographic characteristics were described. Classification tree models were developed to distinguish relic streams and oxbows from other landscape features. All models demonstrated a strong ability to distinguish between target and non-target features with area under the receiver operator curve (AUC) values ≥ 0.82 and correct classification rates ≥ 0.88. Solidity, concavity, and mean height above channel metrics were among the first splits in all trees. To compensate for the noise associated with the final model designation, features were ranked by their conditional probability. The results of this study will provide conservation managers with an improved process to identify candidate restoration sites. View Full-Text
Keywords: off-channel habitats; endangered species; conservation; depression identification; LiDAR off-channel habitats; endangered species; conservation; depression identification; LiDAR
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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L. Zambory, C.; Ellis, H.; L. Pierce, C.; J. Roe, K.; J. Weber, M.; E. Schilling, K.; C. Young, N. The Development of a GIS Methodology to Identify Oxbows and Former Stream Meanders from LiDAR-Derived Digital Elevation Models. Remote Sens. 2019, 11, 12.

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