A LiDAR–optical data fusion approach for identifying and measuring small stream impoundments and dams
Abstract
This article outlines a semi-autonomous approach for using a fusion of light detection and ranging (LiDAR) and optical remote sensing data to identify and measure small impoundments (SIs) and their dams. Quantifying such water bodies as hydrologic network features is critical for ecosystem and species conservation, emergency management, and water resource planning; however, such features are incompletely mapped at national and state levels. By merging an airborne LiDAR-derived point cloud with a normalized water index using airborne optical imagery we demonstrate an improvement upon single-source methods for identifying these water bodies; classification accuracies increased over 10% by using this multi-source fusion method. Furthermore, the method presented here illustrates a cost-effective pathway to improve the National Inventory of Dams (NID) and includes a framework for estimating dam heights, with results showing strong correlations between derived dam heights and those recorded in the NID (r=.78). With the steady increase in available LiDAR coverage, the 87,000+ dams in the NID could be updated using this technique, a method which could also be expanded for global inventories of SIs and dams.
- Authors:
-
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Univ. of Alabama, Huntsville, AL (United States)
- Publication Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1607262
- Grant/Contract Number:
- AC05-00OR22725
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Transactions in GIS
- Additional Journal Information:
- Journal Volume: 24; Journal Issue: 1; Journal ID: ISSN 1361-1682
- Publisher:
- Wiley
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 47 OTHER INSTRUMENTATION
Citation Formats
Swan, Benjamin, and Griffin, Robert. A LiDAR–optical data fusion approach for identifying and measuring small stream impoundments and dams. United States: N. p., 2019.
Web. doi:10.1111/tgis.12595.
Swan, Benjamin, & Griffin, Robert. A LiDAR–optical data fusion approach for identifying and measuring small stream impoundments and dams. United States. https://doi.org/10.1111/tgis.12595
Swan, Benjamin, and Griffin, Robert. Wed .
"A LiDAR–optical data fusion approach for identifying and measuring small stream impoundments and dams". United States. https://doi.org/10.1111/tgis.12595. https://www.osti.gov/servlets/purl/1607262.
@article{osti_1607262,
title = {A LiDAR–optical data fusion approach for identifying and measuring small stream impoundments and dams},
author = {Swan, Benjamin and Griffin, Robert},
abstractNote = {This article outlines a semi-autonomous approach for using a fusion of light detection and ranging (LiDAR) and optical remote sensing data to identify and measure small impoundments (SIs) and their dams. Quantifying such water bodies as hydrologic network features is critical for ecosystem and species conservation, emergency management, and water resource planning; however, such features are incompletely mapped at national and state levels. By merging an airborne LiDAR-derived point cloud with a normalized water index using airborne optical imagery we demonstrate an improvement upon single-source methods for identifying these water bodies; classification accuracies increased over 10% by using this multi-source fusion method. Furthermore, the method presented here illustrates a cost-effective pathway to improve the National Inventory of Dams (NID) and includes a framework for estimating dam heights, with results showing strong correlations between derived dam heights and those recorded in the NID (r=.78). With the steady increase in available LiDAR coverage, the 87,000+ dams in the NID could be updated using this technique, a method which could also be expanded for global inventories of SIs and dams.},
doi = {10.1111/tgis.12595},
journal = {Transactions in GIS},
number = 1,
volume = 24,
place = {United States},
year = {2019},
month = {11}
}
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