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Open AccessArticle

Modeling Barrier Island Habitats Using Landscape Position Information

1
U.S. Geological Survey, Wetland and Aquatic Research Center, Lafayette, LA 70506, USA
2
Louisiana State University, Department of Geography and Anthropology, Baton Rouge, LA 70803, USA
3
U.S. Geological Survey, Wetland and Aquatic Research Center, Baton Rouge, LA 70803, USA
4
Borchert Consulting at the U.S. Geological Survey, Wetland and Aquatic Research Center, Lafayette, LA 70506, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(8), 976; https://doi.org/10.3390/rs11080976
Received: 25 March 2019 / Revised: 19 April 2019 / Accepted: 22 April 2019 / Published: 24 April 2019
Barrier islands are dynamic environments because of their position along the marine–estuarine interface. Geomorphology influences habitat distribution on barrier islands by regulating exposure to harsh abiotic conditions. Researchers have identified linkages between habitat and landscape position, such as elevation and distance from shore, yet these linkages have not been fully leveraged to develop predictive models. Our aim was to evaluate the performance of commonly used machine learning algorithms, including K-nearest neighbor, support vector machine, and random forest, for predicting barrier island habitats using landscape position for Dauphin Island, Alabama, USA. Landscape position predictors were extracted from topobathymetric data. Models were developed for three tidal zones: subtidal, intertidal, and supratidal/upland. We used a contemporary habitat map to identify landscape position linkages for habitats, such as beach, dune, woody vegetation, and marsh. Deterministic accuracy, fuzzy accuracy, and hindcasting were used for validation. The random forest algorithm performed best for intertidal and supratidal/upland habitats, while the K-nearest neighbor algorithm performed best for subtidal habitats. A posteriori application of expert rules based on theoretical understanding of barrier island habitats enhanced model results. For the contemporary model, deterministic overall accuracy was nearly 70%, and fuzzy overall accuracy was over 80%. For the hindcast model, deterministic overall accuracy was nearly 80%, and fuzzy overall accuracy was over 90%. We found machine learning algorithms were well-suited for predicting barrier island habitats using landscape position. Our model framework could be coupled with hydrodynamic geomorphologic models for forecasting habitats with accelerated sea-level rise, simulated storms, and restoration actions. View Full-Text
Keywords: habitat modeling; machine learning; geocomputation; dune; wetlands; marsh; lidar; uncertainty; restoration; monitoring habitat modeling; machine learning; geocomputation; dune; wetlands; marsh; lidar; uncertainty; restoration; monitoring
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  • Externally hosted supplementary file 1
    Doi: 10.5066/P90MACYS
    Link: https://doi.org/10.5066/P90MACYS
    Description: Data developed for this study are publicly archived at https://doi.org/10.5066/P90MACYS.
MDPI and ACS Style

Enwright, N.M.; Wang, L.; Wang, H.; Osland, M.J.; Feher, L.C.; Borchert, S.M.; Day, R.H. Modeling Barrier Island Habitats Using Landscape Position Information. Remote Sens. 2019, 11, 976. https://doi.org/10.3390/rs11080976

AMA Style

Enwright NM, Wang L, Wang H, Osland MJ, Feher LC, Borchert SM, Day RH. Modeling Barrier Island Habitats Using Landscape Position Information. Remote Sensing. 2019; 11(8):976. https://doi.org/10.3390/rs11080976

Chicago/Turabian Style

Enwright, Nicholas M.; Wang, Lei; Wang, Hongqing; Osland, Michael J.; Feher, Laura C.; Borchert, Sinéad M.; Day, Richard H. 2019. "Modeling Barrier Island Habitats Using Landscape Position Information" Remote Sens. 11, no. 8: 976. https://doi.org/10.3390/rs11080976

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