TY - JOUR
T1 - Elevation-based probabilistic mapping of irregularly flooded wetlands along the northern Gulf of Mexico coast
AU - Enwright, Nicholas M.
AU - Cheney, Wyatt C.
AU - Evans, Kristine O.
AU - Thurman, Hana R.
AU - Woodrey, Mark S.
AU - Fournier, Auriel M.V.
AU - Gesch, Dean B.
AU - Pitchford, Jonathan L.
AU - Stoker, Jason M.
AU - Medeiros, Stephen C.
N1 - Funding Information:
This paper is a result of research funded by the National Oceanic and Atmospheric Administration's RESTORE Science Program under award NA19NOS4510195 to Mississippi State University and the USGS . We thank many individuals for providing feedback on draft products or assisting with in situ elevation data product availability, including Karrie Arnold, Eric Brunden, Kevin Buffington, Chris Butler, Jeremy Conrad, Jim Cox, Warren Conway, Mark Danaher, Christopher Gabler, Rebecca Howard, Brita Jessen, Erik Johnson, Kevin Kalasz, Peter Kappes, Joseph Lancaster, Heather Levy, Jonathon Lueck, Jonathan Moczygemba, Jena Moon, Michael Osland, Maulik Patel, Colt Sanspree, Amy Schwarzer, Fred Sklar, Eric Soehren, Camille Stagg, Karen Thorne, Will Underwood, William Vermillion, Jenneke Visser, Barry Wilson, Jennifer Wilson, Bernard Wood, and Woody Woodrow. We thank Neil Ganju from the USGS Woods Hole Coastal and Marine Science Center and three anonymous peer reviewers for their feedback on this work. MSW’s participation and contribution is, in part, supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, Hatch project under accession number 7002261. As such, this publication is considered a contribution of the Mississippi Agricultural and Forestry Experiment Station. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the U.S. Department of Agriculture. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U. S. Government.
Funding Information:
This paper is a result of research funded by the National Oceanic and Atmospheric Administration's RESTORE Science Program under award NA19NOS4510195 to Mississippi State University and the USGS. We thank many individuals for providing feedback on draft products or assisting with in situ elevation data product availability, including Karrie Arnold, Eric Brunden, Kevin Buffington, Chris Butler, Jeremy Conrad, Jim Cox, Warren Conway, Mark Danaher, Christopher Gabler, Rebecca Howard, Brita Jessen, Erik Johnson, Kevin Kalasz, Peter Kappes, Joseph Lancaster, Heather Levy, Jonathon Lueck, Jonathan Moczygemba, Jena Moon, Michael Osland, Maulik Patel, Colt Sanspree, Amy Schwarzer, Fred Sklar, Eric Soehren, Camille Stagg, Karen Thorne, Will Underwood, William Vermillion, Jenneke Visser, Barry Wilson, Jennifer Wilson, Bernard Wood, and Woody Woodrow. We thank Neil Ganju from the USGS Woods Hole Coastal and Marine Science Center and three anonymous peer reviewers for their feedback on this work. MSW's participation and contribution is, in part, supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, Hatch project under accession number 7002261. As such, this publication is considered a contribution of the Mississippi Agricultural and Forestry Experiment Station. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the U.S. Department of Agriculture. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U. S. Government.
Publisher Copyright:
© 2023
PY - 2023/3/15
Y1 - 2023/3/15
N2 - Irregularly flooded wetlands are found above the mean high water tidal datum and are exposed to tides and saltwater less frequently than daily. These wetlands provide important ecosystem services, such as providing habitat for fish and wildlife, enhancing water quality, ameliorating flooding impacts, supporting coastal food webs, and protecting upslope areas from erosion. Mapping irregularly flooded wetlands is challenging given their expansive coverage and dynamic nature. Furthermore, coastal wetlands are expected to change over the coming century due to sea-level rise and changes in the frequency and intensity of extreme storms. Consequently, coastal managers need baseline information on the spatial distribution of wetlands along with efficient and repeatable methods for observing changes. In this study, we used coastal wetlands from existing land use land cover data, best available lidar-derived digital elevation models, and Monte Carlo simulations to incorporate elevation uncertainty to create a probabilistic map of irregularly flooded wetlands along the northern Gulf of Mexico coast (USA). Our approach integrated findings from a review of coastal wetland elevation error in lidar datasets and an analysis of spatial autocorrelations of wetland elevation. We found a positive correlation (r = 0.563, p < 0.0001) when comparing the probability estimated from a digital elevation model and in situ elevation observations. The differences in probability had a mean bias error of −0.04 (i.e., digital elevation model-based probability tends to be slightly lower), a mean absolute error of 0.20, and a root mean square error of 0.26. Beyond this overall validation, we explored error metrics for land cover classes and lidar collection details. To quantify areal coverage of the probabilistic output, we classified the probability values into equal bins using an interval of 0.33. The areal coverage of the lowest probability bin (“unlikely”; probability ≤0.33) was separated into the upper and lower portions of the irregularly flooded wetland zone. Of the coastal wetlands along the northern Gulf of Mexico coast about 38% were classified as unlikely and low with the greatest coverage in south Louisiana and the Everglades and around 33% were classified as unlikely and high with the greatest coverage in the Everglades and Texas. The relative coverage within the highest probability bin (“likely”; probability >0.66) covered around 13%, with the greatest coverage in south Florida, south Louisiana, and Texas. The framework developed in this study can be transferred to other coastal wetland areas and updated to observe changes with sea-level rise.
AB - Irregularly flooded wetlands are found above the mean high water tidal datum and are exposed to tides and saltwater less frequently than daily. These wetlands provide important ecosystem services, such as providing habitat for fish and wildlife, enhancing water quality, ameliorating flooding impacts, supporting coastal food webs, and protecting upslope areas from erosion. Mapping irregularly flooded wetlands is challenging given their expansive coverage and dynamic nature. Furthermore, coastal wetlands are expected to change over the coming century due to sea-level rise and changes in the frequency and intensity of extreme storms. Consequently, coastal managers need baseline information on the spatial distribution of wetlands along with efficient and repeatable methods for observing changes. In this study, we used coastal wetlands from existing land use land cover data, best available lidar-derived digital elevation models, and Monte Carlo simulations to incorporate elevation uncertainty to create a probabilistic map of irregularly flooded wetlands along the northern Gulf of Mexico coast (USA). Our approach integrated findings from a review of coastal wetland elevation error in lidar datasets and an analysis of spatial autocorrelations of wetland elevation. We found a positive correlation (r = 0.563, p < 0.0001) when comparing the probability estimated from a digital elevation model and in situ elevation observations. The differences in probability had a mean bias error of −0.04 (i.e., digital elevation model-based probability tends to be slightly lower), a mean absolute error of 0.20, and a root mean square error of 0.26. Beyond this overall validation, we explored error metrics for land cover classes and lidar collection details. To quantify areal coverage of the probabilistic output, we classified the probability values into equal bins using an interval of 0.33. The areal coverage of the lowest probability bin (“unlikely”; probability ≤0.33) was separated into the upper and lower portions of the irregularly flooded wetland zone. Of the coastal wetlands along the northern Gulf of Mexico coast about 38% were classified as unlikely and low with the greatest coverage in south Louisiana and the Everglades and around 33% were classified as unlikely and high with the greatest coverage in the Everglades and Texas. The relative coverage within the highest probability bin (“likely”; probability >0.66) covered around 13%, with the greatest coverage in south Florida, south Louisiana, and Texas. The framework developed in this study can be transferred to other coastal wetland areas and updated to observe changes with sea-level rise.
KW - Coastal wetlands
KW - Elevation uncertainty
KW - Lidar
KW - Monte Carlo simulations
KW - Spatial autocorrelation
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U2 - 10.1016/j.rse.2023.113451
DO - 10.1016/j.rse.2023.113451
M3 - Article
AN - SCOPUS:85146273381
SN - 0034-4257
VL - 287
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
M1 - 113451
ER -