https://doi.org/10.1109/jstars.2020.2973490

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Faculty Publications

Document Type

Article

Abstract

Light detection and ranging (Lidar) derived digital elevation models are widely used in modeling coastal marsh systems. However, the topographic error in these models can affect simulations of marsh coverage and characteristics. We investigated the relevance and impact of this error in micro- and mesotidal systems. Lidar-derived and RTK-adjusted topography were each used in a dynamic marsh model, and the resulting marsh coverages were examined. For two microtidal sites (Apalachicola, FL, USA, and Grand Bay, MS, USA) using solely lidar-derived topography, the model produced Cohen Kappa values of 0.1 for both estuaries when compared with National Wetland Inventory data indicating “very poor agreement.” Applying the RTK-adjusted topography improved the model marsh coverage results to “substantial agreement” with the values to 0.6 and 0.77, respectively. The mesotidal site in Plum Island, MA, USA, contained similar topographic errors, but the model produced a Cohen Kappa value of 0.73, which categorized it as “very good agreement” with no need for a further topographic adjustment given its present robust biomass productivity. The results demonstrate that marsh models are sensitive to topographic errors when the errors are comparable to the tidal range. The particular sensitivity of the modeling results to topographic error in microtidal systems highlights the need for close scrutiny of lidar-derived topography.

Digital Object Identifier (DOI)

https://doi.org/10.1109/jstars.2020.2973490

Rights

© Copyright 2022 IEEE - All rights reserved. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/

APA Citation

Alizad, K., Medeiros, S. C., Foster-Martinez, M. R., & Hagen, S. C. (2020). Model sensitivity to topographic uncertainty in meso- and microtidal marshes. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 807–814. https://doi.org/10.1109/jstars.2020.2973490

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