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Corrected digital elevation model in coastal wetlands in Nassau and Duval Counties, Florida, 2018

Metadata Updated: July 6, 2024

High-resolution elevation data provide a foundational layer needed to understand regional hydrology and ecology under contemporary and future-predicted conditions with accelerated sea-level rise. While the development of digital elevation models (DEMs) from light detection and ranging data has enhanced the ability to observe elevation in coastal zones, the elevation error can be substantial in densely vegetated coastal wetlands. In response, we developed a machine learning model to reduce vertical error in coastal wetlands for a 1-m DEM from 2018 that covered Nassau and Duval Counties, Florida. Error was reduced by using a random forest regression model within situ observations and predictor variables from optical and radar-based satellite data and elevation derivatives. Vegetation and elevation data were collected using a real-time kinematic global positioning system (RTK GPS) in coastal wetlands at the National Park Service’s Timucuan Ecological and Historic Preserve in summer 2021 and winter 2022 (n = 344). Predictor variables included information on vegetation greenness, wetness, elevation, and vegetation structure. In the extent of coastal wetlands in Nassau and Duval Counties, the original DEM had a mean absolute error of 0.17-m and a 95th percentile error of 0.48 m. Leave-one-out cross-validation was used to assess the accuracy of the corrected DEM. In coastal wetlands, the corrected DEM had a mean absolute error of 0.08 cm and a 95th percentile error of 0.25 m. The random forest model led to a decrease in the mean absolute error by about 50% and a decrease in 95th percentile by 49%.

Access & Use Information

Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Dates

Metadata Created Date October 18, 2023
Metadata Updated Date July 6, 2024

Metadata Source

Harvested from DOI EDI

Additional Metadata

Resource Type Dataset
Metadata Created Date October 18, 2023
Metadata Updated Date July 6, 2024
Publisher U.S. Geological Survey
Maintainer
@Id http://datainventory.doi.gov/id/dataset/b7ecffcc45054d74c61eaa67e0aa8cba
Identifier USGS:64dd3443d34e5f6cd5529321
Data Last Modified 20231002
Category geospatial
Public Access Level public
Bureau Code 010:12
Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
Metadata Catalog ID https://datainventory.doi.gov/data.json
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Harvest Object Id 21fa78c0-6bbd-4af3-8fae-290fb8270d7b
Harvest Source Id 52bfcc16-6e15-478f-809a-b1bc76f1aeda
Harvest Source Title DOI EDI
Metadata Type geospatial
Old Spatial -82.0631,30.1059,-81.3701,30.8383
Publisher Hierarchy White House > U.S. Department of the Interior > U.S. Geological Survey
Source Datajson Identifier True
Source Hash 3b5e8719963f176f6a92d42659339b6608a2840cdbd8406ffe755ea708cb6f26
Source Schema Version 1.1
Spatial {"type": "Polygon", "coordinates": -82.0631, 30.1059, -82.0631, 30.8383, -81.3701, 30.8383, -81.3701, 30.1059, -82.0631, 30.1059}

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