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LEAN-Corrected Collier County DEM for wetlands

Metadata Updated: October 28, 2023

Lidar-derived digital elevation models often contain a vertical bias due to vegetation. In areas with tidal influence the amount of bias can be ecologically significant, for example, by decreasing the expected inundation frequency. We generated a corrected digital elevation model (DEM) for wetlands throughout Collier county using a modification of the Lidar Elevation Adjustment with NDVI (LEAN) technique (Buffington et al. 2016). GPS survey data (15,223 points), NAIP-derived Normalized Difference Vegetation Index (2010), a 10 m lidar DEM from 2007, and a 10 m canopy surface model were used to generate a model of predicted bias across marsh, mangrove, and cypress habitats. The predicted bias was then subtracted from the original lidar DEM, masked by wetlands areas using polygons from the National Wetland Inventory dataset, and merged with the original lidar DEM. Only the area covered by the 2007 lidar was corrected; a piece of the inland area not covered by lidar was interpolated with the GPS survey data and merged with the corrected DEM. Lidar from 2017, which covers a narrow coastal strip, was also incorporated by first masking out the wetland areas and then mosaiking with the final DEM. Across all GPS points, mean initial lidar error was 34.3 cm (SD=28.2) and root-mean squared error (RMSE) was 44.5 cm. After correction with LEAN, mean error was 0 (SD=18.9) and RMSE was 18.9 cm, a 57.3 percent improvement in accuracy. References: Buffington, K.J., Dugger, B.D., Thorne, K.M. and Takekawa, J.Y., 2016. Statistical correction of lidar-derived digital elevation models with multispectral airborne imagery in tidal marshes. Remote Sensing of Environment, 186, pp.616-625.

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 June 1, 2023
Metadata Updated Date October 28, 2023

Metadata Source

Harvested from DOI EDI

Additional Metadata

Resource Type Dataset
Metadata Created Date June 1, 2023
Metadata Updated Date October 28, 2023
Publisher U.S. Geological Survey
Maintainer
@Id http://datainventory.doi.gov/id/dataset/6e7b1eb6b41728cbf21b3fb0213b0a04
Identifier USGS:5d8a4c14e4b0c4f70d0ae668
Data Last Modified 20211116
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 be74e103-b6d0-436f-9cad-35625a592ff4
Harvest Source Id 52bfcc16-6e15-478f-809a-b1bc76f1aeda
Harvest Source Title DOI EDI
Metadata Type geospatial
Old Spatial -82.0132,25.2953,-80.8299,26.4983
Publisher Hierarchy White House > U.S. Department of the Interior > U.S. Geological Survey
Source Datajson Identifier True
Source Hash 8f567d6bb46734ae86d6dea0306fa97a2c948846e3a1f9f42ad53664be76174b
Source Schema Version 1.1
Spatial {"type": "Polygon", "coordinates": -82.0132, 25.2953, -82.0132, 26.4983, -80.8299, 26.4983, -80.8299, 25.2953, -82.0132, 25.2953}

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