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Folders: ROOT > ScienceBase Catalog > USGS Western Ecological Research Center > LEAN-corrected Digital Elevation Models in Tidal Marshes ( Show all descendants )
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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 mode (DEM) for tidal marsh areas around San Francisco Bay using the Lidar Elevation Adjustment with NDVI (LEAN) technique (Buffington et al. 2016). Survey-grade GPS survey data (6614 points), NAIP-derived Normalized Difference Vegetation Index, and original 1 m lidar DEM from 2010 were used to generate a model of predicted bias across tidal marsh areas. The predicted bias was then subtracted from the original lidar DEM and merged with the NOAA...
Categories: Data;
Types: ArcGIS REST Map Service,
ArcGIS Service Definition,
Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Remote Sensing,
San Francisco Bay,
USGS Science Data Catalog (SDC),
biota,
digital elevation models,
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 mode (DEM) for Suisun marsh using a modification of the Lidar Elevation Adjustment with NDVI (LEAN) technique (Buffington et al. 2016). GPS survey data (6912 points, collected across public and private land in 2018), Normalized Difference Vegetation Index (NDVI) derived from an airborne multispectral image (June 2018), a 1 m lidar DEM from September 2018, and a 1 m canopy surface model were used to generate models of predicted bias across the...
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...
Categories: Data;
Types: ArcGIS REST Map Service,
ArcGIS Service Definition,
Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Collier county,
Remote Sensing,
USGS Science Data Catalog (SDC),
biota,
cypress,
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 mode (DEM) for Chesapeake Bay using a modification of the Lidar Elevation Adjustment with NDVI (LEAN) technique (Buffington et al. 2016). GPS survey data (3699 points, collected across four tidal marsh sites (Eastern Neck, Bishops Head, Martin, and Blackwater) in 2010 and 2017, Normalized Difference Vegetation Index (NDVI) derived from an airborne multispectral image (2013), a 1 m lidar DEM and a 1 m canopy surface model were used to generate...
Accurate elevation data in coastal ecosystems are crucial for understanding vulnerability to sea-level rise. Lidar has become increasingly available; however, in tidal wetlands such as mangroves and salt marsh, vertical bias from dense vegetation reduces accuracy of the delivered 'base earth' products. To increase accuracy of elevation models across south Florida, we applied the LEAN technique to six different lidar collections from 2007-2018. On average, LEAN correction increased DEM accuracy by 46.1 percent, reducing the vertical bias. After correction and post-processing, the DEMs were merged together with a bathymetric dataset to create a seamless topobathy product.
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