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William Vervaeke

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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 = 362). For each 0.5-by-0.5 m plot, the following data were collected: 1) percent cover by vegetation species; 2) percent cover by vegetation classes based on height (that is, carpet, herbaceous, and woody); 3) mean height of the dominant species; 4) water depth; 5) marsh class based on dominant vegetation species; and 6) location and elevation (that is, northing, easting, and elevation).
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We characterized coastal wetland responses to flooding stress by measuring vegetation cover, wetland elevation and water elevation in healthy and degrading wetlands dominated by Spartina patens. Wetland elevation was measured using real-time kinematic survey methods. Vegetation cover was determined by visual estimation methods, and water elevation was measured using in situ continuous recorders. In addition to these local-scale responses, we also measured landscape-scale patterns of land and water aggregation or fragmentation using remotely sensed data (Jones et al., 2018). Associated products: Jones, W.R., Hartley, S.B., Stagg, C.L., and Osland, M.J. 2018. Land-water classification for selected sites in McFaddin...
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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...
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This data release includes belowground primary productivity, decomposition, and surface elevation change data from a two-year mesocosm experiment from 2012 to 2014. We conducted experimental greenhouse manipulations of atmospheric CO2 (double ambient CO2) and sediment deposition to simulate a land-falling hurricane under future climate conditions. Experimental greenhouse conditions mimicked a land-falling hurricane under projected future climate conditions by comparing atmospheric to double ambient CO2 and sediment deposition in four communities along a coastal wetland landscape gradient in Louisiana, USA (tidal freshwater forested wetland, forest/marsh mix, marsh, and mudflat).
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The surface elevation table (SET)-marker horizon (MH) approach (SET-MH, together) is a method for quantifying surface elevation change through measurements of surface and subsurface processes that control wetland soil elevation. This dataset combines SET-MH data from five different U.S. Geological Survey efforts to monitor surface elevation change in the coastal wetlands of the Greater Everglades region of south Florida. Data from these efforts have been used in the publications by Cahoon and Lynch (1997), Whelan et al. (2005, 2009), Smith et al. (2009), McKee (2011), Breithaupt et al. (2020), Feher et al. (2020), Howard et al. (2020), and Osland et al. (2020). Although some of these data have previously been released...
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