3 Current Research in Coastal and Nearshore Processes
Sunday, 20 March 2022
8:00 AM - 12:00 PM
Commonwealth Salon 3
ESTIMATION OF TIDAL MARSH BLUE CARBON STORAGE USING A LIDAR AND OPTICAL REMOTE SENSING BASED MODEL
Paper number: 3-9
Program: Theme Sessions
Time: 10:20 AM – 10:40 AM
Authors
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Wenxiu TengUniversity of Massachusetts Amherst, Amherst, MA
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Brian C. YellenUniversity of Massachusetts-Amherst, Amherst, MA
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Qian YuUniversity of Massachusetts Amherst, Amherst, MA
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Jonathan D. WoodruffUniversity of Massachusetts-Amherst, Amherst, MA
Abstract
In June and July 2021, fixed volume soil samples were collected at four northeast tidal marshes with distinctive morphologies to aid in building our predictive models. Tidal creek networks were delineated from 1-m resolution topo-bathy lidar data using a semi-automated workflow in GIS. Sample distance to tidal creeks and flow distance to the marsh edge were then determined. Vegetation and water indices were derived from 2-m WorldView satellite imagery for all sites. Non-linear multivariate regression models were developed to predict soil organic content, bulk density, and carbon density as a function of these predictive metrics at each site and across sites. Results show the topographic parameters perform well at sites with a single connected creek network. Generally, SOC increases with increasing distance from tidal creeks and the marsh edge. Addition of remote sensing parameters results in adjusted-R2 values ranging from 0.67 to 0.72, which outperforms the topographic models and allows for cross-site modeling with greater accuracy. The normalized difference water index (NDWI) is a good indicator of tidal inundation across a marsh and annual particulate organic carbon (POC) appears to represent sediment supply which is a major control of SOC variability across marsh sites. This method will be applied to additional Northeast saltmarsh sites to validate and better understand the predictive power of our models at a regional scale.
Geological Society of America Abstracts with Programs. Vol. 54, No. 3
doi: 10.1130/abs/2022NE-374621
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