Geology topics

Nathan Thomas

Publications and source records attributed to Nathan Thomas.

3 recordsLinked to original sources

USGS2020

Corrigendum to "A remote sensing-based model of tidal marsh aboveground carbon stocks for the conterminous United States" [ISPRS J. Photogram. Rem. Sens.139 (2018) 255-271]

The authors regret that two thirds of the San Francisco Bay biomass data included in the Landsat random forest models were not scaled to the proper units of grams per square meter. This error affects the Landsat-only models in the article, which are models #1-4 shown in Table 6. The authors have thoroughly investigated the error and found that the final random forest model, including the selected dependent and independent variables, is still the most appropriate model for representing CONUS-wide tidal marsh aboveground biomass and carbon (C). Using the properly scaled biomass data we have corrected remote sensing-based estimates of tidal marsh aboveground biomass and C stocks, and we have corrected Tables 4, 6, 7 and 8 and Figures 5, 6, and 9 of the original article.

ISPRS Journal of Photogrammetry and Remote Sensing↗

USGS2019

High-Resolution mapping of biomass and distribution of marsh and forested wetlands in southeastern coastal Louisiana

This study estimates herbaceous and forested wetland coverage and aboveground biomass (AGB) within the Atchafalaya and Terrebonne coastal basins representing sediment rich and sediment poor coastal regions of southern Louisiana. Louisiana coastal wetlands account for approximately one third (37%) of the estuarine wetland area in the conterminous United States, yet the spatial distribution of their extent and aboveground biomass estimates are not well defined. Despite the importance of Louisiana’s coastal wetlands, existing maps on their extent are often outdated yet repeatedly used in national greenhouse gas (GHG) inventories and within regional aboveground biomass and carbon content estimates. Sentinel-2 optical satellite data was used within an object-oriented machine learning approach to classify wetland extent, mapping the spatial distribution of the coastal wetlands at high-resolution. A total wetland extent of 2950 km2 was mapped during a period of peak biomass in September 2017, comprised of forested and herbaceous wetlands (accuracy >90%) and demonstrated considerable differences (793.7 km2) from currently available estimates of wetland area. The distribution of aboveground biomass (AGB) was mapped using class averaged values derived from field data collected during May and September 2015. Total herbaceous biomass at the study site increased by 108.9% from May (668,684.5 Mg) to September (1,396,969.2 Mg) whilst the total woody vegetation biomass (27,667,232.1 Mg) was sampled in May only. This method provides a tractable means of mapping wetland extent and biomass, in a region threatened with wetland loss under projections of increasing sea-level rise and local subsidence.

Louisiana↗

USGS2018

A remote sensing-based model of tidal marsh aboveground carbon stocks for the conterminous United States

Remote sensing based maps of tidal marshes, both of their extents and carbon stocks, have the potential to play a key role in conducting greenhouse gas inventories and implementing climate mitigation policies. Our objective was to generate a single remote sensing model of tidal marsh aboveground biomass and carbon that represents nationally diverse tidal marshes within the conterminous United States (CONUS). We developed the first calibration-grade, national-scale dataset of aboveground tidal marsh biomass, species composition, and aboveground plant carbon content (%C) from six CONUS regions: Cape Cod, MA, Chesapeake Bay, MD, Everglades, FL, Mississippi Delta, LA, San Francisco Bay, CA, and Puget Sound, WA. Using the random forest machine learning algorithm, we tested whether imagery from multiple sensors, Sentinel-1 C-band synthetic aperture radar, Landsat, and the National Agriculture Imagery Program (NAIP), can improve model performance. The final model, driven by six Landsat vegetation indices and with the soil adjusted vegetation index as the most important (n = 409, RMSE = 310 g/m 2 , 10.3% normalized RMSE), successfully predicted biomass for a range of marsh plant functional types defined by height, leaf angle and growth form. Model results were improved by scaling field-measured biomass calibration data by NAIP-derived 30 m fraction green vegetation. With a mean plant carbon content of 44.1% (n = 1384, 95% C.I. = 43.99%–44.37%), we generated regional 30 m aboveground carbon density maps for estuarine and palustrine emergent tidal marshes as indicated by a modified NOAA Coastal Change Analysis Program map. We applied a multivariate delta method to calculate uncertainties in regional carbon densities and stocks that considered standard error in map area, mean biomass and mean %C. Louisiana palustrine emergent marshes had the highest C density (2.67 ± 0.004 Mg/ha) of all regions, while San Francisco Bay brackish/saline marshes had the highest C density of all estuarine emergent marshes (2.03 ± 0.004 Mg/ha). Estimated C stocks for predefined jurisdictional areas ranged from 1023 ± 39 Mg in the Nisqually National Wildlife Refuge in Washington to 507,761 ± 14,822 Mg in the Terrebonne and St. Mary Parishes in Louisiana. This modeling and data synthesis effort will allow for aboveground C stocks in tidal marshes to be included in the coastal wetland section of the U.S. National Greenhouse Gas Inventory. With the increased availability of free post-processed satellite data, we provide a tractable means of modeling tidal marsh aboveground biomass and carbon at the global extent as well.

ISPRS Journal of Photogrammetry and Remote Sensing↗