Abstract
Coastal salt marshes are biologically productive ecosystems that generate and sequester significant quantities of organic matter. Plant biomass varies spatially within a salt marsh and it is tedious and often logistically impractical to quantify biomass from field measurements across an entire landscape. Satellite data are useful for estimating aboveground biomass, however, high-resolution data are needed to resolve the spatial details within a salt marsh. This study used 3-m resolution multispectral data provided by Planet to estimate aboveground biomass within two salt marshes, North Inlet-Winyah Bay (North Inlet) National Estuary Research Reserve, and Plum Island Ecosystems (PIE) Long-Term Ecological Research site. The Akaike information criterion analysis was performed to test the fidelity of several alternative models. A combination of the modified soil vegetation index 2 (MSAVI2) and the visible difference vegetation index (VDVI) gave the best fit to the square root-normalized biomass data collected in the field at North Inlet (Willmott’s index of agreement d = 0.74, RMSE = 223.38 g/m2, AICw = 0.3848). An acceptable model was not found among all models tested for PIE data, possibly because the sample size at PIE was too small, samples were collected over a limited vertical range, in a different season, and from areas with variable canopy architecture. For North Inlet, a model-derived landscape scale biomass map showed differences in biomass density among sites, years, and showed a robust relationship between elevation and biomass. The growth curve established in this study is particularly useful as an input for biogeomorphic models of marsh development. This study showed that, used in an appropriate model with calibration, Planet data are suitable for computing and mapping aboveground biomass at high resolution on a landscape scale, which is needed to better understand spatial and temporal trends in salt marsh primary production.
Highlights
Coastal salt marshes are biologically diverse ecosystems that improve water quality, provide protection from hurricanes and storm surges, and are important habitat for wildlife [1,2,3]
In Akaike information criterion (AIC) analysis, more dependent variables are counted against the model fit, as shown in Equation (2), and some of the variables such as modified soil adjusted vegetation index 2 (MSAVI2) and soil adjusted vegetation index (SAVI) were autocorrelated, violating the assumption of non-multicollinearity
Within North Inlet we found that mean biomass varied by sample location
Summary
Coastal salt marshes are biologically diverse ecosystems that improve water quality, provide protection from hurricanes and storm surges, and are important habitat for wildlife [1,2,3]. As carbon is released from long-term storage through burning of fossil fuels to the atmosphere, understanding how carbon is stored within coastal or marine environment is becoming more important. This form of carbon storage is referred to as “blue carbon” and salt marshes are a large blue carbon reservoir with carbon stored both in above and belowground biomass [4,5]. Biomass data are used in models predicting elevation change within marshes. One such model is the marsh equilibrium model (MEM), which estimates elevation changes within salt marshes in relation to sea-level rise [6]. A fundamental feature of this model is the dependence of biomass production as a function of relative elevation
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