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
]. Furthermore, 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
]. Biomass data are also 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.
Biomass density in a salt marsh is spatially variable and difficult to quantify at the landscape scale. Elevation above sea level is one of the major determinants of primary production and plant health within salt marshes, but other variables such as grazing activity, nutrient availability, and tidal flushing are important as well [6
]. Landscape-scale field analysis of biomass is impractical due to labor-intensive methods and difficulty accessing the entire marsh area. However, remote sensing technologies are well-suited for studies at the landscape scale. Spectral data extracted from satellites allow researchers to estimate aboveground biomass [13
] over large areas at a variety of spatial resolutions. Many of the satellite platforms continuously collect images, making remote sensing data useful for time-series analysis and retrieval of past events.
Many earlier studies about multispectral analyses of salt marsh biomass utilize data from NASA’s Landsat satellite series [13
]. These satellites only have a 30-m resolution and 16-day revisit cycle. The 30-m pixel size limits its capacity to resolve fine-scale variations in a marsh. Landsat imagery is often unusable or only partly usable on cloudy days, which further reduces the image availability. Lastly, satellite images need to be captured during low tide when the salt marsh vegetation is not submerged. Therefore, satellites with higher spatial resolution and shorter repeat times are more desirable.
A company in the United States, Planet, has launched its PlanetScope satellites since 2009. Currently, it has over 100 PlanetScope nanosatellites in orbit, collecting multispectral imagery in blue, green, red, and near-infrared bands. It established a data-sharing program for students and researchers providing them with near-daily, 3-m PlanetScope data [18
], allowing for high spatial and temporal analysis of landscapes. The goal of this study was to test the efficacy of Planet data to accurately predict aboveground biomass within salt marshes in North Inlet-Winyah Bay (North Inlet) National Estuarine Research Reserve and Plum Island Ecosystems (PIE) Long-Term Ecological Research site and its ability to resolve spatial pattern across the marsh landscape. Results from this study will give a better understanding of aboveground biomass within salt marshes, which is useful for a variety of purposes including modeling studies, trends analysis, assessment of marsh health, and potential carbon sequestration.
Planet data were applied successfully to estimate aboveground biomass at North Inlet. A good model fit (d = 0.74, d2 = 0.55, RMSE = 223.38 g/m2) was obtained using the model developed within this study to estimate biomass at the validation sites. The extracted biomass map provided interesting insights about this marsh’s growth dynamics that correlated well with data from marsh organ studies, including correspondence with the vertical growth ranges.
Within North Inlet we found that mean biomass varied by sample location. As indicated in Figure 4
, differences in biomass density were significant due to variable growth conditions within the estuary. STC had the largest biomass concentration and is also the location closest to Winyah Bay. Winyah Bay is an estuary adjacent to North Inlet (Figure 3
) with a large freshwater discharge, and its adjacent marshes are less saline than the majority of North Inlet estuary. The water from Winyah Bay only influences the southern region of North Inlet, due to a tidal node that limits the intrusion of brackish water further into North Inlet [23
]. STC was the only site we sampled that was influenced by Winyah Bay, and the freshwater influence presumably provided for more favorable growth conditions, allowing for larger biomass growth.
There are other sources of variation that have not been fully explained. For instance, total biomass in 2017 was greater than in 2018, however it is unclear what led to this change. The images used in 2017 and 2018 both were taken at low tide (−0.5 m and −0.42 m relative to NAVD, respectively) and in the same season, so influences of tide and time-of-year were minimal. However, it is possible that atmospheric conditions differed between images and were not perfectly or uniformly corrected using atmospheric correction. Alternatively, rainfall may have been a factor [19
]. Based on the Palmer drought severity index for the northeast region in South Carolina, 2017 experienced more “incipient wet spells during the growing season than in 2018, which should have been more favorable for growth. Then in February/March of 2018 there was a period of “mild drought” (Figure 7
], which likely depressed growth. Although there was no notable prolonged drought in 2018, the average summer temperature was slightly cooler in 2017 and springtime wetter than 2018, both of which may have resulted in somewhat more favorable 2017 growing conditions (Figure 8
). Other factors that influence aboveground biomass include herbivory, interannual variation in sea level, and storms [19
]. Thus, the remote imagery has opened up future work on the possible drivers of these inter-annual differences in marsh primary production.
Elevation also played a role in biomass growth, and similar to other studies, biomass followed a parabolic relationship with elevation [6
]. Furthermore, the biomass curve closely follows the curve found in a marsh organ experiment conducted at North Inlet and PIE [9
]. The comparison of results from this study and that of the marsh organ study (shown in Figure 5
a,b) demonstrates that PlanetScope data are useful in deriving biomass growth curves, and can be an alternative to labor-intensive, in situ bioassay experiments. Though the overall harvested biomass at PIE was lower than what was found in a PIE marsh organ experiment, perhaps due to time of harvest, the slope of the two growth curves were similar. For North Inlet, the peak biomass is at mid elevations within its vertical range. As noted earlier, the optimal elevation for S. alterniflora
growth is approximately midway between mean sea level and the level of mean higher high water [9
], which is presumably the least stressful elevation [6
]. This is consistent with our biomass model and supports that Planet data or alternative remote imagery can be utilized to derive a relationship between elevation and biomass production based on vegetation indices and a DEM. This would expand the utility of predictive models such as MEM and allow for better predictions of marsh survival and migration in the face of rising sea level.
There is a north-south elevation gradient within North Inlet (Figure 9
a,b) that may also influence the biomass. As noted above, there are differences in biomass among sample sites, and the mosaic of biomass (Figure 3
) clearly shows a preponderance of high biomass at the south end of North Inlet. The elevation at that end of the estuary (Figure 9
) is close to the optimum (Figure 5
), while elevations at the north end are suboptimal. Consequently, marsh areas at the north end are at greater risk of drowning due to sea-level rise.
The sample size for PIE was possibly too small to realize a significant correlation between the spectral data and aboveground biomass. Another factor may have been the dark organic-rich soils that are characteristic of PIE marshes. Further, based on field observations, biomass, plant form, and stem density at PIE are extremely variable. For instance, biomass was over 160 cm tall at one site and under 20 cm at another. The issues of large biomass variance and possible spectral saturation with tall dense vegetation at PIE could potentially have been overcome using a larger sample size. In addition, a better correlation between satellite data and biomass may have been established if S. patens samples had been included, but the architecture of the two species is radically different. To maintain consistency across sites, only S. alterniflora was included. Future work should be conducted at PIE to include more plant species, larger sample size, and a wider growth range.
The biomass density observed at PIE in this study was lower than what was found in a marsh organ study (Figure 5
b), although the trends with elevation were the same, confirming that growth of S. alterniflora
varies with elevation. However, the growth curves for North Inlet and PIE were different (Figure 5
). Within PIE, S. alterniflora
growth is largely confined to the higher end of its growth range. Moreover, as noted earlier, the tide range at PIE is greater than at North Inlet. Consequently, the potential or fundamental vertical growth range of S. alterniflora
is greater at PIE than at North Inlet. However, S. alterniflora’s
realized growth range in North Inlet spans the entirety of its fundamental range, which our data fully captured, which is possibly another reason for the model’s success at North Inlet and failure at PIE.
Biomass and satellite data from PIE were collected earlier in the annual growth cycle than at North Inlet, which also may have affected the fidelity of the models because the spectral signature of S. alterniflora
varies throughout the year [55
]. Satellite and field data for PIE were collected in July while samples were collected in North Inlet during early autumn. The growing season also differs between the two sites, as discussed above. Therefore, the spectral signatures of PIE and North Inlet plants were likely very different, which would lead to differences in vegetation indices values. To better determine if a universal biomass model using Planet or other spectral data could be used, future work should match field and satellite data during peak VI values at each sample site.
This study supports the use of small satellites as a reliable platform to provide data that may be used to compute and map marsh biomass. The commonly applied medium-resolution data such as Landsat is less helpful since the fine-scale spatial variability of marsh biomass is smoothed in those images. As one example of the rapidly developing small satellite technologies, PlanetScope have data originating in 2009. However, their target for near-daily data was reached in 2017. Planet continues to launch their PlanetScope satellites several times a year ridesharing with other missions. These low-cost small satellites have a lifespan of about three years, which allows the company to update the satellite’s hardware. The low cost and frequent launch of new satellites also reduces the cost risk of a failed mission. An added benefit of PlanetScope data is that the satellites are always in operation, while other high-resolution satellites are often task-based. This feature provides PlanetScope users with high spatial and temporal coverage of data. Data accessibility is an issue. Since Planet is a commercial company, its data are not as easily accessible as NASA’s frequently used Landsat data. In addition, taken with frame cameras, the radiometric accuracy of Planet data may not be as high as that of Landsat. However, this has not yet been widely studied. Despite these drawbacks, the high spatial resolution and temporal frequency of PlanetScope makes it especially useful within heterogeneous wetland systems that are influenced by tides and summer cloud covers.