scholarly journals Estimating Aboveground Biomass and Its Spatial Distribution in Coastal Wetlands Utilizing Planet Multispectral Imagery

2019 ◽  
Vol 11 (17) ◽  
pp. 2020 ◽  
Author(s):  
Gwen J. Miller ◽  
James T. Morris ◽  
Cuizhen Wang
Keyword(s):  
Salt Marsh ◽  
High Resolution ◽  
Salt Marshes ◽  
Aboveground Biomass ◽  
Vegetation Index ◽  
Plant Biomass ◽  
Temporal Trends ◽  
Landscape Scale ◽  
North Inlet ◽  
Vertical Range

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.

scholarly journals Mapping Coastal Wetland Biomass from High Resolution Unmanned Aerial Vehicle (UAV) Imagery

2019 ◽  
Vol 11 (5) ◽  
pp. 540 ◽  
Author(s):  
Cheryl Doughty ◽  
Kyle Cavanaugh
Keyword(s):  
Salt Marsh ◽  
High Resolution ◽  
Aboveground Biomass ◽  
Vegetation Index ◽  
Coastal Wetland ◽  
Spatial Scales ◽  
Biomass Estimation ◽  
Spatial And Temporal Variability ◽  
Estimation Model ◽  
Spatial And Temporal Scales

Salt marsh productivity is an important control of resiliency to sea level rise. However, our understanding of how marsh biomass and productivity vary across fine spatial and temporal scales is limited. Remote sensing provides a means for characterizing spatial and temporal variability in marsh aboveground biomass, but most satellite and airborne sensors have limited spatial and/or temporal resolution. Imagery from unmanned aerial vehicles (UAVs) can be used to address this data gap. We combined seasonal field surveys and multispectral UAV imagery collected using a DJI Matrice 100 and Micasense Rededge sensor from the Carpinteria Salt Marsh Reserve in California, USA to develop a method for high-resolution mapping of aboveground saltmarsh biomass. UAV imagery was used to test a suite of vegetation indices in their ability to predict aboveground biomass (AGB). The normalized difference vegetation index (NDVI) provided the strongest correlation to aboveground biomass for each season and when seasonal data were pooled, though seasonal models (e.g., spring, r2 = 0.67; RMSE = 344 g m−2) were more robust than the annual model (r2 = 0.36; RMSE = 496 g m−2). The NDVI aboveground biomass estimation model (AGB = 2428.2 × NDVI + 120.1) was then used to create maps of biomass for each season. Total site-wide aboveground biomass ranged from 147 Mg to 205 Mg and was highest in the spring, with an average of 1222.9 g m−2. Analysis of spatial patterns in AGB demonstrated that AGB was highest in intermediate elevations that ranged from 1.6–1.8 m NAVD88. This UAV-based approach can be used aid the investigation of biomass dynamics in wetlands across a range of spatial scales.

scholarly journals Plant genotype determines biomass response to flooding frequency in tidal wetlands

2021 ◽  
Vol 18 (2) ◽  
pp. 403-411
Author(s):  
Svenja Reents ◽  
Peter Mueller ◽  
Hao Tang ◽  
Kai Jensen ◽  
Stefanie Nolte
Keyword(s):  
Salt Marsh ◽  
Biomass Production ◽  
Salt Marshes ◽  
Aboveground Biomass ◽  
Plant Biomass ◽  
Growth Parameters ◽  
High Marsh ◽  
Flooding Frequency ◽  
Strong Control ◽  
Marsh Accretion

Abstract. The persistence of tidal wetland ecosystems like salt marshes is threatened by human interventions and climate change. In particular, the threat of accelerated sea level rise (SLR) has increasingly gained the attention of the scientific community recently. However, studies investigating the effect of SLR on plants and vertical marsh accretion are usually restricted to the species or community level and do not consider phenotypic plasticity or genetic diversity. To investigate the response of genotypes within the same salt-marsh species to SLR, we used two known genotypes of Elymus athericus (Link) Kerguélen (low-marsh and high-marsh genotypes). In a factorial marsh organ experiment we exposed both genotypes to different flooding frequencies and quantified plant growth parameters. With increasing flooding frequency, the low-marsh genotype showed higher aboveground biomass production compared to the high-marsh genotype. Additionally, the low-marsh genotype generally formed longer rhizomes, shoots and leaves, regardless of flooding frequency. Belowground biomass of both genotypes decreased with increasing flooding frequency. We conclude that the low-marsh genotype is better adapted to higher flooding frequencies through its ability to allocate resources from below- to aboveground biomass. Given the strong control of plant biomass production on salt-marsh accretion, we argue that these findings yield important implications for our understanding of ecosystem resilience to SLR as well as plant species distribution in salt marshes.

scholarly journals Plant genotype determines biomass response to flooding frequency in tidal wetlands

2020 ◽  
Author(s):  
Svenja Reents ◽  
Peter Mueller ◽  
Hao Tang ◽  
Kai Jensen ◽  
Stefanie Nolte
Keyword(s):  
Salt Marsh ◽  
Biomass Production ◽  
Salt Marshes ◽  
Aboveground Biomass ◽  
Plant Biomass ◽  
Growth Parameters ◽  
High Marsh ◽  
Flooding Frequency ◽  
Strong Control ◽  
Marsh Accretion

Abstract. The persistence of tidal wetland ecosystems like salt marshes is threatened by human interventions and climate change. Particularly the threat of accelerated sea level rise (SLR) has recently gained increasing attention by the scientific community. However, studies investigating the effect of SLR on plants and vertical marsh accretion are usually restricted to the species or community level and do not consider phenotypic plasticity or genetic diversity. To investigate the response of genotypes within the same salt-marsh species to SLR, we used two known genotypes of Elymus athericus (Link) Kerguélen (low-marsh and high-marsh genotypes). In a factorial marsh organ experiment we exposed both genotypes to different flooding frequencies and quantified plant growth parameters. With increasing flooding frequency, the low-marsh genotype showed a higher aboveground biomass production compared to the high-marsh genotype. Additionally, the low-marsh genotype generally formed longer rhizomes, shoots and leaves, regardless of flooding frequency. Belowground biomass of both genotypes decreased with flooding frequency. We conclude that the low-marsh genotype is better adapted to higher flooding frequencies through its ability to allocate resources from below- to aboveground biomass. Given the strong control of plant biomass production on salt-marsh accretion, we argue that these findings yield important implications for our understanding of ecosystem resilience to SLR as well as plant-species distribution in salt marshes.

scholarly journals Using Multispectral Drone Imagery for Spatially Explicit Modeling of Wave Attenuation through a Salt Marsh Meadow

Drones ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 25
Author(s):  
Antoine Mury ◽  
Antoine Collin ◽  
Thomas Houet ◽  
Emilien Alvarez-Vanhard ◽  
Dorothée James
Keyword(s):  
Salt Marsh ◽  
Salt Marshes ◽  
Crop Production ◽  
Vegetation Index ◽  
Near Infrared ◽  
Wave Attenuation ◽  
Coefficient Of Determination ◽  
Coastal Protection ◽  
Surface Model ◽  
Red Edge

Offering remarkable biodiversity, coastal salt marshes also provide a wide variety of ecosystem services: cultural services (leisure, tourist amenities), supply services (crop production, pastoralism) and regulation services including carbon sequestration and natural protection against coastal erosion and inundation. The consideration of this coastal protection ecosystem service takes part in a renewed vision of coastal risk management and especially marine flooding, with an emerging focus on “nature-based solutions.” Through this work, using remote-sensing methods, we propose a novel drone-based spatial modeling methodology of the salt marsh hydrodynamic attenuation at very high spatial resolution (VHSR). This indirect modeling is based on in situ measurements of significant wave heights (Hm0) that constitute the ground truth, as well as spectral and topographical predictors from VHSR multispectral drone imagery. By using simple and multiple linear regressions, we identify the contribution of predictors, taken individually, and jointly. The best individual drone-based predictor is the green waveband. Dealing with the addition of individual predictors to the red-green-blue (RGB) model, the highest gain is observed with the red edge waveband, followed by the near-infrared, then the digital surface model. The best full combination is the RGB enhanced by the red edge and the normalized difference vegetation index (coefficient of determination (R2): 0.85, root mean square error (RMSE): 0.20%/m).

scholarly journals Seasonal variation in apparent conductivity and soil salinity at two Narragansett Bay, RI salt marshes

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8074
Author(s):  
Richard McKinney ◽  
Alana Hanson ◽  
Roxanne Johnson ◽  
Michael Charpentier
Keyword(s):  
Salt Marsh ◽  
Soil Salinity ◽  
Salt Marshes ◽  
Temporal Trends ◽  
Narragansett Bay ◽  
Spatial And Temporal Variability ◽  
Apparent Conductivity ◽  
Marsh Sediments ◽  
Salt Marsh Sediments ◽  
Over Time

Measurement of the apparent conductivity of salt marsh sediments using electromagnetic induction (EMI) is a rapid alternative to traditional methods of salinity determination that can be used to map soil salinity across a marsh surface. Soil salinity measures can provide information about marsh processes, since salinity is important in determining the structure and function of tidally influenced marsh communities. While EMI has been shown to accurately reflect salinity to a specified depth, more information is needed on the potential for spatial and temporal variability in apparent conductivity measures that may impact the interpretation of salinity data. In this study we mapped soil salinity at two salt marshes in the Narragansett Bay, RI estuary monthly over the course of several years to examine spatial and temporal trends in marsh salinity. Mean monthly calculated salinity was 25.8 ± 5.5 ppt at Narrow River marsh (NAR), located near the mouth of the Bay, and 17.7 ± 5.3 ppt at Passeonkquis marsh (PAS) located in the upper Bay. Salinity varied seasonally with both marshes, showing the lowest values (16.3 and 8.3 ppt, respectively) in April and highest values (35.4 and 26.2 ppt, respectively) in August. Contour plots of calculated salinities showed that while the mean whole-marsh calculated salinity at both sites changed over time, within-marsh patterns of higher versus lower salinity were maintained at NAR but changed over time at PAS. Calculated salinity was significantly negatively correlated with elevation at NAR during a sub-set of 12 sample events, but not at PAS. Best-supported linear regression models for both sites included one-month and 6-month cumulative rainfall, and tide state as potential factors driving observed changes in calculated salinity. Mapping apparent conductivity of salt marsh sediments may be useful both identifying within-marsh micro-habitats, and documenting marsh-wide changes in salinity over time.

scholarly journals 14C Wiggle-Match Dating in High-Resolution Sea-Level Research

Radiocarbon ◽  
2001 ◽  
Vol 43 (2A) ◽  
pp. 391-402 ◽  
Author(s):  
O van de Plassche ◽  
R J Edwards ◽  
K van der Borg ◽  
A F M de Jong
Keyword(s):  
Salt Marsh ◽  
High Resolution ◽  
Sea Level ◽  
Time Scales ◽  
Calibration Curve ◽  
Salt Marshes ◽  
Accumulation Rate ◽  
The Other ◽  
Short Term ◽  
Radiocarbon Dates

Comparison of two sets of marsh-accumulation records from each of three Connecticut (USA) salt marshes, one based on individually calibrated dates and the other on wiggle-match dating of the same series of dates, shows that wiggle-match dating results in more precise and objective reconstructions of longer-term (102–103 yr) changes in accumulation rate. On (sub-)century time scales, wiggle-match dating can reveal steps in the calibrated marsh-accumulation envelope as artefacts of the calibration curve, but may also leave real short-term changes in accumulation rate undetected. Wiggle-matches are non-unique, being dependent on the number, quality and distribution of radiocarbon dates in a sequence, how a series of dates is subdivided into groups (representing intervals of uniform accumulation rate), and what is considered a “best match”. Samples from the studied salt-marsh deposits required no correction for reservoir effects prior to calibration.

scholarly journals Identifying Salt Marsh Shorelines from Remotely Sensed Elevation Data and Imagery

2019 ◽  
Vol 11 (15) ◽  
pp. 1795 ◽  
Author(s):  
Amy S. Farris ◽  
Zafer Defne ◽  
Neil K. Ganju
Keyword(s):  
Salt Marsh ◽  
High Resolution ◽  
Salt Marshes ◽  
Spatial Scales ◽  
Remotely Sensed ◽  
Horizontal Resolution ◽  
Unmanned Aircraft ◽  
Maximum Slope ◽  
Marsh Edge ◽  
Elevation Data

Salt marshes are valuable ecosystems that are vulnerable to lateral erosion, submergence, and internal disintegration due to sea level rise, storms, and sediment deficits. Because many salt marshes are losing area in response to these factors, it is important to monitor their lateral extent at high resolution over multiple timescales. In this study we describe two methods to calculate the location of the salt marsh shoreline. The marsh edge from elevation data (MEED) method uses remotely sensed elevation data to calculate an objective proxy for the shoreline of a salt marsh. This proxy is the abrupt change in elevation that usually characterizes the seaward edge of a salt marsh, designated the “marsh scarp.” It is detected as the maximum slope along a cross-shore transect between mean high water and mean tide level. The method was tested using lidar topobathymetric and photogrammetric elevation data from Massachusetts, USA. The other method to calculate the salt marsh shoreline is the marsh edge by image processing (MEIP) method which finds the unvegetated/vegetated line. This method applies image classification techniques to multispectral imagery and elevation datasets for edge detection. The method was tested using aerial imagery and coastal elevation data from the Plum Island Estuary in Massachusetts, USA. Both methods calculate a line that closely follows the edge of vegetation seen in imagery. The two methods were compared to each other using high resolution unmanned aircraft systems (UAS) data, and to a heads-up digitized shoreline. The root-mean-square deviation was 0.6 meters between the two methods, and less than 0.43 meters from the digitized shoreline. The MEIP method was also applied to a lower resolution dataset to investigate the effect of horizontal resolution on the results. Both methods provide an accurate, efficient, and objective way to track salt marsh shorelines with spatially intensive data over large spatial scales, which is necessary to evaluate geomorphic change and wetland vulnerability.

scholarly journals Non-linear power law approach for spatial and temporal pattern analysis of salt marsh evolution

2013 ◽  
Vol 1 (1) ◽  
pp. 1061-1095 ◽  
Author(s):  
A. Taramelli ◽  
L. Cornacchia ◽  
E. Valentini ◽  
F. Bozzeda
Keyword(s):  
Salt Marsh ◽  
Power Law ◽  
Salt Marshes ◽  
Temporal Trends ◽  
Hybrid Approach ◽  
Power Laws ◽  
Time And Space ◽  
Ecological Features ◽  
Time Space ◽  
Vegetation Patches

Abstract. Many complex systems on the Earth surface show non-equilibrium fluctuations, often determining the spontaneous evolution towards a critical state. In this context salt marshes are characterized by complex patterns both in geomorphological and ecological features, which often appear to be strongly correlated. A striking feature in salt marshes is vegetation distribution, which can self-organize in patterns over time and space. Self-organized patchiness of vegetation can often give rise to power law relationships in the frequency distribution of patch sizes. In cases where the whole distribution does not follow a power law, the variance of scale in its tail may often be disregarded. To this end, the research aims at how changes in the main climatic and hydrodynamic variables may influence such non-linearity, and how numerical thresholds can describe this. Since it would be difficult to simultaneously monitor the presence and typology of vegetation and channel sinuosity through in situ data, and even harder to analyze them over medium to large time-space scales, remote sensing offers the ability to analyze the scale invariance of patchiness distributions. Here, we focus on a densely vegetated and channelized salt marsh (Scheldt estuary Belgium–the Netherlands) by means of the sub-pixel analysis on satellite images to calculate the non-linearity in the values of the power law exponents due to the variance of scale. The deviation from power laws represents stochastic conditions under climate drivers that can be hybridized on the basis of a fuzzy Bayesian generative algorithm. The results show that the hybrid approach is able to simulate the non-linearity inherent to the system and clearly show the existence of a link between the autocorrelation level of the target variable (i.e. size of vegetation patches), due to its self-organization properties, and the influence exerted on it by the external drivers (i.e. climate and hydrology). Considering the results of the stochastic model, high uncertainties can be associated to the short term climate influence on the saltmarshes, and the medium-long term spatial and temporal trends seem to be dominated by vegetation with its evolution in time and space. The evolution of vegetation patches (under power law) and channel sinuosity can then be used to forecast potential deviation from steady states in intertidal systems, taking into account the climatic and hydrological regimes.

scholarly journals Multiscale Mapping of the Salt Marshes Using Sentinel-2, Dove and UAV Imagery in the Bay of Mont-Saint-Michel

2021 ◽  
Author(s):  
Antoine Collin ◽  
Dorothée James ◽  
Antoine Mury ◽  
Mathilde Letard ◽  
Thomas Houet ◽  
...  
Keyword(s):  
Salt Marsh ◽  
Salt Marshes ◽  
Vegetation Index ◽  
Near Infrared ◽  
Regional Scale ◽  
Great Promise ◽  
High Temporal Resolution ◽  
Multiscale Approach ◽  
Marsh Vegetation ◽  
Sentinel 2

The salt marshes, lying at the land-sea temperate interface, furnish a plethora of ecosystems services such as biodiversity niche support, ocean-climate change regulation, ornithology recreo-tourism or plant gathering by hand. They undergo significant worldwide losses due to their conversion into crop fields and to their spatial compression between the rising sea-level and the armoring shoreline. Their monitoring however requires to use a suite of remote sensing sensors to embrace the regional scale while capturing the plant details. This research innovatively adopts a multiscale approach using a cascading spaceborne and airborne process, from the 10-m Sentinel-2, through the 3-m Dove, to the 0.03-m unmanned airborne vehicle (UAV) imageries. The high to very high temporal resolution of the Sentinel-2 and Dove enabled to cover twenties and tens of km2 over five and four years, respectively, in the form of normalized difference vegetation index (NDVI) classes, associated with microphytobenthos, low, medium and high salt marsh vegetation, including the opportunistic Elyma genus. The NDVI was then modelled at the UAV scale (a few km2) using a three-layered NN prediction, providing the final near-infrared (NIR), and the intermediate red, green and blue reflectance imageries, calibrated/validated/tested with the Dove reflectance imageries (R2NIR=0.98, R2red=0.88, R2green=0.84, and R2blue=0.90). The 100fold increase in pixel size allowed to detect the decimeter-scale objects of the tidal flats and salt marshes, to enlarge the NDVI class ranges, and hold great promise to model other spectral bands at the UAV scale for further deeply enhancing the salt marsh mapping.

scholarly journals Estimating Ground Elevation and Vegetation Characteristics in Coastal Salt Marshes Using UAV-Based LiDAR and Digital Aerial Photogrammetry

2021 ◽  
Vol 13 (22) ◽  
pp. 4506
Author(s):  
Daniele Pinton ◽  
Alberto Canestrelli ◽  
Benjamin Wilkinson ◽  
Peter Ifju ◽  
Andrew Ortega
Keyword(s):  
Salt Marsh ◽  
High Resolution ◽  
Salt Marshes ◽  
Point Cloud ◽  
Point Clouds ◽  
Vegetation Density ◽  
Marsh Vegetation ◽  
Vegetation Height ◽  
Aerial Photogrammetry ◽  
Vegetation Characteristics

This study evaluates the skills of two types of drone-based point clouds, derived from LiDAR and photogrammetric techniques, in estimating ground elevation, vegetation height, and vegetation density on a highly vegetated salt marsh. The proposed formulation is calibrated and tested using data measured on a Spartina alterniflora-dominated salt marsh in Little Sapelo Island, USA. The method produces high-resolution (ground sampling distance = 0.40 m) maps of ground elevation and vegetation characteristics and captures the large gradients in the proximity of tidal creeks. Our results show that LiDAR-based techniques provide more accurate reconstructions of marsh vegetation (height: MAEVH = 12.6 cm and RMSEVH = 17.5 cm; density: MAEVD = 6.9 stems m−2 and RMSEVD = 9.4 stems m−2) and morphology (MAEM = 4.2 cm; RMSEM = 5.9 cm) than Digital Aerial Photogrammetry (DAP) (MAEVH = 31.1 cm; RMSEVH = 38.1 cm; MAEVD = 12.7 stems m−2; RMSEVD = 16.6 stems m−2; MAEM = 11.3 cm; RMSEM = 17.2 cm). The accuracy of the classification procedure for vegetation calculation negligibly improves when RGB images are used as input parameters together with the LiDAR-UAV point cloud (MAEVH = 6.9 cm; RMSEVH = 9.4 cm; MAEVD = 10.0 stems m−2; RMSEVD = 14.0 stems m−2). However, it improves when used together with the DAP-UAV point cloud (MAEVH = 21.7 cm; RMSEVH = 25.8 cm; MAEVD = 15.2 stems m−2; RMSEVD = 18.7 stems m−2). Thus, we discourage using DAP-UAV-derived point clouds for high-resolution vegetation mapping of coastal areas, if not coupled with other data sources.

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