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 MODELING WAVE ATTENUATION DUE TO SALTMARSH VEGETATION USING A MODIFIED SWAN MODEL

2018 ◽  
pp. 73
Author(s):  
Elizabeth Christie ◽  
Iris Möller ◽  
Tom Spencer ◽  
Marissa Yates
Keyword(s):  
Salt Marsh ◽  
Drag Coefficient ◽  
Salt Marshes ◽  
Wave Attenuation ◽  
Vegetation Type ◽  
Coastal Protection ◽  
Wave Dissipation ◽  
Vegetation Height ◽  
Wave Conditions ◽  
Spatially Varying

Vegetated shorelines have been increasingly recognized for their contribution to natural coastal protection due to their ability to dissipate wave energy. Within the UK, salt marshes are beginning to be included in flood defence schemes. Predicting wave dissipation over vegetation requires accurate representation of salt marsh canopies and the feedback relationship between vegetation and wave conditions. We present a modification to the SWAN vegetation model, which includes a variable drag coefficient and a spatially varying vegetation height. Its application is demonstrated by modelling wave propagation over UK salt marshes. The third generation wave model, SWAN includes a vegetation module for calculation of wave attenuation over vegetation. Wave dissipation is determined based on the vegetation properties and a drag coefficient. This drag coefficient, C_D, is used to calibrate the model, and a fixed value is used per model run. Empirically the drag coefficient has been found to vary with ambient wave conditions. Typically the drag coefficients are defined empirically as a function of either the stem Reynolds number, Rev, or the Keulegan-Carpenter number, KC. The parameter values have been shown to vary with vegetation type. In this paper, we modify the SWAN vegetation module to include a temporally varying CD. This allows the drag coefficient to vary with ambient wave parameters, which gives an improved prediction under time varying wave conditions (e.g. passage of a storm) and includes the change in wave conditions as they travel through the vegetation. We also incorporate spatially varying vegetation height into the model to further improve the representation of the complexity of vegetated shorelines. Using the new formulation we find improved prediction of wave dissipation over both idealized laboratory and field salt marsh vegetation.

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 Evaluation of Fire Severity Indices Based on Pre- and Post-Fire Multispectral Imagery Sensed from UAV

2019 ◽  
Vol 11 (9) ◽  
pp. 993 ◽  
Author(s):  
Fernando Carvajal-Ramírez ◽  
José Rafael Marques da Silva ◽  
Francisco Agüera-Vega ◽  
Patricio Martínez-Carricondo ◽  
João Serrano ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Fire Severity ◽  
Matrix Analysis ◽  
Surface Model ◽  
Chi Square ◽  
Error Matrix ◽  
Red Edge ◽  
The Impact

Fire severity is a key factor for management of post-fire vegetation regeneration strategies because it quantifies the impact of fire, describing the amount of damage. Several indices have been developed for estimation of fire severity based on terrestrial observation by satellite imagery. In order to avoid the implicit limitations of this kind of data, this work employed an Unmanned Aerial Vehicle (UAV) carrying a high-resolution multispectral sensor including green, red, near-infrared, and red edge bands. Flights were carried out pre- and post-controlled fire in a Mediterranean forest. The products obtained from the UAV-photogrammetric projects based on the Structure from Motion (SfM) algorithm were a Digital Surface Model (DSM) and multispectral images orthorectified in both periods and co-registered in the same absolute coordinate system to find the temporal differences (d) between pre- and post-fire values of the Excess Green Index (EGI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Red Edge (NDRE) index. The differences of indices (dEGI, dNDVI, and dNDRE) were reclassified into fire severity classes, which were compared with the reference data identified through the in situ fire damage location and Artificial Neural Network classification. Applying an error matrix analysis to the three difference of indices, the overall Kappa accuracies of the severity maps were 0.411, 0.563, and 0.211 and the Cramer’s Value statistics were 0.411, 0.582, and 0.269 for dEGI, dNDVI, and dNDRE, respectively. The chi-square test, used to compare the average of each severity class, determined that there were no significant differences between the three severity maps, with a 95% confidence level. It was concluded that dNDVI was the index that best estimated the fire severity according to the UAV flight conditions and sensor specifications.

scholarly journals Spaceborne Estimation of Leaf Area Index in Cotton, Tomato, and Wheat Using Sentinel-2

Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 505
Author(s):  
Gregoriy Kaplan ◽  
Offer Rozenstein
Keyword(s):  
Water Vapor ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Near Infrared ◽  
Linear Regression Models ◽  
Area Index ◽  
Estimation Performance ◽  
Red Edge ◽  
Sentinel 2

Satellite remote sensing is a useful tool for estimating crop variables, particularly Leaf Area Index (LAI), which plays a pivotal role in monitoring crop development. The goal of this study was to identify the optimal Sentinel-2 bands for LAI estimation and to derive Vegetation Indices (VI) that are well correlated with LAI. Linear regression models between time series of Sentinel-2 imagery and field-measured LAI showed that Sentinel-2 Band-8A—Narrow Near InfraRed (NIR) is more accurate for LAI estimation than the traditionally used Band-8 (NIR). Band-5 (Red edge-1) showed the lowest performance out of all red edge bands in tomato and cotton. A novel finding was that Band 9 (Water vapor) showed a very high correlation with LAI. Bands 1, 2, 3, 4, 5, 11, and 12 were saturated at LAI ≈ 3 in cotton and tomato. Bands 6, 7, 8, 8A, and 9 were not saturated at high LAI values in cotton and tomato. The tomato, cotton, and wheat LAI estimation performance of ReNDVI (R2 = 0.79, 0.98, 0.83, respectively) and two new VIs (WEVI (Water vapor red Edge Vegetation Index) (R2 = 0.81, 0.96, 0.71, respectively) and WNEVI (Water vapor narrow NIR red Edge Vegetation index) (R2 = 0.79, 0.98, 0.79, respectively)) were higher than the LAI estimation performance of the commonly used NDVI (R2 = 0.66, 0.83, 0.05, respectively) and other common VIs tested in this study. Consequently, reNDVI, WEVI, and WNEVI can facilitate more accurate agricultural monitoring than traditional VIs.

scholarly journals NATURE-BASED COASTAL PROTECTION: WAVE DAMPING BY FLEXIBLE SALT MARSH VEGETATION

2020 ◽  
pp. 9
Author(s):  
Thomas J van Veelen ◽  
Harshinie Karunarathna ◽  
William G Bennett ◽  
Tom P Fairchild ◽  
Dominic E Reeve
Keyword(s):  
Salt Marsh ◽  
Salt Marshes ◽  
Large Scale ◽  
Coastal Vegetation ◽  
Coastal Protection ◽  
Coastal Zones ◽  
Wave Damping ◽  
Marsh Vegetation ◽  
Efficient Manner ◽  
Wave Energy Dissipation

The ability of coastal vegetation to attenuate waves has been well established (Moller et al., 2014). Salt marshes are vegetated coastal wetlands that can act as nature- based coastal defenses. They exhibit a range of plant species, which have been shown to differ in the amount of wave damping they provide (Mullarney & Henderson, 2018). Recent studies have shown that plant flexibility is a key parameter that controls wave energy dissipation (Paul et al., 2016). Yet, no model exists that includes plant flexibility in computationally efficient manner for large-scale coastal zones. Therefore, we have developed a new model for flexible vegetation based on the key mechanisms in the wave-vegetation interaction and applied it to an estuary with diverse salt marsh vegetation.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/AjnFx3aFSzs

Impact of salt marsh and seagrass beds on the sediemnt buget and resilience of coatsal areas

2020 ◽  
Author(s):  
Nicoletta Leonardi ◽  
Carmine Donatelli ◽  
Xiahoe Zhang ◽  
Neil Ganju ◽  
Sergio Fagherazzi
Keyword(s):  
Salt Marsh ◽  
Water Resources ◽  
Salt Marshes ◽  
Numerical Models ◽  
Sediment Budget ◽  
Coastal Protection ◽  
Seagrass Beds ◽  
Geophysical Research ◽  
Coastal System ◽  
Depositional Patterns

<p>Salt marshes and seagrass beds can offer sustainable coastal protection solutions and several ecosystem co-benefits. The delicate balance regulating salt marsh stability depends on several factors including the sediment added to and removed from the coastal system (<em>Donatelli et al., 2018, 2019; Zhang et al., 2019</em>).  Despite the importance of these sediment budget dynamics, many feedbacks between salt marsh presence and sediment availability are still unclear. Here, we use numerical models to simulate changes in depositional patterns of six estuaries along the U.S. coastline to investigate how salt marsh and seagrass beds removal and restoration can alter the sediment budget and resilience of coastal environments. </p><p><em>Donatelli, C., Ganju, N.K., Kalra, T.S., Fagherazzi, S. and Leonardi, N., 2019. Changes in hydrodynamics and wave energy as a result of seagrass decline along the shoreline of a microtidal back-barrier estuary. Advances in Water Resources, 128, pp.183-192.</em></p><p><em>Zhang, X., Leonardi, N., Donatelli, C. and Fagherazzi, S., 2019. Fate of cohesive sediments in a marsh-dominated estuary. Advances in water resources, 125, pp.32-40.</em></p><p><em>Donatelli, C., Ganju, N.K., Fagherazzi, S. and Leonardi, N., 2018. Seagrass impact on sediment exchange between tidal flats and salt marsh, and the sediment budget of shallow bays. Geophysical Research Letters, 45(10), pp.4933-4943.</em></p>

scholarly journals Spectral reflectance indices sense desiccation induced changes in the thalli of Antarctic lichen Dermatocarpon polyphyllizum

2018 ◽  
Vol 8 (2) ◽  
pp. 249-259 ◽  
Author(s):  
Miloš Barták ◽  
Kumud Bandhu Mishra ◽  
Michaela Marečková
Keyword(s):  
Spectral Reflectance ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Spectral Characteristics ◽  
Structural Features ◽  
Coefficient Of Determination ◽  
Curvilinear Relationship ◽  
Structural Deterioration ◽  
Reflectance Indices

Lichens, in polar and alpine regions, pass through repetitive dehydration and rehydration events over the years. The harsh environmental conditions affect the plasticity of lichen’s functional and structural features for their survival, in a species-specific way, and, thus, their optical and spectral characteristics. For an understanding on how dehydration affects lichens spectral reflectance, we measured visible (VIS) and near infrared (NIR) reflectance spectra of Dermatocarpon polyphyllizum, a foliose lichen species, from James Ross Island (Antarctica), during gradual dehydration from fully wet (relative water content (RWC) = 100%) to dry state (RWC = 0%), under laboratory conditions, and compared several derived reflectance indices (RIs) to RWC. We found a curvilinear relationship between RWC and range of RIs: water index (WI), photochemical reflectance index (PRI), normalized difference vegetation index (NDVI), modified chlorophyll absorption in reflectance indices (MCARI and MCARI1), simple ratio pigment index (SRPI), normalized pigment chlorophyll index (NPCI), and a new NIR shoulder region spectral ratio index (NSRI). The index NDVI was initially increased with maxima around 70% RWC and it steadily declined with further desiccation, whereas PRI in-creased with desiccation and steeply falls when RWC was below 10%. The curvilinear relationship, for RIs versus RWC, was best fitted by polynomial regressions of second or third degree, and it was found that RWC showed very high correlation with WI (R2 = 0.94) that is followed by MCARI (R2 = 0.87), NDVI (R2 = 0.83), and MCARI (R2 = 0.81). The index NSRI, proposed for accessing structural deterioration, was almost invariable during dehydration with the least value of the coefficient of determination (R2 = 0.28). This may mean that lichen, Dermatocarpon polyphyllizum, activates protection mechanisms initially in response to the progression of dehydration; however, severe dehydration causes deactivation of photosynthesis and associated pigments without much affecting its structure.

scholarly journals Evaluation of Vegetation Index-Based Curve Fitting Models for Accurate Classification of Salt Marsh Vegetation Using Sentinel-2 Time-Series

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5551
Author(s):  
Chao Sun ◽  
Jialin Li ◽  
Luodan Cao ◽  
Yongchao Liu ◽  
Song Jin ◽  
...  
Keyword(s):  
Time Series ◽  
Salt Marsh ◽  
Curve Fitting ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Coefficient Of Determination ◽  
Salt Marsh Vegetation ◽  
Marsh Vegetation ◽  
Sentinel 2

The successful launch of the Sentinel-2 constellation satellite, along with advanced cloud detection algorithms, has enabled the generation of continuous time series at high spatial and temporal resolutions, which is in turn expected to enable the classification of salt marsh vegetation over larger spatiotemporal scales. This study presents a critical comparison of vegetation index (VI) and curve fitting methods—two key factors for time series construction that potentially influence vegetation classification performance. To accomplish this objective, the stability of five different VI time series, namely Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), Green Normalized Difference Vegetation Index (GNDVI), and Water-Adjusted Vegetation Index (WAVI), was compared empirically; the suitability between three curve fitting methods, namely Asymmetric Gaussian (AG), Double Logistic (DL), and Two-term Fourier (TF), and VI time series was measured using the coefficient of determination, and the salt marsh vegetation separability among different combinations of VI time series and curve fitting methods (i.e., VI time series-based curve fitting model) was quantified using overall the Jeffries–Matusita distance. Six common types of salt marsh vegetation from three typical coastal sites in China were used to validate these findings, which demonstrate: (1) the SAVI performed best in terms of time series stability, while the EVI exhibited relatively poor time series stability with conspicuous outliers induced by the sensitivity to omitted clouds and shadows; (2) the DL method commonly resulted in the most accurate classification of different salt marsh vegetation types, especially when combined with the EVI time series, followed by the TF method; and (3) the SAVI/NDVI-based DL/TF model demonstrated comparable efficiency for classifying salt marsh vegetation. Notably, the SAVI/NDVI-based DL model performed most strongly for high latitude regions with a continental climate, whilst the SAVI/NDVI-based TF model appears to be better suited to mid- to low latitude regions dominated by a monsoonal climate.

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 Crop Type Classification Using Vegetation Indices of RapidEye Imagery

2014 ◽  
Vol XL-7 ◽  
pp. 195-198 ◽  
Author(s):  
M. Ustuner ◽  
F. B. Sanli ◽  
S. Abdikan ◽  
M. T. Esetlili ◽  
Y. Kurucu
Keyword(s):  
Remote Sensing ◽  
Classification Accuracy ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Support Vector ◽  
Red Edge ◽  
Crop Type ◽  
Type Classification

Cutting-edge remote sensing technology has a significant role for managing the natural resources as well as the any other applications about the earth observation. Crop monitoring is the one of these applications since remote sensing provides us accurate, up-to-date and cost-effective information about the crop types at the different temporal and spatial resolution. In this study, the potential use of three different vegetation indices of RapidEye imagery on crop type classification as well as the effect of each indices on classification accuracy were investigated. The Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Red Edge Index (NDRE) are the three vegetation indices used in this study since all of these incorporated the near-infrared (NIR) band. RapidEye imagery is highly demanded and preferred for agricultural and forestry applications since it has red-edge and NIR bands. The study area is located in Aegean region of Turkey. Radial Basis Function (RBF) kernel was used here for the Support Vector Machines (SVMs) classification. Original bands of RapidEye imagery were excluded and classification was performed with only three vegetation indices. The contribution of each indices on image classification accuracy was also tested with single band classification. Highest classification accuracy of 87, 46 % was obtained using three vegetation indices. This obtained classification accuracy is higher than the classification accuracy of any dual-combination of these vegetation indices. Results demonstrate that NDRE has the highest contribution on classification accuracy compared to the other vegetation indices and the RapidEye imagery can get satisfactory results of classification accuracy without original bands.

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