Integrating Imaging Spectrometer and Synthetic Aperture Radar Data for Estimating Wetland Vegetation Aboveground Biomass in Coastal Louisiana

Jensen, Daniel; Cavanaugh, Kyle C.; Simard, Marc; Okin, Gregory S.; Castañeda‐Moya, Edward; Mccall, A.; Twilley, Robert R.

doi:10.3390/rs11212533

Cited by 25 publications

(16 citation statements)

References 70 publications

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“…For example, different airborne radar products have been applied for estimating total river discharge (Altenau et al., 2017; Fayne et al., 2020; Tuozzolo et al., 2019) and the detection of the water surface and its flow through wetland vegetation (Liao et al., 2020). Additionally, airborne imaging spectrometer data has a demonstrated ability to derive accurate and transferable estimates of suspended sediment concentrations (Jensen, Simard, et al., 2019), as well as being able to map vegetation types and aboveground biomass (Jensen, Cavanaugh, Simard et al., 2019, 2021). These various airborne instruments and their datasets will be unified in NASA's Delta‐X Earth Venture Suborbital mission (https://deltax.jpl.nasa.gov/) to develop a regional process‐based accretion model in coastal Louisiana.…”

Section: Discussionmentioning

confidence: 99%

Leveraging the Historical Landsat Catalog for a Remote Sensing Model of Wetland Accretion in Coastal Louisiana

et al. 2022

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A wetland's ability to vertically accrete—capturing sediment and biological matter for soil accumulation—is key for maintaining elevation to counter soil subsidence and sea level rise. Wetland soil accretion is comprised of organic and inorganic components largely governed by net primary productivity and sedimentation. Sea level, land elevation, primary productivity, and sediment accretion are all changing across Louisiana's coastline, destabilizing much of its wetland ecosystems. In coastal Louisiana, analysis from 1984 to 2020 shows an estimated 1940.858 km2 of total loss at an average rate of 53.913 km2/year. Here we hypothesize that remote sensing timeseries data can provide suitable proxies for organic and inorganic accretionary components to estimate local accretion rates. The Landsat catalog offers decades of imagery applicable to tracking land extent changes across coastal Louisiana. This dataset's expansiveness allows it to be combined with the Coastwide Reference Monitoring System's point‐based accretion data. We exported normalized difference vegetation index (NDVI) and red‐band surface reflectance data for every available Landsat 4–8 scene across the coast using Google Earth Engine. Water pixels from the red‐band were transformed into estimates of total suspended solids to represent sediment deposition—the inorganic accretionary component. NDVI values over land pixels were used to estimate bioproductivity—representing accretion's organic component. We then developed a Random Forest regression model that predicts wetland accretion rates (R2 = 0.586, MAE = 0.333 cm/year). This model can inform wetland vulnerability assessments and loss predictions, and is to our knowledge the first remote sensing‐based model that directly estimates accretion rates in coastal wetlands.

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Section: Discussionmentioning

confidence: 99%

Leveraging the Historical Landsat Catalog for a Remote Sensing Model of Wetland Accretion in Coastal Louisiana

et al. 2022

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“…The Wax Lake and Atchafalaya Deltas are located about 175 km south-southeast of Louisiana and each covers an area of about 100 km 2 . These deltas are the few deltas within the Mississippi deltaic floodplain that are prograding [27,46], even as most of coastal Louisiana is experiencing land loss at the alarmingly rapid rate of approximately 57 km 2 /year between 1932 and 2016 [47]. In the case of the Wax Lake Delta, sediments accumulate at the mouth of the Wax Lake Outlet due to a man-made channel dredged by the U.S. Army Corps of Engineers in the early 1940s to reduce flooding in Morgan City [48].…”

Section: Test and Validation Sitesmentioning

confidence: 99%

Orinoco: Retrieving a River Delta Network with the Fast Marching Method and Python

Marshak

Simard

Denbina

et al. 2020

IJGI

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We present Orinoco, an open-source Python toolkit that applies the fast-marching method to derive a river delta channel network from a water mask and ocean delineation. We are able to estimate flow direction, along-channel distance, channel width, and network-related metrics for deltaic analyses including the steady-state fluxes. To demonstrate the capabilities of the toolkit, we apply our software to the Wax Lake and Atchafalaya River Deltas using water masks derived from Open Street Map (OSM) and Google Maps. We validate our width estimates using the Global River Width from Landsat (GRWL) database over the Mackenzie Delta as well as in situ width measurements from the National Water Information System (NWIS) in the southeastern United States. We also compare the stream flow direction estimates using products from RivGraph, a related Python package with similar functionality. With the exciting opportunities afforded with forthcoming surface water and topography (SWOT) data, we envision Orinoco as a tool to support the characterization of the complex structure of river deltas worldwide and to make such analyses easily accessible within a Python remote sensing workflow. To support that end, all the data, analyses, and figures in this paper can be found within Jupyter notebooks at Orinoco’s GitHub repository.

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“…The VIP scores [26] were used to identify the most relevant predictors X (independent original variables) for explaining Y (dependent variable). VIP allows classifying the X-variables according to their explanatory power of Y, enhancing the model interpretability through the identification of the most important predictors [27,28].…”

Section: Statistical Proceduresmentioning

confidence: 99%

Milk Production of Sarda Suckler Cows with Different Calving Period

Acciaro¹,

Dimauro²,

Giovanetti³

et al. 2020

JAST-A

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The study explored the relationship between the performance of calves and calving season in a Mediterranean rangeland-based beef livestock system. Twenty multiparous Sarda cows, grazing on a natural pasture, with two distinct calving periods (group A, 11 animals, calving date 15/10/2016 ± 16 (means ± s.d.), and group W, nine animals, calving date 26/01/2017 ± 11) were used. Meteorological data, herbage quality, daily milk yield (DMY), total milk yield (TMY), body weight (BW) of cows and calf, bodyweight daily gain (ADG) of calves, body condition score (BCS) and calving interval (CI) of cows were assessed. A mixed-effects model was used to DMY and ADG data while TMY, BCS, weaning weight (WW) and CI data were analyzed by a linear model. The most determining factors in the DMY and ADG were detected by means of partial least square regression (PLSR) procedure. Group W showed higher DMY (6.5 ± 0.3 kg/d vs. 4.5 ± 0.3 kg/d, p < 0.001) and TMY (1,189 ± 70 kg vs. 830 ± 60 kg, p = 0.002) than Group A, but this did not result in a greater ADG of calves (Group A: 0.83 ± 0.04 kg/d/animal and Group W: 0.99 ± 0.09 kg/d/animal, p-value not significant) or WW when adjusted for their age (Group A: 216 ± 14 kg/animal and Group W: 250 ± 22 kg/animal, p-value not significant). In contrast, the WW actually measured were higher in Group A than in Group W (257 ± 7 kg vs. 175 ± 8 kg, p < 0.001). The Group W cows experienced a minor CI than Group A cows (288 ± 13 d vs. 320 ± 8 d, p = 0.04). The results of PLSR suggest that the factors with utmost importance for both DMY and ADG were the age and the bodyweight of cows, highlighting the excellent maternal ability of Sarda breed and its good adaptation to environment.

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Integrating Imaging Spectrometer and Synthetic Aperture Radar Data for Estimating Wetland Vegetation Aboveground Biomass in Coastal Louisiana

Cited by 25 publications

References 70 publications

Leveraging the Historical Landsat Catalog for a Remote Sensing Model of Wetland Accretion in Coastal Louisiana

Leveraging the Historical Landsat Catalog for a Remote Sensing Model of Wetland Accretion in Coastal Louisiana

Orinoco: Retrieving a River Delta Network with the Fast Marching Method and Python

Milk Production of Sarda Suckler Cows with Different Calving Period

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