Aerial Image Analysis of Changes in Wetlands between 2019 and 2023 in the Barataria and Breton Sound Basins of Coastal Louisiana ()
1. Introduction
Erosion of marshland soils and vegetation in Louisiana accounts for more than 80% of coastal wetland loss in the continental United States since the 1930s [1] [2]. Land loss threatens the future of the state’s economy which is heavily dependent on coastally situated jobs in the fishing, hydrocarbon (oil and gas) extraction, and waterborne transportation industries. Slowing degradation along wetland margins on the Gulf of Mexico has been at the center of the state’s response to protect and restore coastal communities in the face of future hurricane storm surges and sea level rise.
Subsidence in the Mississippi Delta plain has been mapped for decades [3]. Because sea level in the Gulf of Mexico is rising at rates not observed over the past several thousand years, it has been predicted that much of Delta land cover will be “drowned” in submergence events similar to those documented in the early Holocene [4].
Wave-driven erosion of wetlands is currently a primary physical mechanism for land loss in Louisiana’s coastal estuaries [5], although a highly restricted sediment supply from the Mississippi River channel is the historical causal agent of regional wetland decline [6]. Tropical cyclone (hurricane) events have been shown to be highly destructive to coastal wetlands [7], but also can be a source of mineral sedimentation to organic marshes [8] [9]. In a recent survey report, Sharp (2021) [10] documented Hurricane Ida’s impacts in August 2021 on the monitoring equipment and the marshlands at around 120 long-term monitoring sites in coastal Louisiana. Post-Ida site visits showed that roughly half of the sites were heavily damaged or completely destroyed by Ida’s storm surge in the Barataria and Breton Sound Basins.
A significant compounding cause of wetland degradation has been canal cutting for oil and gas extraction since the 1940s, together with oil spills, such as the Deepwater Horizon (DWH) incident of April 2010 [1]. The DWH incident caused the largest marine oil spill in U.S. history, impacting the entire northern Gulf of Mexico ecosystem. Wetlands in the outer Barataria Basin estuary were the most heavily damaged by the persistence of toxic chemicals [11].
As part of Louisiana’s comprehensive plan for coastal protection and restoration (CPRA, 2017 [1] and updated in 2023), the state now plans to spend several billion dollars constructing sediment diversions from the Mississippi River into both the Barataria and Breton Sound basins. Controlled sediment diversions offer a promising investment to re-establish hydrologic flows from the Mississippi River that carry land-building sediments to nourish marshlands and slow coastal land loss rates. The Mid-Barataria and Mid-Breton diversions, both to be located in Plaquemines Parish and constructed of earthen and concrete levees, will divert sediment-laden water from the Mississippi River and deposit it into the estuary basins to help rebuild, restore, and sustain marshlands [1].
To identify and characterize coastal wetland damage in the Breton Sound basin, Potter and Amer (2020) [2] utilized a multiband-subtraction methodology that calculates a Normalized Difference Water Index (NDWI) from Landsat-8 Operational Land Imager (OLI) images at 30-m pixel resolution. The NDWI was calculated from the blue and short-wave infrared bands and yielded land/water boundary maps. Landsat NDWI time-series analysis by Potter and Amer (2020) [2] estimated that 245 km2 of wetlands in the Breton Sound Basin were transformed to open water from the impacts of Hurricanes Katrina and Gustav, equivalent to one-third the size of the city of New Orleans.
In the present study, high-resolution (<1-m pixel size) imagery, acquired and processed by the United States Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) from 2019 to 2023, was analyzed at the landscape scale to answer two scientific questions:
1) What are the bio-geophysical nature and the areal extent of changes in coastal shorelines and wetlands of the Barataria and Breton Sound basins over the past four years of intensive hurricane activity?
2) How have marshland/water boundaries in the probable impact areas of the Mid-Barataria and Mid-Breton sediment diversions recently changed in the landscape of water and wetland-covered patches?
The 6600 km2 Barataria basin and its watersheds are located adjacent to the west bank of the Mississippi River in southeastern Louisiana and the Gulf of Mexico (Figure 1). The major water bodies of the basin include the northern freshwater Lac des Allemands, the mid-basin Lake Salvador and Little Lake, with Barataria Bay at the southern margin [12] [13]. Freshwater and sediment inputs to the Barataria Basin were largely eliminated by the construction of flood protection levees along the Mississippi River and the closure of Bayou Lafourche at Donaldsonville in the early 1900s. The southern half of the basin consists of tidally influenced marshes connected to a large bay system behind several barrier islands. The average water depth across this basin is 1.5 m and is normally unstratified. The sub-basins most likely to be first impacted by the Mid-Barataria sediment diversion (MBASD) project will be Bayou Dupont, Bayou Grande Cheniere, and Round Lake [1] [14]. The tracts of both Hurricanes Zeta (October 2020) and Ida (August 2021) traversed the Barataria basin (Figure 1).
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Figure 1. Hurricane paths making landfall in the Barataria and Breton Sound Basins of southeastern Louisiana from 2005 to 2022.
The Breton Sound basin in Plaquemines Parish spans 1880 km2 from the eastern Mississippi River to the Mississippi River Gulf Outlet Canal, southeast of the city of New Orleans (Figure 1). Notable hydrologic features of the Breton Sound basin include Bayou Terre aux Boeufs and River aux Chenes, which are abandoned delta distributaries. Between 1940 and 1970, 33 km2 of canals were dredged across the distributary ridges that run from the river to the outer fringes of the basin estuary [15], a disturbance which promoted elevated outflow of fresh water and increased saltwater intrusion. Subsidence combined with sediment deprivation from flood protection levees has contributed to marshland loss in the upper and middle basins, particularly in the Bohemia sub-basin. The sub-basins most likely to be first impacted by the Mid-Breton sediment diversion (MBRSD) project will be River aux Chenes and Spanish Lake [1]. The tracts of Hurricanes Zeta traversed the Breton Sound basin in October 2020 (Figure 1).
2. Methods
The data sources and processing methods for remote-sensing images used in this study are described in this section. Image analysis methods are described for NAIP data collected in the years 2019, 2021 and 2023, followed by methods of marshland gain and loss mapping results.
2.1. Land/Water Cover Index
The NAIP NDWI [16] was calculated from two surface reflectance bands using the equation:
(1)
where, Green is the reflectance band from 0.53 to 0.59 µm, and NIR is the near-infrared reflectance band from 0.83 to 0.92 µm. The difference in land/water area (dNDWI) from one year to another was mapped for every major subbasin and estuary salinity zone inside the study area.
The NDWI values are a ratio that ranges from −1 to 1, with negative values indicating land that includes soil and live vegetation cover, and positive values indicating a majority of water coverage. If NDWI ranges from −1.0 to −0.2, then the pixel is 100% land, and if NDWI ranges from −0.2 to 0.0, then the pixel is a majority land and a minority water (Potter and Amer, 2020). If NDWI ranges from 0.0 to +0.49, then the pixel is a majority water and a minority land, and if NDWI ranges from +0.5 to +1.0, then the pixel is 100% water [16].
While there has been no spectral or atmospheric preprocessing of NAIP imagery regarding calibration across acquisition dates, the conversion of the RGB-NIR NAIP bands into a NDWI index effectively normalizes any spectral inconsistencies between image acquisition dates. A ratio of bands minimizes the absolute reflectance value differences between image acquisition dates and instead represents the NAIP pixel values in relative terms. Furthermore, atmospheric aerosol scattering effects in the NIR band region are weak, and consequently a time series of NDWI is less sensitive to atmospheric effects than is the normalized difference vegetation index (NDVI) [17].
The aircraft collecting different NAIP image data for this study would have passed over at different times of the day during the summer and autumn months, subjecting the images to subtle variations in tidal water levels along the lowest-lying areas of the Barataria and Breton Sound basins. The maximum daily tidal amplitude along the southeastern Louisiana coast is normally less than 20 cm [18], which would potentially introduce a higher level of uncertainty for pixels in the study area that were less than 15 cm elevation above sea level at any given hour and day. Because sea levels are rising in the Gulf of Mexico, the precise elevation relative to the sea level of any NAIP pixel area in the study region is a continuously changing variable over the four-year time period of this study.
2.2. Airborne Color-Infrared Imagery
Aerial image data were acquired from NAIP (www.fsa.usda.gov) as four-band imagery, composed of red, green, blue (RGB), and near-infrared (NIR) reflectance bands. Each NAIP image tile (DOQQ) covers a 3.7-minute quarter quadrangle.
NAIP image data acquired for the years 2019 (June-July), 2021 (November), and 2023 (June-July) were converted to NDWI raster images using Equation 1 for the Plaquemines Parish sections of the Barataria and Breton Sound basins. These NDWI products were regrouped into 10 equal-interval classes for subsequent statistical summaries. This processing step was taken to bin the NDWI values into broad classes of land/water cover and, as such, assure a high level of consistency between images collected on different dates.
2.3. Statistical Methods
Zonal statistics (including mean and standard deviation) for NDWI in each year were computed for the groups of NDWI class pixels that intersect selected a sub-basin polygon in a vector base layer created for the United States Geological Survey (USGS) Watershed Boundary Dataset (WBD). The hydrologic units (HU) in the WBD are arranged in a nested, 12-digit hierarchical system with each HU in the system identified using a unique code [19]. Any HU in the WBD defines the areal extent of surface water drainage to an outlet point on a dendritic stream network or to outlet points where the stream network is not dendritic.
To characterize the wetland plant species that occurred most frequently in each of the ten equal-interval classes for the NAIP NDWI mapped for the Plaquemines Parish sections of Barataria and Breton Sound basins, the point sampling dataset reported by Nyman et al. (2022) [20] was overlaid on the 2019 NDWI raster layer. Aerial surveys of wetland plant species were conducted in early 2021 from a helicopter in transects oriented in a north-south direction and spaced 3 km apart. Visual survey sites were located at a spacing of 0.8 km along these transects. Plant species present were identified, and their abundance was ranked by percent cover at each survey site. Based on species composition and abundance, each marsh survey station was also assigned a marsh type: fresh, intermediate, brackish, or saline marsh. For the present study, a 1-m polygon buffer zone around each marshland survey point was generated and used to calculate zonal statistics for 2019 NAIP NDWI values at a total of 1154 survey points.
2.4. Landscape patch metrics
Landscape metrics for the ten equal-interval classes of NDWI were computed using the FRAGSTATS program [21]. Class-level indices from FRAGSTATS were used to quantify the densities and spatial configuration of patches, providing a variety of metrics for the geometry, complexity, and aggregation levels of the wetland- versus water-covered patches across the study area.
The simplest index of landscape pattern is patch size. Patch size distribution can be summarized at the class level by a variety of metrics (e.g., mean, median, max and variance). The area-weighted mean patch size is also known as correlation length and indicates the distance that one might expect to traverse the map while staying in a particular patch, from a random starting point and moving in a random direction [22]. The boundaries between patches (or edges) represent another fundamental spatial attribute of a class mosaic. The most common measures of patch shape complexity are based fundamentally on the perimeter-to-area ratio (PARA).
The following landscape metrics for class area coverage and edge characteristics were compared for NDWI classes from 2019, 2021, and 2023 NAIP imagery: Percentage of landscape (PLAND), Area-weighted mean patch area (AREA_AM), Coefficient of variation in patch area (AREA_CV), Total edge (TE), Edge Density (ED). PLAND is a measure of how much of the landscape is comprised of a particular class. AREA_AM (m) provides a patch size metric as a function of the number of patches in the class and total class area. TE (m) is an absolute measure of total edge length of a particular patch type. ED (m ha-1) equals the sum of the lengths (m) of all edge segments in the landscape, divided by the total landscape area.
The following landscape metrics for patch shape characteristics were compared for NDWI classes from 2019, 2021, and 2023 NAIP imagery: GYRATE_AM is the area-weighted mean patch radius of gyration (m), which provides a measure of landscape continuity (also known as correlation length). This metric represents the average traversability of the landscape; specifically, the average distance one can move from a random starting point and traveling in a random direction without leaving the patch. CIRCLE is a shape metric that is computed as one minus patch area (m2) divided by the area of the smallest circumscribing circle; this metric approaches 0 for circular patches and approaches 1 for elongated patches. SHAPE is the shape index metric and equals the patch perimeter (m) divided by the square root of patch area. SHAPE corrects for the size problem of the PARA index by adjusting for a square standard and, as a result, is the simplest and perhaps most straightforward measure of shape complexity. SHAPE approaches 1 when the patch is square and increases without limit as patch shape becomes more irregular.
Four representative sub-basin areas (7 km2 each) were selected for FRAGSTATS analysis and visualization of wetland patch dynamics, two in probable impact areas of the Mid-Barataria sediment diversion and two in probable impact areas of the Mid-Breton sediment diversion (Figure 2; geographic coordinates provided in Table 1). The central locations of these sub-basins were selected on the basis of the presence of relatively continuous coverage of wetland vegetation in 2019 adjacent to named bayous, ponds, or lakes.
2.5. Water Level and Surface Elevation Monitoring Data
The state of Louisiana’s Coastwide Reference Monitoring System (CRMS) has been designed to monitor the effectiveness of coastal restoration projects [23]. CRMS includes around 400 sites encompassing a range of fresh, intermediate, brackish, and salt marshes, monitored using standardized data collection techniques and fixed sampling schedules. The CRMS datasets for water level and land surface elevation were accessed and plotted for this study to compare to results for recent NAIP water/land class mapping.
Table 1. Sub-basins of the study area (covering 7 km2 each) selected for FRAGSTATS analysis and visualization of wetland patch dynamics. Center locations of each circular sub-basin area are listed.
Sub-Basin Name |
Diversion |
Latitude |
Longitude |
Bayou McCutchen |
Mid-Barataria |
29.5798 |
−89.9443 |
Bayou Tambour |
Mid-Barataria |
29.5559 |
−89.9262 |
Little Oak Pond |
Mid-Breton |
29.6938 |
−89.9228 |
Lake Batola |
Mid-Breton |
29.6607 |
−89.8006 |
Figure 2. NDWI classes derived from 2019 NAIP imagery for the Barataria Basin portion of the study area.
The Rod Surface Elevation Table (RSET) method implemented within the CRMS network is used to estimate elevation change rates. One RSET benchmark is located at each CRMS site (with the exception of floating marshes and perpetually flooded, flocculent swamps). From the RSET benchmark, surface elevation is measured at nine points in four directions annually.
Surface elevation change is defined as cumulative elevation change since station establishment. For each sampling event, mean cumulative elevation change is calculated for each of the four directions, and an elevation change rate is estimated using linear regression of elevation change against time. The lines in this figure are linear regressions of elevation change (n = 4 per sample date) over time. Rates reported for elevation change are the slopes of the linear regression (cm yr−1).
2.6. CRMS Monitoring Site Surveys After Hurricane Ida
In a recent survey report, Sharp (2021) [10] documented Hurricane Ida’s impacts in August 2021 on the monitoring equipment and the marshlands at around 120 CRMS monitoring sites in coastal Louisiana. The photographic information on damage to marshlands assembled for these sites was compared to changes in NAIP NDWI from the 2019 and (post-Ida) 2021 image acquisitions in 100-m radius circular areas (0.3 km2) centered on each reported station geo-location.
3. Results
3.1. Wetland Plant Cover in NWDI Classes
The frequency of plant species present across the entire study area in 2021, derived from the aerial survey sampling reported by Nyman et al. (2022) [20] overlaid on the 2019 NDWI raster layer (example in Figure 2), showed that the NDWI class 4 (range of −0.2 to −0.4) had about 30% water cover (Table 2) and the highest frequency of freshwater-favoring marsh plants such as Baccharis halimifolia, Colocasia esculenta, Phragmites australis, Sagittaria lancifolia, and Typha domingensis. NDWI class 5 (range of 0.to −0.2) showed 63% water cover and was dominated by Phragmites australis, Spartina alterniflora, and Spartina patens in land cover areas. NDWI classes 6 and 7 (ranges of 0 to 0.2 and 0.2 to 0.4, respectively) both showed about 80% water cover and were dominated by brackish-favoring Spartina alterniflora and Spartina patens in land cover areas. NDWI
Table 2. Frequency of plant species present in NAIP NDWI classes, computed as the percent of sites reporting that species as the primary plant cover, based on the aerial survey sampling reported by Nyman et al. (2022) and overlaid on the 2019 NDWI raster layer of the Barataria and Breton Sound Basins. Key to plant species in Table 2.
USDA Plant Symbol |
Species |
Common Name |
ALPH |
Aletris farinosa |
white colicroot |
AMAU |
Amaranthus tamaulipensis |
tamaulipas amaranth |
BACA |
Bacopa caroliniana |
blue waterhyssop |
BAHA |
Baccharis halimifolia |
groundsel bush sea-myrtle |
CAHY3 |
Calamagrostis hyperborea |
reed grass |
CASE13 |
Calystegia sepium |
hedge false bindweed |
COES |
Colocasia esculenta |
coco yam |
CYPER |
Cyperus sp. |
flatsedge |
DISP |
Dichanthelium sphaerocarpon |
roundseed panicgrass |
ECWA |
Echinochloa walteri |
coast cockspur grass |
FIMBR |
Fimbristylis sp. |
fimbry |
HEAM |
Helenium amarum |
sneezeweed |
ISPA |
Isachne pallens |
bloodgreass |
JUEF |
Juncus effusus |
common rush |
JURO |
Juncus roemerianus |
needlegrass rush |
LUDWI |
Ludwigia sp. |
primrose-willow |
PHAU7 |
Phragmites australis |
roseau cane |
PLFO |
Pluchea foetida |
stinking camphorweed |
POLYG4 |
Polygonum sp. |
knotweed |
SALA |
Sagittaria lancifolia |
bull tongue arrowhead |
SALA2 |
Sagittaria latifolia |
broadleaf arrowhead |
SALIX |
Salix sp. |
willow |
SCAM6 |
Schoenoplectus americanus |
chairmaker's bulrush |
SCHOE6 |
Schoenoplectus sp. |
bulrush |
SCOR5 |
Scapania ornithopodioides |
bird's-foot earwort |
SPAL |
Spartina alterniflora |
smooth cordgrass |
SPPA |
Spartina patens |
saltmeadow cordgrass |
TYPHA |
Typha domingensis |
southern cattail |
VILU3 |
Vigna luteola |
hairy cowpea |
ZIMI |
Zizaniopsis miliacea |
giant cutgrass |
classes 8, 9, and 10 (ranging from 0.4 to 1.0) showed greater than 90% water cover and commonly less than 5% coverage by Spartina sp. or any other wetland plants.
The percent cover of pixels in NAIP images acquired in 2019, 2021, and 2023 for the Barataria Basin portion of the study area was compiled within NDWI equal-interval classes (Table 3) and showed that the greatest magnitude of change
Table 3. Percent cover of pixels in NAIP images from 2019, 2021, and 2023 within NDWI classes of the Barataria Basin of the study area. Coverage classes with the greatest magnitude of change are shaded in gray.
NDWI Class |
NDWI range |
2019 Percent |
2021 Percent |
2023 Percent |
1 |
−0.8 to −1.0 |
0.002 |
0.002 |
0.003 |
2 |
−0.6 to −0.8 |
0 |
0 |
0 |
3 |
−0.4 to −0.6 |
0 |
0.001 |
0.063 |
4 |
−0.2 to −0.4 |
0.286 |
0.181 |
5.525 |
5 |
0 to −0.2 |
11.766 |
24.249 |
31.618 |
6 |
0.2 to 0 |
27.188 |
12.81 |
17.044 |
7 |
0.4 to 0.2 |
6.073 |
5.137 |
44.415 |
8 |
0.6 to 0.4 |
13.51 |
6.098 |
1.311 |
9 |
0.8 to 0.6 |
39.858 |
23.451 |
0.018 |
10 |
1 to 0.8 |
1.316 |
28.072 |
0.004 |
between 2019 and late 2021 (before and after the passage of both Hurricanes Zeta and Ida, respectively) was within NDWI class 5 (range of 0 to −0.2), which showed a 12% increase in marshland area dominated by Phragmites australis and Juncus roemerianus, and also within NDWI class 6 (range of 0 to 0.2), which showed a 16% decrease of marshland area dominated by Spartina sp. by late autumn of 2021. Within NDWI classes 8 and 9, which were relatively shallow water-dominated areas, these zones lost around 6% coverage between 2019 and 2021 and evidently contributed to a 27% increase in deeper water depth (NDWI class 10) coverage by late autumn of 2021, following Hurricanes Zeta (2020) and Ida (2021).
Changes in the percent cover of pixels in NAIP images of the Barataria Basin portion of the study area between late 2021 and mid-2023 showed that the greatest magnitude of change was within NDWI class 7 (range 0.2 to 0.4, 80% water cover) with sparse wetland cover dominated by brackish-favoring Spartina sp. This class 7 coverage increased by nearly 40% over the period since late 2021 (post-Hurricane Ida), whereas NDWI classes 8 - 10 each decreased to 1% or lower cover area in 2023. NDWI classes 5 class, co-dominated by Phragmites australis and Spartina sp., increased by 6% in area over the period since late 2021.
3.2. Comparisons of CRMS Monitoring Sites Following Hurricane Ida
Changes in NAIP NDWI from the 2019 (pre-Ida) and 2021 (post-Ida) image acquisitions at the (total of 85) CRMS sites photographed by Sharp (2021) [10] were summarized in zonal statistics for 100-m radius circular areas (0.3 km2) centered at each reported site (geo-location map in Figure 3). Comparison of frequency distribution plots (Figure 4) of the mean NDWI values for these 85 0.3 km2 areas showed that the majority of sites shifted from mean values to the range NDWI of −0.2 to 0.2 in 2019 to a NDWI range of 0 to 0.4 in 2021, indicating a higher frequency of post-Ida water coverage. Comparison of frequency distribution plots (Figure 4) of the standard deviation of NDWI values for these 85 0.3 km2 areas showed that the majority of sites shifted from values in the range of 0.25 to 0.35 in 2019 to a range of 0.35 to 0.45 in 2021, indicating a higher variability in post-Ida
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Figure 3. Locations of CRMS stations (black dot symbols) surveyed by Sharp (2021) after the passage of Hurricane Ida in 2021. The station numbers were labeled for the four sites that showed the greatest change in mean NDWI from 2019 to 2021 NAIP image acquisitions.
Figure 4. Frequency distribution plots of the mean and standard deviation of NAIP NDWI values for 0.3 km2 areas (N = 85) centered on CRMS stations surveyed by Sharp (2021) before and after the passage of Hurricane Ida in 2021.
land versus water coverage.
The four CRMS station sites that showed the greatest change in mean NDWI from 2019 to 2021 NAIP image acquisitions (labeled with station numbers in map Figure 3) were selected for comparisons to the photographic evidence of marshland change provided by Sharp (2021) [10]. The pre-Ida photos of these locations showed dense coverage of marshland vegetation surrounding the boardwalks and station pole markers at all four CRMS sites (Figure 5 and Figure 6), whereas post-Ida
Figure 5. Photographic evidence of marshland changes surveyed by Sharp (2021) before and after the passage of Hurricane Ida in 2021 at CRMS stations 115 and 161.
Figure 6. Photographic evidence of marshland changes surveyed by Sharp (2021) before and after the passage of Hurricane Ida in 2021 at CRMS stations 4013 and 387.
photos from Sharp (2021) [10] showed widespread removal of plant coverage and replacement by open water surrounding the station markers still remaining after the 2021 storm surge. The disturbance patterns revealed in these before- and after-Ida CRMS photographs verified that NAIP NDWI changes accurately quantified a higher frequency of post-Ida water coverage.
The 2019 to 2021 (post-Ida) NAIP NDWI map comparison at CRMS station 115 (Figure 7), located in the Spanish Lake sub-basin near the Pipeline Canal of inner Breton Sound Basin, showed extensive erosion of the vegetated marshland shoreline and transformation of NDWI class 5 (range of 0 to −0.2) dominated by Phragmites australis and Spartina sp. to NDWI class 8 open water coverage. Likewise, the 2019 to 2021 (post-Ida) NAIP NDWI map comparison at CRMS station 161 (Figure 7), located near the Raphael Pass sub-basin in the transition from Breton Sound Basin to the Chandaleur Sound, showed loss vegetated marshland, widening of several canals, large pond formations, and extensive transformation of NDWI classes 4 and 5 dominated by Spartina sp. to NDWI classes 8 to 10 open water coverage.
The 2019 to 2021 (post-Ida) NAIP NDWI map comparison at CRMS station 4103 (Figure 8), north of the Bayou Dupont sub-basin near a pipeline canal of inner Barataria Basin, showed deep erosion of all vegetated bayou shorelines, extensive pond formation and fragmentation of marshlands, and extensive transformation of most NDWI classes 4 and 5 coverage dominated by Phragmites australis and Spartina sp. to NDWI classes 8 and 9 open water coverage. The 2019 to 2021 (post-Ida) NAIP NDWI map comparison at CRMS stations 387 (sub-stations 1 and 2; Figure 8), located in the Grand Bayou Blue sub-basin of the Terrebonne Basin, showed nearly complete loss of vegetated marshland coverage, shoreline fragmentation, large pond formations, and extensive transformation of most NDWI classes 5 and 6 dominated by Spartina sp. to NDWI classes 9 to 10 open water coverage. These are now “plucked” marshlands that show numerous closely spaced, irregular scours and ponds [24].
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Figure 7. CRMS stations (115 and 161) areas that showed the greatest change in mean NDWI from 2019 to 2021 NAIP image acquisitions, with outlines of 100-m radius circular areas centered on the CRMS geo-location reported by Sharp (2021).
Figure 8. CRMS stations (4103 and 387) areas that showed the greatest change in mean NDWI from 2019 to 2021 NAIP image acquisitions, with outlines of 100-m radius circular areas centered on the CRMS geo-location reported by Sharp (2021).
3.3. Landscape Patch Dynamics in Sub-Basins
Analysis of the area cover and edge complexity of the wetland versus water-covered patches in probable impact areas of the Mid-Barataria and Mid-Breton sediment diversions was carried out for four circular 7 km2 sub-basin areas, each with a diameter of 3 km (Figure 9). FRAGSTATS results (Table 4) showed that the NDWI class 4 (range of −0.2 to −0.4), dominated by freshwater-favoring marsh plants, had a PLAND of just 2.5% in 2019 and a relatively low AREA_AM of 1.4 m2. In contrast, NDWI class 5 (range of 0 to −0.2), dominated by brackish-favoring marsh plants like Spartina sp., covered 15% of these sub-basin areas in 2019 and showed higher ED values along with an AREA_AM of 11 m2. NDWI classes 6 - 8 (ranging from 0 to 0.6) and dominated by 80% - 90% water cover, covered 51% of these sub-basin areas in 2019 and showed the highest edge length (TE and ED) index values of all the NDWI classes. The AREA_AM coverage of these three classes ranged from 14 to 50 m2 in 2019. NDWI class 9 covered 32% of these sub-basins in 2019 with an AREA_AM coverage of 253 m2, whereas the (deepest) water class 10 covered less than 1% of these sub-basins in 2019.
FRAGSTATS analysis of the shape and connectivity levels of the wetland versus water-covered patches in probable impact areas of the Mid-Barataria and Mid-Breton sediment diversions (Table 4) showed that the GYRATE and SHAPE metrics increased with increasing water cover across the ten NDWI classes in 2019. A longer average traversability length and a more irregular overall shape in NDWI classes 7 - 9 patches were discovered, compared to classes 4 - 6 patches. The CIRCLE metric showed that NDWI classes 9 and 10 (deepest water cover) were those with the most circular (least irregular) patch shapes in 2019.
Between 2019 and 2021 NAIP image acquisitions, several notable changes were detected in the landscape metrics for NDWI classes in these same four 7 km2 sub-basins. The most striking shift was in the percent cover (PLAND) and AREA_AM
Figure 9. Locations of four sub-basins used in landscape patch analysis for the Barataria and Breton Sound Basins.
Table 4. (a) Landscape patch metrics from NAIP image analysis from the years 2019, 2021, and 2023 within NDWI classes of the four sub-basin areas shown in Figure 3. Column headings are defined as follows: Percentage of landscape (PLAND; %), Total edge (TE; m), Edge Density (ED; m ha−1), Area-weighted mean patch area (AREA_AM; m2), Coefficient of variation in patch area (AREA_CV). (b) Column headings are defined as follows: Area-weighted mean patch radius of gyration or the average traversability of the landscape (GYRATE_AM; m); Shape index (SHAPE), Circular shape index (CIRCLE).
(a) |
2019 NDWI Class |
PLAND |
TE |
ED |
AREA_AM |
AREA_CV |
3 |
- |
2 |
0 |
0 |
0 |
4 |
2.5 |
660201 |
237.1 |
1.382 |
2674 |
5 |
14.6 |
2328773 |
836.3 |
10.919 |
4648 |
6 |
17.6 |
3565343 |
1280.4 |
13.539 |
3817 |
7 |
16 |
3526448 |
1266.4 |
50.098 |
8486 |
8 |
17.2 |
3793353 |
1362.2 |
32.397 |
11388 |
9 |
31.5 |
2608909 |
936.9 |
253.053 |
13628 |
10 |
0.5 |
450041 |
161.6 |
0.001 |
158 |
2021 NDWI Class |
PLAND |
TE |
ED |
AREA_AM |
AREA_CV |
3 |
0 |
92 |
0 |
0 |
41 |
4 |
0.4 |
198147 |
71.2 |
0.069 |
1146 |
5 |
19.1 |
2972343 |
1067.4 |
8.092 |
2715 |
6 |
6.9 |
3778960 |
1357 |
0.141 |
1306 |
7 |
5.1 |
2302023 |
826.7 |
0.595 |
2737 |
8 |
5.1 |
2253466 |
809.2 |
0.332 |
1857 |
9 |
31.6 |
8711400 |
3128.3 |
126.211 |
22135 |
10 |
31.8 |
7482321 |
2686.9 |
210.641 |
22114 |
2023 NDWI Class |
PLAND |
TE |
ED |
AREA_AM |
AREA_CV |
1 |
0 |
7 |
0 |
- |
- |
2 |
0 |
146 |
0 |
- |
17 |
3 |
0 |
21451 |
0.7 |
0 |
58 |
4 |
6.4 |
3749644 |
119.7 |
0.888 |
4075 |
5 |
28.5 |
6695021 |
213.6 |
31.449 |
7939 |
6 |
23.2 |
6428726 |
205.1 |
40.295 |
18643 |
7 |
41.4 |
4090545 |
130.5 |
236.613 |
15267 |
8 |
0.5 |
730764 |
23.3 |
0 |
59 |
9 |
0 |
9800 |
0.3 |
- |
17 |
10 |
0 |
334 |
0 |
- |
16 |
(b) |
2019 NDWI Class |
GYRATE_AM |
GYRATE_CV |
SHAPE_AM |
SHAPE_CV |
CIRCLE_AM |
CIRCLE_CV |
3 |
0.3 |
0 |
1 |
0 |
0 |
0 |
4 |
48.2 |
259.9 |
9 |
45.03 |
0.75 |
68.97 |
5 |
174.9 |
433.9 |
20.9 |
57.88 |
0.79 |
65.93 |
6 |
180.5 |
452.5 |
34 |
112.45 |
0.78 |
66.34 |
7 |
320.2 |
338 |
39.3 |
95.63 |
0.8 |
60.58 |
8 |
259.4 |
452.5 |
29.7 |
60.75 |
0.78 |
78.24 |
9 |
852.7 |
856.3 |
84.7 |
68.47 |
0.62 |
76.19 |
10 |
1 |
69.7 |
1.4 |
18.13 |
0.56 |
93.56 |
2021 NDWI Class |
GYRATE_AM |
GYRATE_CV |
SHAPE_AM |
SHAPE_CV |
CIRCLE_AM |
CIRCLE_CV |
3 |
0.54 |
10.2 |
1 |
6.99 |
0.12 |
311.18 |
4 |
9.7 |
134.2 |
4.1 |
38.4 |
0.62 |
140.53 |
5 |
167.81 |
451.4 |
24.5 |
90.26 |
0.78 |
86.33 |
6 |
14.68 |
145.4 |
6.4 |
52.97 |
0.71 |
86.69 |
7 |
28.71 |
188.5 |
8.4 |
45.75 |
0.7 |
105.84 |
8 |
28.04 |
226.4 |
7.6 |
51.55 |
0.74 |
107.49 |
9 |
600.44 |
383 |
157.2 |
82.47 |
0.73 |
108.96 |
10 |
642.76 |
399.9 |
162.4 |
103.78 |
0.58 |
101.16 |
2023 NDWI Class |
GYRATE_AM |
GYRATE_CV |
SHAPE_AM |
SHAPE_CV |
CIRCLE_AM |
CIRCLE_CV |
1 |
0.3 |
- |
1 |
- |
- |
- |
2 |
0.3 |
- |
1 |
- |
0.03 |
538.52 |
3 |
0.3 |
17.9 |
1 |
5.67 |
0.23 |
179.09 |
4 |
37.3 |
208.9 |
13.2 |
52.8 |
0.74 |
76.13 |
5 |
258 |
527 |
58.1 |
110.34 |
0.76 |
76.61 |
6 |
316.5 |
450.1 |
55.1 |
51.43 |
0.8 |
92.02 |
7 |
714.7 |
838.2 |
106.5 |
76.29 |
0.6 |
80.69 |
8 |
0.4 |
22.9 |
1.1 |
9.32 |
0.3 |
149.68 |
9 |
0.3 |
2.3 |
1 |
1.44 |
0.03 |
588.7 |
10 |
0.3 |
- |
1 |
- |
0.03 |
576.63 |
of shallow water-dominated NDWI classes 6 - 8 that transitioned into relatively large patches of deeper water-dominated classes 9 and 10 (Table 4). In addition, the TE and ED values of NDWI classes 9 and 10 increased by several-fold between 2019 and 2021, suggesting that longer, more complex shorelines and marshland edge boundaries were developed with classes 5 and 6. The reduction of the GYRATE and SHAPE metric values across all NDWI classes 4 - 9 between 2019 and 2021 further suggested that more compact, less irregular patch shapes of both wetland and water cover were formed during the years 2020 and 2021 in the four sub-basins areas. The CIRCLE metric increased noticeably for all NDWI classes implying more circular (and less irregular) patch shapes were formed in these sub-basins between 2019 and 2021.
Between NAIP image acquisitions in 2021 (post-Hurricane Ida) and in 2023, several noteworthy changes were detected in the landscape metrics for NDWI classes in these same four sub-basins. A distinct shift occurred in the percent cover (PLAND) and AREA_AM of deep water-dominated NDWI classes 9 and 10, transitioning into large patches of relatively shallow water-dominated classes 5 - 7 (Table 4). In addition, the TE and ED values of NDWI classes 9 and 10 decreased considerably between 2021 and 2023, transitioning to the longest, most complex shorelines and marshland edge boundaries in NDWI classes 5 - 7. Associated increases in the GYRATE and SHAPE metric values cross NDWI classes 5 - 7 further suggested that less compact, more irregular patch shapes of wetland cover were formed in the four sub-basins areas between 2021 and 2023. The CIRCLE metric decreased markedly for NDWI classes 8 - 10 (predominantly water cover), implying less circular (and more irregular) patch shapes by 2023 for the deepest water cover zones.
Mapping of NAIP NDWI classes in these four 7 km2 sub-basins over time revealed numerous locations where shoreline erosion and/or wetland drowning have occurred (Figure 10). Extensive shoreline erosion was detected between 2019 and 2021 on the southern margins of the Bayou McCutchen sub-basin and in the center of the Bayou Tambour sub-basin. In the Breton Sound sub-basin areas of Little Oak Pond and Lake Batola, replacement of NDWI class 6 (partially vegetated) marshland patches detected in 2019 by NDWI class 9 and 10 open water cover was extensive by 2021.
Patterns of change between 2019 and 2021 in NDWI classes 7 – 10 could be seen in these four 7 km2 sub-basins, most prominently in the shift from a predominance of shallow water depths to deeper water depths in all sub-basin examples. The absence of smooth gradations from NDWI class 7 to class 8 that were evident in 2019 NAIP images, instead showing deeper NDWI class 9 and 10 areas in 2021 NAIP imagery in these buffer zones adjacent to patches of marshland plants.
Figure 10. Maps of NDWI classes within four sub-basins in the Barataria and Breton Sound Basins, from NAIP imagery collected in 2019, 2021, and 2023.
The changes in patch geometry between 2021 (post-Hurricanes Zeta and Ida) and 2023 in NDWI classes could be seen most prominently in the shift from a predominance of relatively deep-water depths to shallower water depths and a widespread recovery of NDWI class 7 (80% water cover) with sparse wetland cover dominated by brackish-favoring Spartina sp. Patches of NDWI classes 4 and 5, dominated by Phragmites australis and Juncus roemerianus in 2019 NAIP imagery, have failed to recover and regrow extensively since 2021 in these four sub-basins (Figure 10).
Spatial variability in patch metrics, as measured by the coefficient of variation (CV; Table 4), increased for the AREA_AM, SHAPE_AM, and CIRCLE_AM metrics of NDWI classes 9 and 10 between 2019 and 2021. Variability in the patch metrics AREA_AM, GYRATE_AM, and SHAPE_AM of NDWI classes 5 - 7 increased by several-fold between 2021 and 2023.
3.4. Measured Water and Surface Elevation Changes at CRMS
Stations
Plots of water elevation change (Figure 11 from the CRMS data viewer) from the closest CRMS station locations to the four sub-basins in the Barataria and Breton Sound Basins (shown in Figure 9) all showed a substantial rise in water levels immediately following both Hurricane Zeta (2020) and Hurricane Ida (2021).
Figure 11. Plots of water elevation change from the closest Coastwide Reference Monitoring System (CRMS) station locations to the four sub-basins in the Barataria and Breton Sound Basins shown in Figure 2. (a) Bayou McCutchen, (b) Bayou Tambour, (c) Little Oak Pond, (d) Lake Batola. Conversion factor is 1 foot equals 30.5 cm of water level change.
Figure 12. Plots of land elevation change from the closest Coastwide Reference Monitoring System (CRMS) station locations to the four sub-basins in the Barataria and Breton Sound Basins shown in Figure 2. (a) Bayou McCutchen, (b) Bayou Tambour, (c) Little Oak Pond, (d) Lake Batola CRMS monitoring equipment nearest the Lake Batola sub-basin was evidently disabled after the passage of Hurricane Ida in August 2021.
In general, the water elevation at all four stations reached peak measured change levels of 5 to 15 cm over the time period between 2019 and 2023 immediately following these intense storm surges. Water elevation dropped by around 5 cm over the years 2022 - 2023 at all four stations. This difference in water levels after the two hurricanes in 2020 and 2021 verified that the shift from NDWI class 6 and 7 to classes 9 - 10 was associated with deeper water depths detected by NAIP images. It is worth noting that the monitoring equipment nearest the Lake Batola sub-basin was evidently disabled for around 16 months after the passage of Hurricane Ida.
Plots of land surface elevation change (Figure 12 from the CRMS data viewer) from the closest CRMS station locations to the four sub-basins in the Barataria and Breton Sound Basins (shown in Figure 9) displayed more diverse patterns. The CRMS stations closest to the Bayou McCutchen and Lake Batola sub-basins both showed steep negative trends in land elevation levels (and apparent wetland drowning) following Hurricane Zeta, whereas land elevation trends at the stations closest to the Bayou Tambour and Little Oak Pond sub-basins showed minor declines in land elevation following Hurricane Zeta and Hurricane Ida.
4. Discussion
The main findings from this study were that there has been an extensive increase in relatively deep-water coverage (and corresponding losses of land coverage) since 2019 over most of the marshlands and shorelines of the Barataria and Breton Sound Basins in southeastern Louisiana. Deep scouring and widespread erosion of brackish and fresh marshlands were detected after Hurricane Ida in 2021 NAIP imagery [24]. In the Barataria Basin, the combined impacts of the DWH Oil Spill in 2010 [11], Hurricane Zeta in 2020, and Hurricane Ida in 2021, whose eye traveled just west of Bayou Lafourche on the western margins of the Basin, eroded the estuary into progressively larger patches of deeper water cover. Longer shorelines and marshland edge boundaries were developed, particularly in brackish marsh cover areas. In the Breton Sound Basin, the storm surge of Hurricane Zeta in 2020 resulted in widespread drowning of both freshwater and brackish marsh vegetation cover and generated more compact, less irregular patch shapes of both wetland and water cover. Many square kilometers of sparsely vegetated marshland patches dominated by Spartina sp. that were mapped in 2019 were replaced by open water cover following the storm surge of Hurricane Ida in August 2021.
Spatial variability in the patch metrics for area coverage and irregularity of shapes increased in deeper-water areas mapped by NAIP imagery between 2019 and 2021 in the Barataria and Breton Sound Basin study areas. Variability in the patch attributes of sparsely wetland-covered areas increased markedly between 2021 (post-Ida) and 2023, suggesting that there is presently a more fragmented landscape of regrowing marshland patches dominated by Spartina sp. than was present in 2019.
Hurricanes have been documented to damage marsh plants and reduce wetland biomass production which is essential for organic accretion in coastal zones [24] Tropical storm surges may decrease marsh elevation by removing substrates near the shoreline and channel edges through scouring and erosion [25], or conversely, intense storms can increase marsh elevation by moving sediment onto tidal flats [8] [9].
In the survey report by Sharp (2021) [10], photographic evidence revealed uprooting and nearly complete drowning of marsh plant cover at damaged Barataria Basin CRMS stations following Hurricane Ida. Some sites in Breton Sound appeared to be “salt burned”, meaning that saline water introduced into freshwater marsh zones from the storm surge killed much of the vegetation cover. Stations in the Mississippi River bird’s-foot Delta facing Chandaleur Sound commonly saw washed-up wrack (dead reeds and leaf debris mixed with trash) and sediment deposits. This photographic evidence of wetland loss was consistent with the mapping of NDWI class dynamics from NAIP imagery collected in 2019 (pre-Ida) and 2021 (post-Ida).
In previous studies of wetland fragmentation processes, Lam et al. (2018) [26] weak regression results between fragmentation and wetland loss in the Mississippi River Delta plain, with R2 values between 0.20 and 0.45. These authors suggested that additional factors needed to be considered in such fragmentation studies, including (1) testing with different land/water ratio thresholds; (2) elevation effects; (3) distance to the coast, and (4) storm surge effects. In a more conclusive study, Stagg et al. (2020) [27] reported differences in the landscape configuration of vegetated and open water pixels in healthy and degrading wetlands of the Chenier Plain along the Northern Gulf of Mexico. Healthy wetlands were more aggregated, and degrading wetlands were more fragmented. Greater aggregation levels were associated with higher wetland elevation and better drainage, compared to fragmented wetlands, at lower elevations and poorer drainage.
To further compare how changes in marshland versus open water cover from the present study align with or differ from trends observed in past published studies in the Mississippi River Delta region, Potter (2021) [10] reported for subbasins of the western Barataria Basin near the Bayou Lafourche that, based on trends in Landsat NDWI, a baseline total of 42.3 km2 was estimated to fall into the category of wetland loss since 2005. The leading subbasins for these recent wetland-to-water conversions made up most of the southwestern portion of the study area just north of Port Fourchon. Potter and Sukanna (2024) [28] used Landsat NDWI to map the extensive erosion of marshland edges in northern Barataria Bay that has continued from 2013 to 2022. Oiling from the Deepwater Horizon spill event in 2010 has contributed to high erosion rates observed after recent tropical storms in 2020 and 2021 in Barataria Bay. These authors concluded that the combination of Landsat NDWI trend mapping with high-resolution image segmentation of marshland edges and interior features confirms that different types of biophysical damage inflicted on coastal wetlands of southeastern Louisiana from tropical storms can be characterized using this combination of satellite and aerial remote sensing.
As a general overview of wetland disturbance processes in the Mississippi River Delta region, interior marsh pond formation and enlargement, either elongated or irregular in shape, can result from erosion on pond perimeters, flooding of wetlands within a pond, and coalescing of adjacent ponds [29]. Expansion of open water area is often not uniform around an interior pond perimeter, and wetland loss often occurs on a margin that is impacted most by the direction of the wind-driven waves. Coastal Louisiana is a micro-tidal system and the waves interacting with the marsh edges are generated locally within a shallow bay with minimal river current influence [6].
In storm-impacted coastal areas, “plucked” marshlands [24] show numerous small, closely spaced, circular or irregular scours or ponds that are distributed over a wide area, resulting in a pockmarked appearance [30]. Laterally displaced marsh mats and marsh balls can be formed from deep scouring and erosion of brackish and fresh marshlands that commonly form elongated strips or disaggregated clumps of organic matter [24]. High-resolution (<1 m pixel size) aerial imaging such as that from 2021 NAIP data readily resolved and identified such clumps and mats of organic debris in plucked marshlands at CRMS station numbers 4103 and 387.
Marsh collapse and drowning can occur if ponds in the wetlands zone enter a runaway expansion phase [29]. Runaway expansion may occur when ponds are greater than a critical width because edge erosion by waves increases the fetch (pond width), which can feed back to elevate the wave size and further increase marsh edge erosion [30] [31]. However, Mariotti (2016) [30] reported that connected ponds in Terrebonne Bay (just west of the Barataria Basin) were expanding even if their diameter was only 10 - 50 m, suggesting that a threshold width for runaway wave erosion is not well understood. Mariotti (2016) [29] went on to predict that connected ponds of any size would expand if critical inorganic deposition rates were smaller than the rate of sea level rise, a condition common in marshlands of the Mississippi Delta region. High-resolution aerial imaging should readily identify and measure the changes in the areas of small marshland ponds, down to a few square meters coverage.
Marsh edge erosion studies by Sapkota and White (2019) [6] in the Barataria Basin, near the sites measured by Deis et al. (2019) [32] as well, implied that variability in erosion rates, measured at between 0.5 and 3.5 m yr−1, was attributed to wind duration and soil bulk density and organic matter content. Marsh edges adjacent to the open bay experienced greater erosion rates than the protected sites located adjacent to the shallow water bodies in the northern part of the basin. This difference was attributed to the long fetch of unprotected sites, in contrast to shallow bathymetry in more protected sites and small fetches that produce waves with relatively low power. These edge erosion rates from 2017-2018 reported by Sapkota and White (2019) [6] were somewhat lower than the range of (DWH oiled) shoreline erosion rates (at between 2 and 5 m yr−1) measured by Deis et al. (2019) [32] following Hurricane Katrina and the DWH oil spill and mapped at 1-m resolution.
To summarize, aerial remote sensing of hurricane impacts on coastal wetlands can be used to support decision-making processes, such as prioritizing restoration efforts, monitoring the effectiveness of management actions, and assessing the long-term resilience of coastal wetlands to climate change. When individual marshland plant species can be identified and mapped every year over the Mississippi River Delta plain for changes in abundance, better-informed decisions can be formulated about how to remediate areas that are heavily disturbed by intensive hurricane storm surges.
5. Conclusion
This study expanded on remote sensing data collected by satellites over the Barataria and Breton Sound Basins to make increasingly detailed assessments of changes occurring in coastal wetlands of southern Louisiana. Analysis over the past four years (2019 to 2023) using NDWI from NAIP image acquisitions showed that there has been a widespread increase in relatively deep-water coverage (and corresponding losses of land coverage) since 2019 over most of the marshlands and shorelines of the basins. This has evidently resulted from deep scouring and widespread erosion of brackish and fresh marshlands following the storm surges of recent hurricanes in 2020 and 2021. Accelerated fragmentation of shoreline edges and interior pond features indicates that different types of damage inflicted on coastal wetlands of southeastern Louisiana from tropical storms can be characterized using aerial remote sensing.
Data Availability
The datasets generated during the current study are available from the corresponding author on reasonable request.