Beginner’s guide to understanding, accessing, and retrieving Coastwide Reference Monitoring System data
No abstract available.
Louisiana↗
Geology topics
Sarai Piazza
Publications and source records attributed to Sarai Piazza.
Accelerating elevation gain indicates land loss associated with erosion in Mississippi River Deltaic Plain tidal wetlands
In recent years, the Mississippi River Deltaic Plain (MRDP) has experienced the highest rates of wetland loss in the USA. Although the process of vertical drowning has been heavily studied in coastal wetlands, less is known about the relationship between elevation change and land loss in wetlands that are experiencing lateral erosion and the contribution of erosion to land loss in the MRDP. We quantified relationships of elevation change and land change in ten submerging tidal wetlands and found that, despite significant land loss, elevation trajectories in seven of the land loss study sites were positive. Furthermore, we observed an acceleration in elevation gain preceding the conversion from vegetated marsh to open water. To identify regional contributions of lateral erosion to land loss, we quantified the relationship of elevation change and land change in 159 tidal marsh sites in the MRDP. Approximately half the sites were persistently losing land, and 82% of these sites were vulnerable to erosion, identifying erosion as a dominant mechanism of coastal wetland loss in this region. Notably, the sites that were vulnerable to erosion were experiencing land loss while also gaining elevation, and sites with the highest land loss exhibited accelerating elevation gain. Together, these data illustrate that (1) erosion is a dominant mechanism of wetland loss in the MRDP, (2) accelerated elevation gain is an indicator of erosion, and (3) consideration of elevation change trajectories within the context of land change is critical for providing accurate coastal wetland vulnerability assessments.
Louisiana↗
Developing bare-earth digital elevation models from structure-from-motion data on barrier islands
Unoccupied aerial systems can collect aerial imagery that can be used to develop structure-from-motion products with a temporal resolution well-suited to monitoring dynamic barrier island environments. However, topographic data created using photogrammetric techniques such as structure-from-motion represent the surface elevation including the vegetation canopy . Additional processing is required for estimating bare-earth elevation, which is critical for understanding the underlying geomorphology of these islands. In this study, we used a vegetation and elevation survey to produce bare-earth digital elevation models from structure-from-motion-derived elevation products for two sites on Dauphin Island, Alabama (USA). One site was exposed to high wave energy and included a mix of beach, dune, and barrier flat habitats that were dominated by supratidal/upland herbaceous vegetation. The second site was exposed to low wave energy and was dominated by intertidal marsh. Aerial imagery was collected in late fall of 2018 and spring of 2019. We tested several machine learning algorithms for predicting and removing elevation bias for vegetated areas using predictors that included spectral indices from unoccupied aerial systems-based multispectral imagery and landscape position information (e.g., relative topography and distance from shore). Models were developed for each site and season. We also explored how well the model from one season generalized to data from a different season for the same site. For developing initial digital surface models, we found that utilizing a minimum bin algorithm, as opposed to interpolation, led to lower elevation bias. For bias removal, Gaussian process regression performed the best and led to a root mean square error for the bare-earth digital elevation models of around 0.10 m for the high energy site and 0.15 m for the low energy site. Compared to the digital surface models, the root mean square error for the bare-earth digital elevation models was reduced by at least 29 percent for the high energy site and 69 percent for the low energy site. For all models, common predictors included surface elevation, vegetation greenness, and distance from the shoreline. The models produced comparable results when trained using data from a different season. The error estimates for all analyses were within published elevation standards for lidar data for vegetated areas. With calibration, this approach could be portable to other areas or data, such as aerial lidar (conventional or unoccupied), to provide an efficient and repeatable framework for monitoring geomorphology or provide baseline elevations for predicting changes to these environments under future conditions.
Alabama↗
Louisiana Adaptive Management Status and Improvement Report: Vision and Recommendations
As part of the process to increase implementation of adaptive management for ecosystem-based coastal restoration within Louisiana, we aim for this report to be broadly applicable across planning processes as well as funding and implementing entities. It compiles technical knowledge and guidance summarized as key findings through the text which lead to eight priority recommendations to improve application of adaptive management in Louisiana. This report identifies critical linkage points and opportunities for knowledge and data transfers within, and among, agencies in Louisiana. The primary focus is on CPRA and others affiliated with the LA TIG. This report presents a consensus based common vision for adaptive management of coastal restoration implementation in Louisiana. As CPRA is the primary agency responsible for coordinating and facilitating coastal restoration projects within the state of Louisiana, the primary source of information and experience was collated from personnel and processes carried out at CPRA. To capture needs and mechanisms ,across agencies, extensive input was also received from the LA TIG, representing the other state and federal Trustees.
Louisiana↗
Using maintenance records from a long-term sensor monitoring network to evaluate the relationship between maintenance schedule and data quality
Sensor-based environmental monitoring networks are beginning to provide the large-scale, long-term data required to address important fundamental and applied questions in ecology. However, the data quality from deployed sensors can be difficult and costly to ensure. In this study, we use maintenance records from the 12-year history of Louisiana’s Coastwide Reference Monitoring System (CRMS) to assess the relationship between various dimensions of data quality and the frequency of field visits to the sensors. We use hierarchical Bayesian models to estimate the probability of missing data, the probability that a corrective offset of the sensor is required, and the magnitude of required offsets for water elevation and salinity data. We compared these estimates to predetermined risk thresholds to the help identify maintenance schedules that balanced the efficient use of labor resources without sacrificing data quality. We found that the relationship between data quality and increasing maintenance interval varied across metrics. Additionally, for most metrics, the maintenance interval when the metric’s credible interval and risk threshold intersected varied throughout the year and with wetland type. These results suggest that complex maintenance schedules, in which field visits vary in frequency throughout the year and with environmental context, are likely to provide the best tradeoff between labor cost and data quality. This analysis demonstrates that quantitative assessment of maintenance records can positively impact the sustainability of long-term data collection projects by helping identify new potential efficiencies in monitoring program management.
Louisiana↗
Monitoring plans for Louisiana’s system-wide assessment and monitoring program (SWAMP). Version IV
The System-Wide Assessment and Monitoring Program (SWAMP) has been envisioned as a long-term monitoring program to ensure a comprehensive network of coastal data collection activities is in place to support the development, implementation, and adaptive management of the coastal protection and restoration program within coastal Louisiana. The Coastwide Reference Monitoring System (CRMS) and Barrier Island Comprehensive Monitoring (BICM) programs have been implemented under SWAMP, while other aspects of system dynamics, including offshore and inland water-body boundary conditions, nontidal freshwater habitats, riverine conditions, risk status, and protection performance, are not presently the subject of CPRA-coordinated (Coastal Protection and Restoration Authority) monitoring. In order to implement these additional aspects of SWAMP, CPRA partnered with The Water Institute of the Gulf and others to develop 1) a programmatic monitoring plan for evaluating the effectiveness of the coastal protection and restoration program on a coastwide scale, and 2) basinwide monitoring plans that will incorporate the elements of the programmatic plan with specific data collection activities designed to capture effects within the basin. Monitoring plans were developed for Barataria Basin, Pontchartrain Region (includes Breton Sound, Pontchartrain and Mississippi River Delta Basins), and the western basins (Calcasieu-Sabine, Mermentau, Teche-Vermilion, Atchafalaya, and Terrebonne) for both the natural and human systems using a process to identify the monitoring variables, objectives, and sampling design. The monitoring variables and objectives identified fall under the general categories of weather and climate, biotic integrity, water quality, hydrology, physical terrain, population and demographics, housing and community characteristics, economy and employment, ecosystem dependency, residential properties protection, and critical infrastructure and essential services protection. A rigorous statistical analysis, examination of modeling needs, and thorough reviews of previous planning and monitoring efforts were conducted to develop the sampling designs for the natural and human system monitoring plans. The plan relies heavily on the use of existing data, thus, coordination with other agencies (e.g., LDEQ , LDWF) and CPRA’s existing monitoring programs (e.g., BICM, CRMS) is critical to the plan’s success. Implementation of the plans will require development of quality control and quality assurance protocols, specific standardized operating procedures for each of the data collection efforts, a data management plan, and a reporting framework to contribute to decision making and reducing uncertainty in management actions.
Louisiana↗
Vegetation cover, tidal amplitude and land area predict short-term marsh vulnerability in Coastal Louisiana
The loss of coastal marshes is a topic of great concern, because these habitats provide tangible ecosystem services and are at risk from sea-level rise and human activities. In recent years, significant effort has gone into understanding and modeling the relationships between the biological and physical factors that contribute to marsh stability. Simulation-based process models suggest that marsh stability is the product of a complex feedback between sediment supply, flooding regime and vegetation response, resulting in elevation gains sufficient to match the combination of relative sea-level rise and losses from erosion. However, there have been few direct, empirical tests of these models, because long-term datasets that have captured sufficient numbers of marsh loss events in the context of a rigorous monitoring program are rare. We use a multi-year data set collected by the Coastwide Reference Monitoring System (CRMS) that includes transitions of monitored vegetation plots to open water to build and test a predictive model of near-term marsh vulnerability. We found that despite the conclusions of previous process models, elevation change had no ability to predict the transition of vegetated marsh to open water. However, we found that the processes that drive elevation change were significant predictors of transitions. Specifically, vegetation cover in prior year, land area in the surrounding 1 km2 (an estimate of marsh fragmentation), and the interaction of tidal amplitude and position in tidal frame were all significant factors predicting marsh loss. This suggests that 1) elevation change is likely better a predictor of marsh loss at time scales longer than we consider in this study and 2) the significant predictive factors affect marsh vulnerability through pathways other than elevation change, such as resistance to erosion. In addition, we found that, while sensitivity of marsh vulnerability to the predictive factors varied spatially across coastal Louisiana, vegetation cover in prior year was the best single predictor of subsequent loss in most sites followed by changes in percent land and tidal amplitude. The model’s predicted land loss rates correlated well with land loss rates derived from satellite data, although agreement was spatially variable. These results indicate 1) monitoring the loss of small scale vegetation plots can inform patterns of land loss at larger scales 2) the drivers of land loss vary spatially across coastal Louisiana, and 3) relatively simple models have potential as highly informative tools for bioassessment, directing future research, and management planning.
Louisiana↗
Floristic quality index and forested floristic quality index: Assessment tools for restoration projects and monitoring sites in coastal Louisiana
The Floristic Quality Index (FQI) has been used in many areas throughout the United State to assess the integrity of plant communities and restoration projects. Here, we summarize two tools that have been developed for monitoring sites in coastal Louisiana wetlands. The FQI was modified for herbaceous data collected in coastal Louisiana monitoring sites. A second FQI, the Forested Floristic Quality Index (FFQI), was modified and developed to evaluate forested wetland sites that are monitored in coastal Louisiana. These tools, along with other wetland indices developed for coastal Louisiana, will aid managers in coastal restoration and making informed management decisions.
Louisiana↗
Assessing coastal wetland vulnerability to sea-level rise along the northern Gulf of Mexico coast: Gaps and opportunities for developing a coordinated regional sampling network
Coastal wetland responses to sea-level rise are greatly influenced by biogeomorphic processes that affect wetland surface elevation. Small changes in elevation relative to sea level can lead to comparatively large changes in ecosystem structure, function, and stability. The surface elevation table-marker horizon (SET-MH) approach is being used globally to quantify the relative contributions of processes affecting wetland elevation change. Historically, SET-MH measurements have been obtained at local scales to address site-specific research questions. However, in the face of accelerated sea-level rise, there is an increasing need for elevation change network data that can be incorporated into regional ecological models and vulnerability assessments. In particular, there is a need for long-term, high-temporal resolution data that are strategically distributed across ecologically-relevant abiotic gradients. Here, we quantify the distribution of SET-MH stations along the northern Gulf of Mexico coast (USA) across political boundaries (states), wetland habitats, and ecologically-relevant abiotic gradients (i.e., gradients in temperature, precipitation, elevation, and relative sea-level rise). Our analyses identify areas with high SET-MH station densities as well as areas with notable gaps. Salt marshes, intermediate elevations, and colder areas with high rainfall have a high number of stations, while salt flat ecosystems, certain elevation zones, the mangrove-marsh ecotone, and hypersaline coastal areas with low rainfall have fewer stations. Due to rapid rates of wetland loss and relative sea-level rise, the state of Louisiana has the most extensive SET-MH station network in the region, and we provide several recent examples where data from Louisiana’s network have been used to assess and compare wetland vulnerability to sea-level rise. Our findings represent the first attempt to examine spatial gaps in SET-MH coverage across abiotic gradients. Our analyses can be used to transform a broadly disseminated and unplanned collection of SET-MH stations into a coordinated and strategic regional network. This regional network would provide data for predicting and preparing for the responses of coastal wetlands to accelerated sea-level rise and other aspects of global change.
Alabama, Florida, Louisiana, Mississippi, Texas↗
Oiling accelerates loss of salt marshes, southeastern Louisiana
The 2010 BP Deepwater Horizon (DWH) oil spill damaged thousands of km 2 of intertidal marsh along shorelines that had been experiencing elevated rates of erosion for decades. Yet, the contribution of marsh oiling to landscape-scale degradation and subsequent land loss has been difficult to quantify. Here, we applied advanced remote sensing techniques to map changes in marsh land cover and open water before and after oiling. We segmented the marsh shorelines into non-oiled and oiled reaches and calculated the land loss rates for each 10% increase in oil cover (e.g. 0% to >70%), to determine if land loss rates for each reach oiling category were significantly different before and after oiling. Finally, we calculated background land-loss rates to separate natural and oil-related erosion and land loss. Oiling caused significant increases in land losses, particularly along reaches of heavy oiling (>20% oil cover). For reaches with ≥20% oiling, land loss rates increased abruptly during the 2010–2013 period, and the loss rates during this period are significantly different from both the pre-oiling (p < 0.0001) and 2013–2016 post-oiling periods (p < 0.0001). The pre-oiling and 2013–2016 post-oiling periods exhibit no significant differences in land loss rates across oiled and non-oiled reaches (p = 0.557). We conclude that oiling increased land loss by more than 50%, but that land loss rates returned to background levels within 3–6 years after oiling, suggesting that oiling results in a large but temporary increase in land loss rates along the shoreline.
Louisiana↗
Mapping changing distributions of dominant species in oil-contaminated salt marshes of Louisiana using imaging spectroscopy
The April 2010 Deepwater Horizon (DWH) oil spill was the largest coastal spill in U.S. history. Monitoring subsequent change in marsh plant community distributions is critical to assess ecosystem impacts and to establish future coastal management priorities. Strategically deployed airborne imaging spectrometers, like the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), offer the spectral and spatial resolution needed to differentiate plant species. However, obtaining satisfactory and consistent classification accuracies over time is a major challenge, particularly in dynamic intertidal landscapes. Here, we develop and evaluate an image classification system for a time series of AVIRIS data for mapping dominant species in a heavily oiled salt marsh ecosystem. Using field-referenced image endmembers and canonical discriminant analysis (CDA), we classified 21 AVIRIS images acquired during the fall of 2010, 2011 and 2012. Classification results were evaluated using ground surveys that were conducted contemporaneously to AVIRIS collection dates. We analyzed changes in dominant species cover from 2010 to 2012 for oiled and non-oiled shorelines. CDA discriminated dominant species with a high level of accuracy (overall accuracy = 82%, kappa = 0.78) and consistency over three imaging dates (overall 2010 = 82%, overall 2011 = 82%, overall 2012 = 88%). Marshes dominated by Spartina alterniflora were the most spatially abundant in shoreline zones (≤ 28 m from shore) for all three dates (2010 = 79%, 2011 = 61%, 2012 = 63%), followed by Juncus roemerianus (2010 = 11%, 2011 = 19%, 2012 = 17%) and Distichlis spicata (2010 = 4%, 2011 = 10%, 2012 = 7%). Marshes that were heavily contaminated with oil exhibited variable responses from 2010 to 2012. Marsh vegetation classes converted to a subtidal, open water class along oiled and non-oiled shorelines that were similarly situated in the landscape. However, marsh loss along oil-contaminated shorelines doubled that of non-oiled shorelines. Only S. alterniflora dominated marshes were extensively degraded, losing 15% (354,604 m 2 ) cover in oiled shoreline zones, suggesting that S. alterniflora marshes may be more vulnerable to shoreline erosion following hydrocarbon stress, due to their landscape position.
Louisiana↗