Lentz
Erika E.
Lentz
Erika E.
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ArticlePredicted sea-level rise-driven biogeomorphological changes on Fire Island, New York: implications for people and plovers(American Geophysical Union, 2022-03-29) Zeigler, Sara L. ; Gutierrez, Benjamin T. ; Lentz, Erika E. ; Plant, Nathaniel G. ; Sturdivant, Emily ; Doran, Kara S.Forecasting biogeomorphological conditions for barrier islands is critical for informing sea-level rise (SLR) planning, including management of coastal development and ecosystems. We combined five probabilistic models to predict SLR-driven changes and their implications on Fire Island, New York, by 2050. We predicted barrier island biogeomorphological conditions, dynamic landcover response, piping plover (Charadrius melodus) habitat availability, and probability of storm overwash under three scenarios of shoreline change (SLC) and compared results to observed 2014/2015 conditions. Scenarios assumed increasing rates of mean SLC from 0 to 4.71 m erosion per year. We observed uncertainty in several morphological predictions (e.g., beach width, dune height), suggesting decreasing confidence that Fire Island will evolve in response to SLR as it has in the past. Where most likely conditions could be determined, models predicted that Fire Island would become flatter, narrower, and more overwash-prone with increasing rates of SLC. Beach ecosystems were predicted to respond dynamically to SLR and migrate with the shoreline, while marshes lost the most area of any landcover type compared to 2014/2015 conditions. Such morphological changes may lead to increased flooding or breaching with coastal storms. However—although modest declines in piping plover habitat were observed with SLC—the dynamic response of beaches, flatter topography, and increased likelihood of overwash suggest storms could promote suitable conditions for nesting piping plovers above what our geomorphology models predict. Therefore, Fire Island may offer a conservation opportunity for coastal species that rely on early successional beach environments if natural overwash processes are encouraged.
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ArticleUAS-SfM for coastal research : geomorphic feature extraction and land cover classification from high-resolution elevation and optical imagery(MDPI AG, 2017-10-03) Sturdivant, Emily ; Lentz, Erika E. ; Thieler, E. Robert ; Farris, Amy S. ; Weber, Kathryn M. ; Remsen, David P. ; Miner, Simon ; Henderson, Rachel E.The vulnerability of coastal systems to hazards such as storms and sea-level rise is typically characterized using a combination of ground and manned airborne systems that have limited spatial or temporal scales. Structure-from-motion (SfM) photogrammetry applied to imagery acquired by unmanned aerial systems (UAS) offers a rapid and inexpensive means to produce high-resolution topographic and visual reflectance datasets that rival existing lidar and imagery standards. Here, we use SfM to produce an elevation point cloud, an orthomosaic, and a digital elevation model (DEM) from data collected by UAS at a beach and wetland site in Massachusetts, USA. We apply existing methods to (a) determine the position of shorelines and foredunes using a feature extraction routine developed for lidar point clouds and (b) map land cover from the rasterized surfaces using a supervised classification routine. In both analyses, we experimentally vary the input datasets to understand the benefits and limitations of UAS-SfM for coastal vulnerability assessment. We find that (a) geomorphic features are extracted from the SfM point cloud with near-continuous coverage and sub-meter precision, better than was possible from a recent lidar dataset covering the same area; and (b) land cover classification is greatly improved by including topographic data with visual reflectance, but changes to resolution (when <50 cm) have little influence on the classification accuracy.
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ArticleImproving understanding of near-term barrier island evolution through multi-decadal assessment of morphologic change(Elsevier B.V., 2013-02-27) Lentz, Erika E. ; Hapke, Cheryl J. ; Stockdon, Hilary F. ; Hehre, Rachel E.Observed morphodynamic changes over multiple decades were coupled with storm-driven run-up characteristics at Fire Island, New York, to explore the influence of wave processes relative to the impacts of other coastal change drivers on the near-term evolution of the barrier island. Historical topography was generated from digital stereo-photogrammetry and compared with more recent lidar surveys to quantify near-term (decadal) morphodynamic changes to the beach and primary dune system between the years 1969, 1999, and 2009. Notably increased profile volumes were observed along the entirety of the island in 1999, and likely provide the eolian source for the steady dune crest progradation observed over the relatively quiescent decade that followed. Persistent patterns of erosion and accretion over 10-, 30-, and 40-year intervals are attributable to variations in island morphology, human activity, and variations in offshore bathymetry and island orientation that influence the wave energy reaching the coast. Areas of documented long-term historical inlet formation and extensive bayside marsh development show substantial landward translation of the dune–beach profile over the near-term period of this study. Correlations among areas predicted to overwash, observed elevation changes of the dune crestline, and observed instances of overwash in undeveloped segments of the barrier island verify that overwash locations can be accurately predicted in undeveloped segments of coast. In fact, an assessment of 2012 aerial imagery collected after Hurricane Sandy confirms that overwash occurred at the majority of near-term locations persistently predicted to overwash. In addition to the storm wave climate, factors related to variations within the geologic framework which in turn influence island orientation, offshore slope, and sediment supply impact island behavior on near-term timescales.
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ArticleCharacterizing storm response and recovery using the beach change envelope : Fire Island, New York(Elsevier, 2017-08-03) Brenner, Owen T. ; Lentz, Erika E. ; Hapke, Cheryl J. ; Henderson, Rachel E. ; Wilson, Kat E. ; Nelson, Timothy R.Hurricane Sandy at Fire Island, New York presented unique challenges in the quantification of storm impacts using traditional metrics of coastal change, wherein measured changes (shoreline, dune crest, and volume change) did not fully reflect the substantial changes in sediment redistribution following the storm. We used a time series of beach profile data at Fire Island, New York to define a new contour-based morphologic change metric, the Beach Change Envelope (BCE). The BCE quantifies changes to the upper portion of the beach likely to sustain measurable impacts from storm waves and capture a variety of storm and post-storm beach states. We evaluated the ability of the BCE to characterize cycles of beach change by relating it to a conceptual beach recovery regime, and demonstrated that BCE width and BCE height from the profile time series correlate well with established stages of recovery. We also investigated additional applications of this metric to capture impacts from storms and human modification by applying it to several post-storm historical datasets in which impacts varied considerably; Nor'Ida (2009), Hurricane Irene (2011), Hurricane Sandy (2012), and a 2009 community replenishment. In each case, the BCE captured distinctive upper beach morphologic change characteristic of these different beach building and erosional events. Analysis of the beach state at multiple profile locations showed spatial trends in recovery consistent with recent morphologic island evolution, which other studies have linked with sediment availability and the geologic framework. Ultimately we demonstrate a new way of more effectively characterizing beach response and recovery cycles to evaluate change along sandy coasts.
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PreprintEvaluation of dynamic coastal response to sea-level rise modifies inundation likelihood( 2016-02) Lentz, Erika E. ; Thieler, E. Robert ; Plant, Nathaniel G. ; Stippa, Sawyer R. ; Horton, Radley M. ; Gesch, Dean B.Sea-level rise (SLR) poses a range of threats to natural and built environments1, 2, making assessments of SLR-induced hazards essential for informed decision-making3. We develop a probabilistic model that evaluates the likelihood that an area will inundate (flood) or dynamically respond (adapt) to SLR. The broad-area applicability of the approach is demonstrated by producing 30x30 m resolution predictions for more than 38,000 km2 of diverse coastal landscape in the northeastern United States (U.S.). Probabilistic SLR projections, coastal elevation, and vertical land movement are used to estimate likely future inundation levels. Then, conditioned on future inundation levels and the current land-cover type, we evaluate the likelihood of dynamic response vs. inundation. We find that nearly 70% of this coastal landscape has some capacity to respond dynamically to SLR, and we show that inundation models over-predict land likely to submerge. This approach is well-suited to guiding coastal resource management decisions that weigh future SLR impacts and uncertainty against ecological targets and economic constraints.
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ArticleRelationships between regional coastal land cover distributions and elevation reveal data uncertainty in a sea-level rise impacts model(European Geosciences Union, 2019-05-15) Lentz, Erika E. ; Plant, Nathaniel G. ; Thieler, E. RobertUnderstanding land loss or resilience in response to sea-level rise (SLR) requires spatially extensive and continuous datasets to capture landscape variability. We investigate the sensitivity and skill of a model that predicts dynamic response likelihood to SLR across the northeastern US by exploring several data inputs and outcomes. Using elevation and land cover datasets, we determine where data error is likely, quantify its effect on predictions, and evaluate its influence on prediction confidence. Results show data error is concentrated in low-lying areas with little impact on prediction skill, as the inherent correlation between the datasets can be exploited to reduce data uncertainty using Bayesian inference. This suggests the approach may be extended to regions with limited data availability and/or poor quality. Furthermore, we verify that model sensitivity in these first-order landscape change assessments is well-matched to larger coastal process uncertainties, for which process-based models are important complements to further reduce uncertainty.
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ArticleIntegrating Bayesian Networks to forecast sea‐level rise impacts on Barrier Island characteristics and habitat availability(American Geophysical Union, 2022-10-14) Gutierrez, Benjamin T. ; Zeigler, Sara L. ; Lentz, Erika ; Sturdivant, Emily J. ; Plant, Nathaniel G.Evaluation of sea‐level rise (SLR) impacts on coastal landforms and habitats is a persistent need for informing coastal planning and management, including policy decisions, particularly those that balance human interests and habitat protection throughout the coastal zone. Bayesian networks (BNs) are used to model barrier island change under different SLR scenarios that are relevant to management and policy decisions. BNs utilized here include a shoreline change model and two models of barrier island biogeomorphological evolution at different scales (50 and 5 m). These BNs were then linked to another BN to predict habitat availability for piping plovers (Charadrius melodus), a threatened shorebird reliant on beach habitats. We evaluated the performance of the two linked geomorphology BNs and further examined error rates by generating hindcasts of barrier island geomorphology and habitat availability for 2014 conditions. Geomorphology hindcasts revealed that model error declined with a greater number of known inputs, with error rates reaching 55% when multiple outputs were hindcast simultaneously. We also found that, although error in predictions of piping plover nest presence/absence increased when outputs from the geomorphology BNs were used as inputs in the piping plover habitat BN, the maximum error rate for piping plover habitat suitability in the fully‐linked BNs was only 30%. Our findings suggest this approach may be useful for guiding scenario‐based evaluations where known inputs can be used to constrain variables that produce higher uncertainty for morphological predictions. Overall, the approach demonstrates a way to assimilate data and model structures with uncertainty to produce forecasts to inform coastal planning and management.