Matthew M. Barbee

Historical Shoreline Change, Southeast Oahu, Hawaii; Applying Polynomial Models to Calculate Shoreline Change Rates

Abstract Here we present shoreline change rates for the beaches of southeast Oahu, Hawaii, calculated using recently developed polynomial methods to assist coastal managers in planning for erosion hazards and to provide an example for interpreting results from these new rate calculation methods. The polynomial methods use data from all transects (shoreline measurement locations) on a beach to calculate a rate at any one location along the beach. These methods utilize a polynomial to model alongshore variation in the rates. Models that are linear in time best characterize the trend of the entire time series of historical shorelines. Models that include acceleration (both increasing and decreasing) in their rates provide additional information about shoreline trends and indicate how rates vary with time. The ability to detect accelerating shoreline change is an important advance because beaches may not erode or accrete in a constant (linear) manner. Because they use all the data from a beach, polynomial models calculate rates with reduced uncertainty compared with the previously used single-transect method. An information criterion, a type of model optimization equation, identifies the best shoreline change model for a beach. Polynomial models that use eigenvectors as their basis functions are most often identified as the best shoreline change models.

Sea-level rise vulnerability mapping for adaptation decisions using LiDAR DEMs:

Global sea-level rise (SLR) is projected to accelerate over the next century, with research indicating that global mean sea level may rise 18–48 cm by 2050, and 50–140 cm by 2100. Decision-makers, faced with the problem of adapting to SLR, utilize elevation data to identify assets that are vulnerable to inundation. This paper reviews techniques and challenges stemming from the use of Light Detection and Ranging (LiDAR) digital elevation models (DEMs) in support of SLR decision-making. A significant shortcoming in the methodology is the lack of comprehensive standards for estimating LiDAR error, which causes inconsistent and sometimes misleading calculations of uncertainty. Workers typically aim to reduce uncertainty by analyzing the difference between LiDAR error and the target SLR chosen for decision-making. The practice of mapping vulnerability to SLR is based on the assumption that LiDAR errors follow a normal distribution with zero bias, which is intermittently violated. Approaches to correcting discrepancies between vertical reference systems for land and tidal datums may incorporate tidal benchmarks and a vertical datum transformation tool provided by the National Ocean Service (VDatum). Mapping a minimum statistically significant SLR increment of 32 cm is difficult to achieve based on current LiDAR and VDatum errors. LiDAR DEMs derived from ‘ground’ returns are essential, yet LiDAR providers may not remove returns over vegetated areas successfully. LiDAR DEMs integrated into a GIS can be used to identify areas that are vulnerable to direct marine inundation and groundwater inundation (reduced drainage coupled with higher water tables). Spatial analysis can identify potentially vulnerable ecosystems as well as developed assets. A standardized mapping uncertainty needs to be developed given that SLR vulnerability mapping requires absolute precision for use as a decision-making tool.

Lack of suitable coastal plains likely influenced Lapita (~2800 cal. BP) settlement of Sāmoa: Evidence from south-eastern 'Upolu

Between 3050 and 2700 years ago, humans first colonized the islands of south-west Remote Oceania, a region stretching from Vanuatu to Sāmoa. These colonists created a dense archaeological record of Lapita pottery and other artefacts on island coastlines across the region. There is one striking exception to this pattern: Sāmoa, with only a single Lapita pottery colonization site dating to approximately 2800 years ago. There are two competing explanations for the unique Sāmoan colonization record. First, there was a dense Lapita colonization record, now displaced through sedimentation and coastal subsidence. Second, there were few coastal plains suitable for settlement 2800 years ago resulting in the lack of colonization sites. This article describes the first archaeological and geological research designed to systematically test these explanations. The research focuses on the south-eastern coastal plain of ‘Upolu Island, an area where previous geological research and mid-Holocene sea-level indicators predict the least relative subsidence over the last 3000 years. Auger cores and controlled excavation units sampled the geological sequence and archaeological deposits across 700 m of coast. Sedimentary and dating analyses indicate coastal plain formation beginning 1200 years ago with the earliest archaeological deposits, including plain pottery, lithics, shellfish and vertebrate fauna, dating possibly 700 years later. Microfossil analyses identify burning and forest clearance coincident with the earliest archaeological remains. Compared with other Sāmoan archaeological deposits, the cultural materials and ecofacts represent very low-intensity occupation. These results support the proposal that there were few coastal plains suitable for Lapita pottery–bearing colonists approximately 2800 years ago.

Vulnerability Assessment of Hawai'i's Cultural Assets Attributable to Erosion Using Shoreline Trend Analysis Techniques

Abstract KANE, H.H.; FLETCHER, C.H.; ROMINE, B.M.; ANDERSON, T.R.; FRAZER, N.L., and BARBEE, M.M., 2012. Vulnerability assessment of Hawai′i′s cultural assets attributable to erosion using shoreline trend analysis techniques. Hawai‘i’s beaches are a focal point of modern lifestyle as well as cultural tradition. Yet coastal erosion threatens areas that have served as burial grounds, home sites, and other forms of cultural significance. To improve understanding of the convergence of erosion patterns and cultural uses, we mapped shoreline changes from Kawela Bay to Kahuku Point on the capital island of O‘ahu. Shoreline change rates are calculated from historical photographs using the single-transect (ST) and eigenbeaches (EX) method to define the 50- and 100-year erosion hazard zones. To ensure that shoreline change rates reflect long-term trends, we include uncertainties attributable to natural shoreline fluctuations and mapping errors. A hazard zone overlay was compared to cultural data provided by the Hawaii State Historic Preservation Division (SHPD) and the Office of Hawaiian Affairs (OHA) to identify threats to cultural features. Cultural features identified in the study include iwi kupuna (burials), Hawaiian artifacts, and Punaulua (a freshwater spring). Our analysis indicates that, except for Punaulua, all cultural features identified are vulnerable to coastal erosion at historical rates. The data produced in this study may be used as a proactive management tool to rank the vulnerability to threatened cultural features, as well as to develop protocols to appropriately manage cultural assets.

Artificial Neural Network Classification of Sand in all Visible Submarine and Subaerial Regions of a Digital Image

Abstract Factors controlling the distribution of shelf sand as a resource, a component of reef ecosystems, and a dynamic substrate are poorly understood. An initial step in understanding sand accumulation in each of these roles is to identify its areal extent and change through time. Digitized aerial photographs and digital images provide common, inexpensive data sets that are generally underutilized for the purpose of marine substrate classification. Here we use only two bands, blue and green (470 and 550 nm), to demonstrate the utility of simple aerial photography in classifying marine substrate. Although these two are acquired from a hyperspectral data set, they represent blue and green in an RGB image such as commonly available in digitized aerial photographs. We add as a third band the second eigenchannel of a principal components analysis of these bands. Using an artificial neural network classification model, we identify submarine and subaerial sandy substrate in a digital image of a detached reef island in the Red Sea, Gezirat Siyul, Egypt. With careful selection of training and test groups, using small percentages of the total classified image, we create an efficient and accurate classification model. The model, trained to identify two classes, “sand” and “other than sand,” produces a classified image that provides sand locations and approximate areal coverage. Confusion matrices for both training and testing groups have users accuracies in the 90 percentiles, indicating accurate pixel classification.

Bringing Sea-Level Rise into Long Range Planning Considerations on Maui, Hawaii

Maui’s coastal lands, along with many others worldwide, are under tremendous pressure from expanding development and accelerating coastal erosion. While it may be perceived by the public that the lands most at risk from sea-level rise are those immediately bordering the coastline, the threat to low-lying areas from a rising water table inland of the coast may also be great. Maui planning officials have begun to recognize that regardless of the uncertainty over projected rates of sealevel rise, threats associated with rising sea level should be identified and mitigated through a combination of modeling, mapping, and direct observation. This paper provides a review of current sea-level rise science and describes the scientific and management approaches being undertaken by Maui County to better understand potential risks associated with rising seas and account for these projections in long-range planning. INTRODUCTION In 2003, Maui County became the first county in the state of Hawaii to adopt a science-based approach to determining construction setbacks on coastal properties (Norcross-Nu’u and Abbott 2005). High-resolution annual erosion rate data spaced at

Modeling multiple sea level rise stresses reveals up to twice the land at risk compared to strictly passive flooding methods

Planning community resilience to sea level rise (SLR) requires information about where, when, and how SLR hazards will impact the coastal zone. We augment passive flood mapping (the so-called “bathtub” approach) by simulating physical processes posing recurrent threats to coastal infrastructure, communities, and ecosystems in Hawai‘i (including tidally-forced direct marine and groundwater flooding, seasonal wave inundation, and chronic coastal erosion). We find that the “bathtub” approach, alone, ignores 35–54 percent of the total land area exposed to one or more of these hazards, depending on location and SLR scenario. We conclude that modeling dynamic processes, including waves and erosion, is essential to robust SLR vulnerability assessment. Results also indicate that as sea level rises, coastal lands are exposed to higher flood depths and water velocities. The prevalence of low-lying coastal plains leads to a rapid increase in land exposure to hazards when sea level exceeds a critical elevation of ~0.3 or 0.6 m, depending on location. At ~1 m of SLR, land that is roughly seven times the total modern beach area is exposed to one or more hazards. Projected increases in extent, magnitude, and rate of persistent SLR impacts suggest an urgency to engage in long-term planning immediately.

Measuring Historical Shoreline Change, Applying New Polynomial Change Models: Southeast Oahu, Hawaii

Digital aerial photo mosaics and NOAA topographic survey charts (t-sheets) are used to map historical shoreline positions on southeast Oahu, Hawaii. The new PX (Polynomial in alongshore X) and PXT (Polynomial in X and Time) shoreline change rate methods are applied to calculate shoreline change rates from the time series of historical shoreline positions. These new methods utilize all historical shoreline data from a beach to calculate shoreline change rates and can find acceleration in the shoreline change rate with time. The methods are shown here and in previous works to produce more parsimonious models and more statistically significant and defensible rates than the previously used ST (Single-Transect) shoreline change rate calculation method. The ability to model acceleration in shoreline change rates with time provides insight into shoreline change processes, which was previously theoretical or observed in only small-scale studies. An overview of the methods is presented along with results from shoreline change analysis of four beach study sites on the southeast Oahu, Hawaii, shoreline.