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Establishing visual based and datum based shorelines and shoreline uncertainties on a sandy coast: Cape Canaveral, Florida.

机译：在沙质海岸上建立基于视觉和基于基准的海岸线和海岸线不确定性：佛罗里达州卡纳维拉尔角。

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摘要

Shoreline change rates are used by scientists, coastal managers, and coastal municipal agencies to study and manage coasts; but the uncertainties associated with establishing shoreline positions are poorly understood and remain undocumented. These uncertainties arise through data collection, data processing, merging of different data types, and distinguishing short-term shoreline change "noise" from the long term shoreline trend signals. This dissertation examines these sources of uncertainty along the NASA-Kennedy Space Center coast, near Cape Canaveral Florida.;Different survey methods and data collection strategies were tested to compare and contrast their performance in reproducing a geomorphic surface. RTK-GPS elevation uncertainties averaged 5.4 cm (95% confidence) for 22 individual ten minute tests with a data collection frequency of 1 Hz. Cumulative RTKGPS elevation uncertainties for a five-hour test were ∼9.0 cm (95% confidence) for the same data collection frequency. Our RTK-GPS survey, data collection strategy which follows beach face slope breaks in the along shore direction produced a Mean High Water (MHW) DBSL within 2 m of a backpack mounted across shore transect MHW elevation survey. The Slope Break method of collecting topographic data produced more cells within +/- 10 cm of the reference surface than any of the other data collection strategies I tested. This result held true for both complex (cusps and berm) and simple (concave-up) sandy beaches.;To test the annual and seasonal shoreline change envelope two years of monthly and rapid-response storm RTK-GPS surveys were conducted along the 10 km coastal reach of the NASA-KSC site. During the first annual survey period, the average SCE over the entire study area was 13.0 m with a standard deviation of 3.3 m. During the second annual survey period the average SCE was 11.8 m with a standard deviation of 5.2 m. The SCEs for each of the annual study periods also revealed, not unexpectedly, that areas with more net change (either accretion or retreat) have the largest SCE. Three-month running means of SCEs over the two year period show that the months of April, May, and June have the lowest values (3 month SCE <3 m) than those collected during any other 3 month period.;In order to establish a relationship between visual based shoreline (VBS) and digital based shoreline (DBS), I conducted 32 kinematic differential GPS surveys over a two-year period at the NASAKSC site. Two Geoeye I (0.5 m resolution) satellite images were collected 3 days post DBS survey on June 9, 2009 and during a DBS survey on July 28, 2010. June and July beach states were selected for image collection because they have the lowest mean monthly significant wave height (HS) and least variability in HS, providing the highest likelihood of stable beach morphology.;The 10-km study reach exhibits various slope characteristics, beach morphologic states and stability histories. A 2.0 km historically stable beach that is towards a reflective end member, with relatively high slopes (∼ 0.10) is found in the northern portion of the study area. The 4.6 km central portion is characterized by an intermediate beach state and has been historically retreating. The 2.4 km False Cape region comprises a near dissipative beach, with low slopes (∼ 0.06) and is historically accreting. The southernmost 1.0 km region has a mixed history of accretion and retreat, is an intermediate beach, and is characterized by relatively low slopes (∼0.05).;Although the decreased uncertainties make DBS techniques more desirable for coastal change studies, the economic trade off associated with increased cost and sampling frequency is, in some cases, insurmountable. Although LIDAR has become the widely accepted tool for coastal change studies, the combination of satellite imagery and a detailed documentation of its uncertainties is a valuable tool, because it offers the advantage of having an effectively instantaneous collection interval, a precise collection time, and a short return interval (∼ 3 days). (Abstract shortened by UMI.).
机译：科学家，沿海管理人员和沿海市政机构使用海岸线变化率来研究和管理海岸；但是与建立海岸线位置相关的不确定性知之甚少，并且仍然没有记载。这些不确定性是通过数据收集，数据处理，不同数据类型的合并以及将短期海岸线变化“噪声”与长期海岸线趋势信号区分开来产生的。本文研究了佛罗里达州卡纳维拉尔角附近的美国宇航局-肯尼迪航天中心沿岸的这些不确定性来源。测试了不同的调查方法和数据收集策略，以比较和对比它们在再现地貌表面中的性能。 RTK-GPS高度不确定性平均为5.4厘米（95％置信度），适用于22个单独的十分钟测试，数据采集频率为1 Hz。对于相同的数据收集频率，五个小时的测试的RTKGPS累积高度不确定性约为9.0 cm（置信度为95％）。我们的RTK-GPS勘测，沿海岸方向沿海滩面坡度折断的数据收集策略，在横跨岸线MHW高程勘测的背包中2 m内产生了平均高水（MHW）DBSL。与我测试的任何其他数据收集策略相比，用于收集地形数据的Slope Break方法在参考表面+/- 10 cm内产生了更多的像元。该结果对于复杂的（尖峰和堤坝）和简单的（凹形）沙滩都适用。为了测试年度和季节性海岸线变化包络，在10个地区进行了为期两年的月度和快速响应风暴RTK-GPS调查美国国家航空航天局（NASA）-KSC站点的沿海航海距离。在第一个年度调查期间，整个研究区域的平均SCE为13.0 m，标准差为3.3 m。在第二个年度调查期间，平均SCE为11.8 m，标准偏差为5.2 m。毫不意外地，每个年度研究期的SCE也显示出净变化（吸积或退缩）更多的区域具有最大的SCE。两年期间SCE的三个月运行平均值表明，四月，五月和六月月份的值（三个月SCE <3 m）比其他三个月期间的最低值低。基于视觉的海岸线（VBS）和基于数字的海岸线（DBS）之间的关系，我在NASAKSC站点进行了为期两年的32次运动差分GPS测量。在2009年6月9日进行DBS调查后3天以及在2010年7月28日进行DBS调查期间，收集了两张Geoeye I（0.5 m分辨率）卫星图像。选择6月和7月的海滩州进行图像采集是因为它们的平均月度最低显着的波高（HS）和HS的最小变异性，提供了稳定的海滩形态的最大可能性。； 10公里的研究范围展现出各种坡度特征，海滩形态状态和稳定历史。在研究区域的北部发现了一个2.0公里的历史稳定海滩，该海滩朝向反射端构件，具有相对较高的坡度（约0.10）。中心4.6公里，以中间海滩状态为特征，历史上一直在后退。假开普地区长2.4公里，包括一个近耗散的海滩，低坡度（〜0.06），历史上一直在增加。最南端的1.0 km地区具有增生和退缩的混合历史，是一个中间海滩，并且具有相对较低的坡度（〜0.05）.;尽管不确定性降低了DBS技术更适合海岸变化研究，但经济上的平衡在某些情况下，与增加的成本和采样频率相关联是无法克服的。尽管LIDAR已成为海岸变化研究的广泛接受的工具，但结合卫星图像和有关其不确定性的详细文档是一种有价值的工具，因为它具有有效的瞬时采集间隔，精确的采集时间和有效的采集优势。较短的退货间隔（约3天）。 （摘要由UMI缩短。）。

著录项

  • 作者

    MacKenzie, Richard Allen, III.;

  • 作者单位

    University of Florida.;

  • 授予单位 University of Florida.;
  • 学科 Geomorphology.;Geographic information science and geodesy.;Remote sensing.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 261 p.
  • 总页数 261
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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