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Geospatial analysis of spaceborne remote sensing data for assessing disaster impacts and modeling surface runoff in the built-environment.

机译：对星载遥感数据进行地理空间分析，以评估灾害影响并在内置环境中模拟地表径流。

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摘要

Every year, coastal disasters such as hurricanes and floods claim hundreds of lives and severely damage homes, businesses, and lifeline infrastructure. This research was motivated by the 2005 Hurricane Katrina disaster, which devastated the Mississippi and Louisiana Gulf Coast. The primary objective was to develop a geospatial decision-support system for extracting built-up surfaces and estimating disaster impacts using spaceborne remote sensing satellite imagery. Pre-Katrina 1-m Ikonos imagery of a 5km x 10km area of Gulfport, Mississippi, was used as source data to develop the built-up area and natural surfaces or BANS classification methodology. Autocorrelation of 0.6 or higher values related to spectral reflectance values of groundtruth pixels were used to select spectral bands and establish the BANS decision criteria of unique ranges of reflectance values. Surface classification results using GeoMedia Pro geospatial analysis for Gulfport sample areas, based on BANS criteria and manually drawn polygons, were within +/-7% of the groundtruth. The difference between the BANS results and the groundtruth was statistically not significant. BANS is a significant improvement over other supervised classification methods, which showed only 50% correctly classified pixels. The storm debris and erosion estimation or SDE methodology was developed from analysis of pre- and post-Katrina surface classification results of Gulfport samples. The SDE severity level criteria considered hurricane and flood damages and vulnerability of inhabited built-environment. A linear regression model, with +0.93 Pearson R-value, was developed for predicting SDE as a function of pre-disaster percent built-up area. SDE predictions for Gulfport sample areas, used for validation, were within +/-4% of calculated values. The damage cost model considered maintenance, rehabilitation and reconstruction costs related to infrastructure damage and community impacts of Hurricane Katrina. The developed models were implemented for a study area along I-10 considering the predominantly flood-induced damages in New Orleans. The BANS methodology was calibrated for 0.6-m QuickBird2 multispectral imagery of Karachi Port area in Pakistan. The results were accurate within +/-6% of the groundtruth. Due to its computational simplicity, the unit hydrograph method is recommended for geospatial visualization of surface runoff in the built-environment using BANS surface classification maps and elevations data.;Key words. geospatial analysis, satellite imagery, built-environment, hurricane, disaster impacts, runoff.
机译：每年，飓风和洪水等沿海灾害夺去数百人的生命，并严重破坏房屋，企业和生命线基础设施。这项研究的动机是2005年的卡特里娜飓风灾难，这场灾难摧毁了密西西比州和路易斯安那州墨西哥湾沿岸。主要目标是开发一种地理空间决策支持系统，以使用星载遥感卫星图像提取堆积的地面并估算灾害影响。密西西比州格尔夫波特5公里x 10公里区域的卡特里娜飓风之前的1 m Ikonos图像被用作源数据，以开发集结面积和自然表面或BANS分类方法。与地面真实像素的光谱反射率值相关的0.6或更高值的自相关用于选择光谱带并建立反射率值唯一范围的BANS判定标准。根据BANS标准和手动绘制的多边形，使用GeoMedia Pro地理空间分析对Gulfport样本区域进行的表面分类结果在地物的+/- 7％之内。 BANS结果与地面真实性之间的差异在统计学上不显着。 BANS是对其他监督分类方法的重大改进，后者仅显示了50％正确分类的像素。风暴碎片和侵蚀估算或SDE方法是根据对卡特里娜飓风之前和之后的Gulfport样本的表面分类结果进行分析得出的。 SDE严重性级别标准考虑了飓风和洪水的破坏以及居住环境的脆弱性。开发了具有+0.93 Pearson R值的线性回归模型，用于预测SDE作为灾前建筑面积百分比的函数。用于验证的Gulfport样本区域的SDE预测在计算值的+/- 4％之内。破坏成本模型考虑了与卡特里娜飓风造成的基础设施破坏和社区影响有关的维护，修复和重建成本。考虑到新奥尔良主要由洪水引起的破坏，已针对I-10沿线的研究区域实施了开发的模型。 BANS方法已针对巴基斯坦卡拉奇港口地区的0.6米QuickBird2多光谱图像进行了校准。结果是准确的+/- 6％。由于其计算简便性，建议使用单位水位法对建筑环境中使用BANS曲面分类图和高程数据进行的地面径流进行地理空间可视化。地理空间分析，卫星图像，内置环境，飓风，灾难影响，径流。

著录项

  • 作者

    Wodajo, Bikila Teklu.;

  • 作者单位

    The University of Mississippi.;

  • 授予单位 The University of Mississippi.;
  • 学科 Engineering Civil.;Remote Sensing.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 430 p.
  • 总页数 430
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 建筑科学;遥感技术;
  • 关键词

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