東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Measuring and Monitoring Long Term D...

Archer, Reginald S.

FindBook

Google Book

Amazon

博客來

Measuring and Monitoring Long Term Disaster Recovery Using Remote Sensing: A Case Study of Post Katrina New Orleans.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Measuring and Monitoring Long Term Disaster Recovery Using Remote Sensing: A Case Study of Post Katrina New Orleans./
作者:	Archer, Reginald S.
面頁冊數:	227 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-03(E), Section: A.
Contained By:	Dissertation Abstracts International75-03A(E).
標題:	Geography. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3601992
ISBN:	9781303537585

Measuring and Monitoring Long Term Disaster Recovery Using Remote Sensing: A Case Study of Post Katrina New Orleans.
Archer, Reginald S.

Measuring and Monitoring Long Term Disaster Recovery Using Remote Sensing: A Case Study of Post Katrina New Orleans. - 227 p.

Source: Dissertation Abstracts International, Volume: 75-03(E), Section: A.

Thesis (Ph.D.)--University of California, Santa Barbara, 2013.

This item must not be sold to any third party vendors.

This research focuses on measuring and monitoring long term recovery progress from the impacts of Hurricane Katrina on New Orleans, LA. Remote sensing has frequently been used for emergency response and damage assessment after natural disasters. However, techniques for analysis of long term disaster recovery using remote sensing have not been widely explored. With increased availability and lower costs, remote sensing offers an objective perspective, systematic and repeatable analysis, and provides a substitute to multiple site visits. In addition, remote sensing allows access to large geographical areas and areas where ground access may be disrupted, restricted or denied.

ISBN: 9781303537585Subjects--Topical Terms:

524010
Geography.

Measuring and Monitoring Long Term Disaster Recovery Using Remote Sensing: A Case Study of Post Katrina New Orleans.
LDR:04886nmm a2200349 4500 001 2063446
005 20151026143023.5
008 170521s2013 ||||||||||||||||| ||eng d
020 $a 9781303537585
035 $a (MiAaPQ)AAI3601992
035 $a AAI3601992
040 $a MiAaPQ $c MiAaPQ
100 1 $a Archer, Reginald S. $3 3177957
245 1 0 $a Measuring and Monitoring Long Term Disaster Recovery Using Remote Sensing: A Case Study of Post Katrina New Orleans.
300 $a 227 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-03(E), Section: A.
500 $a Adviser: Keith Clarke.
502 $a Thesis (Ph.D.)--University of California, Santa Barbara, 2013.
506 $a This item must not be sold to any third party vendors.
520 $a This research focuses on measuring and monitoring long term recovery progress from the impacts of Hurricane Katrina on New Orleans, LA. Remote sensing has frequently been used for emergency response and damage assessment after natural disasters. However, techniques for analysis of long term disaster recovery using remote sensing have not been widely explored. With increased availability and lower costs, remote sensing offers an objective perspective, systematic and repeatable analysis, and provides a substitute to multiple site visits. In addition, remote sensing allows access to large geographical areas and areas where ground access may be disrupted, restricted or denied.
520 $a This dissertation addressed the primary difficulties involved in the development of change detection methods capable of detecting changes experienced by disaster recovery indicators. Maximum likelihood classification and post-classification change detection were applied to multi-temporal high resolution aerial images to quantitatively measure the progress of recovery. Images were classified to automatically identify disaster recovery indicators and exploit the indicators that are visible within each image. The spectral analysis demonstrated that employing maximum likelihood classification to high resolution true color aerial images performed adequately and provided a good indication of spectral pattern recognition, despite the limited spectral information. Applying the change detection to the classified images was effective for determining the temporal trajectory of indicators categorized as blue tarps, FEMA trailers, houses, vegetation, bare earth and pavement. The results of the post classification change detection revealed a dominant change trajectory from bluetarp to house, as damaged houses became permanently repaired.
520 $a Specifically, the level of activity of blue tarps, housing, vegetation, FEMA trailers (temporary housing) pavement and bare earth were derived from aerial image processing to measure and monitor the progress of recovery. Trajectories of recovery for each individual indicator were examined to provide a better understanding of activity during reconstruction. A collection of spatial metrics was explored in order to identify spatial patterns and characterize classes in terms of patches of pixels. One of the key findings of the spatial analysis is that patch shapes were more complex in the presence of debris and damaged or destroyed buildings. The combination of spectral, temporal, and spatial analysis provided a satisfactory, though limited, solution to the question of whether remote sensing alone, can be used to quantitatively assess and monitor the progress of long term recovery following a major disaster. The research described in this dissertation provided a detailed illustration of the level of activity experienced by different recovery indicators during the long term recovery process. It also addressed the primary difficulties involved in the development of change detection methods capable of detecting changes experienced by disaster recovery indicators identified from classified high resolution true color aerial imagery.
520 $a The results produced in this research demonstrate that the observed trajectories for actual indicators of recovery indicate different levels of recovery activity even within the same community. The level of activity of the long term reconstruction phase observed in the Kates model is not consistent with the level of activity of key recovery indicators in the Lower 9th Ward during the same period. Used in the proper context, these methods and results provide decision making information for determining resources.
520 $a KEYWORDS: Change detection, classification, Katrina, New Orleans, remote sensing, disaster recovery, spatial metrics.
590 $a School code: 0035.
650 4 $a Geography. $3 524010
650 4 $a Remote sensing. $3 535394
650 4 $a Geographic information science and geodesy. $3 2122917
690 $a 0366
690 $a 0799
690 $a 0370
710 2 $a University of California, Santa Barbara. $b Geography. $3 1025686
773 0 $t Dissertation Abstracts International $g 75-03A(E).
790 $a 0035
791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3601992