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

Remote sensing applied to schistosom...

Seto, Edmund Yet Wah.

FindBook

Google Book

Amazon

博客來

Remote sensing applied to schistosomiasis control: The Anning River project.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Remote sensing applied to schistosomiasis control: The Anning River project./
作者:	Seto, Edmund Yet Wah.
面頁冊數:	167 p.
附註:	Source: Dissertation Abstracts International, Volume: 61-07, Section: B, page: 3544.
Contained By:	Dissertation Abstracts International61-07B.
標題:	Public health. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9979801
ISBN:	9780599860773

Remote sensing applied to schistosomiasis control: The Anning River project.
Seto, Edmund Yet Wah.

Remote sensing applied to schistosomiasis control: The Anning River project. - 167 p.

Source: Dissertation Abstracts International, Volume: 61-07, Section: B, page: 3544.

Thesis (Ph.D.)--University of California, Berkeley, 2000.

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

This dissertation presents the use of remote sensing in identifying habitat of the Oncomelania hupensis robertsoni snail, the intermediate host for schistosomiasis in mountainous regions of China. This work is motivated by the construction of the Three Gorges Dam on the Yangtze River, which will be the largest dam in the world, resulting in considerable ecological change, and potentially introducing snail habitat and subsequent schistosomiasis into a previously non-endemic area. Field data from 1994 snail surveys were used to develop a two-tiered classification approach using Isodata clustering and maximum likelihood classification to discriminate between snail habitat and non-habitat from Landsat TM satellite data for the Anning River valley in southwest Sichuan province. A ranking scheme was devised to prioritize areas as to their potential for being snail habitat. A field validation study showed that the classification performed very well in identify potential snail habitat from landscape that was clearly not potential habitat. Within areas identified as potential habitat, sites with higher snail priority rankings generally corresponded to positive snail sites. A large source of potential misclassification was attributed to flooding in the south of the study area the year of the validation study. Measurements of water, soil, and landscape ecology associated with snail habitat were conducted in the field. These data were used to create a classification that required only 4 soil variables (sulfur, phosphorus, magnesium, and silt) to create an accurate distinction between snail habitat and non-habitat This classification was used to classify marginal habitat sites---sites that were thought to be able to support snails by field ecologists, despite there being no snails found. Over 70% of these marginal sites were correctly classified as no-snail sites based on soil data. An ecological interpretation of the remote sensing classification was determined by using 6 land cover variables and 6 soil variables to predict the results of the remote sensing classification.

ISBN: 9780599860773Subjects--Topical Terms:

534748
Public health.

Remote sensing applied to schistosomiasis control: The Anning River project.
LDR:03158nmm a2200313 4500 001 2060214
005 20150917133439.5
008 170521s2000 ||||||||||||||||| ||eng d
020 $a 9780599860773
035 $a (MiAaPQ)AAI9979801
035 $a AAI9979801
040 $a MiAaPQ $c MiAaPQ
100 1 $a Seto, Edmund Yet Wah. $3 3174353
245 1 0 $a Remote sensing applied to schistosomiasis control: The Anning River project.
300 $a 167 p.
500 $a Source: Dissertation Abstracts International, Volume: 61-07, Section: B, page: 3544.
500 $a Chair: Robert C. Spear.
502 $a Thesis (Ph.D.)--University of California, Berkeley, 2000.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a This dissertation presents the use of remote sensing in identifying habitat of the Oncomelania hupensis robertsoni snail, the intermediate host for schistosomiasis in mountainous regions of China. This work is motivated by the construction of the Three Gorges Dam on the Yangtze River, which will be the largest dam in the world, resulting in considerable ecological change, and potentially introducing snail habitat and subsequent schistosomiasis into a previously non-endemic area. Field data from 1994 snail surveys were used to develop a two-tiered classification approach using Isodata clustering and maximum likelihood classification to discriminate between snail habitat and non-habitat from Landsat TM satellite data for the Anning River valley in southwest Sichuan province. A ranking scheme was devised to prioritize areas as to their potential for being snail habitat. A field validation study showed that the classification performed very well in identify potential snail habitat from landscape that was clearly not potential habitat. Within areas identified as potential habitat, sites with higher snail priority rankings generally corresponded to positive snail sites. A large source of potential misclassification was attributed to flooding in the south of the study area the year of the validation study. Measurements of water, soil, and landscape ecology associated with snail habitat were conducted in the field. These data were used to create a classification that required only 4 soil variables (sulfur, phosphorus, magnesium, and silt) to create an accurate distinction between snail habitat and non-habitat This classification was used to classify marginal habitat sites---sites that were thought to be able to support snails by field ecologists, despite there being no snails found. Over 70% of these marginal sites were correctly classified as no-snail sites based on soil data. An ecological interpretation of the remote sensing classification was determined by using 6 land cover variables and 6 soil variables to predict the results of the remote sensing classification.
590 $a School code: 0028.
650 4 $a Public health. $3 534748
650 4 $a Environmental science. $3 677245
650 4 $a Remote sensing. $3 535394
690 $a 0573
690 $a 0768
690 $a 0799
710 2 $a University of California, Berkeley. $3 687832
773 0 $t Dissertation Abstracts International $g 61-07B.
790 $a 0028
791 $a Ph.D.
792 $a 2000
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9979801