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Object-based image analysis for fore...

Czarnecki, Christina.

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Object-based image analysis for forest-type mapping in New Hampshire.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Object-based image analysis for forest-type mapping in New Hampshire./
作者:	Czarnecki, Christina.
面頁冊數:	88 p.
附註:	Source: Masters Abstracts International, Volume: 51-03.
Contained By:	Masters Abstracts International51-03(E).
標題:	Remote Sensing. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1521564
ISBN:	9781267788542

Object-based image analysis for forest-type mapping in New Hampshire.
Czarnecki, Christina.

Object-based image analysis for forest-type mapping in New Hampshire. - 88 p.

Source: Masters Abstracts International, Volume: 51-03.

Thesis (M.S.)--University of New Hampshire, 2012.

The use of satellite imagery to classify New England forests is inherently complicated due to high species diversity and complex spatial distributions across a landscape. The use of imagery with high spatial resolutions to classify forests has become more commonplace as new satellite technology become available. Pixel-based methods of classification have been traditionally used to identify forest cover types. However, object-based image analysis (OBIA) has been shown to provide more accurate results. This study explored the ability of OBIA to classify forest stands in New Hampshire using two methods: by identifying stands within an IKONOS satellite image, and by identifying individual trees and building them into forest stands.

ISBN: 9781267788542Subjects--Topical Terms:

1018559
Remote Sensing.

Object-based image analysis for forest-type mapping in New Hampshire.
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500 $a Adviser: Russell G. Congalton.
502 $a Thesis (M.S.)--University of New Hampshire, 2012.
520 $a The use of satellite imagery to classify New England forests is inherently complicated due to high species diversity and complex spatial distributions across a landscape. The use of imagery with high spatial resolutions to classify forests has become more commonplace as new satellite technology become available. Pixel-based methods of classification have been traditionally used to identify forest cover types. However, object-based image analysis (OBIA) has been shown to provide more accurate results. This study explored the ability of OBIA to classify forest stands in New Hampshire using two methods: by identifying stands within an IKONOS satellite image, and by identifying individual trees and building them into forest stands.
520 $a Forest stands were classified in the IKONOS image using OBIA. However, the spatial resolution was not high enough to distinguish individual tree crowns and therefore, individual trees could not be accurately identified to create forest stands. In addition, the accuracy of labeling forest stands using the OBIA approach was low. In the future, these results could be improved by using a modified classification approach and appropriate sampling scheme more reflective of object-based analysis.
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