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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.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Object-based image analysis for forest-type mapping in New Hampshire./
Author:	Czarnecki, Christina.
Description:	88 p.
Notes:	Source: Masters Abstracts International, Volume: 51-03.
Contained By:	Masters Abstracts International51-03(E).
Subject:	Remote Sensing. -
Online resource:	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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020 $a 9781267788542
035 $a (MiAaPQ)AAI1521564
035 $a AAI1521564
040 $a MiAaPQ $c MiAaPQ
100 1 $a Czarnecki, Christina. $3 2105980
245 1 0 $a Object-based image analysis for forest-type mapping in New Hampshire.
300 $a 88 p.
500 $a Source: Masters Abstracts International, Volume: 51-03.
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.
590 $a School code: 0141.
650 4 $a Remote Sensing. $3 1018559
650 4 $a Natural Resource Management. $3 676989
690 $a 0799
690 $a 0528
710 2 $a University of New Hampshire. $b Natural Resources. $3 2105981
773 0 $t Masters Abstracts International $g 51-03(E).
790 $a 0141
791 $a M.S.
792 $a 2012
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1521564