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Modelling vegetation cover types usi...

Patraw, Kimberly.

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Modelling vegetation cover types using multiseasonal remotely sensed data to compare ecotones at multiple spatial and spectral resolutions (Michigan).

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modelling vegetation cover types using multiseasonal remotely sensed data to compare ecotones at multiple spatial and spectral resolutions (Michigan)./
Author:	Patraw, Kimberly.
Description:	136 p.
Notes:	Source: Masters Abstracts International, Volume: 37-01, page: 0217.
Contained By:	Masters Abstracts International37-01.
Subject:	Physical Geography. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1391029
ISBN:	0591984504

Modelling vegetation cover types using multiseasonal remotely sensed data to compare ecotones at multiple spatial and spectral resolutions (Michigan).
Patraw, Kimberly.

Modelling vegetation cover types using multiseasonal remotely sensed data to compare ecotones at multiple spatial and spectral resolutions (Michigan). - 136 p.

Source: Masters Abstracts International, Volume: 37-01, page: 0217.

Thesis (M.S.)--Utah State University, 1998.

The Army National Guard Bureau has implemented a cooperative project with Utah State University to help with the use, display, and evaluation of environmental data for maintaining land condition. Camp Grayling, Michigan, is comprised of deciduous and evergreen forest types. Use of remote sensing for classification has been limited in this region due to the difficulty of species-level classification using single-date remote-sensing techniques. Also, remote sensing has traditionally focused on mapping homogeneous zones rather than vegetation boundaries, while one of the concerns for land managers is the nature of vegetation edges (ecotones).

ISBN: 0591984504Subjects--Topical Terms:

893400
Physical Geography.

Modelling vegetation cover types using multiseasonal remotely sensed data to compare ecotones at multiple spatial and spectral resolutions (Michigan).
LDR:02320nmm 2200313 4500 001 1850793
005 20051205112324.5
008 130614s1998 eng d
020 $a 0591984504
035 $a (UnM)AAI1391029
035 $a AAI1391029
040 $a UnM $c UnM
100 1 $a Patraw, Kimberly. $3 1938704
245 1 0 $a Modelling vegetation cover types using multiseasonal remotely sensed data to compare ecotones at multiple spatial and spectral resolutions (Michigan).
300 $a 136 p.
500 $a Source: Masters Abstracts International, Volume: 37-01, page: 0217.
500 $a Major Professor: Allan Falconer.
502 $a Thesis (M.S.)--Utah State University, 1998.
520 $a The Army National Guard Bureau has implemented a cooperative project with Utah State University to help with the use, display, and evaluation of environmental data for maintaining land condition. Camp Grayling, Michigan, is comprised of deciduous and evergreen forest types. Use of remote sensing for classification has been limited in this region due to the difficulty of species-level classification using single-date remote-sensing techniques. Also, remote sensing has traditionally focused on mapping homogeneous zones rather than vegetation boundaries, while one of the concerns for land managers is the nature of vegetation edges (ecotones).
520 $a This study analyzed each season and band from multiseasonal satellite imagery for their contribution to separating vegetation type and density classes. Then spectral reflectance values for each vegetation and density class were used in discriminant models that define vegetation cover types and densities. These models were then tested against points within 200 m of vegetation boundaries to determine the performance of the models at edges of vegetation types. The reflectance values for vegetation types on Landsat Thematic Mapper (TM), Landsat MultiSpectral Sensor (MSS), and Advanced Very High Resolution Radiometer (AVHRR) imagery were used. (Abstract shortened by UMI.)
590 $a School code: 0241.
650 4 $a Physical Geography. $3 893400
650 4 $a Biology, Ecology. $3 1017726
650 4 $a Agriculture, Forestry and Wildlife. $3 783690
650 4 $a Library Science. $3 881164
690 $a 0368
690 $a 0329
690 $a 0478
690 $a 0399
710 2 0 $a Utah State University. $3 960049
773 0 $t Masters Abstracts International $g 37-01.
790 1 0 $a Falconer, Allan, $e advisor
790 $a 0241
791 $a M.S.
792 $a 1998
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1391029