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An application of artificial intelli...

Mississippi State University., Forestry.

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An application of artificial intelligence techniques in classifying tree species with LiDAR and multi-spectral scanner data.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	An application of artificial intelligence techniques in classifying tree species with LiDAR and multi-spectral scanner data./
Author:	Posadas, Benedict Kit A., Jr.
Description:	90 p.
Notes:	Adviser: Emily B. Schultz.
Contained By:	Masters Abstracts International47-01.
Subject:	Agriculture, Forestry and Wildlife. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1454234
ISBN:	9780549645337

An application of artificial intelligence techniques in classifying tree species with LiDAR and multi-spectral scanner data.
Posadas, Benedict Kit A., Jr.

An application of artificial intelligence techniques in classifying tree species with LiDAR and multi-spectral scanner data. - 90 p.

Adviser: Emily B. Schultz.

Thesis (M.S.)--Mississippi State University, 2008.

Tree species identification is an important element in many forest resources applications such as wildlife habitat management, inventory, and forest damage assessment. Field data collection for large or mountainous areas is often cost prohibitive, and good estimates of the number and spatial arrangement of species or species groups cannot be obtained. Knowledge-based and neural network species classification models were constructed for remotely sensed data of conifer stands located in the lower mountain regions near McCall, Idaho, and compared to field data. Analyses for each modeling system were made based on multi-spectral sensor (MSS) data alone and MSS plus LiDAR (light detection and ranging) data. The neural network system produced models identifying five of six species with 41% to 88% producer accuracies and greater overall accuracies than the knowledge-based system. The neural network analysis that included a LiDAR derived elevation variable plus multi-spectral variables gave the best overall accuracy at 63%.

ISBN: 9780549645337Subjects--Topical Terms:

783690
Agriculture, Forestry and Wildlife.

An application of artificial intelligence techniques in classifying tree species with LiDAR and multi-spectral scanner data.
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020 $a 9780549645337
035 $a (UMI)AAI1454234
035 $a AAI1454234
040 $a UMI $c UMI
100 1 $a Posadas, Benedict Kit A., Jr. $3 1023728
245 1 3 $a An application of artificial intelligence techniques in classifying tree species with LiDAR and multi-spectral scanner data.
300 $a 90 p.
500 $a Adviser: Emily B. Schultz.
500 $a Source: Masters Abstracts International, Volume: 47-01, page: 0211.
502 $a Thesis (M.S.)--Mississippi State University, 2008.
520 $a Tree species identification is an important element in many forest resources applications such as wildlife habitat management, inventory, and forest damage assessment. Field data collection for large or mountainous areas is often cost prohibitive, and good estimates of the number and spatial arrangement of species or species groups cannot be obtained. Knowledge-based and neural network species classification models were constructed for remotely sensed data of conifer stands located in the lower mountain regions near McCall, Idaho, and compared to field data. Analyses for each modeling system were made based on multi-spectral sensor (MSS) data alone and MSS plus LiDAR (light detection and ranging) data. The neural network system produced models identifying five of six species with 41% to 88% producer accuracies and greater overall accuracies than the knowledge-based system. The neural network analysis that included a LiDAR derived elevation variable plus multi-spectral variables gave the best overall accuracy at 63%.
590 $a School code: 0132.
650 4 $a Agriculture, Forestry and Wildlife. $3 783690
650 4 $a Artificial Intelligence. $3 769149
650 4 $a Remote Sensing. $3 1018559
690 $a 0478
690 $a 0799
690 $a 0800
710 2 $a Mississippi State University. $b Forestry. $3 1023727
773 0 $t Masters Abstracts International $g 47-01.
790 $a 0132
790 1 0 $a Evans, David L. $e committee member
790 1 0 $a Matney, Thomas G. $e committee member
790 1 0 $a Schultz, Emily B., $e advisor
791 $a M.S.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1454234