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Multispectral/multitemporal aerial detection and mapping of red imported fire ant colony mounds in two monoculutural grass types.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Multispectral/multitemporal aerial detection and mapping of red imported fire ant colony mounds in two monoculutural grass types./
Author:	Carruth, Mark Ellis.
Description:	1 online resource (69 pages)
Notes:	Source: Masters Abstracts International, Volume: 73-01.
Contained By:	Masters Abstracts International73-01.
Subject:	Entomology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1491034click for full text (PQDT)
ISBN:	9781124587929

Multispectral/multitemporal aerial detection and mapping of red imported fire ant colony mounds in two monoculutural grass types.
Carruth, Mark Ellis.

Multispectral/multitemporal aerial detection and mapping of red imported fire ant colony mounds in two monoculutural grass types. - 1 online resource (69 pages)

Source: Masters Abstracts International, Volume: 73-01.

Thesis (M.S.)--Mississippi State University, 2011.

Includes bibliographical references

The red imported fire ant (Solenopsis invicta)(RIFA) is a major pest in the United States, causing serious economic and human costs. This study explored the feasibility of using digital aerial remote sensing in multispectral/multitemporal detection and mapping of RIFA mounds. Comparison of photointerpretive mound counts versus ground control counts was performed within two grass types, common Bermuda and tall fescue. Flights collecting digital image data occurred at three intervals in 2009, with ground truth data collected collaterally. Poisson regression count modeling was first utilized for analysis of both datasets. Moran's Index geospatial analysis was applied following the Poisson model. Outcomes in this study from these models demonstrate their ability to robustly support studies for tracking and control of RIFA or other pest populations. Additionally, in one location, type of grass cover appeared to affect detectability of mounds between the two methods. Key words: aerial, image, multispectral, Poisson, Moran's, ants, grass.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9781124587929Subjects--Topical Terms:

615844
Entomology.
Subjects--Index Terms:

AerialIndex Terms--Genre/Form:

542853
Electronic books.

Multispectral/multitemporal aerial detection and mapping of red imported fire ant colony mounds in two monoculutural grass types.
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245 1 0 $a Multispectral/multitemporal aerial detection and mapping of red imported fire ant colony mounds in two monoculutural grass types.
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500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Rodgers, John C.
502 $a Thesis (M.S.)--Mississippi State University, 2011.
504 $a Includes bibliographical references
520 $a The red imported fire ant (Solenopsis invicta)(RIFA) is a major pest in the United States, causing serious economic and human costs. This study explored the feasibility of using digital aerial remote sensing in multispectral/multitemporal detection and mapping of RIFA mounds. Comparison of photointerpretive mound counts versus ground control counts was performed within two grass types, common Bermuda and tall fescue. Flights collecting digital image data occurred at three intervals in 2009, with ground truth data collected collaterally. Poisson regression count modeling was first utilized for analysis of both datasets. Moran's Index geospatial analysis was applied following the Poisson model. Outcomes in this study from these models demonstrate their ability to robustly support studies for tracking and control of RIFA or other pest populations. Additionally, in one location, type of grass cover appeared to affect detectability of mounds between the two methods. Key words: aerial, image, multispectral, Poisson, Moran's, ants, grass.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Entomology. $3 615844
650 4 $a Geographic information science. $3 3432445
650 4 $a Statistics. $3 517247
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773 0 $t Masters Abstracts International $g 73-01.
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