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Quantifying and mapping tree mortality due to mountain pine bark beetles via analyses of remote sensing data in northern Colorado

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dc.contributor.authorTaleb, Hamza A. S., author
dc.contributor.authorLaituri, Melinda, advisor
dc.contributor.authorFassnacht, Steven, committee member
dc.contributor.authorLeisz, Stephen, committee member
dc.contributor.authorGrigg, Neil, committee member
dc.date.accessioned2024-09-09T20:52:08Z
dc.date.available2025-08-16
dc.date.issued2024
dc.description.abstractIn the past two decades, Mountain Pine Bark Beetle (MPBB) infestations have become more pervasive due to increasing temperatures and drought conditions related to climate change causing regional-scale mortality. Insect effects on tree die-off, fuels, and fire behavior can vary widely. A key problem in understanding insect-fire relationships is the lack of empirical maps that show interrelated changes in the distribution of insect infestations and fire zones over space and time. This study demonstrates an approach to tracking and mapping the spread of MPBB by analyses of vegetation indices calculated from Landsat TM data in the study site in northwestern Colorado. These indices were used for calculations in the Random Forest (RF) classifier algorithm and the Support Vector Machine (SVM) classifier algorithm to determine the presence or absence of MPBB and to illustrate the changes in the distribution of infestations with time. A comparison was made between the accuracy of the two classification algorithms (RF and SVM) in tracking and mapping the spread of MPBB. R2 has proved to be a reliable measure of accuracy of regression models. If the statistical accuracy of all the models, (RF vs. SVM and binary vs. regression) are compared, both the regression and binary models based on RF are more accurate. The results of this study can provide a useful tool for forest managers to make decisions about how changing conditions affect potential problems in forest management.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierTaleb_colostate_0053A_18463.pdf
dc.identifier.urihttps://hdl.handle.net/10217/239247
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.rights.accessEmbargo expires: 08/16/2025.
dc.subjectLandsat data analysis
dc.subjectrandom forest classifier
dc.subjecttime series
dc.subjectmountain pine park beetles
dc.subjectbinary vs. regression analysis
dc.subjectsupport vector machine classifier
dc.titleQuantifying and mapping tree mortality due to mountain pine bark beetles via analyses of remote sensing data in northern Colorado
dc.typeText
dcterms.embargo.expires2025-08-16
dcterms.embargo.terms2025-08-16
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineEcology
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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