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Unmanned aerial systems for forest structure mapping: assessments of area-based and individual tree monitoring

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dc.contributor.authorCreasy, Matthew, author
dc.contributor.authorTinkham, Wade T., advisor
dc.contributor.authorVogeler, Jody, committee member
dc.contributor.authorHoffman, Chad, committee member
dc.date.accessioned2020-09-07T10:08:49Z
dc.date.available2020-09-07T10:08:49Z
dc.date.issued2020
dc.description.abstractCharacterization of forest structure is important for management-related decision making, especially in the wake of disturbance. Increasingly, observations of forest structure are needed at both finer resolution and across greater extents in order to support managers in meeting spatially explicit objectives. Current methods of acquiring forest measurements can be limited by a combination of time, expense, and either extent or temporal resolution. Drone or UAS-based photogrammetry provides an airborne method of forest structure data acquisition at a significantly lower cost and time commitment when compared to existing methods of such as airborne laser scanning (LiDAR). A growing body of literature confirms UAS-based photogrammetry models can be as detailed as conventional LiDAR models. However, there exists a knowledge gap in best practice for data acquisition parameters and assessment of accurate characterization within forest photogrammetry. The following two chapters utilize large stem mapped sites to fill that knowledge gap by 1) systematically testing the effects of UAS flight speed and altitude on plot-based aboveground biomass modeling through photogrammetry and 2) evaluating several algorithms for detecting individual tree locations and characterizing crown areas. Results show a strong positive relationship between flight altitude and aboveground biomass modeling, with all UAS flights evaluated above 80 m altitude, providing better results (2-24% more variance explained) than contemporary LiDAR modeling strategies. Additionally, results demonstrate that the probability of detecting individual trees decays moving from the dominant overstory to suppressed trees, corresponding to >97% at the top of the canopy and decreasing to 67% for trees in the understory. Our results indicate the potential for UAS photogrammetry to produce highly detailed maps of forest biomass, as well as capture variation of forest structure through the detection of trees and tree groups. Such high-resolution data has the potential to become a much-needed tool for monitoring forest structures to inform spatially explicit management objectives. Additionally, these studies reinforced how UAS photogrammetry can provide low-cost repeat monitoring of forest conditions.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierCreasy_colostate_0053N_16225.pdf
dc.identifier.urihttps://hdl.handle.net/10217/212060
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.subjectdrone
dc.subjectphotogrammetry
dc.subjectUAS
dc.subjectindividual tree detection
dc.subjectaboveground biomass
dc.subjectstructure from motion
dc.titleUnmanned aerial systems for forest structure mapping: assessments of area-based and individual tree monitoring
dc.typeText
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.disciplineForest and Rangeland Stewardship
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

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