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Carbon storage in urban forests along the Colorado Front Range: an analysis of urban trees in carbon credit markets and the allometric relationships for determining urban forest biomass

dc.contributor.authorMcHale, Melissa R., author
dc.contributor.authorBurke, Ingrid C., advisor
dc.contributor.authorLaituri, Melinda, committee member
dc.contributor.authorRyan, Michael G., committee member
dc.contributor.authorDenning, Scott, committee member
dc.date.accessioned2026-03-26T18:32:24Z
dc.date.issued2007
dc.description.abstractUrbanization is increasing globally with a majority of the world's population living in or around urban centers. It has been shown that a change in regional carbon dynamics occurs with urbanization, and that in semi-arid and arid systems, net primary productivity is stimulated. Urban trees are a potentially large carbon storage pool, but their significance is unknown. Urban tree planting has been recognized as a possible element of carbon mitigation schemes. I used a modeling approach to investigate the cost effectiveness of these projects. I found that some urban tree projects could be cost effective in carbon markets at this time and carbon assimilation rate has the largest influence on cost effectiveness. Urban forest studies have been limited by a lack of information on urban tree allometry. I collected volume data using LiDAR for 11 dominant species in Fort Collins, Colorado and developed allometric equations. I compared my volume estimated to volume equations developed for California's urban trees. Volume equations developed for urban trees outside of Colorado did not accurately predict urban tree volume in Fort Collins. I conducted a sensitivity analysis to determine how important it is to use allometric relationships developed specifically for urban trees when quantifying biomass. A few equations developed for traditional forest trees predicted similar biomass to urban-based predictions, but predictions for individual trees varied by over 200%. Although relative variability declined when I scaled up to a community of street trees, these results indicate that it is important to develop allometric relationships specific for urban areas to accurately quantify carbon storage of urban trees. Finally, I quantified potential carbon storage capacity of Front Range urban forests. My results show that mature, high-density urban forests can store as much carbon as the most productive forests in Colorado. Furthermore major cities along the Front Range cover only 1% of the land area in Colorado, but contain 55% of the human population. Colorado cities can potentially store 1-26 million tonnes of carbon in their lifetime, but that is only enough to offset local carbon emissions for three years.
dc.format.mediumdoctoral dissertations
dc.identifier.urihttps://hdl.handle.net/10217/243853
dc.identifier.urihttps://doi.org/10.25675/3.026540
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
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.licensePer the terms of a contractual agreement, all use of this item is limited to the non-commercial use of Colorado State University and its authorized users.
dc.subjectecology
dc.subjectbiogeochemistry
dc.titleCarbon storage in urban forests along the Colorado Front Range: an analysis of urban trees in carbon credit markets and the allometric relationships for determining urban forest biomass
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.disciplineEcology
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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