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dc.contributor.authorMarczak, Paulina
dc.contributor.otherQueen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.))en
dc.description.abstractClimate warming has led to a need for improved estimates of aboveground carbon accumulation (CA); in Ontario, this is particularly true for uneven-aged, mixedwood temperate forests, where there remains high uncertainty. This study investigated the feasibility of using LiDAR-derived tree lists to initialize a Growth and Yield (G&Y) model at the Petawawa Research Forest (PRF) in eastern Ontario, Canada. We aimed to see if models based on LiDAR-derived estimates of tree attributes provide comparable predictions of aboveground CA in complex temperate forests to those from inventory measurements. Applying a local G&Y model (i.e., FVSOntario), we predicted aboveground carbon stock (CS; tons/ha) and CA (tons/ha/yr) using recurring plot measurements from 2012-2016, FVS1. We then used a suite of statistical predictors derived from LiDAR to predict stem density (SD), stem diameter distribution (SDD), and basal area distribution (BA_dist) using an algorithm. These data, along with measured species abundance, were used to construct tree lists and initialize a second G&Y model (i.e., FVS2). Another G&Y model was tested using LiDAR-initialized tree lists and Forest Resource Inventory (FRI) estimates of species abundance (i.e., FVS3). Models were validated using inventory data at years zero (2012) and four (2016). The inventory-based model predicted equivalent CS at year four compared to validation data at all size-class levels, while LiDAR-based models did not; however, models were statistically dissimilar to validation data for CA. There was no significant difference between LiDAR-based models using inventory and FRI species through a 9-year period at the plot level (p<0.05), although forest type groupings were not always equivalent. We found equivalency of inventory and photo-interpreted models for size classes ≥17 cm. Our results demonstrate the importance of precise tree size information when initializing G&Y models using LiDAR-derived estimates, the possibility of circumventing precise species abundance information when using G&Y models for CA, and that more work is needed on the use of LiDAR to quantify individual tree measurements in G&Y models for accurate predictions of CA. These results provide insight into techniques that can be applied regionally to quantify and forecast CA changes in Ontario, and for more informed carbon mitigation strategies.en_US
dc.relation.ispartofseriesCanadian thesesen
dc.rightsCC0 1.0 Universal*
dc.rightsQueen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canadaen
dc.rightsProQuest PhD and Master's Theses International Dissemination Agreementen
dc.rightsIntellectual Property Guidelines at Queen's Universityen
dc.rightsCopying and Preserving Your Thesisen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectcarbon accumulationen_US
dc.subjectcarbon stocken_US
dc.subjectgrowth-and-yield modelen_US
dc.titlePredicting Carbon Accumulation in Temperate Forests of Ontario Using a LiDAR-Initialized Growth-and-Yield Modelen_US
dc.description.degreeMaster of Scienceen_US
dc.contributor.departmentGeography and Planningen_US

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