Thesis (Ph.D.)--University of Alberta, 2009. / Title from PDF file main screen (viewed on Apr. 1, 2010). A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Geotechnical Engineering, [Department of] Civil and Environmental Engineering, University of Alberta. Includes bibliographical references.

Thesis (Ph. D.)--Michigan State University. Crop & Soil Sciences, 2008. / Title from PDF t.p. (viewed on Aug. 17, 2009) Includes bibliographical references. Also issued in print.

Thesis (M.S.)--Humboldt State University, 2009. / Includes bibliographical references (leaves 55-65). Also available via Humboldt Digital Scholar.

Thesis (M.S.)--Indiana University, 2008. / Department of Earth Sciences, Indiana University-Purdue University Indianapolis (IUPUI). Advisor(s): Pierre-Andre Jacinthe, Lenore P. Tedesco, Philippe G. Vidon. Includes vita. Includes bibliographical references (leaves 95-99).

Thesis (Ph. D.)--Stanford University, 2008. / Submitted to the Department of Geophysics. Copyright by the author.

Thesis (Ph.D.)--University of Wyoming, 2009. / Title from PDF title page (viewed on May 21, 2010). Includes bibliographical references (p. 50-63).

Thesis (M. Sc.(Zoology))--University of Pretoria, 2006. / Includes summary. Includes bibliographical references. Available on the Internet via the World Wide Web.

Thesis (Ph. D.)--Oregon State University, 2011. / Printout. Includes bibliographical references (leaves 126-132). Also available on the World Wide Web.

Individual tree detection and modelling forest parameters using Airborne Laser Scanner data (Light Detection and Ranging (LiDAR) is becoming increasingly important for the monitoring and sustainable management of forests. Remote sensing has been a useful tool for individual tree analysis in the past decade, although inadequate spatial resolution from satellites means that only airborne systems have sufficient spatial resolution to conduct individual tree analysis. Moreover, recent advances in airborne LiDAR now provide high horizontal resolution as well as information in the vertical dimension. However, it is challenging to fully exploit and utilize small-footprint LiDAR data for detailed tree analysis. Procedures for forest biomass quantification and forest attributes measurement using LiDAR data have improved at a rapid pace as more robust and sophisticated modelling used to improve the studies. This thesis contains an evaluation of three approaches of utilizing LiDAR data for individual tree forest measurement. The first explores the relationship between LiDAR metrics and field reference to assess the correlation between LiDAR and field data at the individual-tree level. The intention was not to detect trees automatically, but to develop a LiDAR-AGB model based on trees that were mapped in the field so as to evaluate the relationships between LiDAR-type metrics under controlled conditions for the study sites, and field-derived AGB. A non-linear AGB model based on field data and LiDAR data was developed and LiDAR height percentile h80 and crown width measurement (CW) was found to best fit the data as evidenced by and Adj-R2 value of 0.63, the root mean squared error of the model of 14.8% and analysis of the residuals. This paper provides the foundation for a predictive LiDAR-AGB model at tree level over two study sites, Pasoh Forest Reserve and FRIM Forest Reserve. The second part of the thesis then takes this AGB-LiDAR relationship and combines it with individual tree crown delineation. This chapter shows the contribution of performing an automatic individual tree crown delineation over the wider forest areas. The individual tree crown delineation is composed of a five-step framework, which is unique in its automated determination of dominant crown sizes in a forest area and its adaption of the LiDAR-AGB model developed for the purpose of validation the method. This framework correctly delineated 84% and 88% of the tree crowns in the two forest study areas which is mostly dominated with lowland dipterocarp trees. Thirdly, parametric and non-parametric modelling approaches are proposed for modelling forest structural attributes. Selected modelling methods are compared for predicting 4 forest attributes, volume (V), basal area (BA), height (Ht) and aboveground biomass (AGB) at the species level. The AGB modelling in this paper is extracted using the LiDAR derived variables from the automated individual tree crown delineation, in contrast to the earlier AGB modelling where it is derived based on the trees that were mapped in the field. The selected non-parametric method included, k-nearest neighbour (k-NN) imputation methods: Most Similar Neighbour (MSN) and Gradient Nearest Neighbour (GNN), Random Forest (RF) and parametric approach: Ordinary Least Square (OLS) regression. To compare and evaluate these approaches a scaled root mean squared error (RMSE) between observed and predicted forest attribute sampled from both forest site was computed. The best method varied according to response variable and performance measure. OLS regression was to found to be the best performance method overall evidenced by RMSE after cross validation for BA (1.40 m2), V (1.03 m3), Ht (2.22 m) and AGB (96 Kg/tree) respectively, showed its applicability to wider conditions, while RF produced best overall results among the non-parametric methods tested. This thesis concludes with a discussion of the potential of LiDAR data as an independent source of important forest inventory data source when combined with appropriate designed sample plots in the field, and with appropriate modelling tools.

Plant roots are central to C- and N-cycling in soil. However, (i) plants differ strongly in tissue recalcitrance (e.g. lignin content) affecting their mineralization in soil, and (ii) rhizodeposits also vary strongly in terms of the metabolites that they contain. Therefore, (i) we used 13C labelled ryegrass root and shoot residues as substrates to investigate the impact of tissue recalcitrance on soil processes through controlled incubation of soil, (ii) we assessed variations in root C-deposition between barley genotypes and their respective impacts on soil processes using 13CO2 labelled plants, (iii) using 13C/15N enriched ryegrass root residues as tracer material, we investigated the impacts of barley genotypes on mineralization of recently incorporated plant residues in soil and plant uptake of the residue-derived N, and (iv) we applied a quantitative trait loci analysis approach to identify barley chromosome regions affecting soil microbial biomass and other soil and root related traits. In the first study, addition of root residues resulted in reduced C-mineralization rates, soil microbial activity and soil organic matter (SOM) priming relative to shoot residues. Planted experiments revealed (i) genotype effects on plant-, SOM- and residuederived surface soil CO2-C efflux and showed that incorporation of plant derived-C to the silt-and-clay soil fraction varied between genotypes, indicating relative stabilization of root derived-C as a result of barley genotype, (ii) that plant uptake of residue released N between genotypes was linked to genotype impacts on residue mineralization, and (iii) barley chromosome regions that influence plant-derived microbial biomass C. These results (i) suggest that greater plant tissue recalcitrance can lower soil C-emissions and increase C-storage in soil, and (ii) demonstrate the barley genetic influence on soil microbial communities and C- and N-cycling, which could be useful in crop breeding to improve soil microbial interactions, and thus promote sustainable crop production systems.