Evaluation of alternative applications of LiDAR-based enhanced forest inventory methods
| dc.contributor.author | Kelley, Jason William | |
| dc.contributor.supervisor | Bone, Christopher | |
| dc.contributor.supervisor | Trofymow, John Antonio (Tony) | |
| dc.date.accessioned | 2021-08-31T19:24:16Z | |
| dc.date.copyright | 2021 | en_US |
| dc.date.issued | 2021-04-22 | |
| dc.degree.department | Department of Geography | |
| dc.degree.level | Master of Science M.Sc. | en_US |
| dc.description.abstract | Forests cover a large portion of the global land area and provide critical resources such as timber, food, and medicine in addition to playing a significant role in the global carbon cycle. As such, sustainable forest management practices are required to balance forest economies and climate change mitigation with other non-timber objectives. A key aspect of many sustainable forest management programs is forest monitoring, for which technological and methodological development has led to enhanced forest inventory (EFI) methods, many of which rely on remote sensing data from high-resolution light detection and ranging (LiDAR) and optical imagery. However, to date, current applications of EFI methods have mostly focused on timber attributes with limited research on non-timber attributes or analyses regarding multi-temporal monitoring, method scaling, or method transferability. The objective of this thesis is to expand applications of EFIs in monitoring and analysis through two distinct studies, first evaluating the utility of LiDAR-based EFI methods in multi-temporal silvicultural treatment assessment and secondly in the pre-harvest estimation of merchantable wood and non-merchantable wood left as logging residues. The first study evaluates a process that expands the sampling of fertilization treatment effects on forest stands to the wider treatment area by utilizing paired LiDAR blocks made up of raster cell estimates from a multi-temporal area-based model. Results showed promise for detecting treatment impacts on stand volume, biomass, and height and highlights the potential for the methods to be used as a means to rapidly expand analysis from sample plots to the entire treatment area. The second study focuses on the use of a hybrid area-based and individual tree EFI approach to model merchantable and non-merchantable forest wood volumes while exploring the scalability of these models to harvest blocks and the transferability to additional blocks without prior training. Results from this study indicated that models for both volume attributes are successfully scalable and transferable to harvest blocks. Overall, the research results presented in this thesis demonstrate the potential of enhanced forest inventory methods for the monitoring and assessment of timber attributes, such as wood volume or biomass, as well as alternative attributes, such as stand height, or non-merchantable wood volume, over multiple years. This work further demonstrates the potential for these methods to expand areas of assessment and increase prediction accuracies. | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/13344 | |
| dc.language | English | eng |
| dc.language.iso | en | en_US |
| dc.publisher | Forests | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.subject | LiDAR | en_US |
| dc.subject | Multi-temporal | en_US |
| dc.subject | Enhanced Forest Inventory | en_US |
| dc.subject | Forest Fertilization | en_US |
| dc.subject | Logging Residues | en_US |
| dc.title | Evaluation of alternative applications of LiDAR-based enhanced forest inventory methods | en_US |
| dc.type | Thesis | en_US |