Detecting annual spruce budworm defoliation using digital hemispherical and EO-1 Hyperion hyperspectral imagery
| dc.contributor.advisor | MacLean, David | |
| dc.contributor.author | Donovan, Shawn | |
| dc.date.accessioned | 2023-03-01T16:29:48Z | |
| dc.date.available | 2023-03-01T16:29:48Z | |
| dc.date.issued | 2020 | |
| dc.date.updated | 2023-03-01T15:02:28Z | |
| dc.description.abstract | Estimating defoliation damage during spruce budworm (SBW; Choristoneura fumiferana Clem.) outbreaks is important for implementing effective forest protection strategies, yet methods can be inconsistent. This thesis evaluates digital hemispherical imagery and Earth Observing-1 Hyperion hyperspectral remote sensing data for quantifying annual SBW defoliation, validated using accurate plot-level defoliation measurements from 75 sample plots in Québec, Canada. Hemispherical canopy gap fraction change from May-October, % balsam fir (Abies balsamea (L.) Mill.) basal area, and bioinsecticide spraying were important explanatory variables for modelling percent annual canopy defoliation, with root mean square errors ranging 14–24%. Semi-supervised classification of annual defoliation using the top five hyperspectral vegetation indices resulted in overall accuracies ranging 91–93%. Light (≤30%), moderate (30–70%), and severe (≥70%) defoliation classification accuracy ranged from 86–90%, 93–94%, and 92–96%, respectively. This thesis describes two viable methods that can complement current defoliation surveys in forest protection against SBW. | |
| dc.description.copyright | © Shawn Donovan, 2020 | |
| dc.format | text/xml | |
| dc.format.extent | xii, 113 pages | |
| dc.format.medium | electronic | |
| dc.identifier.uri | https://unbscholar.lib.unb.ca/handle/1882/13975 | |
| dc.language.iso | en_CA | |
| dc.publisher | University of New Brunswick | |
| dc.rights | http://purl.org/coar/access_right/c_abf2 | |
| dc.subject.discipline | Forestry and Environmental Management | |
| dc.title | Detecting annual spruce budworm defoliation using digital hemispherical and EO-1 Hyperion hyperspectral imagery | |
| dc.type | master thesis | |
| thesis.degree.discipline | Forestry and Environmental Management | |
| thesis.degree.fullname | Master of Science in Forestry | |
| thesis.degree.grantor | University of New Brunswick | |
| thesis.degree.level | masters | |
| thesis.degree.name | M.F. |
Files
Original bundle
1 - 1 of 1