Comparison of individual-tree segmentation and area-based approaches using airborne LiDAR for tree density and volume estimation in New Brunswick forests
| dc.contributor.advisor | Kershaw Jr., John A. | |
| dc.contributor.author | Jang, Minji | |
| dc.date.accessioned | 2024-01-04T19:36:11Z | |
| dc.date.available | 2024-01-04T19:36:11Z | |
| dc.date.issued | 2022-08 | |
| dc.description.abstract | Airborne LiDAR (Light Detection and Ranging) is a promising remote sensing technology widely used for forest inventory. In this study we explored three approaches for developing LiDAR derived estimates of volume per ha: Area-based nonlinear regression models; individual-tree segmentation and summation; and a combined approach. On the basis of minimum rMSEs, the combination approach was the best (48.23 m3 /ha rMSE), area-based was second (54.98 m3 /ha rMSE), and segmentation method showed the lowest accuracy (64.62 m3 /ha rMSE). Although the three approaches produced different levels of accuracy, the estimates were statistically equivalent to each other based on the two one-sided t-test of equivalence. While the segmentation approach produced acceptable estimates of volume per ha, density (stems/ha) estimation was very poor with estimates often less than 50% compared to field plot data. The Hamraz algorithm produced more accurate estimates than the other segmentation algorithms explored. In addition to fitting models and accessing goodness-of-fit, we explored stand structural factors that influenced the observed errors. Stand totals (Volume, Basal Area, and Density) were more influential for the area-based approach while mean tree size (quadratic mean diameter and height) and species composition (basal area of hardwood, and softwood) were more influential with the segmentation approaches. | |
| dc.description.copyright | ©Minji Jang, 2022 | |
| dc.format.extent | viii, 83 | |
| dc.format.medium | electronic | |
| dc.identifier.oclc | (OCoLC)1425948793 | en |
| dc.identifier.other | Thesis 11056 | en |
| dc.identifier.uri | https://unbscholar.lib.unb.ca/handle/1882/37630 | |
| dc.language.iso | en | |
| dc.publisher | University of New Brunswick | |
| dc.rights | http://purl.org/coar/access_right/c_abf2 | |
| dc.subject.discipline | Forestry and Environmental Management | |
| dc.subject.lcsh | Optical radar. | en |
| dc.subject.lcsh | Forest management. | en |
| dc.subject.lcsh | Algorithms. | en |
| dc.title | Comparison of individual-tree segmentation and area-based approaches using airborne LiDAR for tree density and volume estimation in New Brunswick forests | |
| dc.type | master report | |
| oaire.license.condition | other | |
| thesis.degree.discipline | Forestry and Environmental Management | |
| thesis.degree.grantor | University of New Brunswick | |
| thesis.degree.level | masters | |
| thesis.degree.name | M.F. |