| 1. | Title | Title | Evaluation of alternative methods for using LiDAR to predict aboveground biomass in mixed species and structurally complex forests in northeastern North America |
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| 2. | Creator | Author / affiliation / country | Rei Hayashi; University of Maine, School of Forest Resources, Orono, ME 04469 USA; United States |
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| 2. | Creator | Author / affiliation / country | John A Kershaw; University of New Brunswick, Faculty of Forestry and Environmental Management, Fredericton, NB Canada E3B 5A3; Canada |
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| 2. | Creator | Author / affiliation / country | Aaron Weiskittel; University of Maine, School of Forest Resources, Orono, ME 04469 USA; United States |
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| 3. | Subject | Keywords | LiDAR; random forest; nonlinear mixed effects models; fixed-radius plots; variableradius plots; Maine; New Brunswick |
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| 3. | Subject | Discipline(s) | Mensuration; Inventory; Remote Sensing |
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| 4. | Description | Abstract | Light detection and ranging (LiDAR) has become a common means for predicting key forest structural attributes, but comparisons of alternative statistical methods and the spatial extent of LiDAR metrics extraction on independent datasets have been minimal. The primary objective of this study was to assess the performance of local and non-local LiDAR aboveground biomass (AGB) prediction models at two locations in the Acadian Forest. Two common statistical techniques, nonlinear mixed effects (NLME) and random forest (RF), were used to fit the prediction models and compared. Finally, this study evaluated the influence of alternative plot radii for LiDAR metrics extraction on model fit and prediction accuracy. AGB models were independently developed at each forest and tested both locally (model applied to same forest used for development) and non-locally (model applied to different forest) using an extensive network of ground-based plots. In general, RF was found to outperform NLME when applied locally, but the differences between the approaches were negligible when applied to the non-local dataset. NLME was found to perform equally well locally and non-locally. LiDAR extraction radius had very little influence on model performance as well. Minimal differences between models developed using fixed- and variable-radius methods were found, while the optimal LiDAR extraction radius was not consistent among forests, statistical technique, or local vs. non-local. Overall, the results highlight the importance of a robust calibration dataset that covers the full range of observed variation for developing accurate prediction models based on remote sensing data. |
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| 5. | Publisher | Publisher | Contemporary Journal Concept Press |
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| 6. | Contributor | Contributors / supporting agencies | US Forest Service; Leading Edge Geomatics; School of Forest Resources at University of Maine; the Cooperative Forestry Research Unit; NB Innovation Fund Research Assistants Initiative |
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| 7. | Date | Publication date | 2015-09-30 |
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| 8. | Type | Type | Text.Serial.Journal; LiDAR; Variable Radius Plot Sampling; |
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| 9. | Format | Format | PDF; application/pdf |
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| 10. | Identifier | Article URL | https://mcfns.com/index.php/Journal/en/article/view/7.6 |
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| 11. | Source | Journal / volume / issue / year / pages | Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS); Vol. 7 No. 2: MCFNS September 30, 2015; 49-65 (17) |
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| 11. | Source | Online ISSN | 1946-7664 |
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| 12. | Language | Language | en |
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| 13. | Relation | Supplementary files | |
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| 14. | Coverage | Geographical coverage | |
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| 15. | Rights | Rights / copyright | Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS) 2015 |
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