Back to Search Start Over

Modeling site index of selected poplar clones using airborne laser scanning data.

Authors :
Tompalski, Piotr
Coops, Nicholas C.
Achim, Alexis
Cosgrove, Cameron F.
Lapointe, Eric
Brochu-Marier, Felix
Source :
Canadian Journal of Forest Research. 2022, Vol. 52 Issue 7, p1088-1097. 10p.
Publication Year :
2022

Abstract

Accurate growth and yield projection for plantations is critical for evaluating management decisions and anticipating future yields. Development of site index (SI) models is often costly and can be problematic when new, short-rotation species are introduced, for example, hybrid poplar plantations, which are increasingly common due to their very fast growth and high productivity. Airborne laser scanning (ALS) allows accurate measurement of tree and stand height and is increasingly being used to develop top height models. In this paper, we demonstrate an approach to develop SI models from ALS data across hybrid poplar plantations in Quebec, Canada. We exploit a single time step ALS acquisition to generate top height estimates at 10 m grid level. Using existing information on planting date and management practices, we developed top height models for unique classes of fertilization treatment and clone. The generic models for unfertilized and fertilized stands showed good fit statistics, with R2 of 0.71 and 0.82, respectively. Clone-specific models showed similar goodness of fit, with the best model resulting in an R2 of 0.89 and relative root mean square error (RMSE) of 16.6%. Analysis of variance (ANOVA) results showed that clone, fertilization status, and the interaction term between clone and fertilization were significant. Our results confirm the development of top height models from a chronosequence of ALS data was successful and offers a new approach to derive SI models in single-species plantation sites. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00455067
Volume :
52
Issue :
7
Database :
Academic Search Index
Journal :
Canadian Journal of Forest Research
Publication Type :
Academic Journal
Accession number :
158477647
Full Text :
https://doi.org/10.1139/cjfr-2021-0257