A1 Journal article (refereed)
Airborne-laser-scanning-derived auxiliary information discriminating between broadleaf and conifer trees improves the accuracy of models for predicting timber volume in mixed and heterogeneously structured forests (2020)


Bont, Leo Gallus; Hill, Andreas; Waser, Lars T.; Bürgi, Anton; Ginzler, Christian; Blattert, Clemens (2020). Airborne-laser-scanning-derived auxiliary information discriminating between broadleaf and conifer trees improves the accuracy of models for predicting timber volume in mixed and heterogeneously structured forests. Forest Ecology and Management, 459, 117856. DOI: 10.1016/j.foreco.2019.117856


JYU authors or editors


Publication details

All authors or editors: Bont, Leo Gallus; Hill, Andreas; Waser, Lars T.; Bürgi, Anton; Ginzler, Christian; Blattert, Clemens

Journal or series: Forest Ecology and Management

ISSN: 0378-1127

eISSN: 1872-7042

Publication year: 2020

Volume: 459

Article number: 117856

Publisher: Elsevier

Publication country: Netherlands

Publication language: English

DOI: http://doi.org/10.1016/j.foreco.2019.117856

Open Access: Publication channel is not openly available

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/68792


Abstract

Managing forests for ecosystem services and biodiversity requires accurate and spatially explicit forest inventory data. A major objective of forest management inventories is to estimate the standing timber volume for certain forest areas. In order to improve the efficiency of an inventory, field based sample-plots can be statistically combined with remote sensing data. Such models usually incorporate auxiliary variables derived from canopy height models. The inclusion of forest type variables, which quantify broadleaf and conifer volume proportions, has been shown to further improve model performance. Currently, the most common way of quantifying broadleaf and conifer forest types is by calculating the proportions of the corresponding areas of the canopy cover. This practice works well for single-layer forests with only a few species, but we hypothesized that this is not best practice for heterogeneously structured and mixed forests, where the area proportion does not accurately reflect the timber volume proportion. To better represent the broadleaf and conifer volume proportions, we introduced two new auxiliary variables in which the area proportion is weighted by height information from a canopy height model.

The main objectives of this study were: (1) to demonstrate the advantage of including forest type (broadleaf/conifer distinction) information in ordinary least squares regression models for timber volume prediction using widely available data sources, and (2) to investigate the hypothesis that including the broadleaf and conifer proportions, weighted by canopy height information, as additional auxiliary variables is favourable over including simple area proportions. The study was conducted in three areas in Switzerland, all of which have heterogeneously structured and mixed forests. Our main findings were that the best model performance can generally be achieved: (1) by deriving conifer and broadleaf proportions from a high-resolution broadleaf/conifer map derived from leaf-off airborne laser scanning data, and (2) by using broadleaf/conifer proportions weighted by height information from a canopy height model. Incorporating the so-derived conifer and broadleaf proportions increased the model accuracy by up to 9 percentage points in root mean square error (RMSE) compared with models not using any forest type information, and by up to 2 percentage points in RMSE compared with models using conifer and broadleaf proportions based solely on the corresponding area proportions, as done in current practice. Our findings are particularly relevant for mixed and heterogeneously structured forests, such as those managed to achieve multiple functions or to adapt effectively to climate change.


Keywords: silviculture; forest management planning; forest mensuration; log scaling; mixed forests; remote sensing; laser scanning

Free keywords: airborne laser scanning; best fit models; canopy height model; forest type map; high-precision forest inventory; image-based point clouds; mixed and heterogeneously structured forest; ordinary least squares regression models; merchantable timber volume


Contributing organizations


Ministry reporting: Yes

Reporting Year: 2020

Preliminary JUFO rating: 3


Last updated on 2020-18-08 at 13:31