A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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, L. G., Hill, A., Waser, L. T., Bürgi, A., Ginzler, C., & Blattert, C. (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, Article 117856. https://doi.org/10.1016/j.foreco.2019.117856
JYU-tekijät tai -toimittajat
Julkaisun tiedot
Julkaisun kaikki tekijät tai toimittajat: Bont, Leo Gallus; Hill, Andreas; Waser, Lars T.; Bürgi, Anton; Ginzler, Christian; Blattert, Clemens
Lehti tai sarja: Forest Ecology and Management
ISSN: 0378-1127
eISSN: 1872-7042
Julkaisuvuosi: 2020
Volyymi: 459
Artikkelinumero: 117856
Kustantaja: Elsevier
Julkaisumaa: Alankomaat
Julkaisun kieli: englanti
DOI: https://doi.org/10.1016/j.foreco.2019.117856
Julkaisun avoin saatavuus: Ei avoin
Julkaisukanavan avoin saatavuus:
Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/68792
Tiivistelmä
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.
YSO-asiasanat: metsänhoito; metsäsuunnittelu; metsänarviointi; puutavaranmittaus; sekametsät; kaukokartoitus; laserkeilaus
Vapaat asiasanat: 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
Liittyvät organisaatiot
OKM-raportointi: Kyllä
Raportointivuosi: 2020
JUFO-taso: 3