Influences of vegetation, model, and data parameters on forest aboveground biomass assessment using an area-based approach

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F22%3A00560448" target="_blank" >RIV/86652079:_____/22:00560448 - isvavai.cz</a>

Alternative codes found

RIV/00216224:14310/22:00126347

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S1574954122002047?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1574954122002047?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ecoinf.2022.101754" target="_blank" >10.1016/j.ecoinf.2022.101754</a>

Result language

angličtina

Original language name

Influences of vegetation, model, and data parameters on forest aboveground biomass assessment using an area-based approach

Original language description

Although aboveground biomass (AGB) estimation using area-based approaches (ABAs) and its application to forestry have been actively researched through three decades, this technology has been little operationalized in the Central European forest sector. That means specific recommendations are needed in order to apply ABA for forest biomass modelling in this region. The present study was directed to filling such gaps while examining the effect of input ABA parameters on AGB model quality in conditions of mixed mountainous forests in Central Europe. Specific objectives were to assess whether the strength of the AGB model can be impacted by 1) canopy conditions (leaf-on and leaf-off), 2) airborne LiDAR point density (2.5, 5.0, 7.5, 10.0 points/m2), 3) field methods to estimate AGB (with regeneration components or without), and 4) machine learning methods (AdaBoost, Random decision forest, multilayer neural network, and Bayesian ridge regression). The results show that canopy conditions and airborne LiDAR point densities did not affect the strength of the AGB model, but that model's strength was affected by the vegetation regeneration component in the field biomass reference and by the machine learning method tested for modelling. AdaBoost and random decision forest were the most successful methods. To evaluate the quality of an AGB model it is recommended to combine several individual evaluation functions into the model score. The study highlights several recommendations to follow when estimating AGB from ALS using an ABA in Central European forests.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20701 - Environmental and geological engineering, geotechnics

Project

<a href="/en/project/QK1910150" target="_blank" >QK1910150: Operational assessment of aboveground biomass in forest ecosystems using advanced remote sensing methods</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Ecological Informatics

ISSN

1574-9541

e-ISSN

1878-0512

Volume of the periodical

70

Issue of the periodical within the volume

SEP

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

12

Pages from-to

101754

UT code for WoS article

000841244600005

EID of the result in the Scopus database

2-s2.0-85135419574