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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/00216224:14310/22:00126347

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20701 - Environmental and geological engineering, geotechnics

Projekt

<a href="/cs/project/QK1910150" target="_blank" >QK1910150: Průběžné hodnocení nadzemní biomasy dřevinného patra lesních ekosystémů pomocí pokročilých metod dálkového průzkumu Země</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Ecological Informatics

ISSN

1574-9541

e-ISSN

1878-0512

Svazek periodika

70

Číslo periodika v rámci svazku

SEP

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

12

Strana od-do

101754

Kód UT WoS článku

000841244600005

EID výsledku v databázi Scopus

2-s2.0-85135419574