Comparing Empirical and Semi-Empirical Approaches to Forest Biomass Modelling in Different Biomes Using Airborne Laser Scanner Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43410%2F17%3A43911225" target="_blank" >RIV/62156489:43410/17:43911225 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.3390/f8050170" target="_blank" >http://dx.doi.org/10.3390/f8050170</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/f8050170" target="_blank" >10.3390/f8050170</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparing Empirical and Semi-Empirical Approaches to Forest Biomass Modelling in Different Biomes Using Airborne Laser Scanner Data

Popis výsledku v původním jazyce

Airborne laser scanner (ALS) data are used operationally to support field inventories and enhance the accuracy of forest biomass estimates. Modelling the relationship between ALS and field data is a fundamental step of such applications and the quality of the model is essential for the final accuracy of the estimates. Different modelling approaches and variable transformations have been advocated in the existing literature, but comparisons are few or non-existent. In the present study, two main approaches to modelling were compared: the empirical and semi-empirical approaches. Evaluation of model performance was conducted using a conventional evaluation criterion, i.e., the mean square deviation (MSD). In addition, a novel evaluation criterion, the model error (ME), was proposed. The ME was constructed by combining a MSD expression and a model-based variance estimate. For the empirical approach, multiple regression models were developed with two alternative transformation strategies: square root transformation of the response, and natural logarithmic transformation of both response and predictors. For the semi-empirical approach, a nonlinear regression of a power model form was chosen. Two alternative predictor variables, mean canopy height and top canopy height, were used separately. Results showed that the semi-empirical approach resulted in the smallest MSD in three of five study sites. The empirical approach resulted in smaller ME in the temperate and boreal biomes, while the semi-empirical approach resulted in smaller ME in the tropical biomes.

Název v anglickém jazyce

Comparing Empirical and Semi-Empirical Approaches to Forest Biomass Modelling in Different Biomes Using Airborne Laser Scanner Data

Popis výsledku anglicky

Airborne laser scanner (ALS) data are used operationally to support field inventories and enhance the accuracy of forest biomass estimates. Modelling the relationship between ALS and field data is a fundamental step of such applications and the quality of the model is essential for the final accuracy of the estimates. Different modelling approaches and variable transformations have been advocated in the existing literature, but comparisons are few or non-existent. In the present study, two main approaches to modelling were compared: the empirical and semi-empirical approaches. Evaluation of model performance was conducted using a conventional evaluation criterion, i.e., the mean square deviation (MSD). In addition, a novel evaluation criterion, the model error (ME), was proposed. The ME was constructed by combining a MSD expression and a model-based variance estimate. For the empirical approach, multiple regression models were developed with two alternative transformation strategies: square root transformation of the response, and natural logarithmic transformation of both response and predictors. For the semi-empirical approach, a nonlinear regression of a power model form was chosen. Two alternative predictor variables, mean canopy height and top canopy height, were used separately. Results showed that the semi-empirical approach resulted in the smallest MSD in three of five study sites. The empirical approach resulted in smaller ME in the temperate and boreal biomes, while the semi-empirical approach resulted in smaller ME in the tropical biomes.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

40102 - Forestry

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

Forests

ISSN

1999-4907

e-ISSN

—

Svazek periodika

8

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

17

Strana od-do

&quot;nestrankovano&quot;

Kód UT WoS článku

000404099300032

EID výsledku v databázi Scopus

2-s2.0-85019958838