Discrimination of Leaves in a Multi-Layered Mediterranean Forest through Machine Learning Algorithms

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41320%2F23%3A97808" target="_blank" >RIV/60460709:41320/23:97808 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2072-4292/15/18/4450" target="_blank" >https://www.mdpi.com/2072-4292/15/18/4450</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/rs15184450" target="_blank" >10.3390/rs15184450</a>

Result language

angličtina

Original language name

Discrimination of Leaves in a Multi-Layered Mediterranean Forest through Machine Learning Algorithms

Original language description

Terrestrial laser scanning (TLS) technology characterizes standing trees with millimetric precision. An important step to accurately quantify tree volume and above-ground biomass using TLS point clouds is the discrimination between timber and leaf components. This study evaluates the performance of machine learning (ML)-derived models aimed at discriminating timber and leaf TLS point clouds, focusing on eight Mediterranean tree species datasets. The results show the best accuracies for random forests, gradient boosting machine, stacked ensemble model, and deep learning models with an average F1 score equal to 0.92. The top-performing ML-derived models showed well-balanced average precision and recall rates, ranging from 0.86 to 0.91 and 0.92 to 0.96 for precision and recall, respectively. Our findings show that Italian maple, European beech, hazel, and small-leaf lime tree species have more accurate F1 scores, with the best average F1 score of 0.96. The factors influencing the timber-leaf discrimination include phenotypic factors, such as bark surface (i.e., roughness and smoothness), technical issues (i.e., noise points and misclassification of points), and secondary factors (i.e., bark defects, lianas, and microhabitats). The top-performing ML-derived models report a time computation ranging from 8 to 37 s for processing 2 million points. Future studies are encouraged to calibrate, configure, and validate the potential of top-performing ML-derived models on other tree species and at the plot level.

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

20705 - Remote sensing

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

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

Name of the periodical

Remote Sensing

ISSN

2072-4292

e-ISSN

2072-4292

Volume of the periodical

15

Issue of the periodical within the volume

18

Country of publishing house

CH - SWITZERLAND

Number of pages

26

Pages from-to

1-26

UT code for WoS article

001072508800001

EID of the result in the Scopus database

2-s2.0-85172922201