<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The difficulty of locating and measuring the over-bark tree bole diameters at heights that are far from the ground, is a serious problem in ground-truth data measurements in the field. This problem could be addressed through the application of intelligent systems methods. The paper explores the possibility of applying the Random Forest regression method (RFr) in order to assess, as accurately as possible, the size of the tree bole diameters at any height above the ground, considering data that can be easily measured in the field. For this purpose, diameter measurements of pine trees (<italic>Pinus brutia</italic> Ten.) from the Seich–Sou urban forest of Thessaloniki, Greece, were used. The effectiveness of the Random Forest regression technique is compared with the results of non-linear regression models that fitted to the available data and evaluated. This research has shown that the RFr method can be a reliable alternative methodology in order to receive accurate information provided by the model, saving time and effort in field.</p>
</abstract>

The difficulty of locating and measuring the over-bark tree bole diameters at heights that are far from the ground, is a serious problem in ground-truth data measurements in the field. This problem could be addressed through the application of intelligent systems methods. The paper explores the possibility of applying the Random Forest regression method (RFr) in order to assess, as accurately as possible, the size of the tree bole diameters at any height above the ground, considering data that can be easily measured in the field. For this purpose, diameter measurements of pine trees (Pinus brutia Ten.) from the Seich–Sou urban forest of Thessaloniki, Greece, were used. The effectiveness of the Random Forest regression technique is compared with the results of non-linear regression models that fitted to the available data and evaluated. This research has shown that the RFr method can be a reliable alternative methodology in order to receive accurate information provided by the model, saving time and effort in field.

Simulation of over-bark tree bole diameters, through the RFr (Random Forest Regression) algorithm

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This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The difficulty of locating and measuring the over-bark tree bole diameters at heights that are far from the ground, is a serious problem in ground-truth data...

Simulation of over-bark tree bole diameters, through the RFr (Random Forest Regression) algorithm

Online veröffentlicht: 05. Aug. 2022

Seitenbereich: 93 - 101

Eingereicht: 12. Jan. 2022

Akzeptiert: 20. Mai 2022

DOI: https://doi.org/10.2478/foecol-2022-0010

Schlüsselwörter
artificial intelligence, non-linear regression, over bark tree bole diameters,

© 2022 Maria J. Diamantopoulou, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Simulation of over-bark tree bole diameters, through the RFr (Random Forest Regression) algorithm

Online veröffentlicht: 05. Aug. 2022

Seitenbereich: 93 - 101

Eingereicht: 12. Jan. 2022

Akzeptiert: 20. Mai 2022

DOI: https://doi.org/10.2478/foecol-2022-0010

Schlüsselwörterartificial intelligence, non-linear regression, over bark tree bole diameters,

© 2022 Maria J. Diamantopoulou, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
artificial intelligence, non-linear regression, over bark tree bole diameters,