1. bookVolumen 49 (2022): Heft 2 (July 2022)
Zeitschriftendaten
License
Format
Zeitschrift
eISSN
1338-7014
Erstveröffentlichung
16 Apr 2017
Erscheinungsweise
2 Hefte pro Jahr
Sprachen
Englisch
access type Uneingeschränkter Zugang

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

Online veröffentlicht: 05 Aug 2022
Volumen & Heft: Volumen 49 (2022) - Heft 2 (July 2022)
Seitenbereich: 93 - 101
Eingereicht: 12 Jan 2022
Akzeptiert: 20 May 2022
Zeitschriftendaten
License
Format
Zeitschrift
eISSN
1338-7014
Erstveröffentlichung
16 Apr 2017
Erscheinungsweise
2 Hefte pro Jahr
Sprachen
Englisch
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.

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