1. bookTom 49 (2022): Zeszyt 2 (July 2022)
Informacje o czasopiśmie
Pierwsze wydanie
16 Apr 2017
Częstotliwość wydawania
2 razy w roku
access type Otwarty dostęp

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

Data publikacji: 05 Aug 2022
Tom & Zeszyt: Tom 49 (2022) - Zeszyt 2 (July 2022)
Zakres stron: 93 - 101
Otrzymano: 12 Jan 2022
Przyjęty: 20 May 2022
Informacje o czasopiśmie
Pierwsze wydanie
16 Apr 2017
Częstotliwość wydawania
2 razy w roku

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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