1 Ocak 2023 Konferans bildirisi Açık Erişim

Aksoy, Samet; Al Shwayyat, Shouq Zuhter Hasan; Topgul, Sule Nur; Sertel, Elif; Unsalan, Cem; Salo, Jari; Holmstrom, Anton; Wallerman, Jorgen; Nilsson, Mats; Fransson, Johan E. S.

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    <subfield code="a">&lt;p&gt;This paper presents the first results of the ongoing development of new forest mapping methods for the Swedish national forest mapping case using Airborne Laser Scanning (ALS) data, utilizing the recent findings in machine learning (ML) and Artificial Intelligence (AI) techniques. We used Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) as ML models. In addition, Neural networks (NN) based approaches were utilized in this study. ALS derived features were used to estimate the stem volume (V), above-ground biomass (AGB), basal area (B), tree height (H), stem diameter (D), and forest stand age (A). XGBoost ML algorithm outperformed RF 1 % to 3 % in the R-2 metric. NN model performed similar to ML model, however it is superior in the estimation of V, AGB, and B parameters.&lt;/p&gt;</subfield>
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