Skip to main content
SLU publication database (SLUpub)

Conference paper2023Peer reviewed

Forest Biophysical Parameter Estimation via Machine Learning and Neural Network Approaches

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.

Abstract

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.

Keywords

Forest; map; global; machine learning; Artificial Intelligence

Published in

IEEE International Geoscience and Remote Sensing Symposium proceedings
2023, pages: 2661-2664 Title: IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM
eISBN: 979-8-3503-2010-7Publisher: IEEE

Conference

IEEE International Geoscience and Remote Sensing Symposium (IGARSS), JUL 16-21, 2023, Pasadena, CA