Wallerman, Jörgen
- Department of Forest Resource Management, Swedish University of Agricultural Sciences
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: 979-8-3503-2010-7
Publisher: IEEE
Conference
IEEE International Geoscience and Remote Sensing Symposium (IGARSS), JUL 16-21, 2023, Pasadena, CA
Nilsson, Mats
- Department of Forest Resource Management, Swedish University of Agricultural Sciences
- Department of Forest Resource Management, Swedish University of Agricultural Sciences
SLU Authors
UKÄ Subject classification
Forest Science
Remote Sensing
Publication identifier
DOI: https://doi.org/10.1109/IGARSS52108.2023.10282899
Permanent link to this page (URI)
https://res.slu.se/id/publ/129041