Forest Biophysical Parameter Estimation via Machine Learning and Neural Network Approaches

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

doi:10.1109/IGARSS52108.2023.10282899

Forest Biophysical Parameter Estimation via Machine Learning and Neural Network Approaches

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

Department of Geomatics Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

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 R² metric. NN model performed similar to ML model, however it is superior in the estimation of V, AGB, and B parameters.

Original language	English
Title of host publication	IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2661-2664
Number of pages	4
ISBN (Electronic)	9798350320107
DOIs	https://doi.org/10.1109/IGARSS52108.2023.10282899
Publication status	Published - 2023
Event	2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 - Pasadena, United States Duration: 16 Jul 2023 → 21 Jul 2023

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2023-July

Conference

Conference	2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023
Country/Territory	United States
City	Pasadena
Period	16/07/23 → 21/07/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Funding

Project ForestMap is supported under the umbrella of ERA-NET Cofund ForestValue by Swedish Governmental Agency for Innovation Systems, Swedish Energy Agency, The Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning, Academy of Finland, and the Scientific and Technological Research Council of Turkey (TUBITAK). ForestValue has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 773324 and from TUBITAK Project No. 221N393. We would like to thank Istanbul Technical University, Scientific Research Unit (ITU-BAP) for supporting Elif Sertel with the project ID. “FHD-2023-44797”.

Funders	Funder number
Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning, Academy of Finland
Horizon 2020 Framework Programme	221N393, 773324
VINNOVA
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu
Energimyndigheten
Istanbul Teknik Üniversitesi
Bilimsel Araştırma Projeleri Birimi, İstanbul Teknik Üniversitesi	FHD-2023-44797

Keywords

Artificial Intelligence
Forest
global
machine learning
map

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/IGARSS52108.2023.10282899

Cite this

Aksoy, S., Hasan Al Shwayyat, S. Z., Nur Topgul, S., Sertel, E., Unsalan, C., Salo, J., Holmstrom, A., Wallerman, J., Nilsson, M., & Fransson, J. E. S. (2023). Forest Biophysical Parameter Estimation via Machine Learning and Neural Network Approaches. In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings (pp. 2661-2664). (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2023-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS52108.2023.10282899

Aksoy, Samet ; Hasan Al Shwayyat, Shouq Zuhter ; Nur Topgul, Sule et al. / Forest Biophysical Parameter Estimation via Machine Learning and Neural Network Approaches. IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 2661-2664 (International Geoscience and Remote Sensing Symposium (IGARSS)).

@inproceedings{af1693615a6b4e52a007d95eb5dda40f,

title = "Forest Biophysical Parameter Estimation via Machine Learning and Neural Network Approaches",

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 R² metric. NN model performed similar to ML model, however it is superior in the estimation of V, AGB, and B parameters.",

keywords = "Artificial Intelligence, Forest, global, machine learning, map",

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

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year = "2023",

doi = "10.1109/IGARSS52108.2023.10282899",

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series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

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Aksoy, S, Hasan Al Shwayyat, SZ, Nur Topgul, S, Sertel, E, Unsalan, C, Salo, J, Holmstrom, A, Wallerman, J, Nilsson, M & Fransson, JES 2023, Forest Biophysical Parameter Estimation via Machine Learning and Neural Network Approaches. in IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2023-July, Institute of Electrical and Electronics Engineers Inc., pp. 2661-2664, 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023, Pasadena, United States, 16/07/23. https://doi.org/10.1109/IGARSS52108.2023.10282899

Forest Biophysical Parameter Estimation via Machine Learning and Neural Network Approaches. / Aksoy, Samet; Hasan Al Shwayyat, Shouq Zuhter; Nur Topgul, Sule et al.
IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. p. 2661-2664 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2023-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Forest Biophysical Parameter Estimation via Machine Learning and Neural Network Approaches

AU - Aksoy, Samet

AU - Hasan Al Shwayyat, Shouq Zuhter

AU - Nur Topgul, Sule

AU - Sertel, Elif

AU - Unsalan, Cem

AU - Salo, Jari

AU - Holmstrom, Anton

AU - Wallerman, Jorgen

AU - Nilsson, Mats

AU - Fransson, Johan E.S.

PY - 2023

Y1 - 2023

N2 - 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 R² metric. NN model performed similar to ML model, however it is superior in the estimation of V, AGB, and B parameters.

AB - 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 R² metric. NN model performed similar to ML model, however it is superior in the estimation of V, AGB, and B parameters.

KW - Artificial Intelligence

KW - Forest

KW - global

KW - machine learning

KW - map

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U2 - 10.1109/IGARSS52108.2023.10282899

DO - 10.1109/IGARSS52108.2023.10282899

M3 - Conference contribution

AN - SCOPUS:85178366425

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 2661

EP - 2664

BT - IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023

Y2 - 16 July 2023 through 21 July 2023

ER -

Aksoy S, Hasan Al Shwayyat SZ, Nur Topgul S, Sertel E, Unsalan C, Salo J et al. Forest Biophysical Parameter Estimation via Machine Learning and Neural Network Approaches. In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. p. 2661-2664. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS52108.2023.10282899

Forest Biophysical Parameter Estimation via Machine Learning and Neural Network Approaches

Abstract

Publication series

Conference

Bibliographical note

Funding

Keywords

UN SDGs

Access to Document

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