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Forest Biophysical Parameter Estimation via Machine Learning and Neural Network Approaches

Aksoy, Samet (author)
Istanbul Technical University, Türkiye
Hasan Al Shwayyat, Shouq Zuhter (author)
Marmara University, Türkiye
Nur Topgül, Şule (author)
Istanbul Technical University, Türkiye
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Sertel, Elif (author)
Istanbul Technical University, Türkiye
Ünsalan, Cem (author)
Marmara University, Türkiye
Salo, Jari (author)
University of Helsinki, Finland
Holmström, Anton (author)
Katam Technologies, Sweden
Wallerman, Jörgen (author)
Swedish University of Agricultural Sciences, Sweden
Nilsson, Mats (author)
Swedish University of Agricultural Sciences, Sweden
Fransson, Johan, Professor, 1967- (author)
Linnéuniversitetet,Institutionen för skog och träteknik (SOT),DISA;DISA-WBT
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 (creator_code:org_t)
IEEE, 2023
2023
English.
In: IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium. - : IEEE. - 9798350320107 - 9798350320091 - 9798350331745 ; , s. 2661-2664
Related links:
https://urn.kb.se/re...
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https://doi.org/10.1...
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  • Conference paper (peer-reviewed)
Abstract Subject headings
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  • 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.

Subject headings

LANTBRUKSVETENSKAPER  -- Lantbruksvetenskap, skogsbruk och fiske -- Skogsvetenskap (hsv//swe)
AGRICULTURAL SCIENCES  -- Agriculture, Forestry and Fisheries -- Forest Science (hsv//eng)

Keyword

Forestry and Wood Technology
Skog och träteknik

Publication and Content Type

ref (subject category)
kon (subject category)

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