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Forest Biophysical ...
Forest Biophysical Parameter Estimation via Machine Learning and Neural Network Approaches
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- Aksoy, Samet (author)
- Istanbul Technical University, Türkiye
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- Hasan Al Shwayyat, Shouq Zuhter (author)
- Marmara University, Türkiye
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- Nur Topgül, Şule (author)
- Istanbul Technical University, Türkiye
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- Sertel, Elif (author)
- Istanbul Technical University, Türkiye
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- Ünsalan, Cem (author)
- Marmara University, Türkiye
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- Salo, Jari (author)
- University of Helsinki, Finland
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- Holmström, Anton (author)
- Katam Technologies, Sweden
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- Wallerman, Jörgen (author)
- Swedish University of Agricultural Sciences, Sweden
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- Nilsson, Mats (author)
- Swedish University of Agricultural Sciences, Sweden
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- 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.
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In: IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium. - : IEEE. - 9798350320107 - 9798350320091 - 9798350331745 ; , s. 2661-2664
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
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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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- By the author/editor
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Aksoy, Samet
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Hasan Al Shwayya ...
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Nur Topgül, Şule
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Sertel, Elif
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Ünsalan, Cem
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Salo, Jari
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Holmström, Anton
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Wallerman, Jörge ...
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Nilsson, Mats
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Fransson, Johan, ...
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- About the subject
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- AGRICULTURAL SCIENCES
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AGRICULTURAL SCI ...
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and Agriculture Fore ...
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and Forest Science
- Articles in the publication
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IGARSS 2023 - 20 ...
- By the university
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Linnaeus University