Sökning: onr:"swepub:oai:DiVA.org:lnu-126400" > Forest Biophysical ...
Fältnamn | Indikatorer | Metadata |
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000 | 02989naa a2200409 4500 | |
001 | oai:DiVA.org:lnu-126400 | |
003 | SwePub | |
008 | 240111s2023 | |||||||||||000 ||eng| | |
024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-1264002 URI |
024 | 7 | a https://doi.org/10.1109/igarss52108.2023.102828992 DOI |
040 | a (SwePub)lnu | |
041 | a engb eng | |
042 | 9 SwePub | |
072 | 7 | a ref2 swepub-contenttype |
072 | 7 | a kon2 swepub-publicationtype |
100 | 1 | a Aksoy, Sametu Istanbul Technical University, Türkiye4 aut |
245 | 1 0 | a Forest Biophysical Parameter Estimation via Machine Learning and Neural Network Approaches |
264 | 1 | b IEEE,c 2023 |
338 | a print2 rdacarrier | |
520 | a 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. | |
650 | 7 | a LANTBRUKSVETENSKAPERx Lantbruksvetenskap, skogsbruk och fiskex Skogsvetenskap0 (SwePub)401042 hsv//swe |
650 | 7 | a AGRICULTURAL SCIENCESx Agriculture, Forestry and Fisheriesx Forest Science0 (SwePub)401042 hsv//eng |
653 | a Forestry and Wood Technology | |
653 | a Skog och träteknik | |
700 | 1 | a Hasan Al Shwayyat, Shouq Zuhteru Marmara University, Türkiye4 aut |
700 | 1 | a Nur Topgül, Şuleu Istanbul Technical University, Türkiye4 aut |
700 | 1 | a Sertel, Elifu Istanbul Technical University, Türkiye4 aut |
700 | 1 | a Ünsalan, Cemu Marmara University, Türkiye4 aut |
700 | 1 | a Salo, Jariu University of Helsinki, Finland4 aut |
700 | 1 | a Holmström, Antonu Katam Technologies, Sweden4 aut |
700 | 1 | a Wallerman, Jörgenu Swedish University of Agricultural Sciences, Sweden4 aut |
700 | 1 | a Nilsson, Matsu Swedish University of Agricultural Sciences, Sweden4 aut |
700 | 1 | a Fransson, Johan,c Professor,d 1967-u Linnéuniversitetet,Institutionen för skog och träteknik (SOT),DISA;DISA-WBT4 aut0 (Swepub:lnu)jofrad |
710 | 2 | a Istanbul Technical University, Türkiyeb Marmara University, Türkiye4 org |
773 | 0 | t IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposiumd : IEEEg , s. 2661-2664q <2661-2664z 9798350320107z 9798350320091z 9798350331745 |
856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-126400 |
856 | 4 8 | u https://doi.org/10.1109/igarss52108.2023.10282899 |
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