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Comparison of linear regression, k-nearest neighbour and random forest methods in airborne laser-scanning-based prediction of growing stock.

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Title: Comparison of linear regression, k-nearest neighbour and random forest methods in airborne laser-scanning-based prediction of growing stock.
Authors: Cosenza, Diogo N1 (AUTHOR) dncosenza@gmail.com, Korhonen, Lauri2 (AUTHOR), Maltamo, Matti2 (AUTHOR), Packalen, Petteri2 (AUTHOR), Strunk, Jacob L3 (AUTHOR), Næsset, Erik4 (AUTHOR), Gobakken, Terje4 (AUTHOR), Soares, Paula1 (AUTHOR), Tomé, Margarida1 (AUTHOR)
Source: Forestry: An International Journal of Forest Research. Apr2021, Vol. 94 Issue 2, p311-323. 13p.
Database: Environment Complete
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DbLabel: Environment Complete
An: 149178293
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  Data: Comparison of linear regression, k-nearest neighbour and random forest methods in airborne laser-scanning-based prediction of growing stock.
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  Data: <searchLink fieldCode="JN" term="%22Forestry%3A+An+International+Journal+of+Forest+Research%22">Forestry: An International Journal of Forest Research</searchLink>. Apr2021, Vol. 94 Issue 2, p311-323. 13p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eih&AN=149178293
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        Value: 10.1093/forestry/cpaa034
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        Text: English
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        PageCount: 13
        StartPage: 311
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      – TitleFull: Comparison of linear regression, k-nearest neighbour and random forest methods in airborne laser-scanning-based prediction of growing stock.
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              Text: Apr2021
              Type: published
              Y: 2021
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            – TitleFull: Forestry: An International Journal of Forest Research
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