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

Approved

Classifications

MinEdu publication type
A1
Category
Artikkelit ja abstraktit
Refereed
Kyllä
Sub category
Tieteelliset aikakauslehtiartikkelit
Type
Alkuperäisartikkeli

Authors of the publication

Number of authors
9
Authors
Cosenza, Diogo N; Korhonen, Lauri; Maltamo, Matti; Packalen, Petteri; Strunk, Jacob L; Næsset, Erik; Gobakken, Terje; Soares, Paula; Tomé, Margarida

Publication channel information

Title of journal/series
Forestry
ISSN (print)
0015-752X
ISSN (electronic)
1464-3626
ISSN (linking)
0015-752X
Publication forum ID
56292
Publication forum level
2
Internationality
Yes

Detailed publication information

Publication year
2020
Reporting year
2020
Journal/series volume number
2021; 94
Journal/series issue number
2
Page numbers
311-323
DOI
10.1093/forestry/cpaa034
Language of publication
English

Co-publication information

International co-publication
Yes
Co-publication with a company
No

Availability

Link to self-archived version
https://erepo.uef.fi/handle/123456789/24230

Classification and additional information

MinEdu field of science classification
4112 Forestry

Source database ID

WoS ID
WOS:000637092800011
Scopus ID
2-s2.0-85095766768