Predicting Forest Inventory Attributes Using Airborne Laser Scanning, Aerial Imagery, and Harvester Data

Atte Saukkola; Timo Melkas; Kirsi Riekki; Sanna Sirparanta; Jussi Peuhkurinen; Markus Holopainen; Juha Hyyppa; Mikko Vastaranta

doi:10.3390/rs11070797

Predicting Forest Inventory Attributes Using Airborne Laser Scanning, Aerial Imagery, and Harvester Data

Atte Saukkola, Timo Melkas, Kirsi Riekki, Sanna Sirparanta, Jussi Peuhkurinen, Markus Holopainen, Juha Hyyppa, Mikko Vastaranta

Forskningsoutput: Tidskriftsbidrag › Artikel › Vetenskaplig › Peer review

Originalspråk	engelska
Artikelnummer	797
Tidskrift	Remote Sensing
Volym	11
Nummer	7
Antal sidor	15
ISSN	2072-4292
DOI	https://doi.org/10.3390/rs11070797
Status	Publicerad - 1 apr. 2019
MoE-publikationstyp	A1 Tidskriftsartikel-refererad

Vetenskapsgrenar

4112 Skogsvetenskap

Åtkomst av dokument

10.3390/rs11070797Licens: CC BY

remotesensing-11-00797Slutlig publicerad version, 1,9 MBLicens: CC BY

Citera det här

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title = "Predicting Forest Inventory Attributes Using Airborne Laser Scanning, Aerial Imagery, and Harvester Data",

keywords = "4112 Forestry, LiDAR, cut-to-length (CTL) harvester, forest planning, wood procurement, tree positioning, k-Most similar neighbor, STAND CHARACTERISTICS, PRECISION FORESTRY, ACCURACY, POSITIONS",

author = "Atte Saukkola and Timo Melkas and Kirsi Riekki and Sanna Sirparanta and Jussi Peuhkurinen and Markus Holopainen and Juha Hyyppa and Mikko Vastaranta",

year = "2019",

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AU - Saukkola, Atte

AU - Melkas, Timo

AU - Riekki, Kirsi

AU - Sirparanta, Sanna

AU - Peuhkurinen, Jussi

AU - Holopainen, Markus

AU - Hyyppa, Juha

AU - Vastaranta, Mikko

PY - 2019/4/1

Y1 - 2019/4/1

KW - 4112 Forestry

KW - LiDAR

KW - cut-to-length (CTL) harvester

KW - forest planning

KW - wood procurement

KW - tree positioning

KW - k-Most similar neighbor

KW - STAND CHARACTERISTICS

KW - PRECISION FORESTRY

KW - ACCURACY

KW - POSITIONS

U2 - 10.3390/rs11070797

DO - 10.3390/rs11070797

M3 - Article

VL - 11

JO - Remote Sensing

JF - Remote Sensing

SN - 2072-4292

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ER -