Tree height estimates in boreal forest using Gaussian process regression

Teemu Mutanen; Laura Sirro; Yrjö Rauste

doi:10.1109/IGARSS.2016.7729450

Tree height estimates in boreal forest using Gaussian process regression

Teemu Mutanen, Laura Sirro, Yrjö Rauste

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

7 Citations (Scopus)

Abstract

Tree height is one of the continuous essential climate model variables. This work applies Gaussian process regression to tree height estimation. Estimates are produced in a coniferous boreal forest area in Southern Finland. GP regression produces both estimate and predictive variance for each pixel. Results show that Gaussian process regression produces estimates which are as good as the ones produced by existing methods. The root-mean-square error from GP regression is lower, estimates have less bias and maximum value is closer to the actual maximum. Predictions can be applied and ingested to carbon modelling where estimates are either direct input parameters or they serve for validation the parameters computed by the models.

Original language	English
Title of host publication	Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	1757-1760
ISBN (Electronic)	978-1-5090-3332-4, 978-1-5090-3331-7
ISBN (Print)	978-1-5090-3333-1
DOIs	https://doi.org/10.1109/IGARSS.2016.7729450
Publication status	Published - 3 Nov 2016
MoE publication type	A4 Article in a conference publication
Event	36th IEEE International Geoscience and Remote Sensing Symposium: Advancing the understanding of our living planet - Beijing, China Duration: 10 Jul 2016 → 15 Jul 2016 Conference number: 36

Conference

Conference	36th IEEE International Geoscience and Remote Sensing Symposium
Abbreviated title	IGARSS
Country/Territory	China
City	Beijing
Period	10/07/16 → 15/07/16

Keywords

boreal forest
Gaussian process
pixel-level uncertainty
regression
tree height

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/IGARSS.2016.7729450

Cite this

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title = "Tree height estimates in boreal forest using Gaussian process regression",

abstract = "Tree height is one of the continuous essential climate model variables. This work applies Gaussian process regression to tree height estimation. Estimates are produced in a coniferous boreal forest area in Southern Finland. GP regression produces both estimate and predictive variance for each pixel. Results show that Gaussian process regression produces estimates which are as good as the ones produced by existing methods. The root-mean-square error from GP regression is lower, estimates have less bias and maximum value is closer to the actual maximum. Predictions can be applied and ingested to carbon modelling where estimates are either direct input parameters or they serve for validation the parameters computed by the models.",

keywords = "boreal forest, Gaussian process, pixel-level uncertainty, regression, tree height",

author = "Teemu Mutanen and Laura Sirro and Yrj{\"o} Rauste",

year = "2016",

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doi = "10.1109/IGARSS.2016.7729450",

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booktitle = "Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International",

publisher = "IEEE Institute of Electrical and Electronic Engineers",

address = "United States",

note = "36th IEEE International Geoscience and Remote Sensing Symposium : Advancing the understanding of our living planet, IGARSS ; Conference date: 10-07-2016 Through 15-07-2016",

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Mutanen, T, Sirro, L & Rauste, Y 2016, Tree height estimates in boreal forest using Gaussian process regression. in Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International . IEEE Institute of Electrical and Electronic Engineers, pp. 1757-1760, 36th IEEE International Geoscience and Remote Sensing Symposium, Beijing, China, 10/07/16. https://doi.org/10.1109/IGARSS.2016.7729450

Tree height estimates in boreal forest using Gaussian process regression. / Mutanen, Teemu; Sirro, Laura; Rauste, Yrjö.
Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International . IEEE Institute of Electrical and Electronic Engineers, 2016. p. 1757-1760.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Tree height estimates in boreal forest using Gaussian process regression

AU - Mutanen, Teemu

AU - Sirro, Laura

AU - Rauste, Yrjö

N1 - Conference code: 36

PY - 2016/11/3

Y1 - 2016/11/3

N2 - Tree height is one of the continuous essential climate model variables. This work applies Gaussian process regression to tree height estimation. Estimates are produced in a coniferous boreal forest area in Southern Finland. GP regression produces both estimate and predictive variance for each pixel. Results show that Gaussian process regression produces estimates which are as good as the ones produced by existing methods. The root-mean-square error from GP regression is lower, estimates have less bias and maximum value is closer to the actual maximum. Predictions can be applied and ingested to carbon modelling where estimates are either direct input parameters or they serve for validation the parameters computed by the models.

AB - Tree height is one of the continuous essential climate model variables. This work applies Gaussian process regression to tree height estimation. Estimates are produced in a coniferous boreal forest area in Southern Finland. GP regression produces both estimate and predictive variance for each pixel. Results show that Gaussian process regression produces estimates which are as good as the ones produced by existing methods. The root-mean-square error from GP regression is lower, estimates have less bias and maximum value is closer to the actual maximum. Predictions can be applied and ingested to carbon modelling where estimates are either direct input parameters or they serve for validation the parameters computed by the models.

KW - boreal forest

KW - Gaussian process

KW - pixel-level uncertainty

KW - regression

KW - tree height

U2 - 10.1109/IGARSS.2016.7729450

DO - 10.1109/IGARSS.2016.7729450

M3 - Conference article in proceedings

SN - 978-1-5090-3333-1

SP - 1757

EP - 1760

BT - Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International

PB - IEEE Institute of Electrical and Electronic Engineers

T2 - 36th IEEE International Geoscience and Remote Sensing Symposium

Y2 - 10 July 2016 through 15 July 2016

ER -

Tree height estimates in boreal forest using Gaussian process regression

Abstract

Conference

Keywords

UN SDGs

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Cite this