Tree height estimates in boreal forest using Gaussian process regression

Teemu Mutanen, Laura Sirro, Yrjö Rauste

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    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 languageEnglish
    Title of host publicationGeoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International
    PublisherIEEE Institute of Electrical and Electronic Engineers
    Pages1757-1760
    ISBN (Electronic)978-1-5090-3332-4, 978-1-5090-3331-7
    ISBN (Print)978-1-5090-3333-1
    DOIs
    Publication statusPublished - 3 Nov 2016
    MoE publication typeA4 Article in a conference publication
    Event36th IEEE International Geoscience and Remote Sensing Symposium: Advancing the understanding of our living planet - Beijing, China
    Duration: 10 Jul 201615 Jul 2016
    Conference number: 36

    Conference

    Conference36th IEEE International Geoscience and Remote Sensing Symposium
    Abbreviated titleIGARSS
    CountryChina
    CityBeijing
    Period10/07/1615/07/16

    Fingerprint

    boreal forest
    coniferous forest
    climate modeling
    pixel
    carbon
    prediction
    modeling
    parameter
    method

    Keywords

    • boreal forest
    • Gaussian process
    • pixel-level uncertainty
    • regression
    • tree height

    Cite this

    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 (pp. 1757-1760). IEEE Institute of Electrical and Electronic Engineers . https://doi.org/10.1109/IGARSS.2016.7729450
    Mutanen, Teemu ; Sirro, Laura ; Rauste, Yrjö. / Tree height estimates in boreal forest using Gaussian process regression. Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International . IEEE Institute of Electrical and Electronic Engineers , 2016. pp. 1757-1760
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    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.",
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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 proceedingConference article in proceedingsScientificpeer-review

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    AU - Sirro, Laura

    AU - Rauste, Yrjö

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    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.

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    Mutanen T, Sirro L, Rauste Y. 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 . 2016. p. 1757-1760 https://doi.org/10.1109/IGARSS.2016.7729450