Timely And Semi-Automatic Detection of Forest Logging Events in Boreal Forest Using All Available Landsat Data

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

    1 Citation (Scopus)

    Abstract

    In this study, we utilized all available Landsat images over two adjacent orbits between 1997 and 2015 for the quasi automatic detection of clearcuts and storm damages in boreal forest of Finland. Landsat time series modelling and analysis was done utilizing the Continuous Change Detection and Classification (CCDC) algorithm with a slight modification for rapid operative conditions. The change maps derived from dense time series analysis showed a good agreement compared to reference maps of clearcuts and storm damages obtained from visual interpretation of Very High Resolution image pairs, by lack of reliable reference in temporal and or spatial domain.
    Original languageEnglish
    Title of host publication2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
    PublisherIEEE Institute of Electrical and Electronic Engineers
    Pages1730-1733
    Number of pages4
    ISBN (Electronic)978-1-5386-7150-4, 978-1-5386-7149-8
    ISBN (Print)978-1-5386-7151-1
    DOIs
    Publication statusPublished - 5 Nov 2018
    MoE publication typeNot Eligible
    EventIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain
    Duration: 22 Jul 201827 Jul 2018

    Conference

    ConferenceIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
    Abbreviated titleIGARSS
    CountrySpain
    CityValencia
    Period22/07/1827/07/18

    Fingerprint

    storm damage
    clearcutting
    boreal forest
    Landsat
    time series analysis
    image resolution
    time series
    modeling
    automatic detection
    detection
    analysis

    Keywords

    • Remote sensing
    • Forestry
    • Time series analysis
    • Earth
    • Artificial satellites
    • Storms
    • Satellites
    • Satellite image time series
    • clearcuts
    • storm damages
    • Landsat
    • Sentinel-2

    Cite this

    Molinier, M., Astola, H., Räty, T., & Woodcock, C. E. (2018). Timely And Semi-Automatic Detection of Forest Logging Events in Boreal Forest Using All Available Landsat Data. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings (pp. 1730-1733). [8518112] IEEE Institute of Electrical and Electronic Engineers . https://doi.org/10.1109/IGARSS.2018.8518112
    Molinier, Matthieu ; Astola, Heikki ; Räty, Tomi ; Woodcock, Curtis E. / Timely And Semi-Automatic Detection of Forest Logging Events in Boreal Forest Using All Available Landsat Data. 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. IEEE Institute of Electrical and Electronic Engineers , 2018. pp. 1730-1733
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    title = "Timely And Semi-Automatic Detection of Forest Logging Events in Boreal Forest Using All Available Landsat Data",
    abstract = "In this study, we utilized all available Landsat images over two adjacent orbits between 1997 and 2015 for the quasi automatic detection of clearcuts and storm damages in boreal forest of Finland. Landsat time series modelling and analysis was done utilizing the Continuous Change Detection and Classification (CCDC) algorithm with a slight modification for rapid operative conditions. The change maps derived from dense time series analysis showed a good agreement compared to reference maps of clearcuts and storm damages obtained from visual interpretation of Very High Resolution image pairs, by lack of reliable reference in temporal and or spatial domain.",
    keywords = "Remote sensing, Forestry, Time series analysis, Earth, Artificial satellites, Storms, Satellites, Satellite image time series, clearcuts, storm damages, Landsat, Sentinel-2",
    author = "Matthieu Molinier and Heikki Astola and Tomi R{\"a}ty and Woodcock, {Curtis E.}",
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    Molinier, M, Astola, H, Räty, T & Woodcock, CE 2018, Timely And Semi-Automatic Detection of Forest Logging Events in Boreal Forest Using All Available Landsat Data. in 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings., 8518112, IEEE Institute of Electrical and Electronic Engineers , pp. 1730-1733, IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018, Valencia, Spain, 22/07/18. https://doi.org/10.1109/IGARSS.2018.8518112

    Timely And Semi-Automatic Detection of Forest Logging Events in Boreal Forest Using All Available Landsat Data. / Molinier, Matthieu; Astola, Heikki; Räty, Tomi; Woodcock, Curtis E.

    2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. IEEE Institute of Electrical and Electronic Engineers , 2018. p. 1730-1733 8518112.

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

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    AU - Molinier, Matthieu

    AU - Astola, Heikki

    AU - Räty, Tomi

    AU - Woodcock, Curtis E.

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    Y1 - 2018/11/5

    N2 - In this study, we utilized all available Landsat images over two adjacent orbits between 1997 and 2015 for the quasi automatic detection of clearcuts and storm damages in boreal forest of Finland. Landsat time series modelling and analysis was done utilizing the Continuous Change Detection and Classification (CCDC) algorithm with a slight modification for rapid operative conditions. The change maps derived from dense time series analysis showed a good agreement compared to reference maps of clearcuts and storm damages obtained from visual interpretation of Very High Resolution image pairs, by lack of reliable reference in temporal and or spatial domain.

    AB - In this study, we utilized all available Landsat images over two adjacent orbits between 1997 and 2015 for the quasi automatic detection of clearcuts and storm damages in boreal forest of Finland. Landsat time series modelling and analysis was done utilizing the Continuous Change Detection and Classification (CCDC) algorithm with a slight modification for rapid operative conditions. The change maps derived from dense time series analysis showed a good agreement compared to reference maps of clearcuts and storm damages obtained from visual interpretation of Very High Resolution image pairs, by lack of reliable reference in temporal and or spatial domain.

    KW - Remote sensing

    KW - Forestry

    KW - Time series analysis

    KW - Earth

    KW - Artificial satellites

    KW - Storms

    KW - Satellites

    KW - Satellite image time series

    KW - clearcuts

    KW - storm damages

    KW - Landsat

    KW - Sentinel-2

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    U2 - 10.1109/IGARSS.2018.8518112

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    BT - 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings

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    Molinier M, Astola H, Räty T, Woodcock CE. Timely And Semi-Automatic Detection of Forest Logging Events in Boreal Forest Using All Available Landsat Data. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. IEEE Institute of Electrical and Electronic Engineers . 2018. p. 1730-1733. 8518112 https://doi.org/10.1109/IGARSS.2018.8518112