Multi-temporal JERS SAR data in boreal forest biomass mapping

    Research output: Contribution to journalArticleScientificpeer-review

    94 Citations (Scopus)

    Abstract

    Multi-temporal JERS SAR data were studied for forest biomass mapping. The study site was located in South-eastern Finland in Ruokolahti. Pre-processing of JERS SAR data included ortho-rectification and radiometric normalization of topographic effects. In single-date regression analysis between backscatter amplitude and stem volume, summer scenes from July to October produced correlation coefficients (r) between 0.63 and 0.81. Backscatter level and the slope of the (linear) regression line were stable from scene to scene. Winter scenes acquired in very cold and dry winter conditions had a very low correlation. One winter scene acquired in conditions where snow is not completely frozen produced a correlation coefficient similar to summer scenes. Multivariate regression analysis with a 6-date JERS SAR dataset produced correlation coefficient of 0.85. A combined JERS–optical regression analysis improved the correlation coefficient to 0.89 and also alleviated the saturation, which affects both SAR and optical data. The stability of the regression results in summer scenes suggests that a simple constant model could be used in wide-area forest biomass mapping if accuracy requirements are low and if biomass estimates are aggregated to large areal units.

    Original languageEnglish
    Pages (from-to)263 - 275
    Number of pages13
    JournalRemote Sensing of Environment
    Volume97
    Issue number2
    DOIs
    Publication statusPublished - 2005
    MoE publication typeA1 Journal article-refereed

    Fingerprint

    boreal forests
    Regression analysis
    boreal forest
    synthetic aperture radar
    Biomass
    biomass
    regression analysis
    backscatter
    winter
    Snow
    summer
    Linear regression
    topographic effect
    snow
    Finland
    Processing
    JERS
    stem
    saturation
    stems

    Keywords

    • remote sensing
    • biomass
    • microwaves
    • boreal forest
    • carbon cycles
    • carbon capture
    • climate change
    • CCS

    Cite this

    @article{4c82dc0aac0d4a65aee6fdf8e271c853,
    title = "Multi-temporal JERS SAR data in boreal forest biomass mapping",
    abstract = "Multi-temporal JERS SAR data were studied for forest biomass mapping. The study site was located in South-eastern Finland in Ruokolahti. Pre-processing of JERS SAR data included ortho-rectification and radiometric normalization of topographic effects. In single-date regression analysis between backscatter amplitude and stem volume, summer scenes from July to October produced correlation coefficients (r) between 0.63 and 0.81. Backscatter level and the slope of the (linear) regression line were stable from scene to scene. Winter scenes acquired in very cold and dry winter conditions had a very low correlation. One winter scene acquired in conditions where snow is not completely frozen produced a correlation coefficient similar to summer scenes. Multivariate regression analysis with a 6-date JERS SAR dataset produced correlation coefficient of 0.85. A combined JERS–optical regression analysis improved the correlation coefficient to 0.89 and also alleviated the saturation, which affects both SAR and optical data. The stability of the regression results in summer scenes suggests that a simple constant model could be used in wide-area forest biomass mapping if accuracy requirements are low and if biomass estimates are aggregated to large areal units.",
    keywords = "remote sensing, biomass, microwaves, boreal forest, carbon cycles, carbon capture, climate change, CCS",
    author = "Yrj{\"o} Rauste",
    year = "2005",
    doi = "10.1016/j.rse.2005.05.002",
    language = "English",
    volume = "97",
    pages = "263 -- 275",
    journal = "Remote Sensing of Environment",
    issn = "0034-4257",
    publisher = "Elsevier",
    number = "2",

    }

    Multi-temporal JERS SAR data in boreal forest biomass mapping. / Rauste, Yrjö.

    In: Remote Sensing of Environment, Vol. 97, No. 2, 2005, p. 263 - 275.

    Research output: Contribution to journalArticleScientificpeer-review

    TY - JOUR

    T1 - Multi-temporal JERS SAR data in boreal forest biomass mapping

    AU - Rauste, Yrjö

    PY - 2005

    Y1 - 2005

    N2 - Multi-temporal JERS SAR data were studied for forest biomass mapping. The study site was located in South-eastern Finland in Ruokolahti. Pre-processing of JERS SAR data included ortho-rectification and radiometric normalization of topographic effects. In single-date regression analysis between backscatter amplitude and stem volume, summer scenes from July to October produced correlation coefficients (r) between 0.63 and 0.81. Backscatter level and the slope of the (linear) regression line were stable from scene to scene. Winter scenes acquired in very cold and dry winter conditions had a very low correlation. One winter scene acquired in conditions where snow is not completely frozen produced a correlation coefficient similar to summer scenes. Multivariate regression analysis with a 6-date JERS SAR dataset produced correlation coefficient of 0.85. A combined JERS–optical regression analysis improved the correlation coefficient to 0.89 and also alleviated the saturation, which affects both SAR and optical data. The stability of the regression results in summer scenes suggests that a simple constant model could be used in wide-area forest biomass mapping if accuracy requirements are low and if biomass estimates are aggregated to large areal units.

    AB - Multi-temporal JERS SAR data were studied for forest biomass mapping. The study site was located in South-eastern Finland in Ruokolahti. Pre-processing of JERS SAR data included ortho-rectification and radiometric normalization of topographic effects. In single-date regression analysis between backscatter amplitude and stem volume, summer scenes from July to October produced correlation coefficients (r) between 0.63 and 0.81. Backscatter level and the slope of the (linear) regression line were stable from scene to scene. Winter scenes acquired in very cold and dry winter conditions had a very low correlation. One winter scene acquired in conditions where snow is not completely frozen produced a correlation coefficient similar to summer scenes. Multivariate regression analysis with a 6-date JERS SAR dataset produced correlation coefficient of 0.85. A combined JERS–optical regression analysis improved the correlation coefficient to 0.89 and also alleviated the saturation, which affects both SAR and optical data. The stability of the regression results in summer scenes suggests that a simple constant model could be used in wide-area forest biomass mapping if accuracy requirements are low and if biomass estimates are aggregated to large areal units.

    KW - remote sensing

    KW - biomass

    KW - microwaves

    KW - boreal forest

    KW - carbon cycles

    KW - carbon capture

    KW - climate change

    KW - CCS

    U2 - 10.1016/j.rse.2005.05.002

    DO - 10.1016/j.rse.2005.05.002

    M3 - Article

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    JO - Remote Sensing of Environment

    JF - Remote Sensing of Environment

    SN - 0034-4257

    IS - 2

    ER -