Multi-temporal JERS SAR data in boreal forest biomass mapping

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    96 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

    Keywords

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

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