Boreal forest snow damage mapping using multi-temporal sentinel-1 Data

Erkki Tomppo (Corresponding Author), Oleg Antropov, Jaan Praks

    Research output: Contribution to journalArticleScientificpeer-review

    21 Citations (Scopus)


    Natural disturbances significantly influence forest ecosystem services and biodiversity. Accurate delineation and early detection of areas affected by disturbances are critical for estimating extent of damage, assessing economical influence and guiding forest management activities. In this study we focus on snow load damage detection from C-Band SAR images. Snow damage is one of the least studied forest damages, which is getting more common due to current climate trends. The study site was located in the southern part of Northern Finland and the SAR data were represented by the time series of C-band SAR scenes acquired by the Sentinel-1 sensor. Methods used in the study included improved k nearest neighbour method, logistic regression analysis and support vector machine classification. Snow damage recordings from a large snow damage event that took place in Finland during late 2018 were used as reference data. Our results showed an overall detection accuracy of 90%, indicating potential of C-band SAR for operational use in snow damage mapping. Additionally, potential of multitemporal Sentinel-1 data in estimating growing stock volume in damaged forest areas were carried out, with obtained results indicating strong potential for estimating the overall volume of timber within the affected areas. The results and research questions for further studies are discussed.

    Original languageEnglish
    Article number384
    JournalRemote Sensing
    Issue number4
    Publication statusPublished - 13 Feb 2019
    MoE publication typeA1 Journal article-refereed


    • Boreal forest
    • Genetic algorithm
    • Improved k-NN
    • Sentinel-1
    • Snow damage
    • Support vector machine
    • Synthetic aperture radar


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