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

Erkki Tomppo, Oleg Antropov, Jaan Praks

Research output: Contribution to journalArticleScientificpeer-review

Abstract

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
Volume11
Issue number4
DOIs
Publication statusPublished - 13 Feb 2019
MoE publication typeNot Eligible

Fingerprint

boreal forest
snow
damage
synthetic aperture radar
disturbance
ecosystem service
forest ecosystem
forest management
timber
logistics
regression analysis
biodiversity
time series
sensor
climate
detection
method

Keywords

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

Cite this

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title = "Boreal forest snow damage mapping using multi-temporal sentinel-1 Data",
abstract = "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.",
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Boreal forest snow damage mapping using multi-temporal sentinel-1 Data. / Tomppo, Erkki; Antropov, Oleg; Praks, Jaan.

In: Remote Sensing, Vol. 11, No. 4, 384, 13.02.2019.

Research output: Contribution to journalArticleScientificpeer-review

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