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Remote Sens. 2019, 11(4), 384; https://doi.org/10.3390/rs11040384

Boreal Forest Snow Damage Mapping Using Multi-Temporal Sentinel-1 Data

1
Department of Electronics and Nanoengineering, Aalto University, P.O. Box 11000, FI-00076 AALTO, 02150 Espoo, Finland
2
VTT Technical Research Centre of Finland, P.O. Box 1000, FI-00076 VTT, 02150 Espoo, Finland
*
Author to whom correspondence should be addressed.
Received: 28 December 2018 / Revised: 2 February 2019 / Accepted: 8 February 2019 / Published: 13 February 2019
(This article belongs to the Special Issue Remote Sensing of Boreal Forests)
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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. View Full-Text
Keywords: boreal forest; snow damage; synthetic aperture radar; Sentinel-1; support vector machine; improved k-NN; genetic algorithm boreal forest; snow damage; synthetic aperture radar; Sentinel-1; support vector machine; improved k-NN; genetic algorithm
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Tomppo, E.; Antropov, O.; Praks, J. Boreal Forest Snow Damage Mapping Using Multi-Temporal Sentinel-1 Data. Remote Sens. 2019, 11, 384.

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