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

Thumbnail Image

Access rights

openAccess
publishedVersion

URL

Journal Title

Journal ISSN

Volume Title

A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)

Authors

Date

2019-02-01

Department

Department of Electronics and Nanoengineering

Major/Subject

Mcode

Degree programme

Language

en

Pages

20

Series

Remote Sensing, Volume 11, issue 4

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.

Description

Keywords

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

Other note

DOI

10.3390/rs11040384

Citation

Tomppo, E, Antropov, O & Praks, J 2019, 'Boreal forest snow damage mapping using multi-temporal sentinel-1 Data', Remote Sensing, vol. 11, no. 4, 384. https://doi.org/10.3390/rs11040384

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-201904022415

Collections

[article-cris] Sähkötekniikan korkeakoulu / ELEC