Boreal Forest Snow Damage Mapping Using Multi-Temporal Sentinel-1 Data
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...
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ftmdpi:oai:mdpi.com:/2072-4292/11/4/384/ 2023-08-20T04:08:43+02:00 Boreal Forest Snow Damage Mapping Using Multi-Temporal Sentinel-1 Data Erkki Tomppo Oleg Antropov Jaan Praks agris 2019-02-13 application/pdf https://doi.org/10.3390/rs11040384 EN eng Multidisciplinary Digital Publishing Institute Forest Remote Sensing https://dx.doi.org/10.3390/rs11040384 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 11; Issue 4; Pages: 384 boreal forest snow damage synthetic aperture radar Sentinel-1 support vector machine improved k-NN genetic algorithm Text 2019 ftmdpi https://doi.org/10.3390/rs11040384 2023-07-31T22:02:32Z 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. Text Northern Finland MDPI Open Access Publishing The Sentinel ENVELOPE(73.317,73.317,-52.983,-52.983) Remote Sensing 11 4 384 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
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ftmdpi |
language |
English |
topic |
boreal forest snow damage synthetic aperture radar Sentinel-1 support vector machine improved k-NN genetic algorithm |
spellingShingle |
boreal forest snow damage synthetic aperture radar Sentinel-1 support vector machine improved k-NN genetic algorithm Erkki Tomppo Oleg Antropov Jaan Praks Boreal Forest Snow Damage Mapping Using Multi-Temporal Sentinel-1 Data |
topic_facet |
boreal forest snow damage synthetic aperture radar Sentinel-1 support vector machine improved k-NN genetic algorithm |
description |
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. |
format |
Text |
author |
Erkki Tomppo Oleg Antropov Jaan Praks |
author_facet |
Erkki Tomppo Oleg Antropov Jaan Praks |
author_sort |
Erkki Tomppo |
title |
Boreal Forest Snow Damage Mapping Using Multi-Temporal Sentinel-1 Data |
title_short |
Boreal Forest Snow Damage Mapping Using Multi-Temporal Sentinel-1 Data |
title_full |
Boreal Forest Snow Damage Mapping Using Multi-Temporal Sentinel-1 Data |
title_fullStr |
Boreal Forest Snow Damage Mapping Using Multi-Temporal Sentinel-1 Data |
title_full_unstemmed |
Boreal Forest Snow Damage Mapping Using Multi-Temporal Sentinel-1 Data |
title_sort |
boreal forest snow damage mapping using multi-temporal sentinel-1 data |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2019 |
url |
https://doi.org/10.3390/rs11040384 |
op_coverage |
agris |
long_lat |
ENVELOPE(73.317,73.317,-52.983,-52.983) |
geographic |
The Sentinel |
geographic_facet |
The Sentinel |
genre |
Northern Finland |
genre_facet |
Northern Finland |
op_source |
Remote Sensing; Volume 11; Issue 4; Pages: 384 |
op_relation |
Forest Remote Sensing https://dx.doi.org/10.3390/rs11040384 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs11040384 |
container_title |
Remote Sensing |
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11 |
container_issue |
4 |
container_start_page |
384 |
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1774721158108676096 |