Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

Growing stock volume (GSV) is a fundamental parameter of forests, closely related to the above-ground biomass and hence to carbon storage. Estimation of GSV at regional to global scales depends on the use of satellite remote sensing data, although accuracies are generally lower over the sparse borea...

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Autores principales:	W. Gareth Rees, Jack Tomaney, Olga Tutubalina, Vasily Zharko, Sergey Bartalev
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	growing stock volume boreal forest Russian arctic tree allometry Sentinel-2 Science Q
Acceso en línea:	https://doaj.org/article/512110190c3e4a32b37ef790a40e6e46
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spelling	oai:doaj.org-article:512110190c3e4a32b37ef790a40e6e462021-11-11T18:58:57ZEstimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification10.3390/rs132144832072-4292https://doaj.org/article/512110190c3e4a32b37ef790a40e6e462021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4483https://doaj.org/toc/2072-4292Growing stock volume (GSV) is a fundamental parameter of forests, closely related to the above-ground biomass and hence to carbon storage. Estimation of GSV at regional to global scales depends on the use of satellite remote sensing data, although accuracies are generally lower over the sparse boreal forest. This is especially true of boreal forest in Russia, for which knowledge of GSV is currently poor despite its global importance. Here we develop a new empirical method in which the primary remote sensing data source is a single summer Sentinel-2 MSI image, augmented by land-cover classification based on the same MSI image trained using MODIS-derived data. In our work the method is calibrated and validated using an extensive set of field measurements from two contrasting regions of the Russian arctic. Results show that GSV can be estimated with an RMS uncertainty of approximately 35–55%, comparable to other spaceborne estimates of low-GSV forest areas, with 70% spatial correspondence between our GSV maps and existing products derived from MODIS data. Our empirical approach requires somewhat laborious data collection when used for upscaling from field data, but could also be used to downscale global data.W. Gareth ReesJack TomaneyOlga TutubalinaVasily ZharkoSergey BartalevMDPI AGarticlegrowing stock volumeboreal forestRussian arctictree allometrySentinel-2ScienceQENRemote Sensing, Vol 13, Iss 4483, p 4483 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	growing stock volume boreal forest Russian arctic tree allometry Sentinel-2 Science Q
spellingShingle	growing stock volume boreal forest Russian arctic tree allometry Sentinel-2 Science Q W. Gareth Rees Jack Tomaney Olga Tutubalina Vasily Zharko Sergey Bartalev Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification
description	Growing stock volume (GSV) is a fundamental parameter of forests, closely related to the above-ground biomass and hence to carbon storage. Estimation of GSV at regional to global scales depends on the use of satellite remote sensing data, although accuracies are generally lower over the sparse boreal forest. This is especially true of boreal forest in Russia, for which knowledge of GSV is currently poor despite its global importance. Here we develop a new empirical method in which the primary remote sensing data source is a single summer Sentinel-2 MSI image, augmented by land-cover classification based on the same MSI image trained using MODIS-derived data. In our work the method is calibrated and validated using an extensive set of field measurements from two contrasting regions of the Russian arctic. Results show that GSV can be estimated with an RMS uncertainty of approximately 35–55%, comparable to other spaceborne estimates of low-GSV forest areas, with 70% spatial correspondence between our GSV maps and existing products derived from MODIS data. Our empirical approach requires somewhat laborious data collection when used for upscaling from field data, but could also be used to downscale global data.
format	article
author	W. Gareth Rees Jack Tomaney Olga Tutubalina Vasily Zharko Sergey Bartalev
author_facet	W. Gareth Rees Jack Tomaney Olga Tutubalina Vasily Zharko Sergey Bartalev
author_sort	W. Gareth Rees
title	Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification
title_short	Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification
title_full	Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification
title_fullStr	Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification
title_full_unstemmed	Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification
title_sort	estimation of boreal forest growing stock volume in russia from sentinel-2 msi and land cover classification
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/512110190c3e4a32b37ef790a40e6e46
work_keys_str_mv	AT wgarethrees estimationofborealforestgrowingstockvolumeinrussiafromsentinel2msiandlandcoverclassification AT jacktomaney estimationofborealforestgrowingstockvolumeinrussiafromsentinel2msiandlandcoverclassification AT olgatutubalina estimationofborealforestgrowingstockvolumeinrussiafromsentinel2msiandlandcoverclassification AT vasilyzharko estimationofborealforestgrowingstockvolumeinrussiafromsentinel2msiandlandcoverclassification AT sergeybartalev estimationofborealforestgrowingstockvolumeinrussiafromsentinel2msiandlandcoverclassification
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Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

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