期刊
REMOTE SENSING
卷 13, 期 21, 页码 -出版社
MDPI
DOI: 10.3390/rs13214483
关键词
growing stock volume; boreal forest; Russian arctic; tree allometry; Sentinel-2
类别
资金
- British-Russian project Multiplatform remote sensing of the impact of climate change on the northern forests of Russia by the Ministry of Science and Higher Education of the Russian Federation [RFMEFI61618X0099]
- British Council [352397111]
- Russian Science Foundation [19-77-30015]
- UK Science and Innovation Network through the British Embassy in Moscow
- EU Transnational Access Interact scheme
- Russian Science Foundation [19-77-30015] Funding Source: Russian Science Foundation
Growing stock volume is a crucial parameter of forests, with estimation at regional to global scales relying on satellite remote sensing data. However, accuracies are lower over sparse boreal forests, particularly in Russia where knowledge of GSV is currently lacking.
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.
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