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  5. Forest growing stock volume of the northern hemisphere: Spatially explicit estimates for 2010 derived from Envisat ASAR

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Article
English
2015

Forest growing stock volume of the northern hemisphere: Spatially explicit estimates for 2010 derived from Envisat ASAR

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English
2015
Remote Sensing of Environment
Vol 168
DOI: 10.1016/j.rse.2015.07.005

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Dmitry Schepaschenko
Dmitry Schepaschenko

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Maurizio Santoro
André Beaudoin
Christian Beer
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Abstract

This paper presents and assesses spatially explicit estimates of forest growing stock volume (GSV) of the northern hemisphere (north of 10°N) from hyper-temporal observations of Envisat Advanced Synthetic Aperture Radar (ASAR) backscattered intensity using the BIOMASAR algorithm. Approximately 70,000 ASAR images at a pixel size of 0.01° were used to estimate GSV representative for the year 2010. The spatial distribution of the GSV across four ecological zones (polar, boreal, temperate, subtropical) was well captured by the ASAR-based estimates. The uncertainty of the retrieved GSV was smallest in boreal and temperate forest (<30% for approximately 80% of the forest area) and largest in subtropical forest. ASAR-derived GSV averages at the level of administrative units were mostly in agreement with inventory-derived estimates. Underestimation occurred in regions of very high GSV (>300m3/ha) and fragmented forest landscapes. For the major forested countries within the study region, the relative RMSE between ASAR-derived GSV averages at provincial level and corresponding values from National Forest Inventory was between 12% and 45% (average: 29%).

How to cite this publication

Maurizio Santoro, André Beaudoin, Christian Beer, Oliver Cartus, Johan E. S. Fransson, Ronald J. Hall, Carsten Pathe, Christiane Schmullius, Dmitry Schepaschenko, А. Shvidenko, Martin Thurner, U. Wegmüller (2015). Forest growing stock volume of the northern hemisphere: Spatially explicit estimates for 2010 derived from Envisat ASAR. Remote Sensing of Environment, 168, pp. 316-334, DOI: 10.1016/j.rse.2015.07.005.

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Publication Details

Type

Article

Year

2015

Authors

12

Datasets

0

Total Files

0

Language

English

Journal

Remote Sensing of Environment

DOI

10.1016/j.rse.2015.07.005

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