Forest growing stock volume of the northern hemisphere: Spatially explicit estimates for 2010 derived from Envisat ASAR
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 70,00...
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| Veröffentlicht in: | Remote sensing of environment Jg. 168; S. 316 - 334 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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01.10.2015
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| ISSN: | 0034-4257, 1879-0704, 1879-0704 |
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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%).
•Wall-to-wall estimates of forest growing stock volume (GSV) north of 10°N•Spatial distribution of GSV well reproduced in four biomes•Percent error of ASAR-derived GSV averages at provincial level: between 12% and 45%.•Underestimation for areas with GSV>300m3/ha and in fragmented forest landscapes |
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| AbstractList | 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%).
•Wall-to-wall estimates of forest growing stock volume (GSV) north of 10°N•Spatial distribution of GSV well reproduced in four biomes•Percent error of ASAR-derived GSV averages at provincial level: between 12% and 45%.•Underestimation for areas with GSV>300m3/ha and in fragmented forest landscapes This paper presents and assesses spatially explicit estimates of forest growing stock volume (GSV) of the northern hemisphere (north of 10 degree 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 degree 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%). This paper presents and assesses spatially explicit estimates of forest growing stock volume (GSV) of the northern hemisphere (north of 10 degrees 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 degrees 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 (>300 m(3)/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%). This paper presents and assesses spatially explicit estimates of forest growing stock volume (GSV) of the northern hemisphere (north of 10 degrees 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 degrees 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 (>300 m(3)/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%). (C) 2015 Elsevier Inc. All rights reserved. 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%). |
| Author | Cartus, Oliver Wegmüller, Urs Thurner, Martin Santoro, Maurizio Fransson, Johan E.S. Beer, Christian Pathe, Carsten Hall, Ronald J. Schmullius, Christiane Beaudoin, André Shvidenko, Anatoly Schepaschenko, Dmitry |
| Author_xml | – sequence: 1 givenname: Maurizio orcidid: 0000-0002-3339-6991 surname: Santoro fullname: Santoro, Maurizio email: santoro@gamma-rs.ch organization: Gamma Remote Sensing, 3073 Gümligen, Switzerland – sequence: 2 givenname: André surname: Beaudoin fullname: Beaudoin, André organization: Laurentian Forestry Centre, Canadian Forest Service, Natural Resources Canada, Sainte-Foy, QC G1V 4C7, Canada – sequence: 3 givenname: Christian surname: Beer fullname: Beer, Christian organization: Department of Environmental Science and Analytical Chemistry (ACES) and Bolin Centre for Climate Research, Stockholm University, 10691 Stockholm, Sweden – sequence: 4 givenname: Oliver surname: Cartus fullname: Cartus, Oliver organization: Gamma Remote Sensing, 3073 Gümligen, Switzerland – sequence: 5 givenname: Johan E.S. surname: Fransson fullname: Fransson, Johan E.S. organization: Department of Forest Resource Management, Swedish University of Agricultural Sciences, 901 83 Umeå, Sweden – sequence: 6 givenname: Ronald J. surname: Hall fullname: Hall, Ronald J. organization: Northern Forestry Centre, Canadian Forest Service, Natural Resources Canada, Edmonton AB T6H 3S5, Canada – sequence: 7 givenname: Carsten surname: Pathe fullname: Pathe, Carsten organization: Department of Earth Observation, Friedrich-Schiller University, 07743 Jena, Germany – sequence: 8 givenname: Christiane surname: Schmullius fullname: Schmullius, Christiane organization: Department of Earth Observation, Friedrich-Schiller University, 07743 Jena, Germany – sequence: 9 givenname: Dmitry surname: Schepaschenko fullname: Schepaschenko, Dmitry organization: International Institute of Applied Systems Analysis, 2361 Laxenburg, Austria – sequence: 10 givenname: Anatoly surname: Shvidenko fullname: Shvidenko, Anatoly organization: International Institute of Applied Systems Analysis, 2361 Laxenburg, Austria – sequence: 11 givenname: Martin surname: Thurner fullname: Thurner, Martin organization: Department of Environmental Science and Analytical Chemistry (ACES) and Bolin Centre for Climate Research, Stockholm University, 10691 Stockholm, Sweden – sequence: 12 givenname: Urs surname: Wegmüller fullname: Wegmüller, Urs organization: Gamma Remote Sensing, 3073 Gümligen, Switzerland |
| BackLink | https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-121862$$DView record from Swedish Publication Index (Stockholms universitet) https://res.slu.se/id/publ/75381$$DView record from Swedish Publication Index (Sveriges lantbruksuniversitet) |
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| Snippet | This paper presents and assesses spatially explicit estimates of forest growing stock volume (GSV) of the northern hemisphere (north of 10°N) from... This paper presents and assesses spatially explicit estimates of forest growing stock volume (GSV) of the northern hemisphere (north of 10 degree N) from... This paper presents and assesses spatially explicit estimates of forest growing stock volume (GSV) of the northern hemisphere (north of 10 degrees N) from... |
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| SubjectTerms | algorithms BIOMASAR Biomass ecological zones Environmental Sciences Envisat ASAR Forest forest inventory Growing stock volume habitat fragmentation landscapes Miljövetenskap MODIS Vegetation Continuous Fields Northern hemisphere remote sensing synthetic aperture radar temperate forests uncertainty |
| Title | Forest growing stock volume of the northern hemisphere: Spatially explicit estimates for 2010 derived from Envisat ASAR |
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