Retrieval of growing stock volume in boreal forest using hyper-temporal series of Envisat ASAR ScanSAR backscatter measurements

Methods for the estimation of forest growing stock volume (GSV) are a major topic of investigation in the remote sensing community. The boreal zone contains almost 30% of global forest by area but measurements of forest resources are often outdated. Although past and current spaceborne synthetic ape...

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Vydané v:	Remote sensing of environment Ročník 115; číslo 2; s. 490 - 507
Hlavní autori:	Santoro, Maurizio, Beer, Christian, Cartus, Oliver, Schmullius, Christiane, Shvidenko, Anatoly, McCallum, Ian, Wegmüller, Urs, Wiesmann, Andreas
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, NY Elsevier Inc 15.02.2011 Elsevier
Predmet:	Algorithms Animal, plant and microbial ecology Applied geophysics automation Backscatter Biological and medical sciences BIOMASAR algorithm Boreal forest boreal forests canopy data collection Earth sciences Earth, ocean, space Envisat ASAR Errors Estimates Exact sciences and technology forest mensuration forest resources Forests Fundamental and applied biological sciences. Psychology General aspects. Techniques Growing stock volume Internal geophysics land cover Mathematical models moderate resolution imaging spectroradiometer MODIS Vegetation Continuous Fields Pixels Quebec remote sensing Retrieval ScanSAR Siberia statistics Sweden Synthetic aperture radar Teledetection and vegetation maps trees Water–Cloud model Canada Russian Federation Sweden Eurasia Europe Quebec Siberia Scandinavia Eastern Canada Russia, Siberia Canada, Quebec Envisat ASAR MODIS Vegetation Continuous Fields Boreal forest BIOMASAR algorithm ScanSAR Water–Cloud model Growing stock volume Backscatter algorithms data Forest resource global Image North America Modeling stocks currents Innovation Synthetic aperture radar news models Water-Cloud model Estimation Independence remote sensing utilization Method Measurement in situ Optimum volume Individual boreal zone Multidate observation Community
ISSN:	0034-4257, 1879-0704
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Abstract	Methods for the estimation of forest growing stock volume (GSV) are a major topic of investigation in the remote sensing community. The boreal zone contains almost 30% of global forest by area but measurements of forest resources are often outdated. Although past and current spaceborne synthetic aperture radar (SAR) backscatter data are not optimal for forest-related studies, a multi-temporal combination of individual GSV estimates can improve the retrieval as compared to the single-image case. This feature has been included in a novel GSV retrieval approach, hereafter referred to as the BIOMASAR algorithm. One innovative aspect of the algorithm is its independence from in situ measurements for model training. Model parameter estimates are obtained from central tendency statistics of the backscatter measurements for unvegetated and dense forest areas, which can be selected by means of a continuous tree canopy cover product, such as the MODIS Vegetation Continuous Fields product. In this paper, the performance of the algorithm has been evaluated using hyper-temporal series of C-band Envisat Advanced SAR (ASAR) images acquired in ScanSAR mode at 100 m and 1 km pixel size. To assess the robustness of the retrieval approach, study areas in Central Siberia (Russia), Sweden and Québec (Canada) have been considered. The algorithm validation activities demonstrated that the automatic approach implemented in the BIOMASAR algorithm performed similarly to traditional approaches based on in situ data. The retrieved GSV showed no saturation up to 300 m 3/ha, which represented almost the entire range of GSV at the study areas. The relative root mean square error (RMSE) was between 34.2% and 48.1% at 1 km pixel size. Larger errors were obtained at 100 m because of local errors in the reference datasets. Averaging GSV estimates over neighboring pixels improved the retrieval statistics substantially. For an aggregation factor of 10 × 10 pixels, the relative RMSE was below 25%, regardless of the original resolution of the SAR data.
AbstractList	Methods for the estimation of forest growing stock volume (GSV) are a major topic of investigation in the remote sensing community. The boreal zone contains almost 30% of global forest by area but measurements of forest resources are often outdated. Although past and current spaceborne synthetic aperture radar (SAR) backscatter data are not optimal for forest-related studies, a multi-temporal combination of individual GSV estimates can improve the retrieval as compared to the single-image case. This feature has been included in a novel GSV retrieval approach, hereafter referred to as the BIOMASAR algorithm. One innovative aspect of the algorithm is its independence from in situ measurements for model training. Model parameter estimates are obtained from central tendency statistics of the backscatter measurements for unvegetated and dense forest areas, which can be selected by means of a continuous tree canopy cover product, such as the MODIS Vegetation Continuous Fields product. In this paper, the performance of the algorithm has been evaluated using hyper-temporal series of C-band Envisat Advanced SAR (ASAR) images acquired in ScanSAR mode at 100 m and 1 km pixel size. To assess the robustness of the retrieval approach, study areas in Central Siberia (Russia), Sweden and Québec (Canada) have been considered. The algorithm validation activities demonstrated that the automatic approach implemented in the BIOMASAR algorithm performed similarly to traditional approaches based on in situ data. The retrieved GSV showed no saturation up to 300 m 3/ha, which represented almost the entire range of GSV at the study areas. The relative root mean square error (RMSE) was between 34.2% and 48.1% at 1 km pixel size. Larger errors were obtained at 100 m because of local errors in the reference datasets. Averaging GSV estimates over neighboring pixels improved the retrieval statistics substantially. For an aggregation factor of 10 × 10 pixels, the relative RMSE was below 25%, regardless of the original resolution of the SAR data. Methods for the estimation of forest growing stock volume (GSV) are a major topic of investigation in the remote sensing community. The boreal zone contains almost 30% of global forest by area but measurements of forest resources are often outdated. Although past and current spaceborne synthetic aperture radar (SAR) backscatter data are not optimal for forest-related studies, a multi-temporal combination of individual GSV estimates can improve the retrieval as compared to the single-image case. This feature has been included in a novel GSV retrieval approach, hereafter referred to as the BIOMASAR algorithm. One innovative aspect of the algorithm is its independence from in situ measurements for model training. Model parameter estimates are obtained from central tendency statistics of the backscatter measurements for unvegetated and dense forest areas, which can be selected by means of a continuous tree canopy cover product, such as the MODIS Vegetation Continuous Fields product. In this paper, the performance of the algorithm has been evaluated using hyper-temporal series of C-band Envisat Advanced SAR (ASAR) images acquired in ScanSAR mode at 100m and 1km pixel size. To assess the robustness of the retrieval approach, study areas in Central Siberia (Russia), Sweden and Québec (Canada) have been considered. The algorithm validation activities demonstrated that the automatic approach implemented in the BIOMASAR algorithm performed similarly to traditional approaches based on in situ data. The retrieved GSV showed no saturation up to 300m³/ha, which represented almost the entire range of GSV at the study areas. The relative root mean square error (RMSE) was between 34.2% and 48.1% at 1km pixel size. Larger errors were obtained at 100m because of local errors in the reference datasets. Averaging GSV estimates over neighboring pixels improved the retrieval statistics substantially. For an aggregation factor of 10×10pixels, the relative RMSE was below 25%, regardless of the original resolution of the SAR data. Methods for the estimation of forest growing stock volume (GSV) are a major topic of investigation in the remote sensing community. The boreal zone contains almost 30% of global forest by area but measurements of forest resources are often outdated. Although past and current spaceborne synthetic aperture radar (SAR) backscatter data are not optimal for forest-related studies, a multi-temporal combination of individual GSV estimates can improve the retrieval as compared to the single-image case. This feature has been included in a novel GSV retrieval approach, hereafter referred to as the BIOMASAR algorithm. One innovative aspect of the algorithm is its independence from in situ measurements for model training. Model parameter estimates are obtained from central tendency statistics of the backscatter measurements for unvegetated and dense forest areas, which can be selected by means of a continuous tree canopy cover product, such as the MODIS Vegetation Continuous Fields product. In this paper, the performance of the algorithm has been evaluated using hyper-temporal series of C-band Envisat Advanced SAR (ASAR) images acquired in ScanSAR mode at 100m and 1km pixel size. To assess the robustness of the retrieval approach, study areas in Central Siberia (Russia), Sweden and Quebec (Canada) have been considered. The algorithm validation activities demonstrated that the automatic approach implemented in the BIOMASAR algorithm performed similarly to traditional approaches based on in situ data. The retrieved GSV showed no saturation up to 300m3/ha, which represented almost the entire range of GSV at the study areas. The relative root mean square error (RMSE) was between 34.2% and 48.1% at 1km pixel size. Larger errors were obtained at 100m because of local errors in the reference datasets. Averaging GSV estimates over neighboring pixels improved the retrieval statistics substantially. For an aggregation factor of 10A-10pixels, the relative RMSE was below 25%, regardless of the original resolution of the SAR data.
Author	Cartus, Oliver Wegmüller, Urs Santoro, Maurizio Wiesmann, Andreas Beer, Christian Schmullius, Christiane McCallum, Ian Shvidenko, Anatoly
Author_xml	– sequence: 1 givenname: Maurizio surname: Santoro fullname: Santoro, Maurizio email: santoro@gamma-rs.ch organization: Gamma Remote Sensing, CH-3073 Gümligen, Switzerland – sequence: 2 givenname: Christian surname: Beer fullname: Beer, Christian organization: Max-Planck Institute for Biogeochemistry, D-07745 Jena, Germany – sequence: 3 givenname: Oliver surname: Cartus fullname: Cartus, Oliver organization: Department of Earth Observation, Friedrich-Schiller University, D-07743 Jena, Germany – sequence: 4 givenname: Christiane surname: Schmullius fullname: Schmullius, Christiane organization: Department of Earth Observation, Friedrich-Schiller University, D-07743 Jena, Germany – sequence: 5 givenname: Anatoly surname: Shvidenko fullname: Shvidenko, Anatoly organization: International Institute of Applied Systems Analysis, A-2361 Laxenburg, Austria – sequence: 6 givenname: Ian surname: McCallum fullname: McCallum, Ian organization: International Institute of Applied Systems Analysis, A-2361 Laxenburg, Austria – sequence: 7 givenname: Urs surname: Wegmüller fullname: Wegmüller, Urs organization: Gamma Remote Sensing, CH-3073 Gümligen, Switzerland – sequence: 8 givenname: Andreas surname: Wiesmann fullname: Wiesmann, Andreas organization: Gamma Remote Sensing, CH-3073 Gümligen, Switzerland
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Keywords	Envisat ASAR MODIS Vegetation Continuous Fields Boreal forest BIOMASAR algorithm ScanSAR Water–Cloud model Growing stock volume Backscatter algorithms data Forest resource global Image North America Modeling stocks currents Innovation Synthetic aperture radar news models Water-Cloud model Estimation Independence remote sensing utilization Method Measurement in situ Optimum volume Individual boreal zone Multidate observation Community
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Snippet	Methods for the estimation of forest growing stock volume (GSV) are a major topic of investigation in the remote sensing community. The boreal zone contains...
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SubjectTerms	Algorithms Animal, plant and microbial ecology Applied geophysics automation Backscatter Biological and medical sciences BIOMASAR algorithm Boreal forest boreal forests canopy data collection Earth sciences Earth, ocean, space Envisat ASAR Errors Estimates Exact sciences and technology forest mensuration forest resources Forests Fundamental and applied biological sciences. Psychology General aspects. Techniques Growing stock volume Internal geophysics land cover Mathematical models moderate resolution imaging spectroradiometer MODIS Vegetation Continuous Fields Pixels Quebec remote sensing Retrieval ScanSAR Siberia statistics Sweden Synthetic aperture radar Teledetection and vegetation maps trees Water–Cloud model
Title	Retrieval of growing stock volume in boreal forest using hyper-temporal series of Envisat ASAR ScanSAR backscatter measurements
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