Biomass Estimation over a Large Area Based on Standwise Forest Inventory Data and ASTER and MODIS Satellite Data: A Possibility to Verify Carbon Inventories. Muukkonen, P. & Heiskanen, J. 107(4):617–624. ![Biomass Estimation over a Large Area Based on Standwise Forest Inventory Data and ASTER and MODIS Satellite Data: A Possibility to Verify Carbon Inventories [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex According to the IPCC GPG (Intergovernmental Panel on Climate Change, Good Practice Guidance), remote sensing methods are especially suitable for independent verification of the national LULUCF (Land Use, Land-Use Change, and Forestry) carbon pool estimates, particularly the aboveground biomass. In the present study, we demonstrate the potential of standwise (forest stand is a homogenous forest unit with average size of 1-3~ha) forest inventory data, and ASTER and MODIS satellite data for estimating stand volume (m3 ha-~1) and aboveground biomass (t ha-~1) over a large area of boreal forests in southern Finland. The regression models, developed using standwise forest inventory data and standwise averages of moderate spatial resolution ASTER data (15~m~×~15~m), were utilized to estimate stand volume for coarse resolution MODIS pixels (250~m~×~250~m). The MODIS datasets for three 8-day periods produced slightly different predictions, but the averaged MODIS data produced the most accurate estimates. The inaccuracy in radiometric calibration between the datasets, the effect of gridding and compositing artifacts and phenological variability are the most probable reasons for this variability. Averaging of the several MODIS datasets seems to be one possibility to reduce bias. The estimates obtained were significantly close to the district-level mean values provided by the Finnish National Forest Inventory; the relative RMSE was 9.9\,%. The use of finer spatial resolution data is an essential step to integrate ground measurements with coarse spatial resolution data. Furthermore, the use of standwise forest inventory data reduces co-registration errors and helps in solving the scaling problem between the datasets. The approach employed here can be used for estimating the stand volume and biomass, and as required independent verification data.
@article{muukkonenBiomassEstimationLarge2007,
title = {Biomass Estimation over a Large Area Based on Standwise Forest Inventory Data and {{ASTER}} and {{MODIS}} Satellite Data: {{A}} Possibility to Verify Carbon Inventories},
author = {Muukkonen, P. and Heiskanen, J.},
date = {2007-04},
journaltitle = {Remote Sensing of Environment},
volume = {107},
pages = {617--624},
issn = {0034-4257},
doi = {10.1016/j.rse.2006.10.011},
url = {https://doi.org/10.1016/j.rse.2006.10.011},
abstract = {According to the IPCC GPG (Intergovernmental Panel on Climate Change, Good Practice Guidance), remote sensing methods are especially suitable for independent verification of the national LULUCF (Land Use, Land-Use Change, and Forestry) carbon pool estimates, particularly the aboveground biomass. In the present study, we demonstrate the potential of standwise (forest stand is a homogenous forest unit with average size of 1-3~ha) forest inventory data, and ASTER and MODIS satellite data for estimating stand volume (m3 ha-~1) and aboveground biomass (t ha-~1) over a large area of boreal forests in southern Finland. The regression models, developed using standwise forest inventory data and standwise averages of moderate spatial resolution ASTER data (15~m~×~15~m), were utilized to estimate stand volume for coarse resolution MODIS pixels (250~m~×~250~m). The MODIS datasets for three 8-day periods produced slightly different predictions, but the averaged MODIS data produced the most accurate estimates. The inaccuracy in radiometric calibration between the datasets, the effect of gridding and compositing artifacts and phenological variability are the most probable reasons for this variability. Averaging of the several MODIS datasets seems to be one possibility to reduce bias. The estimates obtained were significantly close to the district-level mean values provided by the Finnish National Forest Inventory; the relative RMSE was 9.9\,\%. The use of finer spatial resolution data is an essential step to integrate ground measurements with coarse spatial resolution data. Furthermore, the use of standwise forest inventory data reduces co-registration errors and helps in solving the scaling problem between the datasets. The approach employed here can be used for estimating the stand volume and biomass, and as required independent verification data.},
keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-4065356,biomass,finland,forest-biomass,forest-resources,remote-sensing},
number = {4}
}
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In the present study, we demonstrate the potential of standwise (forest stand is a homogenous forest unit with average size of 1-3~ha) forest inventory data, and ASTER and MODIS satellite data for estimating stand volume (m3 ha-~1) and aboveground biomass (t ha-~1) over a large area of boreal forests in southern Finland. The regression models, developed using standwise forest inventory data and standwise averages of moderate spatial resolution ASTER data (15~m~×~15~m), were utilized to estimate stand volume for coarse resolution MODIS pixels (250~m~×~250~m). The MODIS datasets for three 8-day periods produced slightly different predictions, but the averaged MODIS data produced the most accurate estimates. The inaccuracy in radiometric calibration between the datasets, the effect of gridding and compositing artifacts and phenological variability are the most probable reasons for this variability. Averaging of the several MODIS datasets seems to be one possibility to reduce bias. 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In the present study, we demonstrate the potential of standwise (forest stand is a homogenous forest unit with average size of 1-3~ha) forest inventory data, and ASTER and MODIS satellite data for estimating stand volume (m3 ha-~1) and aboveground biomass (t ha-~1) over a large area of boreal forests in southern Finland. The regression models, developed using standwise forest inventory data and standwise averages of moderate spatial resolution ASTER data (15~m~×~15~m), were utilized to estimate stand volume for coarse resolution MODIS pixels (250~m~×~250~m). The MODIS datasets for three 8-day periods produced slightly different predictions, but the averaged MODIS data produced the most accurate estimates. The inaccuracy in radiometric calibration between the datasets, the effect of gridding and compositing artifacts and phenological variability are the most probable reasons for this variability. Averaging of the several MODIS datasets seems to be one possibility to reduce bias. 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