TY - JOUR
T1 - Mapping Alpine Aboveground Biomass from Imaging Spectrometer Data
T2 - A Comparison of Two Approaches
AU - Fatehi, Parviz
AU - Damm, Alexander
AU - Schweiger, Anna Katharina
AU - Schaepman, Michael E.
AU - Kneubühler, Mathias
N1 - Publisher Copyright:
© 2015 EU.
PY - 2015/6/1
Y1 - 2015/6/1
N2 - Aboveground biomass (AGB) of terrestrial ecosystems is an important constraint of global change and productivity models and used to assess carbon stocks and thus the contribution of vegetated ecosystems to the global carbon cycle. Although an indispensable and important requirement for decision makers, coherent and accurate estimates of grassland and forest AGB especially in complex environments are still lacking. In this study, we aim to assess the capability of two strategies to map grassland and forest AGB in a complex alpine ecosystem, i.e., using a discrete as well as a continuous field (CF) mapping approach based on imaging spectroscopy (IS) data. In situ measurements of grassland and forest AGB were acquired in the Swiss National Park (SNP) to calibrate empirical models and to validate AGB retrievals. The selection of robust empirical models considered all potential two narrow-band combinations of the simple ratio (SR) and the normalized difference vegetation index (NDVI) generated from Airborne Prism Experiment (APEX) IS data and in situ measurements. We found a narrow-band SR including spectral bands from the short-wave infrared (SWIR) (1689 nm) and near infrared (NIR) (851 nm) as the best regression model to estimate grassland AGB. Forest AGB showed highest correlation with an SR generated from two spectral bands in the SWIR (1498, 2112 nm). The applied accuracy assessment revealed good results for estimated grassland AGB using the discrete mapping approach [R2 of 0.65, mean RMSE (mRMSE) of 0.91 t · ha-1, and mean relative RMSE (mrRMSE) of 26%]. The CF mapping approach produced a higher R2 (R2 = 0.94), and decreased the mRMSE and the mrRMSE to 0.55 t · ha-1 and 15%, respectively. For forest, the discrete approach predicted AGB with an R2 value of 0.64, an mRMSE of 67.8 t · ha-1, and an mrRMSE of 25%. The CF mapping approach improved the accuracy of forest AGB estimation with R2 = 0.85, mean RMSE = 55.85 t · ha-1, and mean relative RMSE = 21%. Our results indicate that, in general, both mapping approaches are capable of accurately mapping grassland and forest AGB in complex environments using IS data, whereas the CF-based approach yielded higher accuracies due to its capability to incorporate subpixel information (abundances) of different land cover types.
AB - Aboveground biomass (AGB) of terrestrial ecosystems is an important constraint of global change and productivity models and used to assess carbon stocks and thus the contribution of vegetated ecosystems to the global carbon cycle. Although an indispensable and important requirement for decision makers, coherent and accurate estimates of grassland and forest AGB especially in complex environments are still lacking. In this study, we aim to assess the capability of two strategies to map grassland and forest AGB in a complex alpine ecosystem, i.e., using a discrete as well as a continuous field (CF) mapping approach based on imaging spectroscopy (IS) data. In situ measurements of grassland and forest AGB were acquired in the Swiss National Park (SNP) to calibrate empirical models and to validate AGB retrievals. The selection of robust empirical models considered all potential two narrow-band combinations of the simple ratio (SR) and the normalized difference vegetation index (NDVI) generated from Airborne Prism Experiment (APEX) IS data and in situ measurements. We found a narrow-band SR including spectral bands from the short-wave infrared (SWIR) (1689 nm) and near infrared (NIR) (851 nm) as the best regression model to estimate grassland AGB. Forest AGB showed highest correlation with an SR generated from two spectral bands in the SWIR (1498, 2112 nm). The applied accuracy assessment revealed good results for estimated grassland AGB using the discrete mapping approach [R2 of 0.65, mean RMSE (mRMSE) of 0.91 t · ha-1, and mean relative RMSE (mrRMSE) of 26%]. The CF mapping approach produced a higher R2 (R2 = 0.94), and decreased the mRMSE and the mrRMSE to 0.55 t · ha-1 and 15%, respectively. For forest, the discrete approach predicted AGB with an R2 value of 0.64, an mRMSE of 67.8 t · ha-1, and an mrRMSE of 25%. The CF mapping approach improved the accuracy of forest AGB estimation with R2 = 0.85, mean RMSE = 55.85 t · ha-1, and mean relative RMSE = 21%. Our results indicate that, in general, both mapping approaches are capable of accurately mapping grassland and forest AGB in complex environments using IS data, whereas the CF-based approach yielded higher accuracies due to its capability to incorporate subpixel information (abundances) of different land cover types.
KW - Continuous field (CF) mapping
KW - empirical approach
KW - imaging spectroscopy (IS)
KW - vegetation biomass
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U2 - 10.1109/JSTARS.2015.2432075
DO - 10.1109/JSTARS.2015.2432075
M3 - Article
AN - SCOPUS:85027957140
SN - 1939-1404
VL - 8
SP - 3123
EP - 3139
JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
IS - 6
M1 - 7117348
ER -