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Mapping alpine aboveground biomass from imaging spectrometer data: a comparison of two approaches


Fatehi, Parviz; Damm, Alexander; Schweiger, Anna-Katharina; Schaepman, Michael E; Kneubühler, Mathias (2015). Mapping alpine aboveground biomass from imaging spectrometer data: a comparison of two approaches. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6):3123-3139.

Abstract

Aboveground biomass (AGB) of terrestrial ecosys- tems is an important constraint of global change and productivity models and used to assess carbon stocks and thus the contribu- tion of vegetated ecosystems to the global carbon cycle. Although an indispensable and important requirement for decision mak- ers, 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 grass- land 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) gen- erated from Airborne Prism Experiment (APEX) IS data and in situ measurements. We found a narrow-band SR including spec- tral bands from the short-wave infrared (SWIR) (1689 nm) and near infrared (NIR) (851 nm) as the best regression model to esti- mate 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 esti- mation with R2 = 0.85 , mean RMSE = 55.85t · ha−1 , and mean relative RMSE = 21%. Our results indicate that, in gen- eral, 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.

Aboveground biomass (AGB) of terrestrial ecosys- tems is an important constraint of global change and productivity models and used to assess carbon stocks and thus the contribu- tion of vegetated ecosystems to the global carbon cycle. Although an indispensable and important requirement for decision mak- ers, 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 grass- land 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) gen- erated from Airborne Prism Experiment (APEX) IS data and in situ measurements. We found a narrow-band SR including spec- tral bands from the short-wave infrared (SWIR) (1689 nm) and near infrared (NIR) (851 nm) as the best regression model to esti- mate 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 esti- mation with R2 = 0.85 , mean RMSE = 55.85t · ha−1 , and mean relative RMSE = 21%. Our results indicate that, in gen- eral, 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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Date:2015
Deposited On:09 Oct 2015 15:21
Last Modified:05 Apr 2016 19:26
Publisher:Institute of Electrical and Electronics Engineers
ISSN:1939-1404
Publisher DOI:https://doi.org/10.1109/JSTARS.2015.2432075
Permanent URL: https://doi.org/10.5167/uzh-113202

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