Obermeier, W.; Lehnert, L.; Pohl, M.; Gianonni, S.M.; Silva, B.; Seibert, R.; Laser, H.; Moser, G.; Müller, C.; Luterbacher, J. & Bendix, J. (2019): <b>Grassland ecosystem services in a changing environment: The potential of hyperspectral monitoring</b>. <i>Remote Sensing of Environment</i> <b>232</b>, 111273.
Resource Description
Title:
Grassland ecosystem services in a changing environment: The potential of hyperspectral monitoring
FOR816dw ID:
339
Publication Date:
2019-01-01
License and Usage Rights:
Resource Owner(s):
Individual:
W.A. Obermeier
Contact:
email:
webmaster <at> lcrs.de
Individual:
L.W. Lehnert
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email:
webmaster <at> lcrs.de
Individual:
M.J. Pohl
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email:
webmaster <at> lcrs.de
Individual:
S. Makowski Gianonni
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email:
webmaster <at> lcrs.de
Individual:
B. Silva
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email:
webmaster <at> lcrs.de
Individual:
R. Seibert
Contact:
email:
webmaster <at> lcrs.de
Individual:
H. Laser
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email:
webmaster <at> lcrs.de
Individual:
G. Moser
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email:
webmaster <at> lcrs.de
Individual:
C. Müller
Contact:
email:
webmaster <at> lcrs.de
Individual:
J. Luterbacher
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email:
webmaster <at> lcrs.de
Individual:
Jörg Bendix
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email:
bendix <at> staff.uni-marburg.de
Deutschhausstraße 12
Room No. 02 A 48
35032 Marburg
Faculty of Geography
Germany
Abstract:
Provisioning services from grassland ecosystems are strongly linked to physical and chemical grassland traits, which are affected by atmospheric CO2 concentrations ([CO2]s). The influences of increased [CO2]s ([eCO2]s) are typically investigated in Free Air Carbon dioxide Enrichment (FACE) studies via destructive sampling methods. This traditional approach is restricted to sampling plots and harvest dates, while hyperspectral approaches provide new opportunities as they are rapid, non-destructive and cost-effective. They further allow a high temporal resolution including spatially explicit information. In this study we investigated the hyperspectral predictability of 14 grassland traits linked to forage quality and quantity within a FACE experiment in central Germany with three plots under ambient atmospheric [CO2]s, and three plots at [eCO2]s (∼20% above ambient [CO2]s). We analysed the suitability of various normalisation and feature selection techniques to link comprehensive laboratory analyses with two years of hyperspectral measurements (spectral range 600–1600 nm). We applied partial least squares regression and found good to excellent predictive performances (0.49 ≤ leave one out cross-validation R2≤ 0.94), which depended on the normalisation method applied to the hyperspectral data prior to model training. Noteworthy, the models' predictive performances were not affected by the different [CO2]s, which was anticipated due to the altered plant physiology under [eCO2]s. Thus, an accurate monitoring of grassland traits under different [CO2]s (present-day versus future, or within a FACE facility) is promising, if appropriate predictors are selected. Moreover, we show how hyperspectral predictions can be used e.g., within a future phenotyping approach, to monitor the grassland on a spatially explicit level and on a higher temporal resolution compared to conventional destructive sampling techniques. Based on the information during the vegetation period we show how hyperspectral monitoring might be used e.g., to adapt harvest practices or gain deeper insights into physiological plant alterations under [eCO2]s.
