1. Impact of land-use intensity on the relationships between vegetation indices, photosynthesis and biomass of intensively and extensively managed grassland fens.
- Author
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Metzger, C. M. H., Heinichen, J., Eickenscheidt, T., and Drösler, M.
- Subjects
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FEN ecology , *LAND use , *PHOTOSYNTHESIS , *ECOLOGY , *GRASSLANDS , *PLANT biomass - Abstract
Vegetation indices are widely used as model inputs and for non-destructive estimation of biomass and photosynthesis, but there have been few validation studies of the underlying relationships. To test their applicability on temperate fens and the impact of management intensity, we investigated the relationships between normalized difference vegetation index ( NDVI), leaf area index ( LAI), brown and green above-ground biomass and photosynthesis potential ( PP). Only the linear relationship between NDVI and PP was management independent ( R2 = 0·53). LAI to PP was described by a site-specific and negative logarithmic function ( R2 = 0·07-0·68). The hyperbolic relationship of LAI versus NDVI showed a high residual standard error (s.e.) of 1·71-1·84 and differed between extensive and intensive meadows. Biomass and LAI correlated poorly ( R2 = 0·30), with high species-specific variability. Intensive meadows had a higher ratio of LAI to biomass than extensive grasslands. The fraction of green to total biomass versus NDVI showed considerable noise (s.e. = 0·13). These relationships were relatively weak compared with results from other ecosystems. A likely explanation could be the high amount of standing litter, which was unevenly distributed within the vegetation canopy depending on the season and on the timing of cutting events. Our results show there is high uncertainty in the application of the relationships on temperate fen meadows. For reliable estimations, management intensity needs to be taken into account and several direct measurements throughout the year are required for site-specific correction of the relationships, especially under extensive management. Using NDVI instead of LAI could reduce uncertainty in photosynthesis models. [ABSTRACT FROM AUTHOR]
- Published
- 2017
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