Vinsamlegast notið þetta auðkenni þegar þið vitnið til verksins eða tengið í það: http://hdl.handle.net/1946/19864
Gross primary production (GPP) is an important variable to estimate in the global carbon cycle. Estimates of GPP at regional to global scales are critical for understanding ecosystem response to an increased atmospheric CO2 level and for providing objective information for political decisions. The best approach for calculating GPP is through direct measurements of small areas, using either the static-chamber method or eddy covariance technique. Calculating GPP of a whole ecosystem or an entire region is on the other hand problematic. However, scaling up GPP, estimated from direct ground measurements, has increasingly played a role in ecosystem characterization (Lischke et al. 2007). Given that vegetation productivity is directly related to the amount of solar radiation within the plant canopy (Knipling 1970), the simplest method for predicting GPP would be a mathematical function derived from a direct correlation between measured GPP and photosynthetically active radiation (PAR). Many approaches to estimate GPP have been developed based on the work of Monteith (1972), where he suggested that GPP can be expressed as a product of fraction of absorbed photosynthetically active radiation (fAPAR), incident photosynthetically active radiation (PARin) and light use efficiency (LUE), which is the efficiency of the absorbed PAR converted into biomass. Yet an estimate of solar radiation, such as PAR, is not a sufficient indicator of photosynthesis at high northern or southern latitudes because fluctuations in vegetation green mass and solar radiation are not synchronous in time. Several studies have suggested a new remote technique to relate GPP to a product of chlorophyllrelated vegetation indices (VI) and incoming photosynthetic radiation, GPP ∞ VI x PARin, based on Monteith’s logic (Wu et al. 2009, Gitelson et al. 2006, Peng et al. 2013). Numerous vegetation indices are known to indicate the chlorophyll content of vegetation, such as the Red Edge Chlorophyll Index (CIred edge), MERIS terrestrial chlorophyll index (MTCI) (Wu et al. 2009), and the most widely used Normalized Difference Vegetation Index (NDVI) developed by Rouse et al. (1974). Gitelson et al. (2006) successfully estimated GPP with chlorophyll indices, such as NDVI, and indicated GPP as a product of total crop chlorophyll content and PAR. Wu et al. (2009) also verified the utility of chlorophyll content related vegetation indices in the estimation of GPP. The wide acceptance of NDVI, as a proxy for chlorophyll content (e.g. Gutman and Ignatov 1998), and its applicability at both ground and remote levels, make it an attractable option for use in estimating ecosystem productivity. In this study we set out to explore the feasibility of using NDVI alone or NDVI-adjusted PAR for predicting gross photosynthesis of temperate grassland in Iceland through regular ground level measurements of GPP, PAR and NDVI.
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