Abstract
Leaf area index (LAI) is a key ecological indicator for describing the structure of canopies and for modelling energy exchange between atmosphere and biosphere. While LAI of the forest overstory can be accurately assessed over large spatial scales via remote sensing, LAI of the forest understory (LAIu) is still largely ignored in ecological studies and ecosystem modelling due to the fact that it is often too complex to be destructively sampled or approximated by other site parameters. Additionally, so far only few attempts have been made to retrieve understory LAI via remote sensing, because dense canopies with high LAI are often hindering retrieval algorithms to produce meaningful estimates for understory LAI. Consequently, the forest understory still constitutes a poorly investigated research realm impeding ecological studies to properly account for its contribution to the energy absorption capacity of forest stands. This study aims to compare three conceptually different indirect retrieval methodologies for LAIu over a diverse panel of forest understory types distributed across Europe. For this we carried out near-to-surface measurements of understory reflectance spectra as well as digital surface photography over the extended network of Integrated Carbon Observation System (ICOS) forest ecosystem sites. LAIu was assessed by exploiting the empirical relationship between vegetation cover and light absorption (Beer-Lambert- Bouguer law) as well as by utilizing proposed relationships with two prominent vegetation indices: normalized difference vegetation index (NDVI) and simple ratio (SR). Retrievals from the three methods were significantly correlated with each other (r = 0.63–0.99, RMSE = 0.53–0.72), but exhibited also significant bias depending on the LAI scale. The NDVI based retrieval approach most likely overestimates LAI at productive sites when LAIu > 2, while the simple ratio algorithm overestimates LAIu at sites with sparse understory vegetation and presence of litter or bare soil. The purely empirical method based on the Beer-Lambert law of light absorption seems to offer a good compromise, since it provides reasonable LAIu values at both low and higher LAI ranges. Surprisingly, LAIu variation among sites seems to be largely decoupled from differences in climate and light permeability of the overstory, but significantly increased with vegetation diversity (expressed as species richness) and hence proposes new applications of LAIu in ecological modelling.
Highlights
Leaf area index (LAI), defined as one-half the total green leaf area per unit of horizontal ground surface area (Chen and Black, 1992; Fernandes et al, 2014), is an important ecological indicator for analyzing canopy structure and constitutes a key metric for measuring interactions be tween the atmosphere and terrestrial ecosystems (Chen and Black, 1991; Thimonier et al, 2010)
This study aims to compare three conceptually different in direct retrieval methodologies for LAI of the forest understory (LAIu) over a diverse panel of forest understory types distributed across Europe
The majority of sites had low to moderate LAIu values in the range between 0 and 1 (18 sites), 9 sites had LAIu values between 1 and 2, and 2 sites showed relatively higher LAIu values >2 (Table 2)
Summary
Leaf area index (LAI), defined as one-half the total green leaf area per unit of horizontal ground surface area (Chen and Black, 1992; Fernandes et al, 2014), is an important ecological indicator for analyzing canopy structure and constitutes a key metric for measuring interactions be tween the atmosphere and terrestrial ecosystems (Chen and Black, 1991; Thimonier et al, 2010). Tropical woodlands and boreal forest ecosystems are well known examples for this, because the understory can even be more productive than the overstory (Clark et al, 2001; Gower et al, 2001; Black et al, 1996). The overstory and understory vegetation in forest ecosystems needs to be treated differently in carbon cycle modeling, because carbon fixed through net primary productivity has different residence times for different components (Rentch et al, 2003). Ryu et al, 2014) and differences in the greening cycle of the under- and overstory species have been reported to complicate the use of simple vegetation index techniques to determine the start of growing season from Earth Observation data (Doktor et al, 2009). Satellite-derived estimates of total LAI can be strongly confounded when understory LAI information is absent (Ahl et al, 2006; Garrigues et al, 2008; Ryu et al, 2014)
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