Marshall University, USA
Posters & Accepted Abstracts: J Aeronaut Aerospace Eng
Vegetation fraction (VF) greatly influences crucial processes like photosynthesis and evapotranspiration. However, it is among the least studied of the biophysical parameters, yet it is needed in several applications, notably estimating soil erosion, analysis of carbon storage estimates and development of atmospheric models, which have policy implications regarding climate research. Existing methods for deriving VF, e.g., using satellite imagery and spectral mixture analysis have limitations. Consequently, an alternative methodology for quantitatively estimating this parameter for different vegetation types, especially over the growing season, is needed. This study describes a simple technique for estimating VF that combines the power of spectral vegetation indices (VIs) and the simplicity of digital photography. It employs upward-looking hemispherical photographs of twelve deciduous tree canopies and eight VIs calculated from in situ single-leaf level reflectance data to calculate VF. When plotted over time, the VIs showed very similar seasonal patterns as the photo-derived VF of each tree, and the VF values estimated by the VIs were temporally similar for each tree over the growing season. NDVI and SAVI had the highest estimation accuracy. SR showed temporal similarity with the VF of low-foliage trees, and had considerably higher accuracy of prediction. Seasonality and amount of foliage cover significantly affected estimation accuracies. This methodology is simple, inexpensive and could be adapted to other settings. It can complement existing methods and can potentially be used to study other critical parameters such as LAI and biomass in other types of vegetation, but further research is needed to validate it.