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Plant Phenology Index (PPI)

Why a new vegetation index?

Vegetation phenology is difficult to observe from satellite, particularly in boreal landscapes. Currently existing vegetation indices are generally insensitivet to the seasonal variation in dense coniferous canopies. Another problem is snow, which interferes with measurements duing spring and autumn (Jönsson et al. 2010).

About the Plant Phenology Index

The PPI is a physically-based new vegetation index for characterizing terrestrial vegetation canopy green leaf area dynamics. PPI is derived from the solution to a radiative transfer equation, is computed from red and near-infrared (NIR) reflectance, and has a nearly linear relationship with canopy green leaf area index (LAI), enabling it to depict canopy foliage density well. This capability is verified with stacked-leaf measurements, canopy reflectance model simulations, and field LAI measurements from international sites. Snow influence on PPI is shown by modeling and satellite observations to be less severe than on the Normalized Difference Vegetation Index (NDVI) or the Enhanced Vegetation Index (EVI), while soil brightness variations in general have moderate influence on PPI. Comparison of satellite-derived PPI to ground observations of plant phenology and gross primary productivity (GPP) shows strong similarity of temporal patterns over several Nordic boreal forest sites. The proposed PPI can thus serve as an efficient tool for estimating plant canopy growth, and will enable improved vegetation monitoring, particularly of evergreen needle-leaf forest phenology at high northern latitudes.

This figure shows that PPI is minimally affected by snow:

Fig. 6 from Jin and Eklundh (2014).The figure shows linearity of PPI with leaf area index (LAI) (left), in comparison with NDVI (center) and EVI (right). Lines are model data and circles are measurements.
Fig. 6 from Jin and Eklundh (2014).The figure shows linearity of PPI with leaf area index (LAI) (left), in comparison with NDVI (center) and EVI (right). Lines are model data and circles are measurements.

This figure shows that PPI is minimally affected by snow:

Fig. 10 from Jin and Eklundh (2014). The figure shows time series of PPI (red) in comparison to the NDSI snow index, and the indices NDVI and EVI. Note the smoothness of PPI at the ends and beginnings of the snow seasons. The left figure shows data from t
Fig. 10 from Jin and Eklundh (2014). The figure shows time series of PPI (red) in comparison to the NDSI snow index, and the indices NDVI and EVI. Note the smoothness of PPI at the ends and beginnings of the snow seasons. The left figure shows data from t

This figure shows that PPI is strongly related to coniferous GPP, estimated with carbon flux data:

Fig. 11b from Jin and Eklundh (2014). The figure shows time series of PPI (red) in comparison with eddy-covariance measured GPP (black), and the indices NDVI and EVI at the Hyytiälä pine forest.
Fig. 11b from Jin and Eklundh (2014). The figure shows time series of PPI (red) in comparison with eddy-covariance measured GPP (black), and the indices NDVI and EVI at the Hyytiälä pine forest.

 

References

Contact information

Lars Eklundh

E-mail: lars.eklundh [at] nateko.lu.se
Phone: + 46 46 222 96 55

Department of Physical Geography and Ecosystem Science
Lund University
Sölvegatan 12
S-223 62 Lund
Sweden