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Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery

S. Klosterman,K. Hufkens,J. Gray,E. Melaas,O. Sonnentag,I. Lavine,L. Mitchell,R. Norman,M. Friedl,A. Richardson

Published 2014 in Biogeosciences

ABSTRACT

Plant phenology regulates ecosystem services at local and global scales and is a sensitive indicator of global change. Estimates of phenophase transition dates, such as the start of spring or end of fall, can be derived from sensor- based time series, but must be interpreted in terms of bio- logically relevant events. We use the PhenoCam archive of digital repeat photography to implement a consistent proto- col for visual assessment of canopy phenology at 13 temper- ate deciduous forest sites throughout eastern North America, and to perform digital image analysis for time-series-based estimation of phenophase transition dates. We then compare these results to remote sensing metrics of phenophase tran- sition dates derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Reso- lution Radiometer (AVHRR) sensors. We present a new type of curve fit that uses a generalized sigmoid function to es- timate phenology dates, and we quantify the statistical un- certainty of phenophase transition dates estimated using this method. Results show that the generalized sigmoid provides estimates of dates with less statistical uncertainty than other curve-fitting methods. Additionally, we find that dates de- rived from analysis of high-frequency PhenoCam imagery have smaller uncertainties than satellite remote sensing met- rics of phenology, and that dates derived from the remotely sensed enhanced vegetation index (EVI) have smaller uncer- tainty than those derived from the normalized difference veg- etation index (NDVI). Near-surface time-series estimates for the start of spring are found to closely match estimates de- rived from visual assessment of leaf-out, as well as satel- lite remote-sensing-derived estimates of the start of spring. However late spring and fall phenology metrics exhibit larger differences between near-surface and remote scales. Differ- ences in late spring phenology between near-surface and re- mote scales are found to correlate with a landscape metric of deciduous forest cover. These results quantify the effect of landscape heterogeneity when aggregating to the coarser spatial scales of remote sensing, and demonstrate the impor- tance of accurate curve fitting and vegetation index selection when analyzing and interpreting phenology time series.

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