Biogeosciences, 11, 4305-4320, 2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
19 Aug 2014
Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery
S. T. Klosterman1, K. Hufkens1,2, J. M. Gray3, E. Melaas3, O. Sonnentag1,4, I. Lavine5, L. Mitchell6, R. Norman7, M. A. Friedl3, and A. D. Richardson1 1Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
2Isotope Bioscience Laboratory – ISOFYS, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
3Department of Earth and Environment, Boston University, Boston, MA 02215, USA
4Département de géographie, Université de Montréal, Montréal, QC, Canada
5Lafayette College, Easton, PA 18042, USA
6Lincoln University, Jefferson City, MO 65101, USA
7The University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
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 biologically relevant events. We use the PhenoCam archive of digital repeat photography to implement a consistent protocol for visual assessment of canopy phenology at 13 temperate 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 transition dates derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) sensors. We present a new type of curve fit that uses a generalized sigmoid function to estimate phenology dates, and we quantify the statistical uncertainty 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 derived from analysis of high-frequency PhenoCam imagery have smaller uncertainties than satellite remote sensing metrics of phenology, and that dates derived from the remotely sensed enhanced vegetation index (EVI) have smaller uncertainty than those derived from the normalized difference vegetation index (NDVI). Near-surface time-series estimates for the start of spring are found to closely match estimates derived from visual assessment of leaf-out, as well as satellite 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. Differences in late spring phenology between near-surface and remote 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 importance of accurate curve fitting and vegetation index selection when analyzing and interpreting phenology time series.

Citation: Klosterman, S. T., Hufkens, K., Gray, J. M., Melaas, E., Sonnentag, O., Lavine, I., Mitchell, L., Norman, R., Friedl, M. A., and Richardson, A. D.: Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery, Biogeosciences, 11, 4305-4320,, 2014.
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