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| Titel |
Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery |
| VerfasserIn |
S. T. Klosterman, K. Hufkens, J. M. Gray, E. Melaas, O. Sonnentag, I. Lavine, L. Mitchell, R. Norman, M. A. Friedl, A. D. Richardson |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1726-4170
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| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 11, no. 16 ; Nr. 11, no. 16 (2014-08-19), S.4305-4320 |
| Datensatznummer |
250117549
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| Publikation (Nr.) |
 copernicus.org/bg-11-4305-2014.pdf |
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| Zusammenfassung |
| 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. |
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