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Remote Sens. 2017, 9(1), 49; doi:10.3390/rs9010049

Detecting Inter-Annual Variations in the Phenology of Evergreen Conifers Using Long-Term MODIS Vegetation Index Time Series

1
School of GeoSciences, Crew Building, University of Edinburgh, Alexander Crum Brown Road, Edinburgh EH9 3FF, UK
2
Joint Center for Earth Systems Technology (JCET), University of Maryland Baltimore County, Catonsville, MD 20771, USA
3
Global Science & Technology, Greenbelt, MD 20770, USA
4
NASA Goddard Space Flight Center, Earth Sciences, Greenbelt, MD 20771, USA
5
Department of Physics, University of Helsinki, P.O. Box 48, Helsinki 00014, Finland
6
SMEARII, Hyytiälä Forestry Field Station, Deptartment of Physics, University of Helsinki, Hyytiäläntie 124, Korkeakoski FI 35500, Finland
7
Department of Forest Sciences, Viikki Plant Science Center (ViPS), University of Helsinki, P.O. Box 27, Helsinki 00014, Finland
*
Authors to whom correspondence should be addressed.
Academic Editors: Jose Moreno and Prasad S. Thenkabail
Received: 21 November 2016 / Revised: 21 December 2016 / Accepted: 28 December 2016 / Published: 7 January 2017
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Abstract

Long-term observations of vegetation phenology can be used to monitor the response of terrestrial ecosystems to climate change. Satellite remote sensing provides the most efficient means to observe phenological events through time series analysis of vegetation indices such as the Normalized Difference Vegetation Index (NDVI). This study investigates the potential of a Photochemical Reflectance Index (PRI), which has been linked to vegetation light use efficiency, to improve the accuracy of MODIS-based estimates of phenology in an evergreen conifer forest. Timings of the start and end of the growing season (SGS and EGS) were derived from a 13-year-long time series of PRI and NDVI based on a MAIAC (multi-angle implementation of atmospheric correction) processed MODIS dataset and standard MODIS NDVI product data. The derived dates were validated with phenology estimates from ground-based flux tower measurements of ecosystem productivity. Significant correlations were found between the MAIAC time series and ground-estimated SGS (R2 = 0.36–0.8), which is remarkable since previous studies have found it difficult to observe inter-annual phenological variations in evergreen vegetation from satellite data. The considerably noisier NDVI product could not accurately predict SGS, and EGS could not be derived successfully from any of the time series. While the strongest relationship overall was found between SGS derived from the ground data and PRI, MAIAC NDVI exhibited high correlations with SGS more consistently (R2 > 0.6 in all cases). The results suggest that PRI can serve as an effective indicator of spring seasonal transitions, however, additional work is necessary to confirm the relationships observed and to further explore the usefulness of MODIS PRI for detecting phenology. View Full-Text
Keywords: phenology; MODIS; Photochemical Reflectance Index (PRI); Normalized Difference Vegetation Index (NDVI); ecosystem productivity; time series analysis phenology; MODIS; Photochemical Reflectance Index (PRI); Normalized Difference Vegetation Index (NDVI); ecosystem productivity; time series analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Ulsig, L.; Nichol, C.J.; Huemmrich, K.F.; Landis, D.R.; Middleton, E.M.; Lyapustin, A.I.; Mammarella, I.; Levula, J.; Porcar-Castell, A. Detecting Inter-Annual Variations in the Phenology of Evergreen Conifers Using Long-Term MODIS Vegetation Index Time Series. Remote Sens. 2017, 9, 49.

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