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

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Ulsig , L , Nichol , C J , Huemmrich , K F , Landis , D R , Middleton , E M , Lyapustin , A I , Mammarella , I , Levula , J & Porcar-Castell , A 2017 , ' Detecting Inter-Annual Variations in the Phenology of Evergreen Conifers Using Long-Term MODIS Vegetation Index Time Series ' , Remote Sensing , vol. 9 , no. 1 , 49 . https://doi.org/10.3390/rs9010049

Title: Detecting Inter-Annual Variations in the Phenology of Evergreen Conifers Using Long-Term MODIS Vegetation Index Time Series
Author: Ulsig, Laura; Nichol, Caroline J.; Huemmrich, Karl F.; Landis, David R.; Middleton, Elizabeth M.; Lyapustin, Alexei I.; Mammarella, Ivan; Levula, Janne; Porcar-Castell, Albert
Contributor: University of Helsinki, Department of Physics
University of Helsinki, Department of Physics
University of Helsinki, Department of Forest Sciences
Date: 2017-01
Language: eng
Number of pages: 21
Belongs to series: Remote Sensing
ISSN: 2072-4292
URI: http://hdl.handle.net/10138/178864
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 (R-2 = 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 (R-2 > 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.
Subject: phenology
MODIS
Photochemical Reflectance Index (PRI)
Normalized Difference Vegetation Index (NDVI)
ecosystem productivity
time series analysis
LIGHT-USE EFFICIENCY
PHOTOCHEMICAL REFLECTANCE INDEX
SCOTS PINE FOREST
RADIATION USE EFFICIENCY
DECIDUOUS FOREST
CARBON-DIOXIDE
PHOTOSYNTHETIC EFFICIENCY
MONITORING VEGETATION
MEDITERRANEAN FOREST
OPTICAL INDICATOR
114 Physical sciences
4112 Forestry
1172 Environmental sciences
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