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RSS FeedsRemote Sensing, Vol. 11, Pages 2745: Assessment of Spatio-Temporal Patterns of Black Spruce Bud Phenology across Quebec Based on MODIS-NDVI Time Series and Field Observations (Remote Sensing)

 
 

22 november 2019 09:01:28

 
Remote Sensing, Vol. 11, Pages 2745: Assessment of Spatio-Temporal Patterns of Black Spruce Bud Phenology across Quebec Based on MODIS-NDVI Time Series and Field Observations (Remote Sensing)
 




Satellite remote sensing is a widely accessible tool to investigate the spatiotemporal variations in the bud phenology of evergreen species, which show limited seasonal changes in canopy greenness. However, there is a need for precise and compatible data to compare remote sensing time series with field observations. In this study, fortnightly MODIS-NDVI was fitted using double-logistic functions and calibrated using ordinal logit models with the sequential phases of bud phenology collected during 2015, 2017 and 2018 in a black spruce stand. Bud break and bud set were spatialized for the period 2009–2018 across 5000 stands in Quebec, Canada. The first phase of bud break and the last phase of bud set were observed in the field in mid-May and at the beginning of September, when NDVI was 80.5% and 92.2% of its maximum amplitude, respectively. The NDVI rate of change was estimated at 0.07 in spring and 0.04 in autumn. When spatialized on the black spruce stands, bud break was detected earlier in the southwestern regions (April–May), and later in the northeastern regions (mid to end of June). No clear trend was observed for bud set, with different patterns being detected among the years. Overall, the process bud break and bud set lasted 51 and 87 days, respectively. Our results demonstrate the potential of satellite remote sensing for providing reliable timings of bud phenological events using calibrated NDVI time series on wide regions that are remote or with limited access.


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42 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 11, Pages 2746: A Hybrid Bio-Optical Transformation for Satellite Bathymetry Modeling Using Sentinel-2 Imagery (Remote Sensing)
Remote Sensing, Vol. 11, Pages 2744: A Review of Regional and Global Gridded Forest Biomass Datasets (Remote Sensing)
 
 
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