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24 August 2009Monitoring phenology variations of different forest types from 2000 to 2008 in contiguous United States using MODIS LAI measurements
The strong relationships between vegetation phenology and global climate change have been
found in recent years, especially with increasing popularity and availability of satellite data.
Accurate estimates of canopy phenology are critical to quantify carbon and water exchange between
forests and the atmosphere and its response to climate change. The objective of this study is to detect
the spatial distribution of vegetation phenology with remote sensing and to quantitatively examine
the linkage between forest phenology and forest type in contiguous United States. In particular, we
focus on phenology variation between different forest types. To achieve this goal, we utilize LAI
measurements from Moderate Resolution Imaging Spectroradiometer (MODIS) from 2000 to 2007
to identify phenological transition dates. The transition dates are then related to MODIS land cover
type product to assess land cover type dependent phonological variation during 8 years. The results
show that both evergreen forests and deciduous forests have an annual cycle of vegetation phenology.
Greenup onset days vary diversely among different forest types. The phenology variation range of
deciduous needle leaf forests is larger than that of deciduous broadleaf forests. Compared to greenup
days, dormancy days have a little difference between different forest types. Grow length of different
land cover varies obviously during 8 years.
Min Li andJohn J. Qu
"Monitoring phenology variations of different forest types from 2000 to 2008 in contiguous United States using MODIS LAI measurements", Proc. SPIE 7454, Remote Sensing and Modeling of Ecosystems for Sustainability VI, 74540L (24 August 2009); https://doi.org/10.1117/12.826434
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Min Li, John J. Qu, "Monitoring phenology variations of different forest types from 2000 to 2008 in contiguous United States using MODIS LAI measurements," Proc. SPIE 7454, Remote Sensing and Modeling of Ecosystems for Sustainability VI, 74540L (24 August 2009); https://doi.org/10.1117/12.826434