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Detecting leaf phenology of seasonally moist tropical forests with multi-temporal MODIS images

Xiangming Xiao, University of New Hampshire, (Presenting)
Stephen Hagen, University of New Hampshire,
Qingyuan Zhang, NASA GSFC,
Michael Keller, University of New Hampshire,
Stephen Boles, University of New Hampshire,
Berrien Moore III, University of New Hampshire,

Leaf phenology of seasonally moist tropical evergreen forests affects carbon and water fluxes. Because mature tropical forests are rich in species (hundreds of species), tall in tree heights (tens of meters) and high in leaf area index (LAI, 4 m2/m2 and more), phenological studies in the field are extremely challenging. Consequently, there are a limited number of field-based phenological studies for evergreen forests in tropical region. Satellite remote sensing at moderate spatial resolution provides frequent observations that may reveal seasonal changes of vegetation. Here we present image analyses of evergreen forests in the tropical region at site- and regional-scales. At the site-scale for a seasonally moist tropical forest site in the Amazon basin, time series data of Enhanced Vegetation Index (EVI) from the VEGETATION and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors showed an unexpected seasonal pattern, with higher EVI in the late dry season than in the wet season. The results from the regional-scale analysis that used time-series data of EVI from MODIS in 2002 show a large dynamic range and spatial variations of annual maximum EVI for evergreen forest canopies in the region. In seasonally moist tropical forests, maximum EVI in 2002 typically occurs during the late dry season to early wet season. This suggests that leaf phenology in seasonally moist tropical forests is not determined by the seasonality of precipitation. Instead, leaf phonological process may be driven by availability of solar radiation and/or avoidance of herbivory.

Science Theme:  CD (Carbon Dynamics)

Presentation Type:  Poster

Abstract ID: 48

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