Monitoring fall foliage coloration dynamics using time-series satellite data

Fall foliage coloration is a phenomenon that occurs in many deciduous trees and shrubs worldwide. Measuring the phenology of fall foliage development is of great interest for climate change, the carbon cycle, ecology, and the tourist industry; but little effort has been devoted to monitoring the regional fall foliage status using remotely-sensed data. This study developed an innovative approach to monitoring fall foliage status by means of temporally-normalized brownness derived from MODIS (Moderate Resolution Imaging Spectroradiometer) data. Specifically, the time series of the MODIS Normalized Difference Vegetation Index (NDVI) was smoothed and functionalized using a sigmoidal model to depict the continuous dynamics of vegetation growth. The modeled temporal NDVI trajectory during the senescent phase was further combined with the mixture modeling to deduce the temporally-normalized brownness index which was independent of the surface background, vegetation abundance, and species composition. This brownness index was quantitatively linked with the fraction of colored and fallen leaves in order to model the fall foliage coloration status. This algorithm was tested by monitoring the fall foliage coloration phase using MODIS data in northeastern North America from 2001 to 2004. The MODIS-derived timing of foliage coloration phases was compared with in-situ measurements, which showed an overall absolute mean difference of less than 5 days for all foliage coloration phases and about 3 days for near peak coloration and peak coloration. This suggested that the fall foliage coloration phase retrieved from the temporally-normalized brownness index was qualitatively realistic and repeatable.

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