Remote Sensing and Image Interpretation
Remote Sensing and Image Interpretation
BioMon: a Google Earth based continuous biomass monitoring system
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
iGlobe: an interactive visualization and analysis framework for geospatial data
Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications
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Monitoring biomass over large geographic regions for seasonal changes in vegetation and crop phenology is important for many applications. In this paper we a present a novel clustering based change detection method using MODIS NDVI time series data. We used well known EM technique to find GMM parameters and Bayesian Information Criteria (BIC) for determining the number of clusters. KL Divergence measure is then used to establish the cluster correspondence across two years (2001 and 2006) to determine changes between these two years. The changes identified were further analyzed for understanding phenological events. This preliminary study shows interesting relationships between key phenological events such as onset, length, end of growing seasons.