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Phenological event detection from multitemporal image data

Published: 28 June 2009 Publication History

Abstract

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.

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Cited By

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  • (2017)Hierarchical change detection framework for biomass monitoring2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)10.1109/IGARSS.2017.8127030(620-623)Online publication date: Jul-2017
  • (2016)Scalable nearest neighbor based hierarchical change detection framework for crop monitoring2016 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2016.7840735(1309-1314)Online publication date: Dec-2016
  • (2011)iGlobeProceedings of the 2nd International Conference on Computing for Geospatial Research & Applications10.1145/1999320.1999341(1-6)Online publication date: 23-May-2011
  • Show More Cited By

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Published In

cover image ACM Conferences
SensorKDD '09: Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data
June 2009
150 pages
ISBN:9781605586687
DOI:10.1145/1601966
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 28 June 2009

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Author Tags

  1. EM
  2. GMM
  3. MODIS
  4. NDVI
  5. clustering
  6. remote sensing

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Cited By

View all
  • (2017)Hierarchical change detection framework for biomass monitoring2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)10.1109/IGARSS.2017.8127030(620-623)Online publication date: Jul-2017
  • (2016)Scalable nearest neighbor based hierarchical change detection framework for crop monitoring2016 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2016.7840735(1309-1314)Online publication date: Dec-2016
  • (2011)iGlobeProceedings of the 2nd International Conference on Computing for Geospatial Research & Applications10.1145/1999320.1999341(1-6)Online publication date: 23-May-2011
  • (2010)Using Time Series Segmentation for Deriving Vegetation Phenology Indices from MODIS NDVI DataProceedings of the 2010 IEEE International Conference on Data Mining Workshops10.1109/ICDMW.2010.143(202-208)Online publication date: 13-Dec-2010
  • (2009)BioMonProceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/1653771.1653864(536-537)Online publication date: 4-Nov-2009

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