FY2010 - Oak Ridge National Laboratory

Director’s R&D Fund—
Emerging Science and Technology for Sustainable Bioenergy
Results and Accomplishments
We developed a basic biomass monitoring framework on MODIS 16 day time series data. We automated
several tasks to facilitate continuous monitoring of biomass. We developed a novel change detection
technique based on Gaussian Process (GP) learning. We did thorough experimentation and compared
GP-based change detection against three major techniques. Our experimental results showed that
GP-based technique is not only more accurate than others but is also capable of detection crop changing
patterns, which other techniques failed to detect. However, the GP-based change detection technique is
both computationally expensive (O(n 3 )) and memory bound (O(n 2 )). To address computational issues, we
developed not only efficient techniques (O(n 2 )) but also parallelized using shared memory (threads),
distributed memory (MPI), and hybrid (MPI+threads) programming models. These results were presented
at recent NASA conference on intelligent data understanding, and this work was rated as one of the top
papers and was invited for publication in a leading journal. We have developed a data mining–based time
series segmentation method to derive phenology indices from NDVI data and compare it with the
phenology indices derived from the AmeriFlux data. Experimental results showed a significant
correlation (as high as 0.60) between the indices derived from these two different data sources. This study
demonstrated that that data-driven methods could be effectively employed to provide realistic estimates of
vegetation phenology indices using remote sensing data. These results were widely disseminated through
publications in leading conferences, journals, and presentations.
Information Shared
Chandola, V., and R. Vatsavai. 2010. “Scalable Hyper-parameter Estimation for Gaussian Process Based
Time Series Analysis.” 2nd ACM KDD Workshop on Large-Scale Data Mining.
Chandola, V., and R. Vatsavai. 2010. “Multi-temporal Remote Sensing Image Classification—A Multiview
Approach.” NASA Conference on Intelligent Data Understanding (CIDU).
Chandola, V., and R. Vatsavai. 2010. “Scalable Time Series Change Detection For Biomass Monitoring
Using Gaussian.” NASA Conference on Intelligent Data Understanding (CIDU) (chosen as one of the
best papers and invited to a journal).
Chandola, V., and R. Vatsavai. 2010. “An In Depth Scalability Analysis of a Gaussian Process Training
Algorithm.” SC Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems
(ScalA).
Chandola, V., D. Hui, L. Gu, R. R. Vatsavai, and B. Bhaduri, 2010. “Using Time Series Segmentation for
Deriving Vegetation Phenology Indices from MODIS NDVI Data.” 2nd IEEE ICDM Workshop on
Knowledge Discovery from Climate Data: Prediction, Extremes, and Impacts.
Tetrault, Robert, Brad Doorn, Alison Goss Eng, Alex Philp, Budhendra L. Bhaduri, and Ranga Raju
Vatsavai. 2010. “Emerging geospatial science and technology for sustainable bioenergy.”1st
International Conference and Exhibition on Computing for Geospatial Research & Application
(COM.Geo), ACM Press, Washington, DC (panel).
Vatsavai, R. 2010. “Large Scale Remote Sensing Data Mining: Recent Progress in Biomass Monitoring
and Change Detection.” Invited talk at NASA/ESIP federation meeting.
Vatsavai, R. 2010. Invited panel presentation at the 1st International Conference and Exhibition on
Computing for Geospatial Research & Application (COM.Geo).
Vatsavai, R., and B. Bhaduri. 2010. “A Hybrid Classification Scheme for Mining Multisource Geospatial
Data.” GeoInformatica: An International Journal on Advances of Computer Science for Geographic
Information Systems, Springer.
Vatsavai, R., and V. Chandola. 2010. “High-performance Spatiotemporal Data Mining.” Invited poster at
Fall Creek Fall meeting.
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