1. Monitoring Biomass Dynamics at Scale: Emerging Trends and Recent Successes Presented at the CIMMYT Workshop on Remote Sensing for AgricultureBudhendra Bhaduri Corporate Research Fellow R. Vatsavai, A. Cheriyadat, E. BrightDecember 15, 2013 Mexico City, Mexico

2. Acknowledgement People who do the real work Eddie Bright Raju Vatsavai Anil Cheriyadat Amy Rose Marie Urban Steve Fernandez Mark Tuttle Devin White and many othersPeople who make it possible Our sponsors Managed by UT-Battelle for the Department of Energy 3. Managed by UT-Battelle for the Department of Energy 4. Spatial refinement of LandScan GlobalManaged by UT-Battelle for the Department of Energy 5. Developed Land Cover ExamplesManaged by UT-Battelle for the Department of Energy 6. Addis Ababa, Ethiopia2 Xeon Quad core 2.4GHz CPUs + 4 Tesla GPUs + 48GBImage analyzed (0.6m) 40,000x40,000 pixels (800 sq. km) RGB bandsOverall accuracy 93% Settlement class 89% Non-settlement class 94%Total processing timeManaged by UT-Battelle for the Department of Energy 27 seconds 7. Scalable probabilistic approach Bitemporal change Point based at individual pixel (or small neighborhood) Mostly univariate Multivariate techniques produce multi-band change maps Mostly the output is continuous (requires thresholding)New probabilistic approach Model that data as probability distribution Estimate the overlap between two grids (distributions) Computationally efficient and scalable Managed by UT-Battelle for the Department of Energyt1Highly overlapping (no-change) to No overlap (change)t2 8. Kacha Garhi Camp, Pakistan Established 1980 for Afghan Refugees QuickBird (2004 and 2009, 4B, 2.4m)Managed by UT-Battelle for the Department of Energy 9. Comparison of PerformanceDifferenceProbabilistic Managed by UT-Battelle for the Department of EnergyRatio 10. SAR Change Detection SAR Imagery during Ike noise, spatial resolution (1.56m vs. 12.5m)8/31/08 1.56m9/13/08 12.5mFlooded regionsManaged by UT-Battelle for the Department of Energy 11. New Probabilistic Change Detection Predicted changes have good correlation with ground-truth Detected changes Flooded regionsManaged by UT-Battelle for the Department of EnergyNGA Flood Overlaid Shows Good Correlation 12. Managed by UT-Battelle for the Department of Energy 13. Managed by UT-Battelle for the Department of Energy14 14. Online Detection of Anomaly, Change and Change Point from Space-Time DataPotere, D., Feierabend, N., Bright, E., Strahler, A. Walmart from Space: A New Source for Land Cover Change Validation Photogrametric Engineering and Remote Sensing. Vol 74. July 2008.Managed by UT-Battelle for the Department of Energy 15. June 20,2007 July 19,2007Fargo,ND Fargo,NDSunflower SunflowerManaged by UT-Battelle for the Department of EnergyCorn CornSoybeans Soybeans 16. Geocomputation based strategy Design and develop a robust and scalable spatiotemporal data mining framework utilizing high resolution spatial and temporal data streams (MODIS and AWiFS) PreprocessingChange detection Reprojection Atmospheric corrections Time series filtering Time series prediction Unsupervised multidimensional geospatial image clusteringChange characterization Classification Phenology-based Crop Type-basedPeakGoogle Earth Length of growing seasonGreenup OnsetManaged by UT-Battelle for the Department of EnergyDormancy OnsetKey features of crop phenologyNASA World WindOther thin clients 17. Gaussian Process Model MODIS NDVI Time Series from Iowa 6 years (2001 2006) 23 observations per yearTrained for first 5 years and monitored last yearAccuracy was 88% on a validation set consisting of 97 labeled time series with 13 No Change true changes Variance Predicted ObservedVarun Chandola, Ranga Raju Vatsavai: Scalable Time Series Change Detection for Biomass Monitoring Using Gaussian Managed by UT-Battelle Process. NASA CIDU 2010: 69-82 (One of the best for the Department of Energy papers, invited to SADM Journal).Change 18. Wide area biomass monitoring in near real time is becoming a reality MODIS Tile (4800x4800 pixels) 23,040,000 time series 10 trillion at Global scale (432 land tiles)FROST: An SGI Altrix ICE 8200 Cluster at ORNL 128 compute nodes each with 16 virtual cores and 24 GB of RAMMulticore (multithreaded) and Distributed (message passing) computing strategyManaged by UT-Battelle for the Department of EnergySerial 41,105 seconds (11.4 hours)Threads (16) 5,872 seconds (1.6 hours)MPI (96 nodes) 604 seconds (10 minutes)MPI + Threads 34 seconds (1536 cores) 19. Managed by UT-Battelle for the Department of Energy 20. 2005200420032002Managed by UT-Battelle for the Department of Energy2001200025100NDVI175Apple Valley, CA Wal-Mart Distribution Center 21. 100175200320022001200020042005200320042005Year2002200125Managed by UT-Battelle for the Department of Energy200075NDVI15022520052004200320022001200025100 175 250Three Wal-Mart NDVI Time Series