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Monitoring Biomass Dynamics at Scale: Emerging Trends and Recent Successes
 

Monitoring Biomass Dynamics at Scale: Emerging Trends and Recent Successes

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Remote sensing –Beyond images ...

Remote sensing –Beyond images
Mexico 14-15 December 2013

The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)

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  • MODIS 1 KM Landsat 30m SMT-16 meters
  • Basic idea – abstract grids as a statistical distribution, then compare two grids (meaning their statistical distributions to detect changes) – as we are comparing distributions (a) – miss-registrations have less impact, (2) we can compare images of different resolutions.
  • Different types of changes are highlighted – (a) growth, (b) removal
  • Pixel based methods like difference and ratio produces highly noisy images and lot of changes are not real changes.
  • Both noise and speckle noise will influence pixel based methods.
  • NGA Flood Contours: Couldn’t find how they are generated, I will check again and update you soon.Highlight – good (spatial) correlation between predicted changes and NGA flood contour map

Monitoring Biomass Dynamics at Scale: Emerging Trends and Recent Successes Monitoring Biomass Dynamics at Scale: Emerging Trends and Recent Successes Presentation Transcript

  • Monitoring Biomass Dynamics at Scale: Emerging Trends and Recent Successes Presented at the CIMMYT Workshop on Remote Sensing for Agriculture Budhendra Bhaduri Corporate Research Fellow R. Vatsavai, A. Cheriyadat, E. Bright December 15, 2013 Mexico City, Mexico
  • 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 others People who make it possible – Our sponsors – … Managed by UT-Battelle for the Department of Energy
  • Managed by UT-Battelle for the Department of Energy
  • Spatial refinement of LandScan Global Managed by UT-Battelle for the Department of Energy
  • “Developed Land Cover” Examples Managed by UT-Battelle for the Department of Energy
  • Addis Ababa, Ethiopia  2 Xeon Quad core 2.4GHz CPUs + 4 Tesla GPUs + 48GB  Image analyzed (0.6m)  40,000x40,000 pixels (800 sq. km)  RGB bands  Overall accuracy 93%  Settlement class 89%  Non-settlement class 94%  Total processing time Managed by UT-Battelle for the Department of Energy  27 seconds
  • 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 Energy t1 Highly overlapping (no-change) to No overlap (change) t2
  • 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
  • Comparison of Performance Difference Probabilistic Managed by UT-Battelle for the Department of Energy Ratio
  • SAR Change Detection SAR Imagery during Ike – noise, spatial resolution (1.56m vs. 12.5m) 8/31/08 1.56m 9/13/08 12.5m Flooded regions Managed by UT-Battelle for the Department of Energy
  • New Probabilistic Change Detection Predicted changes have good correlation with ground-truth Detected changes – Flooded regions Managed by UT-Battelle for the Department of Energy NGA Flood Overlaid – Shows Good Correlation
  • Managed by UT-Battelle for the Department of Energy
  • Managed by UT-Battelle for the Department of Energy 14
  • Online Detection of Anomaly, Change and Change Point from Space-Time Data Potere, 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
  • June 20,2007 July 19,2007 Fargo,ND Fargo,ND Sunflower Sunflower Managed by UT-Battelle for the Department of Energy Corn Corn Soybeans Soybeans
  • 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) Preprocessing Change detection • Reprojection • Atmospheric corrections • Time series filtering • Time series prediction • Unsupervised multidimensional geospatial image clustering Change characterization • Classification • Phenology-based • Crop Type-based Peak Google Earth Length of growing season Greenup Onset Managed by UT-Battelle for the Department of Energy Dormancy Onset Key features of crop phenology NASA World Wind Other thin clients
  • Gaussian Process Model MODIS NDVI Time Series from Iowa – 6 years (2001 – 2006) – 23 observations per year Trained for first 5 years and monitored last year Accuracy was 88% on a validation set consisting of 97 labeled time series with 13 No Change true changes Variance Predicted Observed Varun 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
  • 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 RAM Multicore (multithreaded) and Distributed (message passing) computing strategy Managed by UT-Battelle for the Department of Energy Serial • 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)
  • Managed by UT-Battelle for the Department of Energy
  • 2005 2004 2003 2002 Managed by UT-Battelle for the Department of Energy 2001 2000 25 100 NDVI 175 Apple Valley, CA – Wal-Mart Distribution Center
  • 100 175 2003 2002 2001 2000 2004 2005 2003 2004 2005 Year 2002 2001 25 Managed by UT-Battelle for the Department of Energy 2000 75 NDVI 150 225 2005 2004 2003 2002 2001 2000 25 100 175 250 Three Wal-Mart NDVI Time Series