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)

1. 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

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 others
People 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 Global
Managed by UT-Battelle
for the Department of Energy

5. “Developed Land Cover” Examples
Managed by UT-Battelle
for the Department of Energy

6. 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

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 Energy
t1
Highly 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 Performance
Difference
Probabilistic
Managed by UT-Battelle
for the Department of Energy
Ratio

10. 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

11. 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

12. Managed by UT-Battelle
for the Department of Energy

13. Managed by UT-Battelle
for the Department of Energy
14

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

15. 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

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)
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

17. 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

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 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)

19. Managed by UT-Battelle
for the Department of Energy

20. 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

21. 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