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The West Africa-America Chamber of Commerce & Industries presents: Big Data & Machine Learning for Societal Benefit

This document discusses using machine learning and combining multiple datasets including historical track issue data, meteorological data, and satellite imagery to monitor and predict drought, flooding, and diseases like malaria. It provides examples of how this approach has been used to track water usage and irrigation needs in agriculture more efficiently, identify mosquito breeding sites to help prevent West Nile virus and malaria, and monitor air pollution levels. Integrating these different data sources allows for more accurate monitoring, prediction, and management of environmental and public health issues.

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Combine historical track issue data and historical high resolution meteorological data with machine learning. Combine Multiple Datasets
Water The Motive Do well with the Details by embracing the Big Picture Prof. David Lary +1 (972) 489-2059 http://davidlary.info david.lary@utdallas.edu Center for Space Science
Drought Has Increased Globally Since 1900
Worst Drought in 1,000 Years Predicted for American West A paddle wheeler and a small motorboat sail on Lake Mead, North America's largest man-made reservoir. The water is at its lowest level since the Hoover Dam was built in the 1930s. The white "bathtub ring" of mineral deposits on the rocks marks past water levels. PUBLISHED FEBRUARY 12, 2015
Western U.S. Drought Prompts Disaster Declarations In 11 States By MICHELLE RINDELS 01/16/14 07:51 PM ET EST LAS VEGAS (AP) — Federal officials have designated portions of 11 drought- ridden western and central states as primary natural disaster areas, highlighting the financial strain the lack of rain is likely to bring to farmers in those regions. The announcement by the U.S. Department of Agriculture on Wednesday included counties in Colorado, New Mexico, Nevada, Kansas, Texas, Utah, Arkansas, Hawaii, Idaho, Oklahoma and California. Rancher Ralph Miller, 79, checks on one of many “stock tanks” of water that are receding due to the severe drought. “I’d say it’s just about as bad as it can get.” Barnhart, Texas
“Water is the new oil” Jim Rogers, chief executive of Duke Energy ... and many others Water crisis in California, Texas threatens US food security Western water scarcity issues becoming more severe Western Farm Press, Jun. 5, 2012 University of Texas at Austin California and Texas produced agricultural products worth $56 billion in 2007, accounting for much of the nation's food production. They also account for half of all groundwater depletion in the U.S., mainly as a result of irrigating crops. The nation’s food supply may be vulnerable to rapid groundwater depletion from irrigated agriculture, according to a new study by researchers at The University of Texas at Austin and elsewhere. http://westernfarmpress.com/irrigation/water-crisis-california-texas-threatens-us-food-security
Since 1980 the population of Texas has more than doubled, but the reservoir capacity has remained almost unchanged. During 2011the reservoir levels were the lowest during Sep-Dec that they have been since 1990. In 2015 we are starting out with lower levels than 2013.
Smarter irrigation control is invaluable! If we can use existing infrastructure it is even better! .... from farm, to corporate campus, to golf course, to your back yard.
When great societal need meets appropriate scalable solution there is much societal and economic benefit to be gained
How? California Children Example
DATE: 12-Feb-2010 DOC NO: 0115056 ISSUE: 02 DMC DATA PRODUCT MANUAL STATUS: FINAL Figure 4: Detector and channel layout of the SLIM-6-22 imager Imager Bank 0 Channel 6 Green Channel 5 Red Channel 4 NIR Imager Bank 1 Channel 1 NIR Channel 2 Red Channel 3 Green Pixel 1 Pixel 14436 Pixel 14436 Pixel 1
uneven irrigation blown valves lead to flooding Sports fields agricultural test plots 22 m resolution
On average, systems have water losses of about 17 percent. 49
Can you tell which grass has had more water? Zooming in
Neighborhood Trees ‘green’ pond golf course gated community dry grass Trees Trees Sports Field
20 lb Airborne hyperspectral imaging system 385 channels between 400-1,700 nm Hyperspectral data cube
J S Famiglietti, and M Rodell Science 2013;340:1300-1301 an accuracy of 1.5 cm equivalent water height. Because GRACE measures changes in total water storage, it integrates the impacts of natural climate fluctuations, global change, and human water use, including groundwater extraction, which in many parts of the world is unmeasured and unmanaged. GRACE- derived rates of groundwater losses in the world’s major aquifer systems (4–6) under- score the critical need to improve monitor- ing and regulation of groundwater systems before they run dry. Regional flooding and drought are driven by the surplus or deficit of water in a river basin or an aquifer, yet few hydrologic observing networks yield sufficient data for comprehensive monitoring of changes in the total amount of water stored in a region. GRACE observations have helped to fill this gap. They have been used to character- ize regional flood potential (8) and to assess water storage deficits in the U.S. Drought Monitor (9) and are included in annual State of the Climate reports (10). As an integrated measure of all surface and groundwater stor- age changes, GRACE data implicitly contain a record of seasonal to interannual water stor- key tools for predicting future water avail- ability, difficult to validate. Low-resolution GRACE data, when combined with higher- resolution model simulations, provide an independent constraint on simulated water balances, while also adding spatial detail to GRACE’s low-resolution perspective (11). They are widely used to evaluate land surface models used by weather and climate forecast- ing centers around the world (12). Evapotranspiration is a key factor in interbasin water allocations, yet because it disperses into the atmosphere in the vapor phase, it confounds standard measurement techniques. The ability of GRACE to weigh changes in water stored in an entire river basin allows evapotranspiration to be esti- mated in a water balance framework (13). Transboundary water availability issues require sharing hydrologic data across politi- cal boundaries. However, national hydrolog- ical records are often withheld for political, socioeconomic, and defense purposes, com- plicating regional water management discus- sions. Several studies have used GRACE data to circumvent international data denial prac- tices, including in those involving lakes (14), higher spatial (<50,000 km ) and tempor (weekly or biweekly) resolution, for exam ple through novel orbital configurations, s that smaller river basins and aquifers can b observed directly.The availability of GRAC data at these finer scales, at which most plan ning decisions are made, would likely ensu their broader use in water management. The GRACE-FO mission is on sched ule for a 2017 launch, but a next-generatio improved GRACE mission is still unde design and as yet unconfirmed. Given i demonstrated contributions to date and th potential for much more, a future without GRACE mission in orbit would be an unfo tunate and unnecessarily risky backward ste for regional water management. References 1. P. J. Durack et al., Science 336, 455 (2012). 2. K. E. Trenberth, Clim. Res. 47, 123 (2011). 3. I. M. Held, B. J. Soden, J. Clim. 19, 5686 (2006). 4. V. M. Tiwari, J. Wahr, S. Swenson, Geophys. Res. Lett. 36, L18401 (2009). 5. B. R. Scanlon et al., Proc. Natl. Acad. Sci. U.S.A. 109, 9320 (2012). 6. K. A. Voss et al., Water Resour. Res. 49, 904 (2013). 7. B. D. Tapley et al., Science 305, 503 (2004). 8. J. T. Reager, J. S. Famiglietti, Geophys. Res. Lett. 36, L23402 (2009). 9. R. Houborg et al., Water Resour. Res. 48, W07525 (2012 10. J. Blunden, D. S. Arndt, Eds., Bull. Am. Meteorol. Soc. 9 S1 (2012). 11. B. F. Zaitchik et al., J. Hydrometeorol. 9, 535 (2008). 12. S. C. Swenson, P. C. D. Milly, Water Resour. Res. 42, W03201 (2006). 13. G. Ramillien et al., Water Resour. Res. 42, W10403 (2006 14. S. Swenson, J. Wahr, J. Hydrol. 370, 163 (2009). 15. J. S. Famiglietti, Abstract GC31D-01, fall meeting, AGU San Francisco, 3 to 7 December 2012. Mixed picture. Between 2003 and 2012, GRACE data show water losses in agricultural regions such as Cali- fornia’s Central Valley (1) ( 1.5 ± 0.1 cm/year) and the Southern High Plains Aquifer (2) ( 2.5 ± 0.2 cm/ year), caused by overreliance on groundwater to supply irrigation water. Regions where groundwater is being depleted as a result of prolonged drought include Houston (3) ( 2.3 ± 0.6 cm/year), Alabama (4) ( 2.1 ± 0.8 cm/year), and the Mid-Atlantic states (5) ( 1.8 ± 0.6 cm/year). Water storage is increasing in the flood- prone Upper Missouri River basin (6) (2.5 ± 0.2 cm/year). See fig. S1 for monthly time series for all hot spots. Data from (15) and from GRACE data release CSR RL05.
Summary • Vegetation Index is dependent on amount of irrigation • Regular (weekly) remote sensing inspection could allow us to: • Appropriate irrigation zones • Help identify regions of over watering • Help identify any burst pipes/valves • Optimize irrigation patterns • Automate sprinkler system controls • Progressively more benefit as a specific history of the plots/site is built up
Stage 2
FUTURE Water Management Why Agriculture? ~80% water use US (USDA 2013) Challenges: Climate change, Drought, Population, non-ag water uses. Water Use Efficiency: ~50% US (USDA 2004) Water Mgmt. “Smart-GRID*” Delivery Models Basin Geodata Water/Crop Status & Forecast Water Need Status & Forecast Water Agric +Others Status & Forecast
Current Water Mgmt. Delivery Models Basin Geodata Water/Crop Status & Forecast Water Status & Forecast Water Agric +Others Status & Forecast Water use based on: Experience Limited estimations No related info CURRENT Water Management
CWMIS Case Example: Water Use vs. Delivery TOP: crop water use vs. water delivery (ac-ft). BOTTOM: water use difference (ac-ft) Typically save at least 10% Can be done on a field by field, campus by campus, home by home, or golf course by golf course basis or for an entire basin. Alfonso Torres
Culex tarsalis West Nile Virus The same data infrastructure can also be used to help combat West Nile Virus by identifying breeding sites.
Malaria Prediction Using Satellite Data
P. vivax is carried by the female Anopheles mosquito
Plasmodium vivax is a protozoal parasite and a human pathogen. The most frequent and widely distributed cause of recurring (Benign tertian) malaria, P. vivax is one of the six species of malaria parasites that commonly infect humans.[1] It is less virulent than Plasmodium falciparum, the deadliest of the six, but vivax malaria can lead to severe disease and death.[2] [3] P. vivax is carried by the female Anopheles mosquito, since it is only the female of the species that bite. Plasmodium vivax Plasmodium falciparum http://www.worldmalariareport.org/
Seasonal climatic suitability for malaria transmission (CSMT) Climatic conditions are considered to be suitable for transmission when the monthly precipitation accumulation is at least 80 mm, the monthly mean temperature is between 18°C and 32°C and the monthly relative humidity is at least 60%. These thresholds are based on a consensus of the literature. In practice, the optimal and limiting conditions for transmission are dependent on the particular species of the parasite and vector. Commentary: Web-based climate information resources for malaria control in Africa Emily K Grover-Kopec, M Benno Blumenthal, Pietro Ceccato, Tufa Dinku, Judy A Omumbo and Stephen J Connor* Malaria Journal 2006, 5:38 doi:10.1186/1475-2875-5-38
0 500 1,000 Km Vectorial Capacity In Zones with Malaria Epidemic Potential 05 August - 12 August 2013 VCAP Values 0 0 - 2 2 - 4 4 - 6 6 - 8 8 - 10 10 - 15 15 - 20 > 20 Country Boundaries
Satellite imagery can be used to track mosquito habitats. High-resolution (5 m) satellite images can identify very small water bodies, wetlands and other malaria-relevant land-cover types. Of the 225 million annual reported cases of the disease, 212 million of these occur in Africa. Of the 800,000 Malaria-related deaths each year, 90% of these fatalities occur in sub-Saharan Africa. http://www.itweb.co.za/index.php?option=com_content&view=article&id=52695
T H R I V E T I M E LY H E A LT H I N D I C AT O R S U S I N G R E M O T E S E N S I N G & I N N O VAT I O N F O R T H E V I TA L I T Y O F T H E E N V I R O N M E N T Why we care so much? Approximately 50 million Americans have allergic diseases, including asthma and allergic rhinitis, both of which can be exacerbated by PM2.5. Every day in America 44,000 people have an asthma attack, and because of asthma 36,000 kids miss school, 27,000 adults miss work, 4,700 people visit the emergency room, 1,200 people are admitted to the hospital, and 9 people die.
Air pollution in Ulaanbaatar, Mongolia
Unprecedented levels of air pollution in Singapore and Malaysia in June led to respiratory illnesses, school closings, and grounded aircraft.  This year it was so bad that in some affected areas there was a 100 percent rise in the number of asthma cases, and the government of Malaysia distributed gas masks. MODIS Aqua July 21, 2013. David Lary
PM2.5 Invisible Killer
TypesofbiologicalMaterialTypesofDustTypesofParticulatesGasMolecules 0.0001 μm 0.001 μm 0.01 μm 0.1 μm 1 μm 10 μm 100 μm 1000 μm Pollen Mold Spores House Dust Mite Allergens Bacteria Cat Allergens Viruses Heavy Dust Settling Dust Suspended Atmospheric Dust Cement Dust Fly Ash Oil Smoke Smog Tobacco Smoke Soot Gas Molecules Decreased Lung Function < 10 μm Skin & Eye Disease < 2.5 μm Tumors < 1 μm Cardiovascular Disease < 0.1 μm Hair Pin Cell 0.0001 μm 0.001 μm 0.01 μm 0.1 μm 1 μm 10 μm 100 μm 1000 μm PM10 particles PM2.5 particles PM0.1 ultra fine particles PM10-2.5 coarse fraction 0.1 mm 1 mm Table!1.!PM!and!health!outcomes!(modified!from!Ruckerl*et*al.!(2006)).! !! Health*Outcomes! Short9term*Studies* Long9term*Studies* PM10! PM2.5! UFP! PM10! PM2.5! UFP! Mortality* !! !! !! !! !! !! !!!!All!causes! xxx!! xxx!! x! xx! xx! x! !!!!Cardiovascular! xxx! xxx! x!! xx! xx! x! !!!!Pulmonary! xxx! xxx! x! xx! xx! x! Pulmonary!effects! !! !! !! !! !! !! !!!!Lung!function,!e.g.,!PEF! xxx! xxx! xx! xxx! xxx! !! !!!!Lung!function!growth! !! !! !! xxx! xxx! !! Asthma!and!COPD!exacerbation! !! !! !! !! !! !! !!!!Acute!respiratory!symptoms! !! xx! x! xxx! xxx! !! !!!!Medication!use! !! !! x! !! !! !! !!!!Hospital!admission! xx! xxx! x! !! !! !! Lung!cancer! !! !! !! !! !! !! !!!!Cohort! !! !! !! xx! xx! x! !!!!Hospital!admission! !! !! !! xx! xx! x! Cardiovascular!effects! !! !! !! !! !! !! !!!!Hospital!admission! xxx! xxx! !! x! x! !! ECG@related!endpoints! !! !! !! !! !! !! !!!!Autonomic!nervous!system! xxx! xxx! xx! !! !! !! !!!!Myocardial!substrate!and!vulnerability! !! xx! x! !! !! !! Vascular!function! !! !! !! !! !! !! !!!!Blood!pressure! xx! xxx! x! !! !! !! !!!!Endothelial!function! x! xx! x! !! !! !! Blood!markers! !! !! !! !! !! !! !!!!Pro!inflammatory!mediators! xx! xx! xx! !! !! !! !!!!Coagulation!blood!markers! xx! xx! xx! !! !! !! !!!!Diabetes! x! xx! x! !! !! !! !!!!Endothelial!function! x! x! xx! !! !! !! Reproduction! !! !! !! !! !! !! !!!!Premature!birth! x! x! !! !! !! !! !!!!Birth!weight! xx! x! !! !! !! !! !!!!IUR/SGA! x! x! !! !! !! !! Fetal!growth! !! !! !! !! !! !! !!!!Birth!defects! x! !! !! !! !! !! !!!!Infant!mortality! xx! x! !! !! !! !! !!!!Sperm!quality! x! x! !! !! !! !! Neurotoxic!effects! !! !! !! !! !! !! !!!!Central!nervous!system!! !! x! xx! !! !! !! x, few studies; xx, many studies; xxx, large number of studies.
Hourly Measurements from 55 countries and more than 8,000 measurement sites from 1997-present
Aqua DeepBlue Rank Source Variable Type 1 Satellite Product Tropospheric NO2 Column Input 2 Satellite Product Solar Azimuth Input 3 Meteorological Analyses Air Density at Surface Input 4 Satellite Product Sensor Zenith Input 5 Satellite Product White-sky Albedo at 470 nm Input 6 Population Density Input 7 Satellite Product Deep Blue Surface Reflectance 470 nm Input 8 Meteorological Analyses Surface Air Temperature Input 9 Meteorological Analyses Surface Ventilation Velocity Input 10 Meteorological Analyses Surface Wind Speed Input 11 Satellite Product White-sky Albedo at 858 nm Input 12 Satellite Product White-sky Albedo at 2,130 nm Input 13 Satellite Product Solar Zenith Input 14 Meteorological Analyses Surface Layer Height Input 15 Satellite Product White-sky Albedo at 1,240 nm Input 16 Satellite Product Deep Blue Surface Reflectance 660 nm Input 17 Satellite Product Deep Blue Surface Reflectance 412 nm Input 18 Satellite Product White-sky Albedo at 1,640 nm Input 19 Satellite Product Sensor Azimuth Input 20 Satellite Product Scattering Angle Input 21 Meteorological Analyses Surface Velocity Scale Input 22 Satellite Product Cloud Mask Qa Input 23 Satellite Product White-sky Albedo at 555 nm Input 24 Satellite Product Deep Blue Aerosol Optical Depth 550 nm Input 25 Satellite Product Deep Blue Aerosol Optical Depth 660 nm Input 26 Satellite Product Deep Blue Aerosol Optical Depth 412 nm Input 27 Meteorological Analyses Total Precipitation Input 28 Satellite Product White-sky Albedo at 648 nm Input 29 Satellite Product Deep Blue Aerosol Optical Depth 470 nm Input 30 Satellite Product Deep Blue Angstrom Exponent Land Input 31 Meteorological Analyses Surface Specific Humidity Input 32 Satellite Product Cloud Fraction Land Input In-situ Observation PM2.5 Target
This is a BigData Problem of Great Societal Relevance • Collecting data in real time from national and global networks requires bandwidth. • With the next generation of wearable sensors and the internet of things this data volume will rapidly increase. • A variety of applications enabled by BigData, higher bandwidth and cloud processing. • Future finer granularity and two way communication will dramatically increase the size of the data bringing air quality to the micro scale, just like weather data. Time Taken 10 Mbps 20 Mbps 50 Mbps 1 Gbps 40 TB training data 4 Gb update 185 days 93 days 37 days 1 day 21 hours 54m 27m 11m 32s
Long-Term Average 1997-present
Four Corners Power Plants Sonoran Dessert Los Angeles Area CentralValley Common Fire Area Close Ups Showing Good Agreement With Observations Alaska (a) (b) (c) (d) Great Salt Lake Desert
Automated traffic patterns, driverless cars routing
Flight  on  Nov  18,  2014  clear  skies Flight  on  Dec  04,  2014  hazy/overcast Japan USA WHO/EU Annual  Avg.  : 15μg/m3 
 Annual  Avg.  : 12μg/m3
 Annual  Avg.  : 25μg/m3   24  hour  Avg.  : 35μg/m3   24  hour  Avg.  : 35μg/m3   Annual  Avg.  :20μg/m3   PM2.5  Air  Quality  Standards Day  within  EPA  Air  Quality  Standards Day  with  exceedance  of  EPA  Air  Quality  Standards
50
Model  Airplane  Details A  12s  5400  mAh  baLery  pack  per  motor  (current  setup)  provides  approximately  8  minutes  of  flight   Pme.  Flight  Pme  can  be  increased  by  using  higher  capacity  baLery  packs.
Flight  Photos
Accomplishments To the best of our knowledge the first time the full sub- pixel aerosol size distribution has been characterized at high spatial resolution (sub meter) and high temporal resolution (every second) using: • A zero emission, low cost, electric remote control model aircraft at multiple vertical levels in the lower most 100 m of the atmosphere. • A car driving daily across a 10 km pixel over an extended period. Satellite Pixel Full Aerosol Size Distribution
G E O L O C AT E D A L L E R G E N S E N S I N G P L AT F O R M G A S P Four objectives: 1. Develop and deploy an array of Internet of Things remote airborne particle sensors within Chattanooga to be used to provide real-time streamed data on hourly particulate levels, both pollen- sized (10-40 micron) and smaller (<2.5 micron) particles. 2. Deploy an in-situ pollen air sampler in Chattanooga to identify specific pollen types. 3. Merge locally streamed data with already-collected, satellite-based NASA data to complement and enhance the newly-collected particulate data and generate Chattanooga-focused particulate maps. 4. Develop web-based visual tools to provide real-time pollen and smaller particle alerts to end users such as asthma patients, health institutions, and businesses and other institutions affected by elevated pollen levels.
Think Big: Holistic & Comprehensive Informatics Bio  InformaPcs Medical  InformaPcs Environmental  InformaPcs THRIVE   MulPple  Big  Data  +  EMR  +  Social  Media  +  Machine  Learning  +  Causality   A  Cross-­‐cuXng  PlaYorm  for  Comprehensive  InformaPcs  for  Data  Driven  Decisions  in  Pa<ent   Centered  Care  facilitated  by  High  Speed  Low-­‐Latency  networks,  mulPple  massive  datasets  from   large  distributed  sensor  networks,  EMR,  and  local  cloud  compu:ng.
Combine historical track issue data and historical high resolution meteorological data with machine learning. Combine Multiple Datasets
Satellite Images can be used to automate the highlighting of vegetation near the tracks Highlight Vegetation
1 Routine satellite acquisition of multispectral and SAR imagery 2 Periodic high resolution ground truth from aerial surveys 3 Image processing & Machine Learning BNSF Decision Support 4 The synergy between routine satellite imagery, periodic high resolution ground truth surveys and automated machine learning and image processing is a powerful combination for decision support. Preparing for Routine Decision Support

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The West Africa-America Chamber of Commerce & Industries presents: Big Data & Machine Learning for Societal Benefit

  • 2.
    Combine historical trackissue data and historical high resolution meteorological data with machine learning. Combine Multiple Datasets
  • 3.
    Water The Motive Do wellwith the Details by embracing the Big Picture Prof. David Lary +1 (972) 489-2059 http://davidlary.info david.lary@utdallas.edu Center for Space Science
  • 4.
    Drought Has IncreasedGlobally Since 1900
  • 5.
    Worst Drought in1,000 Years Predicted for American West A paddle wheeler and a small motorboat sail on Lake Mead, North America's largest man-made reservoir. The water is at its lowest level since the Hoover Dam was built in the 1930s. The white "bathtub ring" of mineral deposits on the rocks marks past water levels. PUBLISHED FEBRUARY 12, 2015
  • 6.
    Western U.S. DroughtPrompts Disaster Declarations In 11 States By MICHELLE RINDELS 01/16/14 07:51 PM ET EST LAS VEGAS (AP) — Federal officials have designated portions of 11 drought- ridden western and central states as primary natural disaster areas, highlighting the financial strain the lack of rain is likely to bring to farmers in those regions. The announcement by the U.S. Department of Agriculture on Wednesday included counties in Colorado, New Mexico, Nevada, Kansas, Texas, Utah, Arkansas, Hawaii, Idaho, Oklahoma and California. Rancher Ralph Miller, 79, checks on one of many “stock tanks” of water that are receding due to the severe drought. “I’d say it’s just about as bad as it can get.” Barnhart, Texas
  • 7.
    “Water is thenew oil” Jim Rogers, chief executive of Duke Energy ... and many others Water crisis in California, Texas threatens US food security Western water scarcity issues becoming more severe Western Farm Press, Jun. 5, 2012 University of Texas at Austin California and Texas produced agricultural products worth $56 billion in 2007, accounting for much of the nation's food production. They also account for half of all groundwater depletion in the U.S., mainly as a result of irrigating crops. The nation’s food supply may be vulnerable to rapid groundwater depletion from irrigated agriculture, according to a new study by researchers at The University of Texas at Austin and elsewhere. http://westernfarmpress.com/irrigation/water-crisis-california-texas-threatens-us-food-security
  • 8.
    Since 1980 thepopulation of Texas has more than doubled, but the reservoir capacity has remained almost unchanged. During 2011the reservoir levels were the lowest during Sep-Dec that they have been since 1990. In 2015 we are starting out with lower levels than 2013.
  • 9.
    Smarter irrigation controlis invaluable! If we can use existing infrastructure it is even better! .... from farm, to corporate campus, to golf course, to your back yard.
  • 10.
    When great societalneed meets appropriate scalable solution there is much societal and economic benefit to be gained
  • 11.
  • 12.
    DATE: 12-Feb-2010 DOC NO:0115056 ISSUE: 02 DMC DATA PRODUCT MANUAL STATUS: FINAL Figure 4: Detector and channel layout of the SLIM-6-22 imager Imager Bank 0 Channel 6 Green Channel 5 Red Channel 4 NIR Imager Bank 1 Channel 1 NIR Channel 2 Red Channel 3 Green Pixel 1 Pixel 14436 Pixel 14436 Pixel 1
  • 14.
    uneven irrigation blown valveslead to flooding Sports fields agricultural test plots 22 m resolution
  • 15.
    On average, systemshave water losses of about 17 percent. 49
  • 16.
    Can you tellwhich grass has had more water? Zooming in
  • 17.
  • 19.
    20 lb Airbornehyperspectral imaging system 385 channels between 400-1,700 nm Hyperspectral data cube
  • 20.
    J S Famiglietti,and M Rodell Science 2013;340:1300-1301 an accuracy of 1.5 cm equivalent water height. Because GRACE measures changes in total water storage, it integrates the impacts of natural climate fluctuations, global change, and human water use, including groundwater extraction, which in many parts of the world is unmeasured and unmanaged. GRACE- derived rates of groundwater losses in the world’s major aquifer systems (4–6) under- score the critical need to improve monitor- ing and regulation of groundwater systems before they run dry. Regional flooding and drought are driven by the surplus or deficit of water in a river basin or an aquifer, yet few hydrologic observing networks yield sufficient data for comprehensive monitoring of changes in the total amount of water stored in a region. GRACE observations have helped to fill this gap. They have been used to character- ize regional flood potential (8) and to assess water storage deficits in the U.S. Drought Monitor (9) and are included in annual State of the Climate reports (10). As an integrated measure of all surface and groundwater stor- age changes, GRACE data implicitly contain a record of seasonal to interannual water stor- key tools for predicting future water avail- ability, difficult to validate. Low-resolution GRACE data, when combined with higher- resolution model simulations, provide an independent constraint on simulated water balances, while also adding spatial detail to GRACE’s low-resolution perspective (11). They are widely used to evaluate land surface models used by weather and climate forecast- ing centers around the world (12). Evapotranspiration is a key factor in interbasin water allocations, yet because it disperses into the atmosphere in the vapor phase, it confounds standard measurement techniques. The ability of GRACE to weigh changes in water stored in an entire river basin allows evapotranspiration to be esti- mated in a water balance framework (13). Transboundary water availability issues require sharing hydrologic data across politi- cal boundaries. However, national hydrolog- ical records are often withheld for political, socioeconomic, and defense purposes, com- plicating regional water management discus- sions. Several studies have used GRACE data to circumvent international data denial prac- tices, including in those involving lakes (14), higher spatial (<50,000 km ) and tempor (weekly or biweekly) resolution, for exam ple through novel orbital configurations, s that smaller river basins and aquifers can b observed directly.The availability of GRAC data at these finer scales, at which most plan ning decisions are made, would likely ensu their broader use in water management. The GRACE-FO mission is on sched ule for a 2017 launch, but a next-generatio improved GRACE mission is still unde design and as yet unconfirmed. Given i demonstrated contributions to date and th potential for much more, a future without GRACE mission in orbit would be an unfo tunate and unnecessarily risky backward ste for regional water management. References 1. P. J. Durack et al., Science 336, 455 (2012). 2. K. E. Trenberth, Clim. Res. 47, 123 (2011). 3. I. M. Held, B. J. Soden, J. Clim. 19, 5686 (2006). 4. V. M. Tiwari, J. Wahr, S. Swenson, Geophys. Res. Lett. 36, L18401 (2009). 5. B. R. Scanlon et al., Proc. Natl. Acad. Sci. U.S.A. 109, 9320 (2012). 6. K. A. Voss et al., Water Resour. Res. 49, 904 (2013). 7. B. D. Tapley et al., Science 305, 503 (2004). 8. J. T. Reager, J. S. Famiglietti, Geophys. Res. Lett. 36, L23402 (2009). 9. R. Houborg et al., Water Resour. Res. 48, W07525 (2012 10. J. Blunden, D. S. Arndt, Eds., Bull. Am. Meteorol. Soc. 9 S1 (2012). 11. B. F. Zaitchik et al., J. Hydrometeorol. 9, 535 (2008). 12. S. C. Swenson, P. C. D. Milly, Water Resour. Res. 42, W03201 (2006). 13. G. Ramillien et al., Water Resour. Res. 42, W10403 (2006 14. S. Swenson, J. Wahr, J. Hydrol. 370, 163 (2009). 15. J. S. Famiglietti, Abstract GC31D-01, fall meeting, AGU San Francisco, 3 to 7 December 2012. Mixed picture. Between 2003 and 2012, GRACE data show water losses in agricultural regions such as Cali- fornia’s Central Valley (1) ( 1.5 ± 0.1 cm/year) and the Southern High Plains Aquifer (2) ( 2.5 ± 0.2 cm/ year), caused by overreliance on groundwater to supply irrigation water. Regions where groundwater is being depleted as a result of prolonged drought include Houston (3) ( 2.3 ± 0.6 cm/year), Alabama (4) ( 2.1 ± 0.8 cm/year), and the Mid-Atlantic states (5) ( 1.8 ± 0.6 cm/year). Water storage is increasing in the flood- prone Upper Missouri River basin (6) (2.5 ± 0.2 cm/year). See fig. S1 for monthly time series for all hot spots. Data from (15) and from GRACE data release CSR RL05.
  • 21.
    Summary • Vegetation Indexis dependent on amount of irrigation • Regular (weekly) remote sensing inspection could allow us to: • Appropriate irrigation zones • Help identify regions of over watering • Help identify any burst pipes/valves • Optimize irrigation patterns • Automate sprinkler system controls • Progressively more benefit as a specific history of the plots/site is built up
  • 22.
  • 23.
    FUTURE Water Management WhyAgriculture? ~80% water use US (USDA 2013) Challenges: Climate change, Drought, Population, non-ag water uses. Water Use Efficiency: ~50% US (USDA 2004) Water Mgmt. “Smart-GRID*” Delivery Models Basin Geodata Water/Crop Status & Forecast Water Need Status & Forecast Water Agric +Others Status & Forecast
  • 24.
    Current Water Mgmt. Delivery Models Basin Geodata Water/Crop Status &Forecast Water Status & Forecast Water Agric +Others Status & Forecast Water use based on: Experience Limited estimations No related info CURRENT Water Management
  • 25.
    CWMIS Case Example:Water Use vs. Delivery TOP: crop water use vs. water delivery (ac-ft). BOTTOM: water use difference (ac-ft) Typically save at least 10% Can be done on a field by field, campus by campus, home by home, or golf course by golf course basis or for an entire basin. Alfonso Torres
  • 27.
    Culex tarsalis West NileVirus The same data infrastructure can also be used to help combat West Nile Virus by identifying breeding sites.
  • 28.
  • 29.
    P. vivax iscarried by the female Anopheles mosquito
  • 30.
    Plasmodium vivax isa protozoal parasite and a human pathogen. The most frequent and widely distributed cause of recurring (Benign tertian) malaria, P. vivax is one of the six species of malaria parasites that commonly infect humans.[1] It is less virulent than Plasmodium falciparum, the deadliest of the six, but vivax malaria can lead to severe disease and death.[2] [3] P. vivax is carried by the female Anopheles mosquito, since it is only the female of the species that bite. Plasmodium vivax Plasmodium falciparum http://www.worldmalariareport.org/
  • 31.
    Seasonal climatic suitabilityfor malaria transmission (CSMT) Climatic conditions are considered to be suitable for transmission when the monthly precipitation accumulation is at least 80 mm, the monthly mean temperature is between 18°C and 32°C and the monthly relative humidity is at least 60%. These thresholds are based on a consensus of the literature. In practice, the optimal and limiting conditions for transmission are dependent on the particular species of the parasite and vector. Commentary: Web-based climate information resources for malaria control in Africa Emily K Grover-Kopec, M Benno Blumenthal, Pietro Ceccato, Tufa Dinku, Judy A Omumbo and Stephen J Connor* Malaria Journal 2006, 5:38 doi:10.1186/1475-2875-5-38
  • 33.
    0 500 1,000Km Vectorial Capacity In Zones with Malaria Epidemic Potential 05 August - 12 August 2013 VCAP Values 0 0 - 2 2 - 4 4 - 6 6 - 8 8 - 10 10 - 15 15 - 20 > 20 Country Boundaries
  • 34.
    Satellite imagery canbe used to track mosquito habitats. High-resolution (5 m) satellite images can identify very small water bodies, wetlands and other malaria-relevant land-cover types. Of the 225 million annual reported cases of the disease, 212 million of these occur in Africa. Of the 800,000 Malaria-related deaths each year, 90% of these fatalities occur in sub-Saharan Africa. http://www.itweb.co.za/index.php?option=com_content&view=article&id=52695
  • 35.
    T H RI V E T I M E LY H E A LT H I N D I C AT O R S U S I N G R E M O T E S E N S I N G & I N N O VAT I O N F O R T H E V I TA L I T Y O F T H E E N V I R O N M E N T Why we care so much? Approximately 50 million Americans have allergic diseases, including asthma and allergic rhinitis, both of which can be exacerbated by PM2.5. Every day in America 44,000 people have an asthma attack, and because of asthma 36,000 kids miss school, 27,000 adults miss work, 4,700 people visit the emergency room, 1,200 people are admitted to the hospital, and 9 people die.
  • 37.
    Air pollution inUlaanbaatar, Mongolia
  • 38.
    Unprecedented levels ofair pollution in Singapore and Malaysia in June led to respiratory illnesses, school closings, and grounded aircraft.  This year it was so bad that in some affected areas there was a 100 percent rise in the number of asthma cases, and the government of Malaysia distributed gas masks. MODIS Aqua July 21, 2013. David Lary
  • 40.
  • 42.
    TypesofbiologicalMaterialTypesofDustTypesofParticulatesGasMolecules 0.0001 μm 0.001μm 0.01 μm 0.1 μm 1 μm 10 μm 100 μm 1000 μm Pollen Mold Spores House Dust Mite Allergens Bacteria Cat Allergens Viruses Heavy Dust Settling Dust Suspended Atmospheric Dust Cement Dust Fly Ash Oil Smoke Smog Tobacco Smoke Soot Gas Molecules Decreased Lung Function < 10 μm Skin & Eye Disease < 2.5 μm Tumors < 1 μm Cardiovascular Disease < 0.1 μm Hair Pin Cell 0.0001 μm 0.001 μm 0.01 μm 0.1 μm 1 μm 10 μm 100 μm 1000 μm PM10 particles PM2.5 particles PM0.1 ultra fine particles PM10-2.5 coarse fraction 0.1 mm 1 mm Table!1.!PM!and!health!outcomes!(modified!from!Ruckerl*et*al.!(2006)).! !! Health*Outcomes! Short9term*Studies* Long9term*Studies* PM10! PM2.5! UFP! PM10! PM2.5! UFP! Mortality* !! !! !! !! !! !! !!!!All!causes! xxx!! xxx!! x! xx! xx! x! !!!!Cardiovascular! xxx! xxx! x!! xx! xx! x! !!!!Pulmonary! xxx! xxx! x! xx! xx! x! Pulmonary!effects! !! !! !! !! !! !! !!!!Lung!function,!e.g.,!PEF! xxx! xxx! xx! xxx! xxx! !! !!!!Lung!function!growth! !! !! !! xxx! xxx! !! Asthma!and!COPD!exacerbation! !! !! !! !! !! !! !!!!Acute!respiratory!symptoms! !! xx! x! xxx! xxx! !! !!!!Medication!use! !! !! x! !! !! !! !!!!Hospital!admission! xx! xxx! x! !! !! !! Lung!cancer! !! !! !! !! !! !! !!!!Cohort! !! !! !! xx! xx! x! !!!!Hospital!admission! !! !! !! xx! xx! x! Cardiovascular!effects! !! !! !! !! !! !! !!!!Hospital!admission! xxx! xxx! !! x! x! !! ECG@related!endpoints! !! !! !! !! !! !! !!!!Autonomic!nervous!system! xxx! xxx! xx! !! !! !! !!!!Myocardial!substrate!and!vulnerability! !! xx! x! !! !! !! Vascular!function! !! !! !! !! !! !! !!!!Blood!pressure! xx! xxx! x! !! !! !! !!!!Endothelial!function! x! xx! x! !! !! !! Blood!markers! !! !! !! !! !! !! !!!!Pro!inflammatory!mediators! xx! xx! xx! !! !! !! !!!!Coagulation!blood!markers! xx! xx! xx! !! !! !! !!!!Diabetes! x! xx! x! !! !! !! !!!!Endothelial!function! x! x! xx! !! !! !! Reproduction! !! !! !! !! !! !! !!!!Premature!birth! x! x! !! !! !! !! !!!!Birth!weight! xx! x! !! !! !! !! !!!!IUR/SGA! x! x! !! !! !! !! Fetal!growth! !! !! !! !! !! !! !!!!Birth!defects! x! !! !! !! !! !! !!!!Infant!mortality! xx! x! !! !! !! !! !!!!Sperm!quality! x! x! !! !! !! !! Neurotoxic!effects! !! !! !! !! !! !! !!!!Central!nervous!system!! !! x! xx! !! !! !! x, few studies; xx, many studies; xxx, large number of studies.
  • 43.
    Hourly Measurements from55 countries and more than 8,000 measurement sites from 1997-present
  • 44.
    Aqua DeepBlue Rank SourceVariable Type 1 Satellite Product Tropospheric NO2 Column Input 2 Satellite Product Solar Azimuth Input 3 Meteorological Analyses Air Density at Surface Input 4 Satellite Product Sensor Zenith Input 5 Satellite Product White-sky Albedo at 470 nm Input 6 Population Density Input 7 Satellite Product Deep Blue Surface Reflectance 470 nm Input 8 Meteorological Analyses Surface Air Temperature Input 9 Meteorological Analyses Surface Ventilation Velocity Input 10 Meteorological Analyses Surface Wind Speed Input 11 Satellite Product White-sky Albedo at 858 nm Input 12 Satellite Product White-sky Albedo at 2,130 nm Input 13 Satellite Product Solar Zenith Input 14 Meteorological Analyses Surface Layer Height Input 15 Satellite Product White-sky Albedo at 1,240 nm Input 16 Satellite Product Deep Blue Surface Reflectance 660 nm Input 17 Satellite Product Deep Blue Surface Reflectance 412 nm Input 18 Satellite Product White-sky Albedo at 1,640 nm Input 19 Satellite Product Sensor Azimuth Input 20 Satellite Product Scattering Angle Input 21 Meteorological Analyses Surface Velocity Scale Input 22 Satellite Product Cloud Mask Qa Input 23 Satellite Product White-sky Albedo at 555 nm Input 24 Satellite Product Deep Blue Aerosol Optical Depth 550 nm Input 25 Satellite Product Deep Blue Aerosol Optical Depth 660 nm Input 26 Satellite Product Deep Blue Aerosol Optical Depth 412 nm Input 27 Meteorological Analyses Total Precipitation Input 28 Satellite Product White-sky Albedo at 648 nm Input 29 Satellite Product Deep Blue Aerosol Optical Depth 470 nm Input 30 Satellite Product Deep Blue Angstrom Exponent Land Input 31 Meteorological Analyses Surface Specific Humidity Input 32 Satellite Product Cloud Fraction Land Input In-situ Observation PM2.5 Target
  • 45.
    This is aBigData Problem of Great Societal Relevance • Collecting data in real time from national and global networks requires bandwidth. • With the next generation of wearable sensors and the internet of things this data volume will rapidly increase. • A variety of applications enabled by BigData, higher bandwidth and cloud processing. • Future finer granularity and two way communication will dramatically increase the size of the data bringing air quality to the micro scale, just like weather data. Time Taken 10 Mbps 20 Mbps 50 Mbps 1 Gbps 40 TB training data 4 Gb update 185 days 93 days 37 days 1 day 21 hours 54m 27m 11m 32s
  • 46.
  • 47.
    Four Corners PowerPlants Sonoran Dessert Los Angeles Area CentralValley Common Fire Area Close Ups Showing Good Agreement With Observations Alaska (a) (b) (c) (d) Great Salt Lake Desert
  • 48.
  • 49.
    Flight  on  Nov  18,  2014  clear  skies Flight  on  Dec  04,  2014  hazy/overcast Japan USA WHO/EU Annual  Avg.  : 15μg/m3 
 Annual  Avg.  : 12μg/m3
 Annual  Avg.  : 25μg/m3   24  hour  Avg.  : 35μg/m3   24  hour  Avg.  : 35μg/m3   Annual  Avg.  :20μg/m3   PM2.5  Air  Quality  Standards Day  within  EPA  Air  Quality  Standards Day  with  exceedance  of  EPA  Air  Quality  Standards
  • 50.
  • 51.
    Model  Airplane  Details A  12s  5400  mAh  baLery  pack  per  motor  (current  setup)  provides  approximately  8  minutes  of  flight   Pme.  Flight  Pme  can  be  increased  by  using  higher  capacity  baLery  packs.
  • 52.
  • 53.
    Accomplishments To the bestof our knowledge the first time the full sub- pixel aerosol size distribution has been characterized at high spatial resolution (sub meter) and high temporal resolution (every second) using: • A zero emission, low cost, electric remote control model aircraft at multiple vertical levels in the lower most 100 m of the atmosphere. • A car driving daily across a 10 km pixel over an extended period. Satellite Pixel Full Aerosol Size Distribution
  • 54.
    G E OL O C AT E D A L L E R G E N S E N S I N G P L AT F O R M G A S P Four objectives: 1. Develop and deploy an array of Internet of Things remote airborne particle sensors within Chattanooga to be used to provide real-time streamed data on hourly particulate levels, both pollen- sized (10-40 micron) and smaller (<2.5 micron) particles. 2. Deploy an in-situ pollen air sampler in Chattanooga to identify specific pollen types. 3. Merge locally streamed data with already-collected, satellite-based NASA data to complement and enhance the newly-collected particulate data and generate Chattanooga-focused particulate maps. 4. Develop web-based visual tools to provide real-time pollen and smaller particle alerts to end users such as asthma patients, health institutions, and businesses and other institutions affected by elevated pollen levels.
  • 55.
    Think Big: Holistic& Comprehensive Informatics Bio  InformaPcs Medical  InformaPcs Environmental  InformaPcs THRIVE   MulPple  Big  Data  +  EMR  +  Social  Media  +  Machine  Learning  +  Causality   A  Cross-­‐cuXng  PlaYorm  for  Comprehensive  InformaPcs  for  Data  Driven  Decisions  in  Pa<ent   Centered  Care  facilitated  by  High  Speed  Low-­‐Latency  networks,  mulPple  massive  datasets  from   large  distributed  sensor  networks,  EMR,  and  local  cloud  compu:ng.
  • 56.
    Combine historical trackissue data and historical high resolution meteorological data with machine learning. Combine Multiple Datasets
  • 57.
    Satellite Images canbe used to automate the highlighting of vegetation near the tracks Highlight Vegetation
  • 58.
    1 Routine satellite acquisitionof multispectral and SAR imagery 2 Periodic high resolution ground truth from aerial surveys 3 Image processing & Machine Learning BNSF Decision Support 4 The synergy between routine satellite imagery, periodic high resolution ground truth surveys and automated machine learning and image processing is a powerful combination for decision support. Preparing for Routine Decision Support