HETEROGENEOUS ROBOT-ASSISTED MEASUREMENT IN DATA SPARSE REGIONS OF - - PowerPoint PPT Presentation

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HETEROGENEOUS ROBOT-ASSISTED MEASUREMENT IN DATA SPARSE REGIONS OF - - PowerPoint PPT Presentation

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HETEROGENEOUS ROBOT-ASSISTED MEASUREMENT IN DATA SPARSE REGIONS OF SOUTHERN INDIA Joshua Peschel a , Sierra Young a , Sally Thompson b , Gopal Penny b , Veena Srinivasan c a Civil and Environmental Engineering, University of Illinois at

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HETEROGENEOUS ROBOT-ASSISTED MEASUREMENT IN DATA SPARSE REGIONS OF SOUTHERN INDIA

Joshua Peschela, Sierra Younga, Sally Thompsonb, Gopal Pennyb, Veena Srinivasanc

aCivil and Environmental Engineering, University of Illinois at Urbana-Champaign bCivil and Environmental Engineering, University of California, Berkeley cAshoka Trust for Research in Ecology and the Environment, Bangalore, India

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LOCATION OF ARKAVATHY BASIN

Arkavathy

Map prepared by ATREE EcoinforaticsLab.

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HYDROLOGICAL CONTEXT

Data source: GSI toposheets, ASTER DEM imagery. Map prepared by ATREE EcoinforaticsLab.

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HYDROLOGICAL CONTEXT

Data source: GSI toposheets, ASTER DEM imagery. Map prepared by ATREE EcoinforaticsLab.

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DECLINING FLOWS IN THE ARKAVATHY

  • Late 20th century, Arkavathy

River flows began declining

  • Caused a shift towards, and

now a reliance on, the Cauvery River for water supply

  • Reasons for the drying of

the Arkavathy are unknown

TG Halli Inflows TG Halli Baseflows

Baseflow months/year Inflows (ML)

Srinivasan, V., S. Thompson, K. Madhyastha, G. Penny, K. Jeremiah, and S. Lele. 2015. Why is the Arkavathy River drying? A multiple-hypothesis approach in a data-scarce region. Hydrol. Earth Syst. Sci., 19, 1905-1917

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DECLINING FLOWS IN THE ARKAVATHY

  • Late 20th century, Arkavathy

River flows began declining

  • Caused a shift towards, and

now a reliance on, the Cauvery River for water supply

  • Reasons for the drying of

the Arkavathy are unknown

TG Halli Inflows TG Halli Baseflows

Baseflow months/year Inflows (ML)

108,000 ML/yr 49,000 ML/yr 25,000 ML/yr

Srinivasan, V., S. Thompson, K. Madhyastha, G. Penny, K. Jeremiah, and S. Lele. 2015. Why is the Arkavathy River drying? A multiple-hypothesis approach in a data-scarce region. Hydrol. Earth Syst. Sci., 19, 1905-1917

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ARKAVATHY LAND USE

Data source: KSRSAC. Map prepared by ATREE EcoinforaticsLab.

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HYPOTHESES FOR DRYING

1.

Changes in precipitation (amount and intensity)

2.

Increasing ET due to increase in temperature

3.

Declining baseflow due to groundwater extraction

4.

Land use changes (specifically increase in Eucalyptus plantations)

5.

Fragmentation of the watershed

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HYPOTHESES FOR DRYING

1.

Changes in precipitation (amount and intensity)

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HYPOTHESES FOR DRYING

2.

Increasing ET due to increase in temperature

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HYPOTHESES FOR DRYING

3.

Declining baseflow due to groundwater extraction

Srinivasan, V., S. Thompson, K. Madhyastha, G. Penny, K. Jeremiah, and S. Lele. 2015. Why is the Arkavathy River drying? A multiple-hypothesis approach in a data-scarce

  • region. Hydrol. Earth Syst. Sci., 19, 1905-1917
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HYPOTHESES FOR DRYING

4.

Land use changes (specifically increase in Eucalyptus plantations)

Srinivasan, V., S. Thompson, K. Madhyastha, G. Penny, K. Jeremiah, and S. Lele. 2015. Why is the Arkavathy River drying? A multiple-hypothesis approach in a data-scarce region. Hydrol. Earth Syst. Sci., 19, 1905-1917

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HYPOTHESES FOR DRYING

5.

Fragmentation of the watershed

TYPE OF STREAM OBSTRUCTION NUMBER IN TG HALLI CATCHMENT CHECK DAM 277 TANK 617 ROAD 9 BRIDGE 58

Data source: Zoomin Tech Report to Cauvery Neeravari Nigam Limited, 2011

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HYPOTHESES FOR DRYING

5.

Fragmentation of the watershed

TYPE OF STREAM OBSTRUCTION NUMBER IN TG HALLI CATCHMENT CHECK DAM 277 TANK 617 ROAD 9 BRIDGE 58

Data source: Zoomin Tech Report to Cauvery Neeravari Nigam Limited, 2011

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TANK STORAGE IN THE ARKAVATHY

  • Tanks = reservoirs
  • Used to serve as a controlled method
  • f irrigation for farmers
  • Determining the volume stored in

these tanks can help us better understand recharge and streamflow

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CURRENT DATA GATHERING PRACTICES

  • LIDAR – Light Detection and Ranging

(expensive and infrequent)

  • Manual bathymetry data gathering

(labor intensive and inefficient)

  • No real improvement towards

sparseness of data

  • Need low cost, high temporal

frequency measurements of tanks

Retrieved from http://proyectojuanchacon.blogspot.com/ April 10, 2016.

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Joshua M. Peschel and Robin R. Murphy. (2013) “On the Human-Machine Interaction of Unmanned Aerial System Mission Specialists”. IEEE Transactions on Human-Machine Systems, 43(1):53–62.

SMALL UNMANNED AERIAL VEHICLES

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Saki Handa. (2015) “Human-Machine Interaction for Unmanned Surface Systems”. Master of Science Thesis, Civil and Environmental Engineering, University of Illinois at Urbana-Champaign. Saki Handa and Joshua M. Peschel. (2015) “On the Human-Machine Interaction of Unmanned Surface Systems”. IEEE Transactions on Human-Machine Systems, in submission.

SMALL UNMANNED SURFACE VEHICLES

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ROBOT-ASSISTED DATA OBJECTIVES

  • Demonstrate use of low-cost, mobile

robotics platforms to collect data accurately and efficiently

  • Complete topographic/bathymetric

mapping of several chosen sites

  • Calculate storage volumes by relating

water surface levels to tank topography/bathymetry

Hadonahalli SM Gollahalli Nelamangala Bangalore

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WORKFLOW

Merge UAV and USV generated surfaces in GIS UAV flights

  • ver dry

areas geotagged aerial imagery Process images into Digital Elevation Model USV deployment in wet areas Geotagged depth readings Interpolate gridded points into 3D surface Storage/surface area calculations

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UAV FLIGHT IMAGERY

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UAV IMAGE PROCESSING RESULTS

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USV MISSION OPS

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USV DATA CAPTURE

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ROBOT-ASSISTED RESULTS

Stage-Storage Relationship for SM GollahalliTank

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ROBOT-ASSISTED RESULTS

Stage-Storage Relationship for SM GollahalliTank

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ROBOT-ASSISTED RESULTS

Stage-Storage Relationship for Hadonahalli Tank

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ROBOT-ASSISTED RESULTS

Stage-Storage Relationship for Hadonahalli Tank

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VALIDATION OF RESULTS

  • UAV elevation data compared to bathymetry data taken with handheld sonar depth

finder (accuracy +/- 1.5 centimeters)

GPS-tagged bathymetry points collected 5 to 10 meters apart

Bathymetry referenced to a common datum Values from DEM extracted for each GPS data point Point-by-Point comparison of elevation values

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VALIDATION OF RESULTS

  • UAV elevation data compared to bathymetry data taken with handheld sonar depth

finder (accuracy +/-1.5 centimeters)

GPS-tagged bathymetry points collected 5 to 10 meters apart

Bathymetry referenced to a common datum Values from DEM extracted for each GPS data point Point-by-Point comparison of elevation values

  • On average, UAV elevation values are within .27m of GPS data for Hadonahalli and

.41m for SM Gollahalli

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OPPORTUNITY COSTS

Chandrakanth, M.G. (2009) “Karnataka State Water Sector Reform: Current Status, Emerging Issues and Needed Strategies”. International Water Management Institute (IWMI)-Tata Water Policy Program.

Crop Cost of surface water withdrawal: Cost of groundwater withdrawal: (includes electricity for pumping, amortization, and

negative externalities from failed wells)

Maize 87 Rs./ha $1.32 /ha 6,000 Rs./ha $90.83 /ha Areca nut 148 Rs./ha $2.24 /ha 8,000 Rs./ha $121.11 /ha Paddy 247 Rs./ha $3.74 /ha 11,500 Rs./ha $174.10 /ha

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CHANGE TO INFRASTRUCTURE COSTS TO A FARMER

1Nagaraj, N. and Chandrakanth, M.G. (1997). Intra- and Inter-Generational Equity Effects of Irrigation Well Failures Farmers in Hard Rock

Areas of India. Economic and Political Weekly, March 29, 1997 .

2Varghese, S.K. et al. (2013). Estimating the causal effect of water scarcity on the groundwater use efficiency of rice farming in South India.

Ecological Economics, 86: 55-64.

3Improving the Economic Condition of Farmers. Report of the Official Group of Government of Karnataka, 2007.

  • Average investment cost of borewell: 25,000 – 50,000 INR ($375.00 - $750.00)1,2
  • Frequency of failed wells = 50%1
  • Average life of well ~ 5 years3
  • Average yearly income of agricultural household in Karnataka = $585 - $780.002,3
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CONTRIBUTIONS

  • Successfully demonstrated the complete topographic/bathymetric data capture for 3

sites in the Arkavathy using multiple robotics platforms

  • This surface data of tanks will help us understand the extent of surface

capture/diversion and groundwater recharge

  • Providing information on this spatial scale at a low cost helps inform and manage

water resources

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NEXT STEPS

  • Automation of USV and UAV data fusion in a GIS
  • Per data point cost and time analysis of this data collection method against current

standard practices (e.g., LIDAR)

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NSF Grant Number 1427420 CNIC: US-India Collaborative Research Linking Remote Sensing, Citizen Science, and Robotics to Address Critical Environmental Problems in Data Sparse Regions.

ACKNOWLEDGMENTS