Tom HarmonEnvironmental Systems Grad Program

Sierra Nevada Research InstituteUniversity of California, Merced

& UCLA Center for Embedded Networked Sensing (CENS)

National Science FoundationFunding: Center for Embedded Networked Sensing, WATERS Network Design Team, Pan-American Advanced Studies Institute

Technology Session 1: Assessing and Evaluating Ecosystem Function

Precision Sustainability…

• Precision Agriculture: Using technology to enable adding precise amounts of water, fertilizer, pesticides to minimize costs while maximizing crop yield

• Precision Sustainability: Using technology to enable precise management of water from the perspectives of municipal, industrial, agricultural, and environmental needs

Outline Sensor Systems (remote + embedded)

What is possible now and in the near future? How do we assess aquatic and terrestrial

ecosystem functions more efficiently? Multi-scale assessment technologies (Integrated

Sensor-Model Systems) How do we evaluate ecosystem function in the

context of human well-being as objectively aspossible? Participatory sensing (enabling the citizen scientist)

Remote sensing (satellites)

Current Capabilities of Earth Observing Satellites

Spatial: 0.6 to 4 m10 to 30 m100 to 500 m1 to 8 km

Temporal: 0.5 hrDailyWeeklyBimonthly

Spectral: Panchromatic (1)Multispectral (2-6)Hyperspectral (10s-100s)

example: evapotranspiration potential forecasted using hourly GOES and MODIS and calibrated using weather station data(courtesy of Susan Ustin, UC Davis)

Remote Sensing and Water Quality• Aircraft-based

multispectral imaging to achieve necessary spatial resolution

• Turbidity, TSS, chlorophyll-a, CDOM

• There is more potential here

Brezonik and co-workersSee: http://water.umn.edu/pres_pubs.html

Other air/ground-based remote sensing

• Lidar – high resolution DEM, vegetation topology, snow/ice cover

• Thermal infrared remote sensing – thermal gradients, mixing phenomena, pollution sources

• CUAHSI Hydrologic Measurement Facility (http://www.cuahsi.org/hmf.html)

– Geophysical: electromagnetic toolbox (em induction, ground-penetrating radar, electrical resisitivity imaging)

– Evapotranspiration Suite• Integrated Cavity Output Spectroscopy (ICOS)

– water vapor isotopes for quantifying and partitioning large scale ET fluxes

• Large Aperture Scintillometer (LAS) – large-scale sensible heat fluxes, atmospheric turbulence

• Codar – HF radar – large scale surface water velocity, wave action

Source: John JensenU South Carolina

Bonner, Maidment, Minsker, et al.WATERS Test Bed Site at

Corpus Christi Bay

Ground-based Sensors Status and Outlook

Physical Sensors…increasingly smaller, cheaper

Chemical Sensors: gross concentrations, changes

Acoustic and Image data samples

Acoustic, Image sensors with on board analysis

Chemical Sensors: trace concentrations, universal sensors

(lab-on-a-chip)

DNA microarrays onboard embedded device, universal sensors

Sensor triggered sample collection (bridging technology)

present future

Organism tagging, tracking

ab

ioti

cb

ioti

c

DNA biosensors for targeted microorganism

Physical Sensors

ParameterField-Readiness Scalability Cost

Temperature High High 50–100Moisture Content High High 100–500Flow rate, Flow velocity High Medium–High 1,000–10,000Pressure High High 500–1,000Light Transmission (Turbidity) High High 800–2,000

Goldman et al. (2007) White Paper

ChemicalSensors

ParameterField-

Readiness Scalability Cost ($)Dissolved Oxygen High High 800–2,000Electrical Conductivity High High 800–2,000pH High High 300–500Oxidation Reduction Potential Medium High 300–500Major Ionic Species (Cl-, Na+) Low–Medium High 500–800Nutrients (Nitrate, Ammonium) Low–Medium Low–High 500–25000Heavy metals Low Low NASmall Organic Compounds Low Low NALarge Organic Compounds Low Low NA

Scalable nitrate sensor fabrication efforts

7 µm diam. carbon fiber-based nitrate

microsensor

Bendikov et al. Sensors and Actuators B: Chemical (2005; 2007)

Allen et al. Bioscience 57(10) (2007)“Soil Sensor Technology: Life within a Pixel”

Biosensors• Not field-ready sensors for

direct observations of key organisms (e.g, pathogens)

• Flow-thru instruments:– FlowCAM: phytoplankton, etc.

by image recognition

• Surrogates– Fluorescence (Chlorophyll,

CDOM, etc.) images from:Fluid Imaging Technologies

Much activity in basic sensor development!• e.g., Chemical Reviews special issue Feb 2008

– Optical Chemical Sensors, McDonagh et al. [288 refs]– Potentiometric Ion Sensors, Bobacka et al. [324 refs]– Chemical Sensors with Integrated Circuits, Joo and Brown [115 refs]– Surface Plasmon Resonance Sensors … Chemical and Biological Species,

Homola [335 refs]– DNS Biosensors and Microarrays, Sassolas et al. [444 refs]

McDonagh et al. Chemical Reviews, 2008, 108(2)

DNA/Biosensor exampleDNA detection utilizing a chrono-coulometric detection method

Redox-cycling current is detected at the electrodes over time – when labeled target DNA is hybridized with the probe DNA, current change occurs

Schienle et al. IEEE J. Solid-State Circ. (2004)

chip dimensions: 6.4 x 4.5 mm

publications for DNAbiosensors and

microarrays

Sassolas et al. Chemical Reviews (2008)

Embedded Sensor Systems• Arrays of static sensors

– Continuous in time, nested in space– Fusion with intermittent remote sensing

data– Aggregate signal is higher order

information than that form individual sensors (e.g., ET sensor suite)

• Mobile sensors– Continuous in space, periodic or event-

triggered in time– Fusion with static and remote sensing

data– Calibration for multidimensional fluid

dynamic models• Human in-the-loop mobile sensors

– Campaign-driven (periodic or event-driven)

– Synergize with educational aspects– Collect social science data

simultaneously

A. Sanderson, RPI

W. Kaiser, CENS-UCLA

T. Harmon, CENS-Merced

Outline Sensor Systems (remote + embedded)

What is possible now and in the near future? How do we assess aquatic and terrestrial

ecosystem functions more efficiently? Multi-scale assessment technologies (Integrated

Sensor-Model Systems) How do we evaluate ecosystem function in the

context of human well-being as objectively aspossible? Participatory sensing (enabling the citizen scientist)

Understanding and assessing ecosystems & their functions

• Plan to observe

• Observe

• Analyze data (e.g. test your models)

• Modify observation plan

• Observe more

• Analyze more data

• Repeat until you’re finished or (more likely) out of time

• Plan to observe• Observe• Analyze data (e.g. test your models)• Modify observation plan• Observe more• Analyze more data• Repeat until you’re finished or (more likely) out of time

We must learn to compress the timeline for this process

How? Multi-scale Observations• Multi-scale observatories

– Remote sensing + on-the-ground sensing– Integrated with models

• The total information is greater than the sum of the sensor data!– Systems of sensors become “virtual sensors” for higher order

information

• Provides 25% of US agriculture production, most from just three counties (based on $)

• Soil salinization trajectory shows lack of sustainability (decades timescale)

• Population growth, urbanization, climate change on top of this…

“The sustainability of irrigated agriculture in many arid and semiarid areas of the world is at risk because of a combination of several interrelated factors, including lack of fresh water, lack of drainage, the presence of high water tables, and salinization of soil and groundwater resources.”

Schoups et al. (2005), Proc. National Academy of Sciences, vol. 102 no. 43.

Example 1: Salinity and observation and management in a watershed Soil Salinization

San Joaquin Valley, CA

Agricultural drainage on San Joaquin River, CA

• Provides 25% of US agriculture production, most from just three counties (based on $)

• Soil salinization trajectory shows lack of sustainability (decades timescale)

• Population growth, urbanization, climate change on top of this…

Example 1: Salinity and observation and management in a watershed Soil Salinization

San Joaquin Valley, CA

Agricultural drainage on San Joaquin River, CA

“Nowhere in the United States are these issues more apparent than in the San Joaquin Valley of California.”

Schoups et al. (2005), Proc. National Academy of Sciences, vol. 102 no. 43.

Backbone observation efforts in place

Salt Slough

Mud Slough

SJR near Vernalis

Stanislaus River

Tuolumne River

Merced River

Courtesy of Nigel QuinnLawrence Berkeley National Lab & USBR

California Digital Exchange Center (CDEC):

- Hydrologic database- Some parts real-time- Some water quality sensors (EC, temp)

- Spatially sparse (10s of km granularity)

Studies on the Merced River

watershed

Mixing zone study:Merced-San Joaquin confluence(Harmon, Kaiser et al.)

Dairy manure application study (Harmon, Castillo et al)

USGS/NAWQA agricultural gw-sw flow path site(USGS and UCM)

Sierra Nevada Hydrologic Observatory (Bales et al)

Wetlands salinity management sites (Quinn, Harmon et al.)

Mine tailings and habitat restoration (Dunne et al. UCSB)

Lower Merced River Dairy Sensor System

Manure lagoon

• Production dairies large part of Central California• How to make dairy operations sustainable?• OBJECTIVE: To observe, understand, and manage the application of manure as fertilizer [and how is it connected to groundwater and the river?]

Sensor and Data Acquisition Hardware

• Off-the-shelf:– Moisture– Temperature– Soil salinity– Meteorology– Nitrate– Ammonium

• also some developmental nitrate sensors

Deploy forMonths to

years

Deploy fordays

Data-logger with radio or

cellular modem

Irrigation events

Salinity eventually carried to depth by water

For soil salinity:

• Observed patterns are as expected

• Simulation models are mature for flow, chemical, and energy transport in soils

• So, we can automate analysis

• In fact, for agricultural and engineered systems, we can use sensor feedback and models to control the system (precision agriculture or precision sustainability

Precision Agriculture with Reclaimed Water

• Palmdale water reuse experimental site (not in the SJV, but could be…)

• Microclimate + soil pylons (moisture, temp, short-term nitrate and ammonium)

• sensor feedback, model calibration, model forecast

• Receding horizon control (look ahead multiple management steps)

Soil

moi

stur

e (c

m3 /c

m3 )

Management step 40.245

0.24

0.235

0.23

0.225

0.22

0.215

0.21

time (min)0 5 10 15 20 25 30

estimatedmeasuredestimatedmeasured

time (min)

estimatedmeasuredestimatedmeasured

Management step 30.225

0.22

0.215

0.21 0 5 10 15 20 25 30

Soil

moi

stur

e (c

m3 /c

m3 )

Studies on the Merced River

watershed

Mixing zone study:Merced-San Joaquin confluence(Harmon, Kaiser et al.)

Dairy manure application study (Harmon, Castillo et al)

USGS/NAWQA agricultural gw-sw flow path site(USGS and UCM)

Sierra Nevada Hydrologic Observatory (Bales et al)

Wetlands salinity management sites (Quinn, Harmon et al.)

Mine tailings and habitat restoration (Dunne et al. UCSB)

Wetland Ecosystem Functions(here, a heavily managed ecosystem)

Grasslands Ecological Area, Central California

Patrick Rahilly 2008 MS Thesis (UC Merced)

Wetlands Project Overview

• Study the effects of delaying the seasonal release of pond water (it is warmer and saltier, but the river flow is higher and able to dilute it)

• Assess the impact of the change on wetland plant ecology (mostly for the benefit of the wildfowl populations)

Objectives

Multi-scale sensing system• Remote sensing: high resolution aerial imagery (RBG and near-infrared)

• Human-in-the-loop sensing: electro-magnetic scanning of soil (moisture, salinity)

• Embedded sensing: meteorology, soil moisture, temperature, salinity

Transects walked 15m apart auto sampling every 4m

Soil salinity mapping methods

Trimble Ag114

Geonics EM-38 MK1

Typical results: plant productivity, soil salinity, micro-topography (not shown)

TWO (2) SETS OF FALSE COLOR COMPOSITE AERIAL PHOTOGRAPHS TAKEN

…MAY 11, 2006 & …JUNE 09, 2006

PROJECT DESCRIPTION

Key plant species correlates with aerial imagery product (as long as you time the flyover well)

NDVI is a common veg index based on near infrared band

So far, we see signs that the delayed drawdown may alter plant ecology, but we are repeating in 2008 to be certain and to clarify the role of salinity

Studies on the Merced River

watershed

Mixing zone study:Merced-San Joaquin confluence(Harmon, Kaiser et al.)

Dairy manure application study (Harmon, Castillo et al)

USGS/NAWQA agricultural gw-sw flow path site(USGS and UCM)

Sierra Nevada Hydrologic Observatory (Bales et al)

Wetlands salinity management sites (Quinn, Harmon et al.)

Mine tailings and habitat restoration (Dunne et al. UCSB)

Example Application: San Joaquin-Merced Confluence Tests (2005-2007)

Sensor System:• Networked Info-mechanical

system (NIMS), a 2D robotic system

• Can carry ADP, water quality sondes, water sampling system

Objectives:• Cross-sectional velocity

fields (informing models, sediment transport, geomorphology)

• Gradient mapping– Flow adjustment to

optimize mixing, reaeration (reservoir operation)

• Mass balances over river sections, reaches– Groundwater loss/gain– Chemical fluxes from

groundwater

2007 Mass balance and mixing study

The bottom of the watershed at the San Joaquin confluence

San Joaquin-Merced River Confluence

55 m span NIMS RD- Sontek ADV- Hydrolab sonde- 1 low res scan- 2 high res scans

NIMS 2D river scanning system (for relatively low energy flow, < 1 m/s)

Coupled velocity-conductance readings(integrated to yield a total salt load)

Day 2:9.33 kg/s

Day 1:9.30 kg/s

Harmon et al. Environ. Eng. Science,24(2), 2007

San Joaquin side Merced side

Salinity gradient and integrated salt load in the mixing zone

Other quick examples in terrestrial ecology

Phil Rundel, William Kaiser, UCLA; Mike Allen, UC Riverside, Michael Hamilton, UC Blue Oaks Reserve, Eric Graham CENS, Tom Harmon, UC Merced

TEOS- Terrestrial Ecological Observing System

Goals

Evaluation of capabilities and limitations of arrayed systems, integrating sensors, observations, and static measurements:

C, H2O, energy fluxes, and integration with organisms and ecological processes

TEOS: NIMS and Soil Energy Balance

NIMS RD was used at the James Reserve to measure soil surface and sub-surface temperatures. Heat storage varied significantly between seasons, primarily due to water content. Such detailed soil energy balance data for large areas in the understory are unique to NIMS and AMARSS.

scandirection

Imagers as Environmental Sensors

Visible light cameras can capture quantitative information on the behavior of animals: time-to-fledging for cavity nesting birds to reptile diversity.

Visible light cameras can also capture quantitative information in forest understory locations for calculation of Energy Balance parameters. We are just beginning to explore using remote sensing technology for ground based imaging.

Algal growth potential in an urban stream

• Distributed networks of light, temperature, and novel algal growth potential biosensors

• Actuated sampling system followed by lab chemical analyses

• Linking urban runoff (point, non-point source) to water quality

Bioassay Response vs Temp*Light*NO3(Scaled based on Bioassay Respose Curve)

R2 = 0.881

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 0.05 0.1 0.15 0.2 0.25 0.3Temp (F) * NO3(uM) * Ln(intensity)

Gilbert and Ambrose, UCLA

CENS Prototype EcoPDAGoal is to provide reliable, simple, robust option instead of paper for field crews. In collaboration with TEAM network• Multiple interface options• SQL-lite database on the device• Interactive mapping feature• Flexible export to xml, txt, etc.

Simple MappingInterface

Familiar SpreadsheetInterface

(Web) FormInterface

Outline Sensor Systems (remote + embedded)

What is possible now and in the near future? How do we assess aquatic and terrestrial

ecosystem functions more efficiently? Multi-scale assessment technologies (Integrated

Sensor-Model Systems) How do we evaluate ecosystem function in the

context of human well-being as objectively aspossible? Participatory sensing (enabling the citizen scientist)

Participatory Sensing(e.g., citizenry science using smart phones)

• How many cell phones are there in the world?!

• Image, video, audio, GPS, cell ID, motion band, Bluetooth, text, time, battery level

• Interfaces for many common sensor outputs are there or coming

• Image recognition & classification software

• Make it easy for the user by doing any analysis and visualization on the back-end Web interface

daily trace of an individual

Dietary record

Burke, Estrin et al (UCLA-CENS)

2009 PASIPan-American Sensors for Environmental

Observations (PASEO)

• Sensor fabricators, sensor networkers, ecologists, and environmental scientists

• Sequence of theory and hands-on experiences in:– Building novel sensors– Data transport/communicatiions– Scientific design considerations

• Bahia Blanca, Argentina in April or May 2009• Website: https://eng/ucmerced.edu/paseo/