Developing an Australian phenology monitoring network, Tim Brown, ACEAS Grand 2014

Building an Australian Phenocam NetworkA Report from the 2014 ACEAS Phenology Monitoring Working Group

Tim Brown (Australian National University)

ACEAS Workshop Participants – March, 2014Trevor Keenan (Co-Organizer), Alison Specht, Mike Liddell, Natalia Restrepo Coupe, Ivan Hanigan, Jeff Taylor, Yu Liu, Eva Van Gorsel, Albert Van Dijk, Remko Duursma, Caitlin Moore, Stefan Meier, Grant Thorpe, Andrew Richardson, Oliver Sonnentag.

Complex Systems• Complex systems solve problems based on the

rate of information exchange, complexity of connective networks and quality of information available

Research as a complex systemCurrent ecological questions are too hard to be understood with existing data and methods

Barriers to knowledge discovery in ecology:


Barriers to knowledge discovery in ecology:• Rate of data discovery: Tools available and ease of use



• How easy is it to find existing data: Journal Paywalls




• Ease of using existing data: Unavailable/non-interoperable data





• Data quality: Time period monitored; precision; spatial resolution






• Data Analysis How hard is it to work with data, ask new questions and answer them Wikipedia/Google/Smartphones






• Data Analysis How hard is it to work with data, ask new questions and answer them Wikipedia/Google/Smartphones

• The bottom line: How hard (and costly) is it to answer a given question? Reducing these barriers increases knowledge discovery

Traditionally field ecology has had very limited capacity


• Low spatial/time resolution data


• Low spatial/time resolution data• Limited sensors other than weather


• Low spatial/time resolution data• Limited sensors other than weather• Sampling is manual; subjective


• Low spatial/time resolution data• Limited sensors other than weather• Sampling is manual; subjective • Observations not-interoperable • little or no data sharing• often proprietary



• Repeat experiments to verify results are often at different site by different observers




What % of data from the last century of ecology is available for reuse?




What % of data from the last century of ecology is available for reuse?

(even your own data)

“Next-Gen” ecology

“Next-Gen” ecology• Large, long-term field projects with standardized instruments and

data products (“Big Data”): TERN, NEON, FluxNet


data products (“Big Data”): TERN, NEON, FluxNet• NEON:• 106 sites around the US• 30-years• Each site has the same suite of 100’s of types of calibrated

sensors; coupled with on-the-ground measurements, annual aerial overflights

• Billions of data points per year• All data public





• Requires: Public Data, Data Standards and Synthesis





• Requires: Public Data, Data Standards and Synthesis• Enables: Data Sharing and collaboration ; Increases our ability to

solve complex problems

Big Data is not just for “big” projects

National Arboretum Phenomic & Environmental Sensor Array(Canberra, ACT)

National Arboretum Sensor Array

National Arboretum Sensor Array• 20-node Wireless mesh sensor network (Temp, Hg, PAR, Soil T/H)

National Arboretum Sensor Array• 20-node Wireless mesh sensor network (Temp, Hg, PAR, Soil T/H)• High resolution dendrometers on 20 trees

National Arboretum Sensor Array• 20-node Wireless mesh sensor network (Temp, Hg, PAR, Soil T/H)• High resolution dendrometers on 20 trees• (3) Gigapixel timelapse cameras: Leaf phenology for > 1,000 trees

National Arboretum Sensor Array• 20-node Wireless mesh sensor network (Temp, Hg, PAR, Soil T/H)• High resolution dendrometers on 20 trees• (3) Gigapixel timelapse cameras: Leaf phenology for > 1,000 trees• Sequence every tree on site for < $50 tree

National Arboretum Sensor Array• 20-node Wireless mesh sensor network (Temp, Hg, PAR, Soil T/H)• High resolution dendrometers on 20 trees• (3) Gigapixel timelapse cameras: Leaf phenology for > 1,000 trees• Sequence every tree on site for < $50 tree• 5 full weather stations

National Arboretum Sensor Array• 20-node Wireless mesh sensor network (Temp, Hg, PAR, Soil T/H)• High resolution dendrometers on 20 trees• (3) Gigapixel timelapse cameras: Leaf phenology for > 1,000 trees• Sequence every tree on site for < $50 tree• 5 full weather stations• All data live online in realtime

National Arboretum Sensor Array• 20-node Wireless mesh sensor network (Temp, Hg, PAR, Soil T/H)• High resolution dendrometers on 20 trees• (3) Gigapixel timelapse cameras: Leaf phenology for > 1,000 trees• Sequence every tree on site for < $50 tree• 5 full weather stations• All data live online in realtime• Annual LIDAR

National Arboretum Sensor Array• 20-node Wireless mesh sensor network (Temp, Hg, PAR, Soil T/H)• High resolution dendrometers on 20 trees• (3) Gigapixel timelapse cameras: Leaf phenology for > 1,000 trees• Sequence every tree on site for < $50 tree• 5 full weather stations• All data live online in realtime• Annual LIDAR • UAV overflights (monthly?)

• Georectified Google Earth/GIS image layers• 5mm resolution DEM/3D point cloud of site in time-series

National Arboretum Sensor Array• 20-node Wireless mesh sensor network (Temp, Hg, PAR, Soil T/H)• High resolution dendrometers on 20 trees• (3) Gigapixel timelapse cameras: Leaf phenology for > 1,000 trees• Sequence every tree on site for < $50 tree• 5 full weather stations• All data live online in realtime• Annual LIDAR • UAV overflights (monthly?)

• Georectified Google Earth/GIS image layers• 5mm resolution DEM/3D point cloud of site in time-series

Total Cost ~$200K

Who cares about data standards anyway?


• Data management and synthesis is essential to doing science in the 21st century


• Data management and synthesis is essential to doing science in the 21st century

• New technology lets us measure the world in unprecedented detail but it creates so much data we have to organize it better

Phenocams

Phenocam at Capitol Reef Field Station, Utah, USA (Live view: bit.ly/CRFS2)

http://bit.ly/CRFS2

What is a phenocam?

Richardson et al (2009), Near-surface remote sensing of spatial and temporal variation in canopy phenology, Ecological Applications, 19(6), 1417-1428.

What is a phenocam?• Phenology: Study of periodic plant and animal

life-cycle events and how these are influenced by seasonal and interannual variations in climate. (source: wikipedia)


What is a phenocam?• Phenology: Study of periodic plant and animal

life-cycle events and how these are influenced by seasonal and interannual variations in climate. (source: wikipedia)

• Phenocam: Low-cost, automated, consumer digital camera for capturing environmental change in the field (A. Richardson: US PhenoCam network)


What can you measure with phenocams?


Color-based vegetation change driven by biological response to environment or disturbance events (drought, herbivory, fire, etc)• Flowering, Leaf and bark color changes, canopy color

variation, grassland and understory greenup



variation, grassland and understory greenup• Most used for monitoring temperate deciduous

forest and satellite validation.



variation, grassland and understory greenup• Most used for monitoring temperate deciduous

forest and satellite validation. • Less work on vegetation types that don’t show

strong canopy-wide seasonal change such Australian dry/wet sclerophyll forests, grasslands, etc.

Phenocam Science• Characterize relation between environmental drivers and plant

response (data from co-located sensors: CO2 flux, microclimate, etc.)



• Influence of seasonal plant cycles on ecosystem carbon budgets



• Influence of seasonal plant cycles on ecosystem carbon budgets• Scaling of ground-based phenology to satellite (and back)



• Influence of seasonal plant cycles on ecosystem carbon budgets• Scaling of ground-based phenology to satellite (and back)• Fractional cover of green vegetation (nadir cameras)



• Influence of seasonal plant cycles on ecosystem carbon budgets• Scaling of ground-based phenology to satellite (and back)• Fractional cover of green vegetation (nadir cameras)• Leaf area index (upwards facing cameras).



• Influence of seasonal plant cycles on ecosystem carbon budgets• Scaling of ground-based phenology to satellite (and back)• Fractional cover of green vegetation (nadir cameras)• Leaf area index (upwards facing cameras). • Flowering phenology



• Influence of seasonal plant cycles on ecosystem carbon budgets• Scaling of ground-based phenology to satellite (and back)• Fractional cover of green vegetation (nadir cameras)• Leaf area index (upwards facing cameras). • Flowering phenology• Snow cover monitoring



• Influence of seasonal plant cycles on ecosystem carbon budgets• Scaling of ground-based phenology to satellite (and back)• Fractional cover of green vegetation (nadir cameras)• Leaf area index (upwards facing cameras). • Flowering phenology• Snow cover monitoring • Capture Rare events



• Influence of seasonal plant cycles on ecosystem carbon budgets• Scaling of ground-based phenology to satellite (and back)• Fractional cover of green vegetation (nadir cameras)• Leaf area index (upwards facing cameras). • Flowering phenology• Snow cover monitoring • Capture Rare events• Fire (occurrence and recovery)



• Influence of seasonal plant cycles on ecosystem carbon budgets• Scaling of ground-based phenology to satellite (and back)• Fractional cover of green vegetation (nadir cameras)• Leaf area index (upwards facing cameras). • Flowering phenology• Snow cover monitoring • Capture Rare events• Fire (occurrence and recovery)• Identify outlier individuals for analysis/sequencing

Richardson, Klosterman, and Toomey. Near-Surface Sensor-Derived Phenology. (2013). In Phenology: An Integrative Environmental Science. Springer Netherlands, 2013. 413-430.

Fall in Canberra, April 25, 2014

See the full panorama online here: bit.ly/CBR-TS

http://bit.ly/CBR-TS

Why a working group?Everyone is installing cameras now because they are cheap

Why a working group?Everyone is installing cameras now because they are cheap• US standards are being developed (US PhenoCam

and NEON)


and NEON)BUT no global standards or global network


and NEON)BUT no global standards or global network• Non-US networks exist but limited data online.


and NEON)BUT no global standards or global network• Non-US networks exist but limited data online. • Lots of cameras at random field sites globally that

aren’t indexed



aren’t indexed• No standards or data management plans for

Australia (yet)



aren’t indexed• No standards or data management plans for

Australia (yet)• Non-deciduous forests are not well represented

2014 Workshop: Goals

2014 Workshop: Goals• Summarized cameras and existing data sets

• Preliminary analysis of 15 cameras around Australia• Mix of fixed internet-enabled cameras (flux towers), “game cameras”• Primary Contributors (camera data):

Alfredo Huete (Natalia Restrepo Coupe) Jason Berringer (Caitlin Moore) Supersite network (Myself, Mike Liddell)




• What data products can we create from Australian phenocams?





• Metadata standards and long term storage• Ivan Hanigan (supersite manager) is primary lead on this





• Metadata standards and long term storage• Ivan Hanigan (supersite manager) is primary lead on this

• Collect all existing camera data • Email me if you have cameras

(Preliminary) Results


• Whole forest analysis doesn’t yield much but looking at individuals is promising



• Track individual trees or groups of species (requires new tools)




• Green-up of understory




• Green-up of understory• Fire monitoring




• Green-up of understory• Fire monitoring• Grasslands and fields and other open areas




• Green-up of understory• Fire monitoring• Grasslands and fields and other open areas• Need better data:• Imagery is difficult enough to analyse: low quality images and

moving FOV make it harder• Timestamps inconsistent (GMT vs Local vs Daylight)• Large gaps in data

Outcomes: Recommendations

Outcomes: Recommendations• The image is the data! -- field of view (FOV) is your sample plot• You wouldn’t move your study plot at random every 6

months


months• Need protocols and standards for hardware, data capture and

how data gets harvested


months• Need protocols and standards for hardware, data capture and

how data gets harvested

• Also pay attention to how things are replaced, matching FOVs• Simple things like how you replace the batteries on a camera

can greatly impact data quality

Outcomes: Moving forward

Outcomes: Moving forward• Data Standards

Outcomes: Moving forward• Data Standards• Tools for managing the images (1000’s of images)

Outcomes: Moving forward• Data Standards• Tools for managing the images (1000’s of images)• Tools for analysing the data• What data products can provide from phenocam images?• TERN/Supersites/Flux sites are good for this since they have lots

of other stuff being measured• Less well characterized for Australian environments

Outcomes: Moving forward• Data Standards• Tools for managing the images (1000’s of images)• Tools for analysing the data• What data products can provide from phenocam images?• TERN/Supersites/Flux sites are good for this since they have

lots of other stuff being measured• Less well characterized for Australian environments

• Persistent storage

Outcomes: Moving forward• Data Standards• Tools for managing the images (1000’s of images)• Tools for analysing the data• What data products can provide from phenocam images?• TERN/Supersites/Flux sites are good for this since they have lots

of other stuff being measured• Less well characterized for Australian environments

• Persistent storage• Results sharing and tools that can synthesize the wider

network’s data and results

Outcomes: Moving forward• Data Standards• Tools for managing the images (1000’s of images)• Tools for analysing the data• What data products can provide from phenocam images?• TERN/Supersites/Flux sites are good for this since they have lots of

other stuff being measured• Less well characterized for Australian environments

• Persistent storage• Results sharing and tools that can synthesize the wider

network’s data and results• Collaborate with NEON/US PhenoCam Network• Also get EU, India, China, etc on board

Data sharing: Embargos and other challenges


• TERN, NEON, etc: data is designed to be public• Very important because it gives everyone a level playing field for

examining questions together




• But, for many other sources of data there are (may be) good reasons for it to be proprietary• Data costs money! • People need to get credit for creating quality data sets

Initial credit in publications Citation credit for the data itself if you create a good dataset




• But, for many other sources of data there are (may be) good reasons for it to be proprietary• Data costs money! • People need to get credit for creating quality data sets

Initial credit in publications Citation credit for the data itself if you create a good dataset

• Providing resources and tools that create standardized data and streamline analysis benefit everyone• Private datasets can be easily pushed to the public when ready

Thanks to all the workshop participants

Photo: Yu Liu (CERN)

Credits & Thanks

Justin Borevitz and the Borevitz Lab, ANU

2014 ACEAS Phenocam Workshop participants

Funding - ACEAS: Phenocam workshop - FNQ Camera (TERN) - NEON/NSF: US Phenocam Workshop - NCRIS: Phenocam camera server - ANU Major Equipment Grant: National Arboretum sensor array

Email me your phenocam info: [email protected]

Find me here: bit.ly/Tim_ANUOr Google: Tim Brown anu

Borevitz Lab: borevitzlab.anu.edu.au

http://bit.ly/Tim_ANU

http://bit.ly/Tim_ANU

http://borevitzlab.anu.edu.au/

Developing an Australian phenology monitoring network, Tim Brown, ACEAS Grand 2014

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