Developing an Australian phenology monitoring network using near-surface remote sensing. Tim Brown and Trevor Keenan. ACEAS Grand 2014

1. Building an Australian Phenocam Network
A Report from the 2014 ACEAS Phenology Monitoring Working Group
Tim Brown (Australian National University)
ACEAS Workshop Participants – March, 2014
Trevor 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.

2. Complex Systems
• Complex systems solve problems based on the
rate of information exchange, complexity of
connective networks and quality of
information available

3. Research as a complex system
Current ecological questions are too hard to be understood
with existing data and methods
Barriers to knowledge discovery in ecology:

4. Research as a complex system
Current ecological questions are too hard to be understood
with existing data and methods
Barriers to knowledge discovery in ecology:
• Rate of data discovery: Tools available and ease of use

5. Research as a complex system
Current ecological questions are too hard to be understood
with existing data and methods
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

6. Research as a complex system
Current ecological questions are too hard to be understood
with existing data and methods
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

7. Research as a complex system
Current ecological questions are too hard to be understood
with existing data and methods
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

8. Research as a complex system
Current ecological questions are too hard to be understood
with existing data and methods
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

9. Research as a complex system
Current ecological questions are too hard to be understood
with existing data and methods
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
The bottom line: How hard (and costly) is it to answer a given question?
 Reducing these barriers increases knowledge discovery

10. Research as a complex system
Current ecological questions are too hard to be understood
with existing data and methods
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
The bottom line: How hard (and costly) is it to answer a given question?
 Reducing these barriers increases knowledge discovery

11. Traditionally field ecology has had very limited capacity

12. Traditionally field ecology has had very limited capacity
• Low spatial/time resolution data

13. Traditionally field ecology has had very limited capacity
• Low spatial/time resolution data
• Limited sensors other than weather

14. Traditionally field ecology has had very limited capacity
• Low spatial/time resolution data
• Limited sensors other than weather
• Sampling is manual; subjective

15. Traditionally field ecology has had very limited capacity
• Low spatial/time resolution data
• Limited sensors other than weather
• Sampling is manual; subjective
• Observations not-interoperable
• little or no data sharing
• often proprietary

16. Traditionally field ecology has had very limited capacity
• 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

17. Traditionally field ecology has had very limited capacity
• 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?

18. Traditionally field ecology has had very limited capacity
• 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?
(even your own data)

19. “Next-Gen” ecology

20. “Next-Gen” ecology
• Large, long-term field projects with standardized instruments and
data products (“Big Data”): TERN, NEON, FluxNet

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

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

23. “Next-Gen” ecology
• Large, long-term field projects with standardized instruments and
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
• Enables: Data Sharing and collaboration ; Increases our ability to
solve complex problems

24. Big Data is not just for “big” projects
National Arboretum Phenomic & Environmental Sensor Array
(Canberra, ACT)

25. National Arboretum Sensor Array

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

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

28. 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

29. 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

30. 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

31. 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

32. 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

33. 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

34. 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

35. Who cares about data standards anyway?

36. Who cares about data standards anyway?
• Data management and synthesis is essential to
doing science in the 21st century

37. Who cares about data standards anyway?
• 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

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

39. 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.

40. 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)
Richardson et al (2009), Near-surface remote
sensing of spatial and temporal variation in
canopy phenology, Ecological Applications,
19(6), 1417-1428.

41. 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)
Richardson et al (2009), Near-surface remote
sensing of spatial and temporal variation in
canopy phenology, Ecological Applications,
19(6), 1417-1428.

42. What can you measure with phenocams?

43. 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

44. 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
• Most used for monitoring temperate deciduous
forest and satellite validation.

45. 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
• 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.

46. Phenocam Science
• Characterize relation between environmental drivers and
plant response (data from co-located sensors: CO2 flux,
microclimate, etc.)

47. 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

48. 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
• Scaling of ground-based phenology to satellite (and back)

49. 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
• Scaling of ground-based phenology to satellite (and back)
• Fractional cover of green vegetation (nadir cameras)

50. 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
• Scaling of ground-based phenology to satellite (and back)
• Fractional cover of green vegetation (nadir cameras)
• Leaf area index (upwards facing cameras).

51. 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
• Scaling of ground-based phenology to satellite (and back)
• Fractional cover of green vegetation (nadir cameras)
• Leaf area index (upwards facing cameras).
• Flowering phenology

52. 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
• 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

53. 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
• 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

54. 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
• 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)

55. 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
• 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

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

57. Fall in Canberra, April 25, 2014
See the full panorama online here: bit.ly/CBR-TS

58. Fall in Canberra, April 25, 2014
See the full panorama online here: bit.ly/CBR-TS

59. Fall in Canberra, April 25, 2014
See the full panorama online here: bit.ly/CBR-TS

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

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

62. Why a working group?
Everyone is installing cameras now because
they are cheap
• US standards are being developed (US PhenoCam
and NEON)
BUT no global standards or global network

63. Why a working group?
Everyone is installing cameras now because
they are cheap
• US standards are being developed (US PhenoCam
and NEON)
BUT no global standards or global network
• Non-US networks exist but limited data online.

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

65. Why a working group?
Everyone is installing cameras now because
they are cheap
• US standards are being developed (US PhenoCam
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
• No standards or data management plans for
Australia (yet)

66. Why a working group?
Everyone is installing cameras now because
they are cheap
• US standards are being developed (US PhenoCam
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
• No standards or data management plans for
Australia (yet)
• Non-deciduous forests are not well represented

67. 2014 Workshop: Goals

68. 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)

69. 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?

70. 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

71. 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
• Collect all existing camera data
• Email me if you have cameras

72. (Preliminary) Results

73. (Preliminary) Results
• Whole forest analysis doesn’t yield much but
looking at individuals is promising

74. (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)

75. (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

76. (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
• Fire monitoring

77. (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
• Fire monitoring
• Grasslands and fields and other open areas

78. (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
• 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

79. Outcomes: Recommendations

80. 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

81. 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
• Need protocols and standards for hardware, data capture and
how data gets harvested

82. 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
• 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

83. Outcomes: Moving forward

84. Outcomes: Moving forward
• Data Standards

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

86. 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

87. 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

88. 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

89. 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

90. Data sharing: Embargos and other challenges

91. 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

92. 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

93. 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
• Providing resources and tools that create standardized
data and streamline analysis benefit everyone
• Private datasets can be easily pushed to the public when
ready

94. Thanks to all the workshop participants
Photo: Yu Liu (CERN)

95. 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: tim.brown@anu.edu.au
Find me here: bit.ly/Tim_ANU
Or Google: Tim Brown anu
Borevitz Lab: borevitzlab.anu.edu.au