IN-SITU SENSING OF VEGETATION GROWTH AND PHENOLOGY

IN-SITU SENSING OF VEGETATION GROWTH AND PHENOLOGY

IN-SITU SENSING OF VEGETATION
GROWTH AND PHENOLOGY
Workshop: Environmental Sensing in the ACT
February 2015
Darius Culvenor
Environmental Sensing Systems
Melbourne, Australia
Contributors and Collaborators
• Malcolm Gale and Adam Leavesley - ACT Parks &
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Conservation
Marta Yebra, Tim Brown, Cris Brack, Lauren de Waal Australian National University
Alex Held - TERN AusCover and CSIRO
Glenn Newnham and Eva van Gorsel - CSIRO
Susan Kidnie, Danni Martin and Jude Alexander - CFA
Outline
• Multispectral sensing
• Phenological imaging
• Dendrometers
• Data Management
• Grass curing trial
• Tumbarumba
• Future directions
SENSORS
An overview of some sensors being deployed in and
around the ACT
Monitoring ‘Greenness’
• Multispectral sensors
Multispectral Sensors
• Downward Field of View (FOV) ± 30°
• 3m above the ground, approx. 3.5m diameter FOV
• Sensors currently not calibrated to physical units
• Reflectance measurements normalised relative to
intensity of incoming light during post-processing
• Vegetation indices calculated from normalised reflectance
Phenological Camera
• ‘Raspberry Pi’ camera (5 MP, RGB)
• Integrated with base station
• Hourly photos from 10am to 5pm
Phenological Camera
• Time-lapse imaging and visual analysis
• Quantitative analysis using the Green Chromatic
Coordinate (GCC) index
• GCC = Green / (Red + Green + Blue)
Dendrometers
• 20 radial dendrometers soon to
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be installed at the ANU research
forest at the National Arboretum
Measuring long-term stem
diameter growth & diurnal
variation (link to climate, soil
moisture availability, insect
activity, competition…)
0.01 mm (10 um) resolution
Nominal 5 min. sample frequency
Temperature compensation
800
700
600
100
2
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0
-100
01-Oct-08
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01-Nov-08
Radial position (um)
Dendrometers (example data)
15 y.o. Pinus radiata plantation, New Zealand
Tree 11
Tree 17
Tree 31
Rainfall in the last 24 hours
500
400
300
200
Time
Data courtesy of Geoff Downes, Forest Quality (formerly CSIRO)
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-2
DATA MANAGEMENT
• XBee transceivers used to send data from sensors to
base station, range typically < 1km
• All sensors have time-stamped local data storage in-case
wireless transmission to base station fails
• Accuracy: ~5 min drift per year
• Time-stamping by sensors and base station in local time
• Sensor real-time clock (RTC) currently doesn’t adjust for daylight
savings
• Base station can adjust for daylight savings (or not)
Example data string received by base station:
2013-11-04T23:09:25.656845
0013a2004030e554 001c033d002f001400120001000e0021000e000c00010008
2013-11-04T23:09:32.237400
0013a2004030e55c
2013-11-04T23:09:43.854448
2013-11-04T23:11:24.965629
0013a2004030455c 001c034000170009000700000009000c0005000400000002
0013a2004030e554 001d033d002f001400110001000e0021000e000c00010008
2013-11-04T23:11:31.535350
0013a2004030e55c
001d0344000b0004000300000002001d000c000b0001000a
2013-11-04T23:11:43.366718
0013a2004030455c
001d03400017000b000800000009000b0004000400000002
2013-11-04T23:13:24.256292
0013a2004030e554 001e033d002f001400120001000e0020000f000c00010009
2013-11-04T23:13:30.838069
0013a2004030e55c
001e0344000b0004000300000003001c000d000b0001000a
2013-11-04T23:13:42.885438
0013a2004030455c
001e03400017000a000800010009000b0005000400000002
2013-11-04T23:15:23.559046
0013a2004030e554 001f033d002f001300120001000e0022000f000d00010008
2013-11-04T23:15:30.138822
0013a2004030e55c
001f0344000b0004000300000002001d000d000b0001000a
2013-11-04T23:15:42.402183
0013a2004030455c
001f03400017000b000800010009000b0004000400000002
001c0344000b0005000400000002001c000d000a0001000a
Date/time stamp
Unique ID (network address) for
each datalogger/transceiver
Data block contains sensor battery
status and sensor data in hexadecimal
format
GRASS CURING TRIAL
Can in-situ NDVI sensors provide estimates of grass
curing comparable to an experienced field observer?
Base station
View from the Raspberry Pi camera
Grass Curing Trial
http://www.sensingsystems.com.au/public/grassact/GrassCuringACT_NDVI.html
TUMBARUMBA
Can we improve satellite-based estimates of forest Leaf
Area Index (LAI) from in-situ monitoring of understorey
and overstorey dynamics
Tumbarumba Phenocam
Rainfall events?
Sensor 7
Sensor 2
FUTURE DIRECTIONS
Future Directions
• Improvements to data formatting and accessibility
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(comments?)
Better handling of outliers in camera and multispectral
sensor data
Phenocams and/or spectrometers to improve
understanding of red flush events in eucalypts and
differentiation from reddening due to stress/decline
In-situ monitoring of grass & understorey biomass/fuel in
association with estimates of curing (lidar)
Addition of SWIR band to multispectral sensors?
Thank-You
Questions and comments
welcome
[email protected]
Mobile: +61 409 973 993