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Field Calibration/Validation Data » Leaf Data Protocol

Leaf Data Protocol

Last modified by Matt Paget on 2014/04/30 17:43

 
                 

Alfredo Huete

Natalia Restrepo-Coupe

Field work at Chowilla (Jan 29 –Feb 2, 2012)

In collaboration with site PIs and technical personnel: D. Chittleborough, W. Meyer, and G. Whiteman

 

 Over the entire year, there are seasonal changes in the following landscape properties:
 i. whole canopy LAI, percent cover,
 ii. whole canopy fAPAR,
 iii. understory/ overstory,
 iv. leaf optics/ leaf age, and
 v. sun angle (including direct/diffuse and total radiation)

Approach for validating observed satellite seasonality (phenology) patterns:
 i. LAI and percent cover per overstory and understory layers (to be sampled by separate teams)
 ii. leaf sampling to assess seasonality in leaf age and traits (size, colour, chlorophyll)**;
 iii. digital camera observations for monitoring overstory/understory dynamics over seasons**,
 iv. tower observations of fAPAR, PAR(incident), absorbed PARhelp, reflected PAR, and albedo, and
 v. diurnal radiometric measurements of understory reflectances
[n1]  with ASD portable spectroradiometer.

 

We group the above-mentioned tasks in three main measurement activities:

1.       Leaf Spectral Measurements: Leaf optical property sampling of dominant species and functional classes in order to complement current validation efforts in validating satellite retrievals of vegetation foliage amounts, in terms of 3D Leaf Area Index (LAI) and 2D Foliage Projected Coverage (FPC).  Leaf optical and leaf trait sampling represent the missing factors linking satellite spectral observations with landscape biophysical properties; and recognizes that not all LAI nor FPC are the same. 

2.      Understory Spectral Measurements: Hyperspectral optical sampling of understory (leaves, bark, litter, and soil) will also enable a more effective interpretation of hyperspectral- broadband sensor data, thus providing a fusion of MODIS with hyperspectral airborne campaigns.

3.      Automated RGB cameras: Installation of three permanent cameras will provide daily near-surface remote sensing of overstory and understory phenology.

 

Methods

 

 

1.  Leaf Spectral Measurements: Sample enough leaves (3-5 per branch: youngest - middle and oldest leaf) and several branches (3-4 bottom to crown), sampling from lower to upper branches gives us a proxy for age. Sample only fully expanded (“mature”) leaves at 2 to 3 trees per statistically significant species (focus on 5 dominant understory and 5 overstory species, increase number of species if possible) to determine if whole tree/canopy leaves seasonally change their optical/biologic properties (leaf spectra, chlorophyll, leaf colour, SLA, etc.).

2. Understory Spectral Measurements: Sample understory optical properties along LAI transect. Sample understory at different times of the day (effect of solar angle on spectral measurements).

3. RGB camera: Install oblique and nadir cameras**

Site selection process:

1.  Leaf Spectral Measurements: Select species at tower footprint.

2. Understory Spectral Measurements: Four transects to capture optical properties (understory) along the LAI (hemispherical camera and LI-2200) transects.

3. RGB camera: Three automated cameras will record hourly and daily changes on vegetation. To be installed looking at the understory and hole ecosystem. This would be in collaboration and coordination with site PIs and technical personal, Chittleborough, D., W. Meyer, and G. Whiteman of the OZflux Calperum-Chowilla tower (Figure 1).

 

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Figure 1: View from the Calperum-Chowilla tower and corner right, understory species (note large quantities of woody debris) [Chittleborough et al., 2012]

 

 

 

Sampling times:

1. Leaf Spectral Measurements: For validation of phenology products, a minimum of 2 to 3 sampling periods are required since phenology measures rates and magnitudes of changes over a season to verify that the satellite phenology products are depicting the same rates and direction of change as well as the correct trends and switches in growth.

Ideally continue with the sampling during key phenological events (start of the growing season, growing season peak and dormant season).  Branches and trees suitable for resample should be labelled (aluminium tags). Therefore, measurements can continue during key phenological events.

2. Understory Spectral Measurements: Similar to Leaf Spectral Measurements continue sampling at key phonological events

3. RGB camera: The cameras will take 1-hourly image from 8:00 – 16:00 hours every day.

 

[2] Sampling protocols:

1.      Leaf Spectral Measurements: Focus on the parameters required by most Leaf Optical Properties Spectra models (SM) and Fuel Moisture Content (FMC) models. Parameters measured by previous works relating leaves spectra and leaf traits (LT) [Asner et al., 2009; Doughty et al., 2010].

Performed at leaf-level at five leaves per specie (see sampling for individuals), see:

Protocol1.0LeafSampling.111005 document for protocol.

 

Data

Protocol

Equipment

 

 

GPS

Ladder

Hand pruner

Extension pruner

Aluminum tags

1.1.            Leaf Reflectance

Protocol1.5&6.Reflectance&TransmitanceLI1800

ASD-HH

LI800 Integrating sphere

1.2.            Leaf shape: area, maximum width and length

Protocol1.1.LeafShape

Scanner

 

1.3.            Digital photo (RGB record) of sampled leaf[n3] 

Protocol1.11.DigitalrecordRGB

RGB camera

Spectralon

Tripod

 

To be done on site (see leaf collection protocol)

1)                  Digital photo of the leaf location and image of the leaf on site (if possible)

2)                  Leaf collection

3)                  Gas exchange (if leaf is selected)

4)                  Optical properties

5)                  Scan leaf

 

2.      Understory Spectral Measurements

Focus on validation of phenology products and parameters and information required by canopy bidirectional reflectance model (SAIL and PARCINOPY[n4] ) parameters [Combal et al., 2003].  Along proposed LAI transects.

 

Data

Protocol

Equipment

 

 

GPS

2.1.            Understory reflectance (in situ)

Protocol1.10.ReflectanceInSitu

ASD HH

2.2.            Reflectance soil and litter (wet and dry)

Protocol1.10.ReflectanceInSitu

ASD HH

2.3.            Digital photo (RGB record)

Protocol1.11.DigitalrecordRGB

RGB camera

Spectralon

Tripod

 

To be done on site (see leaf collection protocol)

1)      Follow the STAR transect

2)      Every 2 m. Record GPS coordinates and do a set of spectral measurements

3)      Every 10 m. Record GPS coordinates and measure absorbed and reflected PAR (LI-1400).

4)      Run the same transects at different times of the day.

 

3. RGB camera:  

Data

Protocol

Equipment

 

 

Boom

Crossboom

Level

Compass

Clinometer

2.2.            Understory and canopy (up tower) phenol-cam

Protocol1.12.wingscapesRGB

Wingscapes

Solar panel

SDcard

Bateries

 

To be done on site (see leaf collection protocol)

1)      Install the cameras (understory and tower).

2)      Test the cameras for 1-2 days saving at a frequency of image/10 minutes.

3)      Set the camera to take one image every hour from 8:00 to 17:00.

 

Results:

Relationships between optical properties with leaf traits at various phenostages.

The role of leaf spectral/ biophysical properties on satellite-phenology (seasonality)?

 

References

Asner, G. P., R. E. Martin, A. J. Ford, D. J. Metcalfe, and M. J. Liddell (2009), Leaf chemical and spectral diversity in Australian tropical forests, Ecological Applications, 19(1), 236-253, doi:10.1890/08-0023.1.

Chittleborough, D., W. Meyer, and G. Whiteman (2012), Calperum-Chowilla Flux Tower, Calperum-Chowilla Flux Tower. [online] Available from: https://calperumchowilla.wordpress.com (Accessed 20 January 2012)

Combal, B., F. Baret, M. Weiss, A. Trubuil, D. Macé, A. Pragnère, R. Myneni, Y. Knyazikhin, and L. Wang (2003), Retrieval of canopy biophysical variables from bidirectional reflectance: Using prior information to solve the ill-posed inverse problem, Remote Sensing of Environment, 84(1), 1-15, doi:10.1016/S0034-4257(02)00035-4.

Doughty, C. E., G. P. Asner, and R. E. Martin (2010), Predicting tropical plant physiology from leaf and canopy spectroscopy, Oecologia, 165, 289-299, doi:10.1007/s00442-010-1800-4.

 



[n1]whole canopy / landscape


[2]

 

Other simple measures are - leaf size/ elongation.  Leaf colour using Munsell color charts; branch photos; SLA.

Also- any other qualitative observations?


[n3]Leaf color using Munsell color charts based on the RGB image


[n4]Maximum number of leaf, maximum LAI value, maximum canopy height, plant stage, and plant density

 

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