The BOREAS Information System

Canopy Profiles of Foliage Nitrogen and PAR


This data set describes the relationship between PAR and foliage nitrogen in the canopies of the NSA-OBS, NSA-UBS, NSA-YJP, NSA-OJP AND NSA-OASP.

Table of Contents

  1. Data Set Overview
  2. Investigator(s)
  3. Theory of Measurements
  4. Equipment
  5. Data Acquisition Methods
  6. Observations
  7. Data Description
  8. Data Organization
  9. Data Manipulations
  10. Errors
  11. Notes
  12. Application of the Data Set
  13. Future Modifications and Plans
  14. Software
  15. Data Access
  16. Output Products and Availability
  17. References
  18. Glossary of Terms
  19. List of Acronyms
  20. Document Information

1. Data Set Overview

1.1 Data Set Identification
Light/N profile data.

1.2 Data Set Introduction

1.3 Objective/Purpose
The purpose of this project was to quantify the relationship between percent PAR levels and leaf nitrogen of three boreal forest tree species, black spruce, jack pine and aspen at the BOREAS Northern Study Area.

1.4 Summary of Parameters
Downwelling photosynthetically active radiation (PAR), FPAR, leaf nitrogen (N) concentration, specific leaf nitrogen (N) (i.e. N per unit leaf area)

1.5 Discussion
Functional convergence predicts that the investment into leaf nitrogen is limited by the amount of available light (PAR) which in turn determines photosynthetic capacity. This study tests this hypothesis through measurements of the relationship between FPAR, PAR and N.
The downwelling radiation data from the northern study area (NSA) of the BOREAS study area were collected to characterize the fpar levels at different canopy levels of six boreal canopy cover types: young jack pine, old jack pine, young aspen, old aspen, old (lowland) black spruce and upland TE black spruce. Leaf samples were harvested at each light level, in order to quantify specific leaf nitrogen content by leaf/needle age class. Radiation data were collected between May 27, 1994 and September 17, 1994.

1.6 Related Data Sets
There will be a data file that summarizes the data for each sampling site (6) per field campaign. Data for each transfer are in separate files.

Return to top of document.

2. Investigator(s)

2.1 Investigator(s) Name and Title
Hank Margolis
Ph.D. Universite Laval
Faculte de foresterie et geomatique
Pavillon Abitibi-Price
Sainte-Foy, Quebec
G1K 7P4 Canadatel.
(418) 656-7120

2.2 Title of Investigation
Relationship between FPAR and leaf nitrogen for black spruce, jack pine and aspen stands at the BOREAS Northern Study Area.

2.3 Contact Information

Marie R. Coyea, Ph.D.
Universite Laval
Faculte de foresterie et geomatique
Pavillon Abitibi-Price Sainte-Foy,
Quebec G1K 7P4 
(418)656-2131, poste 6546

Return to top of document.

3. Theory of Measurements

The photosynthetically active radiation measurements were obtained by point quantum sensors. Data was relayed to a data acquisitionsystem (CR10) for storage and data manipulation prior to downloading.
The methodology for measuring leaf area has been described in Appendix K of the BOREAS Experiment Plan, version 3.0. Aspen foliagehas been expressed as hemi-surface area leaf area, while conifer foliage has been expressed as total surface area leaf area.
Kjeldahl procedures will be used to measure leaf nitrogen.

Return to top of document.

4. Equipment:

4.1 Sensor/Instrument Description
Point quantum sensors, millivolt adaptors; extension cables, BNC connectors (LI-COR)
Data acquisition system (CR10 measurement and control module with wiring panel and 12 volt power supply, CR10 keyboard and display, sc12 cable, datalogger support software, CR10 manual, screwdrivers, sc532 data transfer interface, data storage module (SM192), Campbell Scientific)
LadderExtendible pruning shears2 extendible support polespolyethylene storage bagsfreezer spacedrying ovenoptical image analysis system (AgVision, Decagon Inc.)top loading weighing balance.
Laboratory equipped for Kjeldahl N analysis

4.1.1 Collection Environment

4.1.2 Source/Platform
A quantum sensor was supported at the top of a pole that was extended to different levels in the canopy, following a vertical profile from the top of the canopy to the groundcover and understory shrubs. These levels were variable depending on the type of understory vegetation and the canopy profile. One point quantum sensor was used to obtain simultaneous 100% light readings. This sensor was placed on a pole extended from a canopy access tower or a pole that was extended sufficiently above the tree canopy level to avoidany shading during the sampling period. The data acquisition system was kept at the sampling level while using canopy access towers, or on the ground.

4.1.3 Source/Platform Mission Objectives
(As outlined in the Experiment Plan, Version 3, Appendix N, page N-34) To examine the relationship betweeen net photsynthetic (net Ps) capacity, N concentration and percent photsynthetically active radiation of three major boreal forest cover types (jack pine, black spruce and aspen) as well as their link to remotely-sensed and land-based measures of reflected and absorbed radiation.
To establish general relationships which can be used to predict the vertical distribution of the net Ps capacity for these cover types, including their understories, when neither water nor temperature is greatly limiting.

4.1.4 Key Variables
PAR, FPAR, and N.

4.1.5 Principles of Operation
A quantum sensor under full sky exposure was set out under cloudy (diffuse) sky conditions in each stand type. A second quantum sensor was moved vertically throughout the canopy profile starting at ground level. Vegetation at each light harvesting level was removed in order to determine specific N content.
LI-COR quantum sensors measure photosynthetically active radiation (PAR) in the 400 to 700 nm wave band. The unit of measurements ismicromoles per second per square meter (umol/s/m2). The quantum sensor is designed to measure PAR received on a plane surface. The indicated sensor response corresponds to the expected photosynthetic response of plants for which data is available. A silicon photodiode with an enhanced response in the visible wavelengths is used as the sensor. A visible bandpass interference filter in combination with coloured glass filters is mounted in a cosine corrected head.
Quantum sensors were connected to a data acquisition system. This system (CR10) was programmed to convert a differential voltage measurement (rather than a single ended measurement because of noise reduction) to a radiation value. Each quantum sensor has a different calibration constant provided by the manufacturing company that must be incorporated into the programming of the CR10.
The CR10 is a fully programmable datalogger/controller that permits a user to convert electronic responses of a sensor to comprehensible information about our physical environment (e.g. temperature, radiation). It allows the user to store data and program the retrieved data.
The AgVision System is an image analysis system that works by first looking at an object through a video camera, then processing theimage into discrete numerical information with a digitizer and microcomputer, and finally displaying the image or other informationon a monitor for examination.
The Kjeldahl method, the most common method for nutrient analysis, will be used for measuring total N.

4.1.6 Sensor/Instrument Measurement Geometry
Quantum sensors were pointed face up to the canopy (zenith angle) to obtain downwelling radiation.

4.1.7 Manufacturer of Sensor/Instrument
Point quantum sensors Li-190SA, millivolt adaptors; extension cables, BNC connectors:

LI-COR inc.
4421 Superior Street
P.O. Box 4425
Lincoln, NE. 68504 USA
tel. 1-800-447-3576
CR10 data acquisition system CR10 measurement and control module with wiring panel and 12 volt power supply, cr10 keyboard and display, sc12 cable, datalogger support software, cr10 manual, screwdrivers, sc532 data transfer gadget, data storage module (SM192):
Campbell Scientific Canada Corp.
11564 149 Street
Edmonton, Alberta
Canada T5M 1W7
tel. 403-454-2505
Leaf area measurement system/Optical Image Analysis System (AgVision, Monochrome system, root and leaf analysis)
Decagon Devices, Inc.
P.O. Box 835
Pullman, Washington 99163 USA
tel. 1-800-755-2751

4.2 Calibration
Quantum sensors that were used in the field were brand new. Calibration was completed by the manufacturing company, and recalibration of radiation sensors is normally recommended every two years. The calibration data is unique to each sensor and so this was incorporated into the programming when connected to a data acquisition system.
Prior to the season's field sampling, all quantum sensors were compared to confirm that no differences in measurements existed. This involved comparing radiation readings from the same light source. Following a comparison of electrical environmental conditions (outside a building versus inside the photosynthetic lab), a differential mode configuration rather than a single ended voltage configuration was found to reduce response fluctuations (due to electrical noise primarily). Therefore, the differential mode programming and wiring set up were used. In the field, a quick comparison among quantum sensors under similar light conditions was made.
The optical image analysis system was calibrated according to instrument specifications each time the system was opened or after it was left for a period of time. A fine ruler and flat disks of known area were used in the calibration.
Calibration of Kjeldahl instrumentation is unknown at this time.

4.2.1 Specifications
The quantum sensors had a resolution capability of 1 umol m-2 s-1. The absolute calibration is +/- 5% traceable to the US National Institute of Standards and Technology.
The weighing balance was accurate to within 0.0001 g.
The leaf area system was accurate to within 1%.
The shape factor used for black spruce leaf area measurements was 4, according to the BOREAS Experiment Plan, appendix K, Version 3). Based on observations of two cross sections of two needles per fascicle for five fascicles for six jack pine trees from Thompson, Manitoba, we calculated an average shape factor of 4.59 (+/- 0.07).
Specifications of Kjeldahl instrumentation is unknown at this time. Tolerance
The acceptable range for our radiation measurements is between 0 and 2000 umol m-2 s-1. The quantums are typically sensitive to 8 uA per 1000 umol m-2 s-1. There is a maximum deviation of 1% up to 10,000 umol/s/m2.

4.2.2 Frequency of Calibration
Field checks of quantum sensor readings were carried out upon installation of the quantums and everytime data was transferred.
Quantum sensor calibration was completed by the manufacturing company, and recalibration of radiation sensors is normally recommended every two years.

4.2.3 Other Calibration Information
Information regarding calibration for N analysis using the Kjeldahl method is unavailable at this time.

Return to top of document.

5. Data Acquisition Methods

Downwelling radiation data for the northern study area (NSA) of the BOREAS study area were collected to characterize the vertical profile of fpar (the fraction of above canopy PAR) of six boreal forest canopy cover types: young jack pine, old jack pine, young aspen, old aspen, old (lowland) and upland black spruce. These data were collected during diffuse light conditions (overcast/cloudy days) during the three field campaigns between May 27, 1994 and September 17, 1994.

For each stand, the following activities took place:

Return to top of document.

6. Observations

6.1 Data Notes

6.2 Field Notes
None at this time.

Return to top of document.

7. Data Description

7.1 Spatial Characteristics
The data was collected from 6 principal sites in the northern study area (NSA) of Manitoba. The NSA is approximately 100 km by 80 km, and is located 735 km north of Winnipeg. The principal sites in this study were:

Quantum sensors (on poles) were used on the canopy access towers in the bottom, middle and top canopy. In the case of the young jack pine site, where a canopy access tower did not exist, sensors were located within 1 km of the flux tower.

7.1.1 Spatial Coverage
Data acquisition at all six sites was repeated for each of the three intensive field campaigns. The overall period extended from May 24 to September 17, 1994.

7.1.2 Spatial Coverage Map

7.1.3 Spatial Resolution
During the first IFC, the following sites were measured: YJP, May 29; OJP, May 31; YASP, June 14; OASP, June 12; OBS, June 12; TE-BS, June 3.
During the second IFC, the following sites were measured:YJP, July 24; OJP, July 19, 20; YASP, July 26,30; OASP, July 21, 24; OBS, July 25; TE-BS, July 22, 23.
During the third IFC, the following sites were measured: YJP, September 8, 11, 12; OJP, September 5, 6; YASP, September 8,11; OASP, September 2, 5, 6; OBS, September 13;TE-BS, September 12.

7.1.4 Projection

7.1.5 Grid Description

7.2 Temporal Characteristics

7.2.1 Temporal Coverage

7.2.2 Temporal Coverage Map

7.2.3 Temporal Resolution

7.3 Data Characteristics

7.3.1 Parameter/Variable
See 7.3.4.

7.3.2 Variable Description/Definition

7.3.3 Unit of Measurement
See 7.3.2.

7.3.4 Data Source
Second #
1 = top of canopy
2 = middle of canopy
3 = bottom third of canopy
4 = at understory level (below crown canopy)
5= at ground cover level
When a level 6 exists, this corresponds to measurements taken at ground cover, and level 5 corresponds to measurements taken just above a second understory canopy level, i.e. another level of sampling had been added in, always in vertical profile order.

7.3.5 Data Range
Not avaialble at this time.

7.4 Sample Data Record

species site  ifc  stid age      logPAR  nitrogen(mg/g)  PAR(%)  nitrogen(gN/m2) 
aspen   oasp  ifc1 4101 current  4.514   27.59           91.29   1.5

Return to top of document.

8. Data Organization

8.1 Data Granularity

8.2 Data Format(s)
The data has been saved using Microsoft Excel (Version 5.0 for IBM) in text format.

Return to top of document.

9. Data Manipulations

9.1 Formulae
FPAR (%)=((PAR at 100% exposure - PAR reading below canopy)/PAR at 100% exposure)*100. For example, if the maximum PAR (no shading)was 890 and the PAR value underneath canopy was 230, then FPAR under the canopy = 74.16% = ((890-230)/890) x 100

9.1.1 Derivation Techniques and Algorithms
None at this time.

9.2 Data Processing Sequence

9.2.1 Processing Steps
Data from quantum sensors was first converted from electrical (voltage) data to a radiation value in a program (given by the user) for the CR10. This program is specific to this data acquisition system. See the manual for programming if you want to learn how to program (Campbell Scientific).
Each minute the CR10 was programmed to take the data which was being retrieved every second, and do the following:

Data was then transferred from the field site to a storage module which was brought back to a computer for data transfer and verification.
All raw data was printed. Column headings were inserted. Subsequently, all lines that were not part of the sampling scheme were erased. A code that had been entered into the CR10 program in place of the site location enabled us to identify when the relevant light data was not being taken. This code was normally 1.2 and has no other meaning other than to signify that the light values at this time are not useful. For example, the sensor may at this time have been between two different light levels.
Lines that contained zero readings for the quantum sensors were erased (using the autofilter function in Excel, version 5.0). Where only 1 minute or less of data existed per level, these lines were not removed (to avoid blank data cells) Data was then sorted in order of the light profile. Light readings by minute for a level were then averaged together (using the subtotals function in Excel,version 5.0), so that at each change in SITEID the function AVERAGE was used to subtotal all the columns of data.
%PAR and FPAR values were calculated. Year and cdate (calendar date) were inserted as columns.
Manitoba time data was converted from a numeric format to a time format (because original data was in numeric format). The following command was used in Excel, version 5 to do this step: time(mid(a1;1;len(a1)-2);right(a1;2);0). Where a1 refers to the data cell where the time data was found. This value may be f1, for example, depending on what column the data was found. Manitoba time was then converted to Greenwich Mean Time = Thompson, Manitoba time + 6 hours.
Shoot silhouette data, specific leaf area and leaf area data were then transferred into the data files. (At this point there is only one line of data for each desired light level). Data was then checked with original data sheets.
Note that the SLA samples corresponding to the YJP stand in IFC 1 (May 29, 1994) were either misplaced, ground or lost. In order tocompensate for this lost SLA data, the SLA data from the photosynthesis data (May 27, group TE-9B), were substituted. The SLA represent the same sampling levels, but only 4 repetitions were available for this set. Photosynthesis was measured on 3 branches at each canopy level for 5 yjp trees. An average sla (based on 3 branches for each canopy level) was used.

9.2.2 Processing Changes
See 9.2.1.

9.3 Calculations
See 9.1.

9.3.1 Special Corrections/Adjustments
None at this time.

9.3.2 Calculated Variables

9.4 Graphs and Plots
None submitted.

Return to top of document.

10. Errors

10.1 Sources of Error
If the wiring to the CR10 is not fixed securely, loose wires can lead to dubious data results. Likewise if wires have been chewed up by rodents or by hungry canopy tower people, results may be questionable. We had no evidence of these activities during the field season.
If the battery charge is below 9.6 volts, we can expect errors in the data set. We checked for this in our data set, but it is doubtful that this happened since at every data transfer in the field this value was checked.
We can expect errors in the data set when the CR10 module temperature is greater than 50 C. We checked for this in our data set. However, despite the warm and dry conditions that were experienced this summer, it was unlikely that this everoccurred. The storage module was placed in a shady understory location.

10.2 Quality Assessment

10.2.1 Data Validation by Source
All possible errors were checked and removed.

10.2.2 Confidence Level/Accuracy Judgement
Data are preliminary. Pease contact us if used for publication.

10.2.3 Measurement Error for Parameters
Not available at this time

10.2.4 Additional Quality Assessments
None at this time.

10.2.5 Data Verification by Data Center

Return to top of document.

11. Notes

11.1 Limitations of the Data

11.2 Known Problems with the Data
Not available at this time.

11.3 Usage Guidance
Not available at this time.

11.4 Other Relevant Information
Not available at this time.

Return to top of document.

12. Application of the Data Set

Return to top of document.

13. Future Modifications and Plans

Return to top of document.

14. Software

14.1 Software Description

14.2 Software Access

Return to top of document.

15. Data Access

15.1 Contact Information
Marie R. Coyea, Ph.D.
Universite Laval
Faculte de foresterie et geomatique
Pavillon Abitibi-Price Sainte-Foy,
Quebec G1K 7P4 Canadatel.
(418) 656-2131, poste 6546

15.2 Data Center Identification

15.3 Procedures for Obtaining Data
Users may place requests by letter, telephone, electronic mail, fax or personal visit. If the data is taken from BORIS, discussions with either Marie Coyea or Hank Margolis should definitely take place before using the data.

15.4 Data Center Status/Plans

Return to top of document.

16. Output Products and Availability

16.1 Tape Products

16.2 Film Products

16.3 Other Products

Return to top of document.

17. References

Bremner, J.M. and Mulvaney, C.S. 1982. Nitrogen-total. pp. 595-624. In Page, A.L., Miller, R.H. and Keeney, D.R. (eds.). Methods of Soil Analysis - Part 2. Chemical and microbiological properties (2e ed.). Agron. No. 9. ASA-SSSA, Madison, Wisconsin

17.1 Platform/Sensor/Instrument/Data Processing Documentation
Campbell Scientific Canada Corp. 1992. CR10 measurement and control module operator's manual. Revision 3-31-92. Edmonton, Alberta.

Decagon Devices Inc. 199? AgVision monochrochrom system, root and leaf analysis, operator's manual. Pullman, Washington, USA.

LI-COR, inc. 1991. LI-COR Radiation sensors instruction manual. Publication No. 8609-56. Lincoln, Nebraska, USA.

17.2 Journal Articles and Study Reports
Q.L. Dang, H. Margolis, M.R. Coyea, M. Sy, G.J. Collatz and C. Walthall 1996. Profiles of photosynthetically active radiation, nitrogen, and photosynthetic capacity in the boreal forest: implications for scaling from leaf to canopy. J. Geophys. Res., BOREAS Special Issue (accepted with minor revision, July 29, 1996).

17.3 Archive/DBMS Usage Documentation

Return to top of document.

18. Glossary of Terms

Canopy profile, nitrogen profile, PAR profile, PAR-nitrogen relation, nitrogen allocation

Return to top of document.

19. List of Acronyms

BOREAS  - BOReal Ecosystem-Atmosphere Study
BORIS   - BOREAS Information System
DAAC    - Distributed Active Archive Center
EOS     - Earth Observing System
EOSDIS  - EOS Data and Information System
FPAR    - Fraction of photosynthetically active radiation
GSFC    - Goddard Space Flight Center
IPAR    - Intercepted PAR
NASA    - National Aeronautics and Space Administration
OASP    - Old aspen canopy access tower site
OJP     - Old jack pine flux tower site
ORNL    - Oak Ridge National Laboratory
PAR     - Photosynthetically active radiation
PARFPAR - Fraction of absorbed 
Ps      - Photosynthesis
TE-BS   - Terrestrial ecology black spruce canopy access tower site
URL     - Uniform Resource Locator
YJP     - Young jack pine flux tower site

Return to top of document.

20. Document Information

20.1 Document Revision Date

20.2 Document Review Date(s)
BORIS Review:
Science Review:

20.3 Document ID

20.4 Citation
Please contact us.

20.5 Document Curator

20.6 Document URL

Return to top of document.

E-Mail a comment on this page to the curator
Return to the BOREAS Home Page
Last Updated: July 22, 1997