NASA/TMm2000-209891, Vol. 53

Technical Report Series on the

Boreal Ecosystem-Atmosphere Study (BOREAS)

Forrest G. Hall and Jaime Nickeson, Editors

Volume 53

BOREAS RSS-7 Landsat TM

LAI Images of the SSA and NSA

Jing Chen and Josef Cihlar

Canada Centre for Remote Sensing, Ottawa, Ontario, Canada

National Aeronautics and

Space Administration

Goddard Space Flight Center

Greenbelt, Maryland 20771

August 2000

https://ntrs.nasa.gov/search.jsp?R=20010022101 2018-05-14T18:54:28+00:00Z
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NASA/TMm2000-209891, Vol. 53

Technical Report Series on the

Boreal Ecosystem-Atmosphere Study (BOREAS)

Forrest G. Hall and Jaime Nickeson, Editors

Volume 53

BOREAS RSS-7 Landsat TM

LAI Images of the SSA and NSA

Jing Chen and Josef Cihlar

Canada Centre for Remote Sensing, Ottawa, Ontario, Canada

National Aeronautics and

Space Administration

Goddard Space Flight Center

Greenbelt, Maryland 20771

August 2000
NASA Center for AeroSpace Information

7121 Standard Drive

Hanover, MD 21076-1320

Price Code: A17

Available from:

National Technical Information Service

5285 Port Royal Road

Springfield, VA 22161

Price Code: A10
BOREAS RSS-7 Landsat TM LAI Images of the SSA and NSA

Jing M. Chen, Josef Cihlar

Summary

The BOREAS RSS-7 team used Landsat TM images processed at CCRS to produce images of LAI for

the BOREAS study areas. Two images acquired on 06-Jun and 09-Aug-1991 were used for the SSA,

and one image acquired on 09-Jun- 1994 was used for the NSA. The LAI images are based on ground

measurements and Landsat TM RSR images. The data are stored in binary image-format files.

Note that some of the data files on the BOREAS CD-ROMs have been compressed using the Gzip

program. See Section 8.2 for details.

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

BOREAS RSS-07 Landsat TM LAI Images of the SSA and NSA

1.2 Data Set Introduction

These Leaf Area Index (LAI) images were generated in response to the need within the BOReal

Ecosystem-Atmosphere Study (BOREAS) modeling community for adequate spatial and temporal

coverage of estimated vegetation indices across the BOREAS region.

1.3 Objective/Purpose

The objective of this study was to provide spatially referenced LAI maps for landscape and

regional ecosystem analysis and modeling in the Southern Study Area (SSA) and Northern Study Area

(NSA) of the BOREAS.

Page 1
1.4 Summary of Parameters

LAI images

1.5 Discussion

This data set was prepared as a part of continuous investigation by the Remote Sensing Science

(RSS)-07 team. LAI maps were calculated using Landsat Thematic Mapper (TM) images acquired on

06-Jun-1991 (SSA), 09-Aug-1991 (SSA), and 09-Jun-1994 (NSA). These LAI maps are produced

with atmospherically corrected Landsat TM band 3, 4, and 5 data and field measurements of LAI. The

link between the image and field measurements is based on the correlation between the Reduced

Simple Ratio (RSR) and the field LAI values. RSR is the simple ratio (SR), near-infrared (NIR)

reflectance/red reflectance, reduced by a factor based on the mid-infrared (MIR) reflectance (TM band

5), i.e.

RSR = SR(1-(MIR-MIRmin)/(MIRmax-MIRmin))

where MIRmin and MIRmax are the minimum and maximum MIR reflectance in the image, determined

from the histogram of each image. They are 0.05 and 0.22 for the 06-Jun and 09-Aug images, and

0.06 and 0.24 for the 09-Jun image. Generally the correlation takes the following form:

LAI = intercept + Slope * RSR

In this LAI calculation, the following parameters were used for both the SSA and the NSA: intercept =

1.75, slope = 0.46. Unlike our previous techniques (i.e., regional LAI maps from the Advanced Very

High Resolution Radiometer (AVHRR), no land cover information was used in the LAI calculation.

This may reduce the error propagation caused by misclassification or grouping of spectral data and

provides more details of spatial variation of LAI because of the sensitivity of the MIR band to

vegetation cover. The disadvantage of not using the land cover map is that in areas of low canopy

density, the unknown background reflectance can affect the determination of LAI; however, this bias is

very small. For a small number of pixels, the calculated LAI is unreasonably high, and therefore an

upper limit of an LAI of 6 is imposed. The advantages of using RSR are discussed by Brown et al.

(1999).

The theories of field LAI measurements are documented in the BOREAS RSS-07 LAI, Gap

Fraction, and FPAR Data document. The LAI data sets have been geometrically rectified. The LAI

maps were produced with encoded LAI range values. Image processing and penetration of LAI maps

were conducted at the Canada Centre for Remote Sensing (CCRS).

1.6 Related Data Sets

BOREAS Level-3b Landsat TM Imagery: At-sensor Radiance in BSQ Format

BOREAS RSS-07 Regional LAI and FPAR Images From Ten-Day AVHRR-LAC Compsites

2. Investigator(s)

2.1 Investigator(s) Name and Title

Dr. Jing M. Chen

Mr. Xiaoyuan Geng (Ph.D. Candidate)

2.2 Title of Investigation

Retrieval of Boreal Forest Leaf Area Index From Multiple Scale Remotely Sensed Vegetation

Indices

Page 2
2.3 Contact Information

Contact 1:

Jing M. Chen

Canada Centre for Remote Sensing

588 Booth Street, 4th Floor

Ottawa, Ontario

K1AOY7 Canada

(613) 947-1266

(613) 947-1406 (fax)

jing.chen@ ccrs.nrcan.gc.ca

Contact 2:

Mr. Xiaoyuan Geng

Canada Centre for Remote Sensing

588 Booth Street, 4th Floor

Ottawa, Ontario

K1AOY7 Canada

(613) 947-1231

(613) 947-1406 (fax)

xgeng@ccrs.nrcan.gc.ca

Contact 3:

Jaime Nickeson

Raytheon ITSS

NASA GSFC

Code 923

Greenbelt, MD 20771

(301) 286-3373

(301) 286-0239 (fax)

Jaime.Nickeson@ gsfc.nasa.gov

3. Theory of Measurements

The theory of Landsat TM has been discussed in the BOREAS documentation for the Landsat Level-3b

data. Relevant portions have been repeated throughout.

The Landsat TM sensor collects imagery of Earth in seven spectral bands ranging from the blue to

the thermal IR portion of the electromagnetic spectrum. Multispectral classification of this imagery can

be performed by identifying areas that are representative of land cover types to be classified. Statistics

can be computed in these spectral bands for each feature type. A maximum likelihood classifier can

then be applied by identifying the most likely land cover type for each pixel in the image based on the

statistics of the training fields.

The following spectral bands are collected by the TM sensor:

Channel Wavelength (_) Primary Use

1 0.45

2 0.52

3 0.63

4 0.76

- 0.52

- 0.90

Coastal water mapping, soil vegetation

differentiation, deciduous/coniferous

differentiation.

Green reflectance by healthy vegetation.

Chlorophyll absorption for plant species

differentiation.

Biomass surveys, water body delineation.

Page 3
5 1.55 - 1.72 Vegetation moisture measurement, snow

cloud differentiation.

6 10.4 - 12.5 Plant heat stress measurement, other

thermal mapping.

7 2.08 - 2.35 Hydrothermal mapping.

4. Equipment

4.1 Sensor/Instrument Description

The TM sensor system records radiation from the seven bands in the electromagnetic spectrum

described in Section 3. It has a telescope that directs the incoming radiant flux obtained along a scan

line through a scan line collector to the visible and NIR focal plane, or to the MIR and thermal-IR

cooled focal plane. The detectors for the visible and NIR bands (1 to 4) are four staggered linear

arrays, each containing 16 silicon detectors. The two MIR detectors are 16 indium-antimonide cells in

a staggered linear array, and the thermal-IR detector is a four-element array of

mercury-cadmium-telluride cells.

4.1.1 Collection Environment

The Landsat satellite orbits Earth at an altitude of 705 km.

4.1.2 Source/Platform

Landsat TM satellite.

4.1.3 Source/Platform Mission Objectives

The Landsat TM is designed to respond to and measure both reflected and emitted Earth surface

radiation to enable the investigation, survey, inventory, and mapping of Earth's natural resources.

4.1.4 Key Variables

Reflected radiation, emitted radiation, and temperature.

4.1.5 Principles of Operation

The TM is a scanning optical sensor operating in the visible and IR wavelengths. It contains a scan

mirror assembly that directly projects the reflected Earth radiation onto detectors arrayed in two focal

planes. The TM achieves better imagery resolution, sharper color separation, and greater in-flight

geometric and radiometric accuracy for seven spectral bands simultaneously than the previous

generation sensor, the Multispectral Scanner (MSS). Data collected by the TM sensor are transmitted

back to Earth receiving stations for processing.

4.1.6 Sensor/Instrument Measurement Geometry

The TM sensor depends on the forward motion of the spacecraft for the along-track scan and uses

a moving mirror assembly to scan in the cross-track direction (perpendicular to the spacecraft). The

instantaneous field of view (IFOV) for each detector from bands 1 through 5 and band 7 is equivalent

to a 30-m square when projected to the ground; band 6 (the thermal-IR band) has an IFOV equivalent

to a 120-m square.

4.1.7 Manufacturer of Sensor/Instrument

NASA GSFC

Greenbelt, MD 20771

Hughes Aircraft Corporation

Santa Barbara, CA

Page 4
4.2 Calibration

The internal calibrator, a flex-pivot-mounted shutter assembly, is synchronized with the scan

mirror, oscillating at the same 7-Hz frequency. During the turn-around period of the scan mirror, the

shutter introduces the calibration source energy and a black direct-current restoration surface into the

100-detector field of view.

The calibration signals for bands 1 through 5 and band 7 are derived from three regulated

tungsten-filament lamps. The calibration source for band 6 is a blackbody with three temperature

selections, commanded from the ground. The method for transmitting radiation to the moving

calibration shutter allows the tungsten lamps to provide radiation independently and to contribute

proportionately to the illumination of all detectors.

4.2.1 Specifications

Band

Radiometric

Sensitivity [NE(dP)]*

1 0.8%

2 0 .5%

3 0 .5%

4 0 .5%

5 1.0%

6 0.5 K

7 2 .4%

[NE (dT) ]

Ground IFOV

Avg. altitude

Data rate

Quantization levels

Orbit angle

Orbital nodal

Period

Scan width

Scan angle

Image overlap

30 m (Bands 1-5,

120 m (Band 6)

699.6 km

85 Mbps

256

8.15 degrees

98.88 minutes

185 km

14.9 degrees

7.6 %

7)

* N.B. The radiometric sensitivities are the noise-equivalent reflectance differences for the reflective

channels expressed as percentages [NE(dP)] and temperature differences for the thermal-IR bands

[NE(dT)].

4.2.1.1 Tolerance

The TM channels were designed for a noise-equivalent differential represented by the radiometric

sensitivity shown in Section 4.2.1.

4.2.2 Frequency of Calibration

The absolute radiometric calibration between bands is maintained by using internal calibrators that

are located between the telescope and the detectors and are sampled at the end of a scan.

4.2.3 Other Calibration Information

Relative within-band radiometric calibration, to reduce "striping," is provided by a scene-based

procedure called histogram equalization. The absolute accuracy and relative precision of this calibration

scheme assumes that any changes in the in the optics of the primary telescope or the "effective

radiance" from the internal calibrator lamps are insignificant in comparison to the changes in detector

sensitivity and electronic gain and bias with time and that the scene-dependent sampling is sufficiently

precise for the required within-scan destriping from histogram equalization.

Page 5
EachTM reflectivebandandtheinternalcalibratorlampswerecalibratedprior to launchusing

lampsin integratingspheresthatwerein turncalibratedagainstlampstraceableto calibratedNational

Bureauof Standardslamps.Sometimestheabsoluteradiometriccalibrationconstantsin the

"short-term"and"long-termparameters"filesusedfor groundprocessinghavebeenmodifiedafter

launchbecauseof inconsistencywithin orbetweenbands,changesin theinherentdynamicrangeof

thesensors,or adesireto makequantizedandcalibratedvaluesfrom onesensormatchthosefrom

another.

5. Data Acquisition Methods

These data were acquired from the Landsat 5 TM sensor and received from CCRS who purchased it

from the Earth Observation Satellite Corporation (EOSAT). As received from CCRS, the image had

been processed from raw telemetry to a systematically corrected product within the CCRS MOSAICS

system. Atmospheric correction was applied to the systematically corrected data. Surface reflectance

was calculated using the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) (Vermote

et al., 1997) model, using inputs of a continental airmass, midlatitude summer, a uniform target, and

30 km atmospheric visibility. Modified SR was calculated using maximum IR reflectance of 22% and

24%, and minimum IR reflectance of 5% and 6% for the SSA and NSA, respectively (Brown et al.,

1999). Field LAI data were taken from the published data of Chen (1996) and Brown et al. (1999).

Detailed procedures are documented in Chen et al. (1997).

6. Observations

6.1 Data Notes

The radiometric quality of the imagery is acceptable.

6.2 Field Notes

None given.

7. Data Description

7.1 Spatial Characteristics

7.1.1 Spatial Coverage

The calculated LAI images cover portions of a full Landsat TM scene. The data set boundaries have

well-defined coordinates.

The North American Datum of 1927 (NAD27) Universal Transverse Mercator (UTM) coordinates

of the TM image of NSA acquired on 09-Jun-1994 are:

UTM UTM

Easting Northing

Northwest 501220.5 6239553.0

Southeast 562660.5 6178113.5

The NAD27 UTM coordinates of the TM image of SSA acquired on 09-Aug- 1991:

UTM UTM

Easting Northing

Northwest 451000 6042000

Southeast 556000 5937000

Page 6
The NAD27 UTM coordinates of the TM image of SSA acquired on 06-Jun-1991:

UTM UTM

Easting Northing

Northwest 471200 6016960

Southeast 532640 5955520

7.1.2 Spatial Coverage Map

Not available.

7.1.3 Spatial Resolution

These data were gridded to a cell size of 30 meters from the original nominal resolution of 28.5

meters by the MOSAICS system.

7.1.4 Projection

Universal Transverse Mercator (UTM).

7.1.5 Grid Description

SSA: UTM Zone 13, NAD27

NSA: UTM Zone 14, NAD27

7.2 Temporal Characteristics

7.2.1 Temporal Coverage

The images used were acquired in 1991 for the SSA, and in 1994 for the NSA.

7.2.2 Temporal Coverage Map

Image WRS* Solar Elevation Solar Azimuth Radiometric

Date (Path/Row) (degrees) (degrees) Quality

06-Jun-1991 37/22-23 47.41 139.83 Good

09-Aug-1991 37/22 43.25 141.88 Good

09-Jun-1994 33/21 37.65 140.81 Good

* WRS -- World Reference System

7.2.3 Temporal Resolution

The Landsat TM satellite revisit frequency is 16 days for each path/row; however, in the BOREAS

region, the overlap between adjacent scene paths is about 50%. Thus, the frequency for some areas

can be 8 days.

7.3 Data Characteristics

7.3.1 Parameter/Variable

Leaf Area Index (LAI)

7.3.2 Variable Description/Definition

The definition of LAI is one half the total leaf area per unit ground surface area. The reduced simple

ratio is:

RSR = SR * (1-(MIR-MIRmin)/(MIRmax-MIRmin))

where MIRmin and MIRmax are the minimum and maximum MIR reflectance in an image.

Page 7
7.3.3 Unit of Measurement

LAI - squaremetersof leafarea/ square meter of ground surface

7.3.4 Data Source

Landsat TM 5 sensor.

7.3.5 Data Range

LAI : 1 - 55 (0 for water) for LAI 0 - 5.4

7.4 Sample Data Record

Not applicable for image data.

8. Data Organization

8.1 Data Granularity

The smallest unit of data tracked by the BOREAS Information System (BORIS) is a binary and

TIFF image pair acquired on a single day.

8.2 Data Format(s)

8.2.1 Uneompressed Files

This data set contains the following six files:

File Description/Name Format Npix Nlines File Size

NSA LAI 94JUNE.IMG Raw Binary 2048 2048 4Mb

NSA LAI 94JUNE.TIFF TIFF 2Mb

SSA LAI 91JUNE.IMG Raw Binary 2048 2048 4Mb

SSA LAI 91JUNE.TIFF TIFF 2Mb

SSA LAI 91AUG.IMG Raw Binary 3500 3500 12Mb

SSA LAI 91AUG.TIFF TIFF 6Mb

The *.IMG files are all 8-bit binary images. The file architecture is the same as that of any common

image file: the first byte is the first pixel of the first line, the second byte is the second pixel of the first

line,.(the first pixel in the second line of image 1 is the 2049th byte). The *.TIFF files are TIFF

versions of the *.IMG files and are for display purposes only.

8.2.2 Compressed CD-ROM Files

On the BOREAS CD-ROMs, the image files been compressed with the Gzip (GNU zip)

compression program (file_name.gz). These data have been compressed using gzip version 1.2.4 and

the high compression (-9) option (Copyright (C) 1992-1993 Jean-loup Gailly). Gzip uses the

Lempel-Ziv algorithm (Welch, 1994) also used in the zip and PKZIP programs. The compressed files

may be uncompressed using gzip (with the -d option) or gunzip. Gzip is available from many Web

sites (for example, the ftp site prep.ai.mit.edu/pub/gnu/gzip-*.*) for a variety of operating systems in

both executable and source code form. Versions of the decompression software for various systems

are included on the CD-ROMs.

Page 8
9. Data Manipulations

9.1 Formulae

9.1.1 Derivation Techniques and Algorithms

LAI maps were calculated using Landsat TM images acquired on 06-Jun-1991 (SSA),

09-Aug-1991 (SSA), and 09-Jun-1994 (NSA). These LAI maps are produced with atmospherically

corrected Landsat TM band 3, 4, and 5 data and field measurements of LAI. The link between the

image and field measurements is based on the correlation between the RSR and the field LAI values.

RSR is the SR, NIR reflectance/red reflectance, reduced by a factor based on the MIR reflectance (TM

band 5), i.e.

RSR = SR(1-(MIR-MIRmin)/(MIRmax-MIRmin))

where MIRmi n and MIRma x are the minimum and maximum MIR reflectance in the image determined

from the histogram of each image. They are 0.05 and 0.22 for the 06-Jun and 09-Aug images, and

0.06 and 0.24 for the 09-Jun image. Generally, the correlation takes the following form:

LAI = intercept + Slope * RSR

In this LAI calculation, the following parameters were used for both the SSA and the NSA: intercept =

1.75, slope = 0.46.

9.2 Data Processing Sequence

9.2.1 Processing Steps

BORIS staff copied the American Standard Code for Information Interchange (ASCII) and

compressed the binary files for release on Compact Disk - Read-Only Memory (CD-ROM).

9.2.2 Processing Changes

None.

9.3 Calculations

9.3.1 Special Corrections/Adjustments

None.

9.3.2 Calculated Variables

RSR = SR(1-(MIR-MIRmin)/(MIRmax-MIRmin))

LAI = intercept + Slope * RSR

9.4 Graphs and Plots

None.

10. Errors

10.1 Sources of Error

The sources of error in the LAI maps could result from a number of factors. The correlation

between LAI and modified SR is empirical. Also, the use of MIR channel in the empirical relationship

may be influenced by the surface water conditions. Other potential sources of error, such as those

caused by image correction and projection, are minor in comparison.

Page 9
10.2 Quality Assessment by Source

10.2.1 Data Validation by Source

Even though we have inspected the final LAI values for about 10 known sites on each image, no

extensive validation has been done. Since our source of RSR data came from the scenes 06-Jun-1991

(SSA) and 09-Jun-1994 (NSA), we believe LAI images of these two dates are most useful.

10.2.2 Confidence Level/Accuracy Judgment

None given.

10.2.3 Measurement Error for Parameters

Ground LAI measurement errors are about 25% (Chen et al., 1997). The RSR may be sensitive to

rainfall events. In particular, the 09-Aug-1991 scene was not used in the RSR-LAI algorithm

development, so it is less reliable.

10.2.4 Additional Quality Assessment

Not applicable.

10.2.5 Data Verification by Data Center

Data were examined for general consistency and clarity.

11. Notes

11.1 Limitations of the Data

None given.

11.2 Known Problems with the Data

Clouds in the 06-Jun-1991 scene of the SSA show up in a diagonal strip across the image. The

areas under the clouds have LAI in the range of 2.5-3.0. The 09-Aug-1991 scene, which covers the

same area, is clear and is therefore provided here as a remedy for the cloud effect. It is known,

however, that summer scenes are less reliable for LAI retrieval because of the increased background

effect, which overestimates LAI by about 15-25%, and reduces the spatial variation of LAI.

For jack pine stands, the LAI in the 09-Aug-1991 scene of the SSA seems to be about 20% high

because of understory development, while this background effect in other stand types is more

suppressed by the use of the MIR (TM band 5) data.

11.3 Usage Guidance

Spring scenes (06-Jun-1991 and 09-Jun-1991) are the most suitable for LAI mapping in the SSA

and NSA (Chen and Cihlar, 1996). However, in the SSA, the 06-Jun-1991 scene is affected by

clouds. The 09-Aug-1991 scene is therefore provided as a reference to remove the cloud effects. The

LAI change is small for conifer stands and higher for deciduous and grassland.

Before uncompressing the Gzip files on CD-ROM, be sure that you have enough disk space to

hold the uncompressed data files. Then use the appropriate decompression program provided on the

CD-ROM for your specific system.

11.4 Other Relevant Information

None.

12. Application of the Data Set

These images can be used to investigate the spatial distribution of LAI over the mapped areas.

Page 10
Nonegiven.

13. Future Modifications and Plans

14. Software

14.1 Software Description

Gzip (GNU zip) uses the Lempel-Ziv algorithm (Welch, 1994) used in the zip and PKZIP

commands.

14.2 Software Access

Gzip is available from many Web sites across the Internet (for example, ftp site

prep.ai.mit.edu/pub/gnu/gzip-*.*) for a variety of operating systems in both executable and source

code form. Versions of the decompression software for various systems are included on the

CD-ROMs.

15. Data Access

The Landsat TM LAI images of the SSA and NSA are available from the Earth Observing System Data

and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active

Archive Center (DAAC).

15.1 Contact Information

For BOREAS data and documentation please contact:

ORNL DAAC User Services

Oak Ridge National Laboratory

P.O. Box 2008 MS-6407

Oak Ridge, TN 37831-6407

Phone: (423) 241-3952

Fax: (423) 574-4665

E-mail: ornldaac@ornl.gov or ornl@eos.nasa.gov

15.2 Data Center Identification

Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory

(ORNL) Distributed Active Archive Center (DAAC) for Biogeochemical Dynamics

http://www-eosdis.ornl.gov/.

15.3 Procedures for Obtaining Data

Users may obtain data directly through the ORNL DAAC online search and order system

[http://www-eosdis.ornl.gov/] and the anonymous FTP site [ftp://www-eosdis.ornl.gov/data/] or by

contacting User Services by electronic mail, telephone, fax, letter, or personal visit using the contact

information in Section 15.1.

15.4 Data Center Status/Plans

The ORNL DAAC is the primary source for BOREAS field measurement, image, GIS, and

hardcopy data products. The BOREAS CD-ROM and data referenced or listed in inventories on the

CD-ROM are available from the ORNL DAAC.

Page 11
16. Output Products and Availability

16.1 Tape Products

The data can be made available on 8-ram or Digital Archive Tape (DAT) media.

16.2 Film Products

None.

16.3 Other Products

These data are available on the BOREAS CD-ROM series.

17. References

17.1 Platform/Sensor/Instrument/Data Processing Documentation

PACE Image Analysis Kernel Version 6.2. 1997. PCI, Inc. Richmond Hill, Ontario.

Welch, T.A. 1984. A Technique for High Performance Data Compression. IEEE Computer, Vol. 17,

No. 6, pp. 8-19.

17.2 Journal Articles and Study Reports

Brown, L., J.M. Chen, S.G. Leblanc, and J. Cihlar. 1999. Application of the shortwave infrared for

LAI retrieval in boreal forests: an image and model analysis. Submitted to Remote Sens. Environ.

Chen, J.M. 1996. Evaluation of vegetation indices and modified simple ratio for Boreal applications.

Canadian Journal of Remote Sensing 22:229-242.

Chen, J.M. and J. Cihlar. 1996. Retrieving leaf area index of boreal conifer forests using Landsat TM

images. Remote Sens. Environ. 55:153-162.

Chen, J.M., P.M. Rich, S.T. Gower, J.M. Norman, and S. Plummer. 1997. Leaf area index of

boreal forests: Theory, techniques, and measurements. Journal of Geophysical Research. 102(D24):

29,429-29,443.

Newcomer, J., D. Landis, S. Conrad, S. Curd, K. Huemmrich, D. Knapp, A. Morrell, J. Nickeson,

A. Papagno, D. Rinker, R. Strub, T. Twine, F. Hall, and P. Sellers, eds. 2000. Collected Data of The

Boreal Ecosystem-Atmosphere Study. NASA. CD-ROM.

Sellers, P. and F. Hall. 1994. Boreal Ecosystem-Atmosphere Study: Experiment Plan. Version

1994-3.0, NASA BOREAS Report (EXPLAN 94).

Sellers, P. and F. Hall. 1996. Boreal Ecosystem-Atmosphere Study: Experiment Plan. Version

1996-2.0, NASA BOREAS Report (EXPLAN 96).

Sellers, P., F. Hall, and K.F. Huemmrich. 1996. Boreal Ecosystem-Atmosphere Study: 1994

Operations. NASA BOREAS Report (OPS DOC 94).

Sellers, P., F. Hall, and K.F. Huemmrich. 1997. Boreal Ecosystem-Atmosphere Study: 1996

Operations. NASA BOREAS Report (OPS DOC 96).

Page 12
Sellers,P.,F. Hall, H. Margolis,B. Kelly, D. Baldocchi,G. denHartog,J.Cihlar, M.G. Ryan,B.

Goodison,P.Crill, K.J.RansomD. Lettenmaier,andD.E. Wickland. 1995.Theboreal

ecosystem-atmospherestudy(BOREAS):anoverviewandearlyresultsfrom the1994field year.

Bulletinof theAmericanMeteorologicalSociety.76(9):1549-1577.

Sellers,P.J.,F.G.Hall, R.D.Kelly, A. Black, D. Baldocchi,J. Berry, M. Ryan,K.J. Ranson,P.M.

Crill, D.P.Lettenmaier,H. Margolis,J.Cihlar, J.Newcomer,D. Fitzjarrald,P.G.Jarvis,S.T.

Gower,D. Halliwell, D. Williams, B. Goodison,D.E.Wickland, andF.E.Guertin.1997.BOREAS

in 1997:ExperimentOverview,ScientificResultsandFutureDirections.Journalof Geophysical

Research102(D24):28,731-28,770.

Vermote,E.,D. Tanre,andJ.Morcrette.1997.Secondsimulationof thesatellitesignalin thesolar

spectrum,6S: anoverview.IEEETrans.Geosci.RemoteSens.,vol. 35,no. 3,pp. 675.

17.3 Archive/DBMS Usage Documentation

None.

None given.

18. Glossary of Terms

6S

ASCII

AVHRR

BOREAS

BORIS

BSQ

CCRS

CD-ROM

DAAC

DAT

DN

EOS

EOSAT

EOSDIS

EROS

FPAR

GIS

GSFC

IFC

IFOV

IR

ISLSCP

LAI

LCC

Mbps

MIR

MRSC

MSS

NAD27

19. List of Acronyms

- Second Simulation of the Satellite Signal in the Solar Spectrum

- American Standard Code for Information Interchange

- Advanced Very High Resolution Radiometer

- BOReal Ecosystem-Atmosphere Study

- BOREAS Information System

- Band Sequential

- Canada Centre for Remote Sensing

- Compact Disk-Read-Only Memory

- Distributed Active Archive Center

- Digital Archive Tape

- Digital Number

- Earth Observing System

- Earth Observation Satellite Corporation

- EOS Data and Information System

- Earth Resources Observation System

- Fraction of PAR absorbed by plant canopies

- Geographic Information System

- Goddard Space Flight Center

- Intensive Field Campaign

- Instantaneous Field-of-View

- Infrared

- International Satellite Land Surface

- Leaf Area Index

- Lambert Conformal Conic

- Megabytes per second

- Mid-Infrared

- Manitoba Remote Sensing Centre

- Multispectral Scanner

- North American Datum of 1927

Climatology Project

Page 13
NAD83

NASA

NDVI

NEdT

NIR

NOAA

NSA

ORNL

PANP

PAR

PASS

RSR

RSS

SR

SSA

TM

URL

UTM

WRS

- North American Datum of 1983

- National Aeronautics and Space Administration

- Normalized Difference Vegetation Index

- Noise Equivalent Differential Temperature

- Near-Infrared

- National Oceanic and Atmospheric Administration

- Northern Study Area

- Oak Ridge National Laboratory

- Prince Albert National Park

- Photosynthetically Active Radiation

- Prince Albert Satellite Station

- Reduced Simple Ratio

- Remote Sensing Science

- Simple Ratio

- Southern Study Area

- Thematic Mapper

- Uniform Resource Locator

- Universal Transverse Mercator

- World Reference System

20. Document Information

20.1 Document Revision Date

Written: 11-Jan- 1998

Last Updated: 17-Sep- 1999

20.2 Document Review Date(s)

BORIS Review: 25-Feb- 1999

Science Review:

20.3 Document ID

20.4 Citation

Any publication of these data should acknowledge the source of the data as Dr. Jing M. Chen, Mr.

Xiaoyuan Geng, and CCRS. Also, please include citations of relevant papers in Section 17.2.

If using data from the BOREAS CD-ROM series, also reference the data as:

Chen, J. and X. Geng, "Retrieval of Boreal Forest Leaf Area Index From Multiple Scale Remotely

Sensed Vegetation Indices" In Collected Data of The Boreal Ecosystem-Atmosphere Study. Eds. J.

Newcomer, D. Landis, S. Conrad, S. Curd, K. Huemmrich, D. Knapp, A. Morrell, J. Nickeson, A.

Papagno, D. Rinker, R. Strub, T. Twine, F. Hall, and P. Sellers. CD-ROM. NASA, 2000.

Also, cite the BOREAS CD-ROM set as:

Newcomer, J., D. Landis, S. Conrad, S. Curd, K. Huemmrich, D. Knapp, A. Morrell, J.

Nickeson, A. Papagno, D. Rinker, R. Strub, T. Twine, F. Hall, and P. Sellers, eds. Collected Data of

The Boreal Ecosystem-Atmosphere Study. NASA. CD-ROM. NASA, 2000.

20.5 Document Curator

20.6 Document URL

Page 14
REPORT DOCUMENTATION PAGE FormApproved

OMB No. 0704-0188

Public reporting burden for this collection of informationis estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources,

gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this

collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson

Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188), Washington, DC 20503.

1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE 3. REPORT TYPE AND DATES COVERED

August 2000 Technical Memorandum

4. TITLE AND SUBTITLE 5. FUNDING NUMBERS

Technical Report Series on the Boreal Ecosystem-Atmosphere Study (BOREAS)

BOREAS RSS-7 Landsat TM LAI IMages of the SSA and NSA

6. AUTHOR(S)

Jing Chen and Josef Cihlar

Forrest G. Hall; and Jaime Nickeson, Editors

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS (ES)

Goddard Space Flight Center

Greenbelt, Maryland 20771

9. SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS (ES)

National Aeronautics and Space Administration

Washington, DC 20546-0001

923

RTOP: 923-462-33-01

8. PEFORMING ORGANIZATION

REPORT NUMBER

2000-03136-0

10. SPONSORING / MONITORING

AGENCY REPORT NUMBER

TM--2000-209891

Vol. 53

11. SUPPLEMENTARY NOTES

J. Chen and J. Cihlar: Canada Centre for Remote Sensing; J. Nickeson: Raytheon ITSS

12a. DISTRIBUTION / AVAILABILITY STATEMENT

Unclassifie_Unlimited

Subject Category: 43

Report available from the NASA Center for AeroSpace Information,

7121 Standard Drive, Hanover, MD 21076-1320. (301) 621-0390.

12b. DISTRIBUTION CODE

13. ABSTRACT (Maximum 200 words)

The BOREAS RSS-7 team used Landsat TM images processed at CCRS to produce images of LAI for

the BOREAS study areas. Two images acquired on 06-Jun and 09-Aug-1991 were used for the SSA,

and one image acquired on 09-Jun-1994 was used for the NSA. The LAI images are based on ground

measurements and Landsat TM RSR images. The data are stored in binary image-format files.

14. SUBJECT TERMS

BOREAS, remote sensing science, LAI, Landsat.

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