BOREAS RSS-07 Landsat TM LAI Images of the SSA and NSA Summary The BOREAS RSS-07 team used Landsat TM images processed at CCRS to produce images of LAI for the BOREAS study areas. Two images acquired on June 6 and August 9, 1991 were used for the SSA, and one image acquired on June 9, 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. 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. 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 LAI, Gap Fraction, and FPAR Data 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 2.3 Contact Information Contact 1 --------- Jing M. Chen Canada Centre for Remote Sensing Ottawa, Ontario Canada (613) 947-1266 (613) 947-1406 (fax) jing.chen@ccrs.nrcan.gc.ca Contact 2 --------- Mr. Xiaoyuan Geng Canada Centre for Remote Sensing Ottawa, Ontario Canada (613) 947-1231 (613) 947-1406 (fax) xgeng@ccrs.nrcan.gc.ca Contact 3 -------------- Jaime Nickeson Raytheon ITSS NASA GSFC Greenbelt, MD (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 (µ) Primary Use ------- --------------- ----------------------------------------- 1 0.45 - 0.52 Coastal water mapping, soil vegetation differentiation, deciduous/coniferous differentiation. 2 0.52 - 0.60 Green reflectance by healthy vegetation. 3 0.63 - 0.69 Chlorophyll absorption for plant species differentiation. 4 0.76 - 0.90 Biomass surveys, water body delineation. 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 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 [NE(dT)] 7 2.4% Ground IFOV 30 m (Bands 1-5, 7) 120 m (Band 6) Avg. altitude 699.6 km Data rate 85 Mbps Quantization levels 256 Orbit angle 8.15 degrees Orbital nodal Period 98.88 minutes Scan width 185 km Scan angle 14.9 degrees Image overlap 7.6 % * 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. Each TM reflective band and the internal calibrator lamps were calibrated prior to launch using lamps in integrating spheres that were in turn calibrated against lamps traceable to calibrated National Bureau of Standards lamps. Sometimes the absolute radiometric calibration constants in the "short-term" and "long-term parameters" files used for ground processing have been modified after launch because of inconsistency within or between bands, changes in the inherent dynamic range of the sensors, or a desire to make quantized and calibrated values from one sensor match those from 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., submitted 1998). 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 Southease 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 Southease 556000 5937000 The NAD27 UTM coordinates of the TM image of SSA acquired on 06-Jun-1991: UTM UTM Easting Northing Northwest 471200 6016960 Southease 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. 7.3.3 Unit of Measurement LAI - square meters of leaf area / 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 BORIS is a binary and TIFF image pair acquired on a single day. 8.2 Data Format(s) 8.2.1 Uncompressed Files This data set contains the following 6 files: File Description/Name Format Npix Nlines Block Size File Size NSA_LAI_94JUNE.IMG Raw Binary 2048 2048 2048 4Mb NSA_LAI_94JUNE.TIFF TIFF 512 2Mb SSA_LAI_91JUNE.IMG Raw Binary 2048 2048 2048 4Mb SSA_LAI_91JUNE.TIFF TIFF 512 2Mb SSA_LAI_91AUG.IMG Raw Binary 3500 3500 3500 12Mb SSA_LAI_91AUG.TIFF TIFF 512 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. 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 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. 9.2 Data Processing Sequence 9.2.1 Processing Steps None given. 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. 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 of 06-Jun-1991 for the SSA and 09-Jun-1994 for the NSA, we believe LAI images of these two dates are most useful. 10.2.2 Confidence Level/Accuracy Judgement 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 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. 13. Future Modifications and Plans None given. 14. Software 14.1 Software Description Gzip (GNU zip) uses the Tempel-Ziv algorithm (Welch, 1994) used in the zip and PKZIP commands. 14.2 Software Access Gzip is available from many web sites across the net (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 15.1 Contact Information For BOREAS data and documentation please contact: ORNL DAAC User Services Oak Ridge National Laboratory Oak Ridge, TN 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, or fax. 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. 16. Output Products and Availability 16.1 Tape Products The data can be made available on 8-mm 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 Satellite/Instrument/Data Processing Documentation PACE Image Analysis Kernal 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, S. and Plummer. 1997. Leaf area index of boreal forests: Theory, techniques, and measurements. Journal of Geophysical Research. 102(D24): 29,429-29,443. 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). Sellers, P., F. Hall, H. Margolis, B. Kelly, D. Baldocchi, G. den Hartog, J. Cihlar, M.G. Ryan, B. Goodison, P. Crill, K.J. Ranson, D. Lettenmaier, and D.E. Wickland. 1995. The boreal ecosystem-atmosphere study (BOREAS): an overview and early results from the 1994 field year. Bulletin of the American Meteorological Society. 76(9): 1549-1577. Sellers, P.J., F.G. Hall, R.D. Kelly, A. Black, D. Baldocchi, J. Berry, H. Margolis, M. Ryan, J. Ranson, P.M. Crill, D.P. Lettenmeier, J. Cihlar, J. Newcomer, D. Halliwell, D. Fitzjarrald, P.G. Jarvis, S.T. Gower, D. Williams, B. Goodison, D.E. Wickland, and F.E. Guertin. 1997. BOREAS in 1997: Scientific results, experiment overview and future directions.Journal of Geophysical Research, 102(D24): 28,731-28,770. Vermote, E., D. Tanre, and J. Morcrette. 1997. Second simulation of the satellitesignal in the solar spectrum, 6S: an overview. IEEE Trans. Geosci. Remote Sens., vol. 35, no. 3, pp. 675. 17.3 Archive/DBMS Usage Documentation None. 18. Glossary of Terms None given. 19. List of Acronyms 6S - Second Simulation of the Satellite Signal in the Solar Spectrum ASCII - American Standard Code for Information Interchange AVHRR - Advanced Very High Resolution Radiometer BOREAS - BOReal Ecosystem-Atmosphere Study BORIS - BOREAS Information System BSQ - Band Sequential CCRS - Canada Centre for Remote Sensing CD-ROM - Compact Disk-Read-Only Memory DAAC - Distributed Active Archive Center DAT - Digital Archive Tape DN - Digital Number EOS - Earth Observing System EOSAT - Earth Observation Satellite Corporation EOSDIS - EOS Data and Information System EROS - Earth Resources Observation System FPAR - Fraction of PAR absorbed by plant canopies GSFC - Goddard Space Flight Center IFC - Intensive Field Campaign IFOV - Instantaneous Field-of-View IR - Infrared ISLSCP - International Satellite Land Surface Climatology Project LAI - Leaf Area Index LCC - Lambert Conformal Conic Mbps - Megabytes per second MIR - Mid-Infrared MRSC - Manitoba Remote Sensing Centre MSS - Multispectral Scanner NAD83 - North American Datum of 1983 NASA - National Aeronautics and Space Administration NDVI - Normalized Difference Vegetation Index NEdT - Noise Equivalent Differential Temperature NIR - Near-Infrared NOAA - National Oceanic and Atmospheric Administration NSA - Northern Study Area ORNL - Oak Ridge National Laboratory PANP - Prince Albert National Park PAR - Photosynthetically Active Radiation PASS - Prince Albert Satellite Station RSS - Remote Sensing Science RSR - Reduced Simple Ratio SR - Simple Ratio SSA - Southern Study Area TM - Thematic Mapper URL - Uniform Resource Locator UTM - Universal Transverse Mercator WRS - World Reference System 20. Document Information 20.1 Document Revision Date Written: 11-Jan-1998 Last Updated: 05-May-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 the authors and CCRS. If using data from the BOREAS CD-ROM series, also reference the data as: Chen, Jing 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. CD-ROM. NASA, 2000. 20.5 Document Curator 20.6 Document URL Keywords: AVHRR-LAC LAI NOAA REFLECTANCE RSS07_TM_LAI.doc.doc 06/09/99