BOREAS TE-18 Landsat TM Physical Classification Image of the NSA Summary The objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the NSA. A Landsat-5 TM image from 21-Jun-1995 was used to derive the classification. A technique was implemented that uses reflectances of various land cover types along with a geometric optical canopy model to produce spectral trajectories. These trajectories are used as training data to classify the image into the different land cover classes. The data are provided in a binary, image file format. 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 TE-18 Landsat TM Physical Classification Image of the NSA 1.2 Data Set Introduction This data set classifies the BOReal Ecosystem-Atmosphere Study (BOREAS) Northern Study Area (NSA) into 13 land cover classes. These classes include wet conifer, dry conifer, deciduous, mixed (deciduous and conifer), fen, and various regeneration and other classes. The pixel resolution of this data set is 30 meters and the data set is georeferenced in the Albers Equal Area Conic (AEAC) projection. 1.3 Objective/Purpose The objective of this data set is to provide BOREAS investigators with a land cover product for use in modeling activities. The technique that was used to produced this data set can also be used to determine the amount of canopy cover within the given class and makes it possible to derive other biophysical parameters from the imagery. 1.4 Summary of Parameters and Variables In a joint meeting of the BOREAS Terrestrial Ecosystem (TE) modelers and the Remote Sensing Science (RSS) algorithm developers in Columbia, MD, July 1992, several land cover classes were identified as necessary inputs to the TE models. One exception to this is the fire- blackened class which is a consequence of spectral distinctness. The classification was performed using bands 3, 4, and 5 of the Landsat-5 Thematic Mapper (TM) scene. The radiometric status of this scene was acceptable. The parameter that is being described in this data set is the land cover class for each 30 meter pixel. The classes that are used in this data set are: Image Value Class ------------------------------ 1 Conifer (Wet) 2 Conifer (Dry) 3 Mixed (Coniferous and Deciduous) 4 Deciduous 5 Fen 6 Water 7 Disturbed 8 Fire Blackened 9 New Regeneration Conifer 10 Medium-Age Regeneration Conifer 11 New Regeneration Deciduous 12 Medium-Age Regeneration-Deciduous 13 Grass 1.5 Discussion The objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the NSA. This data set can be used for modeling purposes. The technique that was used to produce this classification is based on the work of Dr. Forrest Hall. This technique involves the use of reflectances of various land cover types along with a geometric optical canopy model to model the amount of shadow. The reflectance data and the model are used to produce spectral trajectories of the various land cover classes. The trajectories are used in a way that is similar to training data. Each image pixel is compared to the various points of each trajectory. The pixel is assigned to the class of the point to which it is closest in red/near-infrared reflectance space. 1.6 Related Data Sets BOREAS Forest Cover Data Layers of the NSA-MSA in Raster Format BOREAS TE-18 Landsat TM Physical Classification Image of the SSA 2. Investigator(s) 2.1 Investigator(s) Name and Title Dr. Forrest G. Hall NASA Goddard Space Flight Center (GSFC) 2.2 Title of Investigation TE-18 Regional Scale Carbon Flux from Modeling and Remote Sensing 2.3 Contacts Contact 1 ---------------- Dr. Forrest G. Hall NASA Goddard Space Flight Center Greenbelt, MD Tel.: (301) 286-2974 FAX: (301) 286-0239 Forrest.G.Hall@gsfc.nasa.gov Contact 2 ---------------- David Knapp NASA Goddard Space Flight Center Greenbelt, MD Tel: (301) 286-1424 FAX: (301) 286-0239 David.Knapp@gsfc.nasa.gov 3. Theory of Measurements The Landsat-5 TM sensor collects imagery of Earth in seven spectral bands ranging from the blue to the thermal infrared portion of the electromagnetic spectrum. This image was classified from Landsat-5 TM imagery using a technique described by Dr. Forrest Hall (Hall, et al., in press). In this technique, end member reflectances of canopy, background, and shadow are used with a geometric canopy model to compute simulated pixel reflectances for increasing amounts of canopy cover. These simulated reflectances can be plotted as a continuous trajectory for each class (e.g., wet conifer, deciduous, etc.) from 0% to 100% canopy cover. The imagery pixels were classified based on their proximity to the trajectories, with the pixel being assigned to the class of the closest trajectory. 4. Equipment 4.1 Instrument Description. The Landsat-5 TM sensor system records radiation from the seven bands described in Section 4.2.1. It has a telescope that directs the incoming radiant flux obtained along a scan line through a scan line collector to the visible and near-infrared focal plane, or to the mid-infrared and thermal-infrared cooled focal plane. The detectors for the visible and near-infrared bands (1 - 4) are four staggered linear arrays, each containing 16 silicon detectors. The two mid- infrared detectors are 16 indium-antimonide cells in a staggered linear array, and the thermal-infrared detector is a four-element array of mercury-cadmium- telluride cells. 4.1.1 Collection Environment The data that were used to produce this classification were collected by the Landsat-5 Thematic Mapper on 21-Jun-1995. Landsat-5 orbits Earth at an altitude of approximately 705 kilometers. 4.1.2 Source/Platform Landsat-5 satellite 4.1.3 Source/Platform Mission Objectives The mission of the Landsat-5 satellite is to measure reflected radiation from Earth’s surface at a spatial resolution of 30 meters and to measure the temperature of Earth’s surface at a spatial resolution of 120 meters. 4.1.4 Key Variables Reflected radiation. Emitted radiation. Temperature. 4.1.5 Principles of Operation The TM is a scanning optical sensor operating in the visible and infrared 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 inflight geometric and radiometric accuracy for seven spectral bands simultaneously than the previous Multispectral Scanner (MSS). Data collected by the sensor are transmitted to Earth-receiving stations for processing. 4.1.6 Sensor/Instrument Measurement Geometry The TM depends on the forward motion of the spacecraft for the along-track scan and uses 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 1-5 and band 7 is equivalent to a 30-m square when projected to the ground; band 6 (the thermal-infrared band) has an IFOV equivalent to a 120-meter square. 4.1.7 Manufacturer of Sensor/Instrument NASA Goddard Space Flight Center 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 turnaround period of the scan mirror, the shutter introduces the calibration source energy and a black direct-current restoration surface into the 100 detector fields of view. The calibration signals for bands 1 - 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 The following spectral bands are collected by the TM sensor: Channel Wavelength (um) 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. 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 infrared 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 on both sensors is maintained by using internal calibrators that are physically 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 change in the optics of the primary telescope or the "effective radiance" from the internal calibrator lamps is 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 the Canadian Centre for Remote Sensing (CCRS) who purchased it from the Earth Observation Satellite Company (EOSAT). As received from CCRS, the image had been processed from raw telemetry to a systematically corrected product within the CCRS MOSAICS system. After original delivery to the BOREAS data system, CCRS reprocessed these data which produced minor differences in the pixel values. The data that were used to produce this data product are from the original data delivery, not the TM image product that currently exists in the BOREAS data set. 6. Observations 6.1 Data Notes This imagery was collected on 21-Jun-1995. This scene is Path 33, Row 21 in the Landsat Worldwide Reference System (WRS). The solar elevation angle at the time of image acquisition was 40.1 degrees. The solar azimuth angle was 146 degrees. The radiometric quality of this imagery was acceptable. The TM image from which this classification was produced was atmospherically corrected using aerosol optical thickness data measured by sunphotometers in the study area. These optical thickness data were used in the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) program to determine the spherical albedo, path radiance, gaseous transmission, and scattering transmission. These parameters were used to determine surface reflectance based on equations 4a and 4b of Markham, et al. (1992). 6.2 Field Notes Not applicable. 7. Data Description 7.1 Spatial Characteristics 7.1.1 Spatial Coverage The classified image covers an area that is approximately 129 km by 86 km and includes areas just west of Thompson, Manitoba. The corners of the data set are as follows. These coordinates are in the BOREAS Grid projection. BOREAS Grid NAD83 Corner X Y Long. Lat. --------------------------------------------------- Northwest 740.000 650.000 98.983W 56.262N Northeast 850.010 650.000 97.240W 56.081N Southwest 740.000 569.990 99.202W 55.555N Southeast 850.010 569.990 97.489W 55.377N 7.1.2 Spatial Coverage Map Not available. 7.1.3 Spatial Resolution Each pixel represents a 30-meter by 30-meter area on the ground. 7.1.4 Projection The area mapped is projected in the BOREAS Grid projection which is based on the ellipsoidal version of the Albers Equal Area Conic (AEAC) projection. The projection has the following parameters: Datum: NAD83 Ellipsoid: Geodetic Reference System of 1980 (GRS80) or Worldwide Geodetic System of 1984 (WGS84) Origin: 111.000°W 51.000°N Standard Parallels: 52° 30' 00"N 58° 30' 00"N Units of Measure: kilometers 7.1.5 Grid Description The data are referenced to the projection described in section 7.1.4. 7.2 Temporal Characteristics 7.2.1 Temporal Coverage This original spectral imagery was collected on 21-Jun-1995. The scene is from Path 33, Row 21 in the Landsat WRS. The solar elevation angle at the time of image acquisition was 38.2 degrees. The solar azimuth angle was 136.3 degrees. The radiometric quality of this imagery was acceptable. 7.2.2 Temporal Coverage Map Not applicable. 7.2.3 Temporal Resolution This data set represents the land cover as it appeared on 21-Jun-1995. 7.3 Data Characteristics 7.3.1 Parameter/Variable Land cover type. 7.3.2 Variable Description/Definition Each pixel in the classification image contains a number between 0 and 13. This number represents one of the following land cover classes: Class Descriptions 0 No Data This area is not covered in the classification. This area is most likely blank fill on the edges of the image frame. 1 Conifer (Wet) Primarily black spruce and jack pine on three major different soil substrates: (i) moderately well drained soils with feather moss over clay, (ii) poorly drained soils with sphagnum on clay, and (iii) sparsely treed fens with a very deep moss layer. Overstory biomass density varies considerably within this class. 2 Conifer (Dry) Dry Conifer is an area that contains coniferous trees (primarily jack pine) with a lichen (cladina) background. These areas have sandy soils that are well drained. Areas of permafrost supporting conifers with a lichen background are also included in this class. 3 Mixed Deciduous and Coniferous Mixed deciduous and coniferous contains coniferous and aspen/birch (populus tremuloides/betula papyrifera) trees. The composition of this class contains less than 80% of the dominant species. 4 Deciduous The deciduous class contains primarily aspen/birch. The composition of this class is generally greater than 80% deciduous trees. 5 Fen The Fen/Bog class is characterized by areas with a water table very near or at the surface. Fens experience lateral water transport, whereas bogs are enclosed landforms experiencing only vertical transport. Fens typically contain sedges, moss, and bog birch associated with sparse to medium dense tamarack (larix laricina) stands. Bogs are usually 6 Water Water bodies such as ponds, lakes, and streams. 7 Disturbed The disturbed class consists of areas that are dominated by bare soil, recently logged areas, or rock outcrops. This class also includes roads, airports, and urban areas. 8 Fire Blackened Areas that have been burned in the last 5 or 6 years. Distinguishable for their charred sphagnum background they are usually areas of very intense burn where little or no vegetation survived. 9 New Regeneration Conifer This class consists primarily of conifers that are regrowing after a burn. It may also include conifer stands where there are a few remaining trees after a low- to medium-intensity burn. 10 Medium-Age Regeneration Conifer Areas that are predominantly young jack pine or young black spruce. This class typically occurs in stands that were cleared or burned and have been growing back for approximately 10 years. 11 New Regeneration deciduous This class consists of aspen that is starting to regrow after a recent clearing. This class is younger than the young aspen class. The aspen in this class may also include grasses or other herbaceous vegetation. 12 Medium-Age Regeneration deciduous The class consists of areas that were cleared or burned and have been growing back as aspen. These stands typically contain 10 year old aspen where the background is almost completely obscured and thinning has not yet taken place. 13 Grass This class consists primarily of grasses, agricultural fields that have been planted, or shrub-like vegetation. 7.3.3 Unit of Measurement Unitless but coded value. 7.3.4 Data Source Landsat-5 TM scene on 21-Jun-1995 from the CCRS 7.3.5 Data Range Land cover type: 13 different land cover classes (pixel values from 0 to 13). 7.4 Sample Data Record Not applicable for image data. 8. Data Organization 8.1 Data Granularity The smallest amount of data that can be ordered is the entire data set. 8.2 Data Format 8.2.1 Uncompressed Data Files The NSA classification product contains two files as follows: File 1: (80-byte American Standard Code for Information Interchange (ASCII) text records) Text file listing the files on tape. File 2: (2,667 records of 3,667 bytes each) (1 byte per pixel) Classified image with values from 0 to 13. 8.2.2 Compressed CD-ROM Files On the BOREAS CD-ROMs, file 1 listed above is stored as ASCII text; however, file 2 has been compressed with the Gzip 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 (GNU zip) uses the Lempel-Ziv algorithm (Welch, 1994) used in the zip and PKZIP programs. The compressed files may be uncompressed using gzip (-d option) or gunzip. Gzip is available from many websites (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. 9. Data Manipulations 9.1 Formulae Not applicable. 9.1.1 Derivation Techniques and Algorithms The techniques that were used to classify this image are described in sections 1.5, 3, and 6.1. 9.2 Data Processing Sequence 9.2.1 Processing Steps 1) The imagery was converted to surface reflectance before the classification was performed. Atmospheric correction coefficients were computed using optical depths from a sunphotometer in conjunction with 6S (Markham et al., 1992). 2) End member reflectances were the same as those used for the Southern Study Area (SSA) classification. 3) Trajectories were computed based on end member reflectances, solar geometry, tree height to width ratio, and tree form (i.e. cone or cylinder). 4) Additional trajectories for regeneration classes were added using data from regeneration areas of the SSA. No end member reflectances were used to characterize the regeneration and water classes (classes 6 - 13). 5) The trajectories were used as input to the image classifier. 6) Post-processing techniques to classify any remaining null-classed pixels were applied. 7) The classified image was mapped into the AEAC projection using nearest neighbor resampling. 8) The classification image was written to tape. 9) Copy the ASCII and compress the binary files for release on CD-ROM. 9.2.2 Processing Changes None. 9.3 Calculations 9.3.1 Special Corrections/Adjustments None. 9.4 Graphs and Plots None. 10. Errors 10.1 Sources of Error The sources of error in this classification can be attributed to several factors. In many cases, the reflectance of one feature could be similar to the reflectance of another feature, resulting in confusion. The similarity in reflectances could be the result of similar background components and variations in tree density. Error could also be a result of spectral mixing of various features that fall within a 30 meter pixel. 10.2 Quality Assessment 10.2.1 Data Validation by Source The imagery was spot checked at various locations and the image class was compared to the forest cover map. An error assessment was performed on the classification. The auxiliary sites and a few randomly selected sites were used as ground truth. The location of each ground truth site was identified on the georeferenced image as a 3 by 3 pixel area. Each of the 9 pixels in these areas represents a test point. Some classes were not represented by auxiliary sites or randomly selected sites. 10.2.2 Confidence Level/Accuracy Judgment Although efforts have been made to make this classification as accurate as possible, there is bound to be some confusion between classes. In some areas, new regeneration conifer can be confused with fen because of differences in canopy density. Also, many of the age classes within the deciduous or conifer classes can be confused because of minor variations in background. 10.2.3 Measurement Error for Parameters The following tables and statistics were derived to assess the accuracy of the classification: Confusion Matrix Classification Class 1 2 3 4 5 6 7 8 9 10 11 12 13 Truth ---------------------------------------------------------------------------- Wet Conifer(1) 93 0 0 8 0 0 0 0 0 0 9 0 0 Dry Conifer(2) 0 0 0 0 1 0 0 0 7 0 0 0 0 Mixed (3) 0 0 21 5 0 0 0 0 0 0 2 2 0 Deciduous (4) 0 0 0 85 0 0 0 0 1 0 0 0 0 Fen (5) 0 0 6 22 80 0 0 0 0 0 9 0 0 Water (6) 0 0 0 0 0 51 0 0 0 0 0 0 0 Bare Soil (7) 0 1 0 0 0 0 18 0 0 0 0 0 0 Fire Black.(8) 0 0 0 0 0 0 0 0 0 0 0 0 0 New Regen. Conifer (9) 0 0 0 3 1 0 0 0 67 0 0 0 0 Med. Age Regen. Con.(10) 0 0 7 0 0 0 0 0 0 47 0 0 0 New Regen. Deciduous (11) 0 0 0 0 9 0 0 0 0 0 39 0 0 Med. Age Regen. Deciduous (12) 0 0 0 0 0 0 0 0 0 0 0 21 0 Grass (13) 0 0 0 0 0 0 0 0 0 0 0 0 0 Class % Correct Wet Conifer 84 % Dry Conifer 0 % Mixed 70 % Deciduous 99 % Fen 68 % Water 100 % Bare Soil 95 % Fire Blackened Not represented in NSA New Regen. Conifer 94 % Med. Age Regen. Con. 87 % New Regen. Decid. 81 % Med. Age Regen. Decid. 100 % Grass Not represented in NSA Overall 85 % Kappa = 0.83 or 83 % better than chance agreement (Campbell, 1987). 10.2.4 Additional Quality Assessments None. 10.2.5 Data Verification by Data Center The imagery was spot checked at various locations and the image class was compared to the forest cover maps from Manitoba Natural Resources. 11. Notes 11.1 Limitations of the Data This data set is based on an image that was collected on 21-Jun-1995 and only represents the land cover as it existed on that day. Please see Section 10.2.1 to determine how the amount of error in this product may affect your results from using it. 11.2 Known Problems With the Data Clouds in this classification show up in the disturbed class, and cloud shadows show up in the water class. The scene is mostly clear, so this problem has a very limited impact. 11.3 Usage Guidance 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 This data set may be used for modeling purposes. It can also be used to analyze measurements from aircraft to determine the land cover that was under the aircraft at locations along the aircraft’s path. 13. Future Modifications and Plans None. 14. Software 14.1 Software Description Programs written at NASA GSFC to run under EASI/PACE image processing software from PCI, Inc. were used to classify the image. The trajectories were computed using Microsoft Excel (Version 4.0). Questions related to the specific details of the software written to process this data set should be addressed to David Knapp (see Section 2.3). Microsoft Excel (Version 4.0) is a spreadsheet program. Gzip (GNU zip) uses the Lempel-Ziv algorithm (Welch, 1994) used in the zip and PKZIP commands. 14.2 Software Access EASI/PACE is a proprietary software package developed by PCI, Inc. Contact PCI for details. PCI, Inc. 50 West Wilmot St. Richmond Hill Ontario, Canada L4B 1M5 Phone: (905) 764-0614 FAX: (905) 764-9604 Microsoft Excel is a proprietary software package that is widely available in the commercial software market. Gzip is available from many websites 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 Ms. Beth Nelson BOREAS Data Manager NASA GSFC Greenbelt, MD (301) 286-4005 (301) 286-0239 (fax) Elizabeth.Nelson@gsfc.nasa.gov 15.2 Data Center Identification See Section 15.1 15.3 Procedures for Obtaining Data Users may place data requests by telephone, electronic mail, or fax. 15.4 Data Center Status/Plans The NSA physical classification is available from the EOSDIS ORNL DAAC (Earth Observing System Data and Information System) (Oak Ridge National Laboratory) (Distributed Active Archive Center). The BOREAS contact at ORNL is: ORNL DAAC User Services Oak Ridge National Laboratory Oak Ridge, TN (423) 241-3952 ornldaac@ornl.gov ornl@eos.nasa.gov 16. Output Products and Availability 16.1 Tape Products These data can be made available on 8mm, DAT, or 9-track tapes. 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 Hall, F.G., D.E. Knapp and K.F. Huemmrich. Physically-Based Classification and Satellite Mapping of Biophysical Characteristics in the Southern Boreal Forest.JGR. BOREAS Special Issue (in press). Hall, F.G., Y.E. Shimabukuro and K.F. Huemmrich. 1995. Remote sensing of forest biophysical structure using mixture decomposition and geometric reflectance models. Ecological Applications 5(4):993-1013. Markham, B.L., R.N. Halthornea and S.J. Goetz. 1992. Surface reflectance retrieval from satellite and aircraft sensors: Results of sensor and algorithm comparisons during FIFE. FIFE Special Issue. American Geophysical Union. 18785- 18795. PACE Image Analysis Kernal Version 5.2. 1993. PCI Inc. Richmond Hill, Ontario. Richards, J. A. 1986. Remote Sensing Digital Image Analysis: An Introduction. Springer Verlag. 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 Campbell, J. B. 1987. Introduction to Remote Sensing. Guilford Press. p.349. 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. 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, and F. E. Guertin. 1997. BOREAS in 1997: Experiment Overview, Scientific Results and Future Directions. Journal of Geophysical Research 102 (D24): 28, 731-28,770. 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. 17.3 Archive/DBMS Usage Documentation None. 18. Glossary of Terms None. 19. List of Acronyms AEAC - Albers Equal-Area Conic ASCII - American Standard Code for Information Interchange BOREAS - BOReal Ecosystem-Atmosphere Study BORIS - BOREAS Information System BPI - Bytes per inch CCRS - Canadian Centre for Remote Sensing CD-ROM - Compact Disk-Read-Only Memory DAAC - Distributed Active Archive Center DAT - Digital Archive Tape DEM - Digital Elevation Model EOS - Earth Observing System EOSAT - Earth Observation Satellite Company EOSDIS - EOS Data and Information System GMT - Greenwich Mean Time GRS80 - Geodetic Reference System of 1980 GSFC - Goddard Space Flight Center IFOV - Instantaneous Field of View MSA - Modeling Sub-Area MSS - Multispectral Scanner NAD27 - North American Datum of 1927 NAD83 - North American Datum of 1983 NASA - National Aeronautics and Space Administration NSA - Northern Study Area PANP - Prince Albert National Park ORNL - Oak Ridge National Laboratory SSA - Southern Study Area RSS - Remote Sensing Science 6S - Second Simulation of the Satellite Signal in the Solar Spectrum TE - Terrestrial Ecology TM - Thematic Mapper URL - Uniform Resource Locator UTM - Universal Transverse Mercator WGS84 - World Geodetic System of 1984 WRS - Worldwide Reference System WWW - World Wide Web 20. Document Information 20.1 Document Revision Dates Written: 06-Apr-1995 Last Updated: 30-Jul-1998 20.2 Document Review Dates BORIS Review: 09-Jan-1998 Science Review: 20.3 Document ID 20.4 Citation This classification image was produced for the BOREAS project as part of the research of Dr. Forrest Hall of NASA Goddard Space Flight Center. Please contact Dr. Hall or David Knapp before using these data in a publication. 20.5 Document Curator 20.6 Document URL Keywords LAND COVER LANDSAT TM CLASSIFICATION TE18_NSA_Class_Traj.doc 08/18/98