BOREAS TE-18 Biomass Density Image of the SSA Summary: This biomass density image covers almost the entire BOREAS SSA. The pixels for which biomass density is computed include areas that are in conifer land cover classes only. The biomass density values represent the amount of overstory biomass (i.e. tree biomass only) per unit area. It is derived from a Landsat-5 TM image collected on 02-Sep-1994. The technique that was used to create this image is very similar to the technique that was used to create the physical classification of the SSA. 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 Biomass Density Image of the SSA 1.2 Data Set Introduction This data set depicts biomass density for the BOReal Ecosystem-Atmosphere Study (BOREAS) Southern Study Area (SSA). It is derived from a Landsat-5 Thematic Mapper (TM) scene collected on 02-Sep-1994 based on a technique developed by Dr. Forrest Hall. 1.3 Objective/Purpose The objective of this study was to develop a set of biophysical parameter maps from Landsat-5 TM imagery. It is hoped that these products may be useful for determining amounts of carbon in the boreal ecosystem and will provide data for model input. 1.4 Summary of Parameters Biomass Density 1.5 Discussion This biomass density image covers almost the entire BOREAS SSA. The pixels for which biomass density is computed include areas that are in conifer land cover classes only. The biomass density values represent the amount of overstory biomass (i.e., tree biomass only) per unit area. The technique that was used to create this image is very similar to the technique that was used to create the physical classification of the SSA. This technique involves the use of trajectories that can be thought of as a set of points in red/near-infrared space. Each of these points represents a linear combination of reflectances of three end members that make up the land surface. The three main surface features include sunlit canopy, sunlit background, and shadow. The points of the trajectory range from 0% canopy to 100% canopy. A geometric optical canopy model was used to determine the areal proportions of each of these elements. The “nearest” trajectory point to each pixel of the Landsat TM image was determined to derive the biomass density based on the amount of canopy that exists in a pixel. 1.6 Related Data Sets BOREAS TE-18 Landsat TM Maximum Likelihood Classification Image of the NSA BOREAS TE-18 Landsat TM Maximum Likelihood Classification Image of the SSA BOREAS TE-18 Landsat TM Physical Classification image of the SSA 2. Investigator(s) 2.1 Investigator(s) Name and Title Dr. Forrest 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 Contact Information Contact 1 ---------------- Dr. Forrest G. Hall NASA Goddard Space Flight Center Greenbelt, MD Phone: (301) 286-2974 FAX: (301) 286-0239 Forrest.G.Hall@gsfc.nasa.gov Contact 2 ---------------- David Knapp NASA Goddard Space Flight Center Greenbelt, MD Phone: (301) 286-1424 FAX: (301) 286-0239 David.Knapp@gsfc.nasa.gov 3. Theory of Measurements This biomass density image was derived from a Landsat-5 TM scene collected on 02-Sep-1994. A classification image was produced using end member reflectance trajectories. Please refer to the document for the BOREAS TE-18 Landsat TM physical classification image of the BOREAS SSA. The general theory behind the method for deriving biomass density is very similar to the way that the classification image was created. The reflectance of a pixel is represented in this method as a linear combination of the reflectance of sunlit canopy, sunlit background, and shadow. A model of the canopy geometries was used for the various classes. This model computes the proportions of canopy, background, and shadow that occupy the area in a pixel based on solar and canopy geometry. Using ground and satellite reflectance measurements and the proportions of the end members, model reflectances were produced for each class. These model reflectances form a trajectory in red/near-infrared space within each class as the proportions of canopy cover go from 0% to 100%. Ground measurements of biomass density were made for several sites in the SSA. These measurements were used to define a relationship between the biomass density and the amount of canopy cover at each site. A scaling factor was determined based on this relationship. The amount of canopy cover at each pixel was determined by finding the model reflectance that has the shortest distance in red/near-infrared space. The scaling factor is then applied to the amount of canopy cover for each pixel, resulting in an estimate of biomass density for that pixel. 4. Equipment 4.1 Instrument Description The Landsat-5 TM sensor system records radiation in seven bands of the electromagnetic spectrum. 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 TM on 02-Sep-1994. 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 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 - 5 and band 7 is equivalent to a 30-meter 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 (µm) 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 nodalPeriod 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. 6. Observations 6.1 Data Notes This imagery was collected on 02-Sep-1994. This scene is Path 37, Row 22-23 (shifted) 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 Solution 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 image area that was classified covers an area that is approximately 144 km by 114 km and includes areas just north of Prince Albert, Saskatchewan. The corners of the data set are as follows. The BOREAS Grid coordinates are in the Albers Equal-Area Conic (AEAC) projection described in section 7.1.4. BOREAS Grid NAD83 Corner X Y Long. Lat. --------------------------------------------------- Northwest 297.810 392.490 106.401W 54.438N Northeast 441.810 392.490 104.190W 54.333N Southwest 297.810 278.490 106.515W 53.417N Southeast 441.810 278.490 104.357W 53.314N 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 AEAC projection. The projection has the following parameters: Datum: NAD83 Ellipsoid: Geodetic Reference System of 1980 (GRS80) or Worldwide Geodetic Sysem 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 imagery was collected on 02-Sep-1994. This scene is Path 37, Row 22-23 (shifted) in the Landsat 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. 7.2.2 Temporal Coverage Map Not applicable. 7.2.3 Temporal Resolution This data set represents the biomass density as it existed on 02-Sep-1994. 7.3 Data Characteristics 7.3.1 Parameter/Variable Biomass density 7.3.2 Variable Description/Definition Biomass density: The amount of biomass per unit area. 7.3.3 Unit of Measurement Tenths of kilograms of biomass per square meter 7.3.4 Data Source Landsat-5 TM scene on 02-Sep-1994 from the CCRS. 7.3.5 Data Range Data are stored as 8-bit integers ranging from 0 to 255. Values of 254 and 255 represent pixels for which biomass density was not computed (i.e., not conifer). Values of 254 represent water pixels. Valid biomass density values in this image range from 0 to 253 (0.0 to 25.3 kg of overstory biomass per square meter). 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 SSA 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: (3,800 records of 4,800 bytes each) (1 byte per pixel) Image with values from 0 to 255. 8.2.2 Compressed CD-ROM Files On the BOREAS, 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 used to create this biomass image are described in Sections 1.5, 3, and 6.1. 9.2 Data Processing Sequence 9.2.1 Processing Steps 1) The TM imagery was converted to surface reflectance before the biomass density image was created. Atmospheric correction coefficients were computed with 6S using optical depths from a sunphotometer (Markham, et al., 1992). 2) End member reflectances were collected or compiled. 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) Ground measurements of biomass density were used to find the relationship between biomass density and canopy cover. 5) The trajectories and the image classification were used as input to the image biomass program. 6) The biomass image was mapped into the AEAC projection using nearest neighbor resampling. 7) The biomass image was written to tape. 8) Copy the ASCII and compress the binary file 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 errors in the biomass density estimate. The similarity in reflectances could be the result of differences in 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 10.2.2 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, resulting in errors in the biomass density estimates. In some areas, especially around the SSA Old Jack Pine site, there might be large overestimates of biomass density. This is a result of the pixels not matching the model properly. Although efforts have been made to minimize this error, it can still occur in pixels that do not match the model output. 10.2.3 Measurement Error for Parameters Comparison with ground measurements of biomass density showed that the root mean squared error of biomass density for this image is approximately 3.75 kg/m2 where the tree species is very homogenous. However, errors can vary significantly because of variations in pixel reflectance. This technique is thought to be reasonably accurate, especially in areas where black spruce is relatively pure and not mixed with other species. In areas where different tree species are mixed together, errors can be significant. 10.2.4 Additional Quality Assessments None. 10.2.5 Data Verification by Data Center The imagery was visually checked at various locations to see that the biomass density values were reasonable. 11. Notes 11.1 Limitations of the Data This data set is based on an image that was collected on 02-Sep-1994 and only represents the biomass density 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 to get a rough idea of the variation in biomass density in the BOREAS SSA. 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 Goddard Space Flight Center Greenbelt, Maryland (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 SSA biomass density image 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 8 mm, Digital Archive Tape (DAT), or 9-track tapes at 6250 or 1600 BPI. 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., Y.E. Shimabukuro, K.F. Huemmrich. 1995. Remote sensing of forest biophysical structure using mixture decomposition and geometric reflectance models. Ecological Applications 5(4):993-1013. Hall, F.G., D.E. Knapp, K.F. Huemmrich. 1997. Physically-Based Classification and Satellite Mapping of Biophysical Characteristics in the Southern Boreal Forest. Journal of Geophysical Research. Special Issue:29,567-29,580. PACE Image Analysis Kernal Version 5.2. 1993. PCI, Inc. Richmond Hill, Ontario. Markham, B.L., R.N. Halthorne, 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. Richards, John 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 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, K.F. Huemmrich. 1996. Boreal Ecosystem-Atmosphere Study: 1994 Operations. NASA BOREAS Report (OPS DOC 94). Sellers, P., F. Hall, 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, 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. 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 Observing 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 ORNL - Oak Ridge National Laboratory PANP - Prince Albert National Park RSS - Remote Sensing Science 6S - Second Simulation of the Satellite signal in the Solar Spectrum SSA - Southern Study Area 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: 27-Aug-1997 Last Updated: 30-Jul-1998 20.2 Document Review Dates BORIS Review: 05-Jan-1998 Science Review: 20.3 Document ID 20.4 Citation This biomass image was produced as part of the research of Dr. Forrest Hall of NASA Goddard Space Flight Center. This image was produced for the BOREAS project. Please contact Dr. Hall or David Knapp before using these data in a publication. 20.5 Document Curator 20.6 Document URL Keywords BIOMASS LAND COVER LANDSAT TM CLASSIFICATION TE18_SSA_Biomass_Dens.doc 08/18/98