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