The Edit 9 forest scene, a computer processing technique, and its capability to map timber types in the Sam Houston National Forest, are evaluated. Special efforts were made to evaluate existing computer processing techniques in mapping timber types using ERTS-1 and aircraft data, and to provide an opportunity to open up new research and development areas in forestry data

Kan, E. P.

Reeves, C. A.

English

NASA Technical Reports Server

Ref: 641-655
Contract NAS 9-12200
Job Order 75-315
NASA CR-
TECHNICAL MEMORANDUM
MULTISPECTRAL SCANNER DATA PROCESSING OVER SAM HOUSTON
N NATIONAL FOREST: A PROGRESS REPORT
By
C. A. Reeves
E. P. Kan
E-'H CN
ZwUWa APPROVED: 
___
Z 4 U I. E. Duggan
Sa Acting Supervisor
no 0 Forestry Applications Section
H Distribution:
4 E JSC/L. F. Childs
;HM A. W. Patteson
J. 1I. Sasser
R. L. Smelser
4 LEC/O. N. Brandt
U)- I. E. Duggan
E. P. Kan (3)
oLn R. E. Tokerud
Sa FAP team members (12)
Job Order File
M LEC/ASD Technical Library (2)
REDAF (3)
-'[4 0
December 1974
LEC-5265
https://ntrs.nasa.gov/search.jsp?R=19750008886 2020-03-22T22:53:19+00:00Z
CONTENTS
Section Page
1.0 SUMMARY . ........... 
...... 1
2.0 OBJECTIVES . ...... . . ......... . . 1
3.0 SCOPE . . . . . . . . . . . . . . . . . . . 1
4.0 TECHNICAL APPROACH. . ........... 2
5.0 RESULTS . ............. 
.... 6
5.1 Data Processing Results . . . . . . . 6
5.2 New Research and Development Areas . 10
6.0 CONCLUSION. . ............. 
... 11
7.0 REFERENCES . . . . . . . . . . . . . . . 13
i
1.0 SUMMARY
This memorandum reports on the progress of the Forestry
Applications Project on Timber Resources (FAP/TR), Task
11.2.1 and Task 11.2.2, as of December 1974 (ref. 1,2).
Using existing computer processing techniques, two sets
of multispectral scanner (MSS) data were used in the
analysis; one set being a 3-date Earth Resources
Technology Satellite (ERTS-1) MSS data, the other being a
1-date aircraft MSS data. Both data sets were over a
selected area in Sam Houston National Forest (SHNF).
2.0 OBJECTIVES
Being part of FAP/TR, there are two objectives to Task
11.2.1 of Timber Type-Mapping 
- ERTS-1 MSS, and Task 1I.2.2
of Timber Type-Mapping 
- Aircraft MSS/24:
* To evaluate existing computer processing techniques
in mapping timber types using ERTS-1 and aircraft
data.
* To afford the opportunity to open up new research
and development (R&D) areas, while forestry data
is being closely examined.
3.0 SCOPE
The "Edit 9" forest scene (ref. 2) over SHNF, which
covers approximately 11 square kilometers (5 square miles),
was examined. Three dates of ERTS-1 MSS data were used,
27 November 1972 (ERTS-1 scene ID #1127-16253), 25 February
1973 (ID #1217-16254), and 8 May 1973 (ID #1289-16254).
1
MSS data collected by Mission M230 flown by the C-130
aircraft on 21 March 1973 at 10,000 feet altitude was used.
The Bendix 24-channel scanner was on board.
There are seven timber types/condition classes in
Edit 9. These features are some of those intended for
study in FAP/TR (ref. 1). Using the same notation as in
ref. 1, these seven features are tabulated in Table I.
The size of ERTS-1 data for Edit 9 is approximately
30 x 80 pixels (30 lines and 80 pixels/line). The size of
the aircraft MSS-24 data for the same area is approximately
250 x 700 pixels.
The spectral coverage by the ERTS-1 scanner is in
Table II. The 24 channels of the Bendix MSS/24 scanner
cover the visible and infrared spectrum. However, only
12 of these channels were operating during M230. They are
channels 1-11 and 13 on the scanner; but they are renum-
bered here 1-12. The spectral coverages of these channels
are in Table III.
4.0 TECHNICAL APPROACH
The raw MSS data was preprocessed before classification/
analysis. For aircraft MSS/24 data, the preprocessing
comprised (a) calibration, using the BENMSS and REFORM
computer programs; (b) scan-angle correction, using the
LYKIT, LUVIT, LUMPIT and FIXIT computer programs; and
(c) registration to ground, using the ADAS and ADJUST
computer programs (ref. 1). For ERTS-1 data, the November
data was registered to ground using longitude-latitude
2
TABLE I. - TIMBER TYPES/CONDITION
CLASSES OF INTEREST IN EDIT 9
Type ID Description
1.3 Shortleaf pine, immature sawtimber
2.3 Loblolly pine, seedling and. sapling, adequately
stocked
2.5 Loblolly pine, immature sawtimber
2.6 Loblolly pine, mature sawtimber
3.1 Laurel oak - willow oak, immature sawtimber
4.2 Sweetgum-nuttal oak - willow oak, immature sawtimber
7.2 Cutover land, not site prepared
3
TABLE II. - SPECTRAL COVERAGES OF THE
4 CHANNELS ON ERTS-1 MSS
Channel # Spectral Coverage (micrometer)
1 0.5 - 0.6
2 0.6 - 0.7
3 0.7 - 0.8
4 0.8 - 1.1
TABLE III. - SPECTRAL COVERAGES OF THE 12 CHANNELS
OF THE EDIT 9 MSS AIRCRAFT DATA
Channel # Spectral Coverage (micrometer)
1 0.375 - 0.405
2 0.40 - 0.44
3 0.466 - 0.495
4 0.53 - 0.58
5 0.588 - 0.643
6 0.65 - 0.69
7 0.72 - 0.76
8 0.770- 0.810
9 0.82 - 0.88
10 0.981 - 1.045
11 1.20 - 1.30
12 2.10 - 2.36
4
information on the Earth Resources Interactive Processing
System (ERIPS). Then the February and May data were
registered and composed with the November data, on ERIPS,
to form a 12-channel data set registered to ground.
Two classification/analysis approaches were then
taken:
I Classification by training class statistics.
II Classification by representative cluster
statistics of training class.
ERIPS was used for data processing. Rectangular and
nonrectangular training fields were picked from the tele-
vision display of ERIPS. Statistics of these fields and
thus. classes were then calculated by the system.
In approach I, the best set of channels from the avail-
able set of channels were obtained by a "divergence"
calculation (ref. 3, 4) on the training class statistics.
For single-data ERTS data analysis, no channel selection was
made. For aircraft data analysis, the best six of the
available 12 channels were chosen. Six channels were chosen
to provide sufficient separation between classes. The
training class statistics and the best channel set were then
used in the classification of the data. The classification
rule on ERIPS is a maximum likelihood classification (ref. 3,
5).
In approach II, each training field of each class was
clustered into five clusters. Five was chosen arbitrarily,
with the contention that all possible heterogeneity would be
5
described by the 5-cluster representation. The three most
significant clusters of each training class were then
selected manually; this was achieved by overlaying cluster
statistics plots one on the top of another for identifying
equivalent clusters, and then, insignificant clusters were
eliminated by checking the population of the cluster in
the training field/class. The cluster 'statistics thus
chosen were then used for channel selection and then classi-
fication on ERIPS.
5.0 RESULTS
5.1 Data Processing Results
5.1.1 MSS/24 Data Processing. There were three main
categories of activities performed on Edit 9. A 3-channel
color rendition of Edit 9 is shown in Figure 1 with the
ground-truth timber stand boundaries; the boundaries were
extracted from U.S. Forest Service information.
Preprocessing
As planned in ref. 1, the original Edit 9 was calibrated,
scan-angle corrected and registered to ground. The resulting
data resolution, i.e. size on ground of one pixel, is (8.47
meters) 2 . A 3-channel color rendition of this resulting
data is shown in Figure 1. The imagery before preprocessing
is not available at the time of this reporting.
6
APPROXIMATE SCALE 1:28,000
1 0 1
I I- I, I STATUTE MILES1 0
50 1 2 3
SSA T KILOMETERS
Figure 1. - Timber stand and compartment map over Sam Houston
National Forest Edit 9; a 3-channel color rendi-
tion of the MSS/24 data of M230. (Refer to table
I for timber type codes.)
7
Classification Approach I
Training fields of all seven classes (c.f. section 3.0)
were picked all over Edit 9. Divergence calculations
produced the optimum set of channels 2, 5, 6, 7, 10 and 12.
Classification of the entire Edit 9 using.this channel set,
class statistics and using 2 percent, 5 percent, 10 percent,
and 12 percent thresholds, resulted in classification maps.
Figure 2 is a generic classification map. These maps were
then interpreted by a skilled photo-interpreter who
delineated the boundaries of homogeneous areas, which are
also shown by black lines in figure 2. Visual judgement by
ten different individuals led to the conclusion that the
classification map corresponding to 5 percent thresholding
was the best classification map, compared to the ground-
truth map of figure 1. It is also obvious from the maps
that the 2.'5 fields (refer to table I for timber type codes
such as 2.5) on the right are considerably confused with the
1.3 fields, and are spectrally different from the 2.5 fields
on the left of the scene. This suggests that the scan-angle
correction previously performed on the raw Edit 9 data is
inadequate.
Classification Approach II
Each training field of each class was clustered into
five clusters on ERIPS using the settings of PERCENT = 0.8,
SEP = 1.0, STDMAX = 0.5, DLMIN = 3.2, NMIN = 5 and ITMAX = 10
in the iterative clustering procedure. By the process stated
in section 4.0, the three most significant clusters of each
class were identified, and then used to classify the indi-
vidual training classes. The resulting classification
images were found to contain more "holes" than the maps
8
Figure 2. -A classification map over Edit 9 Black lines
denote timber stand boundaries delineated by a
photo-interpreter. Interpretations for colorsare: yellow = 1.3; red = 2.3; turquiose = 2.5;blue = 2.6; white = 3.1; green = 4.2; brown = 7.2.(Refer to table I for timber type codes.)
9
~T: ii 
~ ~Ja
obtained by classifying training classes using the training
class statistics; "holes" refer to thresholded pixels, i.e.,
pixels considered not to belong to the class. Due to
technical difficulties, classification of the entire Edit 9
by cluster statistics of all seven classes has not been
performed at the time of reporting.
5.1.2 ERTS-1 Data Processing. The three ERTS-1 data
sets were preprocessed and classified on ERIPS.
Preprocessing
The November, February and May ERTS-1 data sets were
registered to one another and to ground, as discussed in
section 4.0. The resulting data resolution, i.e. size on
ground of one pixel, is (80 meters) 2
Classification
Only Approach I was taken, i.e. classification by
training class statistics. Approach II of cluster-statistics-
classification was not performed because it was felt that
the training data did not have sufficient variability to
call for clustering. Classification was done season-by-
season, i.e. 1-date (4 channels) at a time. All three
season classifications showed that pine and hardwood could
be separated from one another; pine pertains to the totality
of 1.3, 2.3, 2.5, 2.6, and hardwood pertains to 3.1 and 4.2.
The May classification seemed to be better than the November
and February classification. And, the 2.3 and 7.2 class
were very well distinguished from each other and from the
other features in all seasons. Hardcopies of classification
maps were not available at the time of this reporting.
10
5.2 New Research and Development Areas,
As a result of the intensive data processing activities
on forestry data, problem areas that require research and
development opened up and became more well defined. Four
such problem areas are listed as follows:
(a) Mixture modeling - The extreme complexities in the
aircraft data (at (8 meters) 2 resolution) call for
new and improved modeling of forestry data,
especially at high resolution. Also, a systematic,
algorithmic way to identify equivalent clusters,
significant and insignificant clusters need to be
developed. These problems are being investigated
and partial solutions are reported in ref. 6.
(b) Data resolution versus classification - It appears
that classification is made difficult by the high
resolution in the aircraft data. It also appears
that classification is gross in the low resolution
ERTS-1 data. A natural question arises: "What
is the optimal data resolution for best forestry
analysis?" Modeling and simulation of data with
intermediate resolution are being performed. Some
results are reported in ref. 7.
(c) Boundary finding - Boundaries between timber stands
in classification maps normally are fuzzy. Boundary
finding is an art in human photo-interpretation,
and is far from being well defined inmachine
processing. Ideas are being explored in using
lower resolution data for boundary finding.
11
(d) Scan-angle correction - The Edit 9 aircraft data
is found to be inadequately corrected for scan-
angle effects. Signatures on the left side of the
scene does not seem to be extendable to the right
side. Closer look at the process of scan-angle
correction is called for.
6.0 CONCLUSION
The Edit 9 forest scene over Sam Houston National Forest
has been examined intensively using aircraft and ERTS-1 data,
for the two objectives of FAP/TR: (a) evaluate existing
computer techniques for forestry data processing; (b) afford
an opportunity to open up new research and development areas.
Although the first objective is not yet completely
fulfilled, the 6-month's study in this reporting period has
opened up at least two major research and development areas.
These are (a) modeling of forestry data, and (b) data reso-
lution versus classification study; these works have been
reported in ref. 6 and 7.
Should manpower be made available in the next 6 months
to these two Tasks 11.2.1 and 11.2.2 of FAP/TR, data
processing and analysis effort should be expended to complete
what has been left undone here, and also to study other
available data sets of Sam Houston National Forest.
12
7.0 .REFERENCES
1. Kan, E. P.: The Forestry Applications Project on Timber
Resources (FAP/TR): The FY75 Investigation Plan.
Lockheed Electronics Co., Inc., Houston, Texas, Tech.
Rep. #LEC 3567, June 1974.
2. Kan, E. P.: The Forestry Applications Project on Timber
Resources (FAP/TR): The FY75 Interim Report. Lockheed
Electronics Co., Inc., Houston, Texas, Tech. Rep.
#LEC 5135, Dec. 1974.
3. Earth Resources Interactive Processing System: A User's
Manual. International Business Machines Corporation,
Federal Systems Division, Houston, Texas, Contract No.
NAS 9-996, Dec. 1972.
4. Marill, T. and Green, D. M.: On the Effectiveness of
Receptors in Recognition Systems. IEEE Transactions
on Information Theory, Vol. IT-9, Jan. 1963.
5. Anderson, T. W.: An Introduction to Multivariate Statis-
tical Analysis. John Wiley and Sons, New York, N.Y.
1958.
6. Basu, J. P. and Kan, E.P.: Statistical Modeling and
Analysis of Forestry Data: Pure and Mixed Pixels.
Part I. Lockheed Electronics Co., Inc., Houston,
Texas, Tech. Memo. #LEC 5185, Dec. 1974
7. Kan, E. P. and Ball, D. L.: Data Resolution versus
Forestry Classification. Lockheed Electronics Co.,
Inc., Houston, Texas, Tech, Rep. #LEC 5168, Dec. 1974.
13


Multispectral scanner data processing over Sam Houston National Forest

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