The author has identified the following significant results. Recent signature identification and refinement techniques indicate that with automated classification of MSS CCT data commercial stands of cottonwood and white spruce can be identified with 80% accuracy in the Bonanza Creek experimental forest. Since that forest is representative of the vast interior Alaska forests, this finding has substantial economic importance to public and private forestry interests in Alaska

Mckendrick, J. D.

English

NASA Technical Reports Server

E7.4- 1 0.29.
'Mie svffwMfe gadt MS A *WW NINTH BI-MONTHLY PROGRESS REPORT
In the interest of early and wide dis- UNIVERSITY OF ALASKA
semination of Earth Resources Survey ERTS PROJECT NO. 110-2
Program informAtion and without liability FEBRUARY 6, 1974
for any use made thereot."
A. TITLE OF INVESTIGATION: Identification of Phenological Stages and Vege-
tative Types for Land Use Classification
B. PRINCIPAL INVESTIGATOR/GSFC ID: Jay D. McKendrick/UN 641
C. PROBLEMS IMPEDING INVESTIGATION:
A serious limitation slowing our progress is the inoperative CDU. In
, I view of our contract expiration date (February 13, 1974) and the current
o i
inoperative condition of the CDU we expect to obtain for this project no more
a digital displays, other than the three already obtained,
P1 D. PROGRESS REPORT:
1. Accomplishments during reporting period.
SThis is our last bi-monthly report due to the pending expiration of the
Scontract. Therefore, the final report will include maps and illustrations
4 P4 W
E )U of our findings. We wish to stress that MSS signature data contained in
S- o m this report have not been applied beyond the original training sets, i.e. used
P4 0 to classify MSS data. These refined signatures and those being derived and
0 0 refined between now and February 13, 1974 will probably be verified via methods
Ir 4 other than the CDU for inclusion in the final report.
V P Selected ground truth areas from NASA aircraft data were mapped on plas-
P FA 04 tic overlays to the scale of digital printouts for 13 CDU scenes via the
oO Bausch and Lomb Zoom Transfer Scope. Those overlays were needed to locate
(n H training sets for MSS signature extractions. Due to the lack of man-made
.4 w features on most of the Alaskan landscape, it is extremely difficult to pre-
0 4 W
r- m O cisely locate training sets in specific vegetation types without the aid of
the Zoom Transfer Scope. Also confounding the problem is the varied nature of
the Alaskan vegetation. In order to overcome our uncertainty in extracting
signatures we have statistically analyzed the signature variation within
https://ntrs.nasa.gov/search.jsp?R=19740007917 2020-03-23T12:59:35+00:00Z
-2-
bands and vegetation types and developed minimum sample size requirements
(Table 1). The assumptions for our model are the standard statistical analysis
assumptions: (1) picel intensities within training sets are normally distri-
buted about the mean, (2) our selection of test areas was random with types
and (3) by adequately sampling for a 99% confidence of the true mean of the
population of intensities, we would obtain an adequate estimate of the popula-
tion parameters of picel intensities, To insure adequate sampling error pro-
tection the final sample size was computed to the next highest multiple of 6
because training sets must include an equal number of samples from each of
the 6 scanners to balance interscanner errors.
Based on those training set intensity samples we developed MSS signatures
for automated classification of the features which are useful for identifying
locations having agricultural potential (Table 2) and other commercial inter-
ests. Water signatures must be included because water features serve as re-
liable geographical location references.
Signature refinement was essential for distinction among features. Also
due to the MSS data overlap in birch-spruce and scrubby spruce, signatures for
those two types were combined. That overlap probably resulted from a large
inherent variability within the deciduous-coniferous mixtures. In addition
to the value of these classifications to crop land and grazing land enter-
prises, the possibility of identifying commercial cottonwood and commercial
spruce stands in ERTS-1 data is of particular value to the U.S. Forest Service,
Bureau of Land Management, the State of Alaska and the Alaskan native corpora-
tions. The highest probability of identifying commercial stands of timber
was calculated to be about 80-82% (Table 3). The vastness of the Alaskan
forest and paucity of timber volume estimates would appear to impel urgent
consideration of automated means for obtaining those estimates. The 80%
accuracy in automated classification of commercial stands would have an
-3-
economical advantage of at least an order of magnitude over the costly aircraft
and relatively slow visual interpretation methods,
Statistical analyses of features' signatures within and among MSS bands
showed certain peculiarities (Tables 4, 5, 6 and 7) which indicated the prob-
able value of data transformation to improve automated classification poten-
tials. Bands 4 and 5 are quite "flat", i.e. the signature variances are low.
The treeless bog and muskeg types (definitely poor agricultural sites) had
skewed distributions of intensities in band 4, i.e, the median, mean and mode
were relatively distant from each other compared to those respective parameters
for other vegetation types. That probably reflects dissimilarities within our
grouping of types and may indicate they possibly could be separated in data
obtained during another season of the year.
The birch-spruce and scrub-type signatures in band 4 had a broad var-
iance, reflecting the above-mentioned problem resulting from combining signatures
among types, Within this classification type occurs lands with agricultural
potential where topography and soil depth permits, Therefore, identifying
this type is important to our project objectives related to agriculture.
Although band 5 was considered relatively "flat" as compared to bands 6
and 7, the signature variances in band 5 are larger than those in band 4,
and the signatures appear to have relatively normal distributions, except
for the clear water signature. The clear water signature may be showing the
effects of an edge-effect in shallow lakes and/or inadequate sampling since this
particular CDU scene contained few lakes large enough to sample.
Signatures in band 6 consistently contained the largest variation com-
pared to those of the other bands. However, data in band 6 are important for
separating commercial white spruce from scrubby spruce, a capability valuable
to forestry interests in Alaska.
Signatures in band 7 as a rule were distributed normally with the excep-
tion of the aforementioned clear water, a slight skewness in the birch-spruce
-4-
and scrub combination and the commercial spruce signature, Howeverp those
characteristics did not significantly reduce the respective classification
accuracies as much as those in band 6 (Table 3),
Two color digital imagery samples have been processed by the Dicomed
Corporation of Minneapolis. Their service was found to be very good and
quite valuable to our project objectives, In our final report we expect to
include quantitative evaluation of that process compared to optically enlarged
color imagery.
To assist our mapping efforts, Mr. Peter C. Scorup visited the U.S.
Geological Survey Cartographic Division in Denver, Colorado, while in that
state on personal business. We compared their methods to those of the Soil
Conservation Service in Portland, Oregon and explored the possibility of using
U.S.G.S. plates containing township and range lines and topographic contours
in conjunction with our vegetation themes.
In our continuing efforts to educate local people about ERTS, we have
recently guided three high-school age groups (about 50 students) through our
Palmer facility. Following the tours, the high school teacher expressed great
interest in our work and indicated that he intended to incorporate our lab
into his field trip plans for his future earth science classes, Other visitors
included Alaska's State Senator Jalmar Kerttula and William Lewis, Director of
the Alaska State Division of Agriculture. Both Senator Kerttula and Mr, Lewis
expressed interest in our project, a willingness to support our expansion
efforts, and stated that current and future results from ERTS would be of
great value not only to Alaskan residents but also to others with interests in
Alaska. The Alaska Rural Development Council, an organization composed of
state, federal and private groups with interests in agriculture, was presented
with a report on our project. As a result of that report, the council passed
a resolution favoring the continuation of our work (see appendix).
-5-
F. PUBLICATIONS;
(a) In preparation;
McKendrick, Jay D. and Peter C, Scorup. 1974. From ERTS-1--A Super
Bird's-Eye View of Alaska, Agroborealis 6(1) _. (see
attached manuscript),
(b) In Press: None
(c) Published:
McKendrick, Jay D. 1973, Mapping Alaskan Vegetation from ERTS-1
Data. A report to the Alaska Rural Development Council.
December 6, 1973 Anchorage, Alaska (xerox). (see previous
report for copy).
G. RECOMMENDATIONS: None
H. CHANGES IN STANDING ORDER FORMS; None
I. ERTS IMAGE DESCRIPTORS FORMS: (see attached appendix)
J. DATA REQUEST FORMS: None
-C--
TABLE 1. Number of picels required for an adequate sample (d=2 and P40.01) of
digital data training sets in ERTS-1 MSS CCT data for each of eight fea-
tures in scene 1033-21011.
MINIMUM SIZE
---------------- BANDS---------------- OF
FEATURE 4 5 6 7 TRAINING SET
Commercial cottonwood 4 3 26 7 30
Birch & aspen 5 5 92 14 96
Tall shrub 4 4 25 13 30
Muskeg & treeless bogs 39 8 35 11 42
Birch-spruce & scrub 71 8 71 35 72
Commercial spruce 3 7 43 20 48
Clear water 4 8 28 12 30
Silty water 7 15 6 3 18
- d = confidence interval; P = probability level
TABLE 2. Observed (o) and refined (R) MSS signatures for eight features in training sets
from CCT data ERTS-1 scene 1033-21011,
--------------------MSS BANDS--------------------
4 5 6 7
FEATURE o R o R o R o R
Commercial cottonwood 14-17 14-17 8-10 8-10 22-29 22-29 13-17 15-17
Deciduous forest 14-18 14-18 7-11 7-10 30-40 30-40 18-27 18-27
Tall shrub 16-19 16-19 10-13 11-13 27-37 27-37 16-22 17-22
Treeless bog 15-39 15-21 9-15 11-15 20-31 24-31 11-18 11-16
Scrub & birch-spruce mix 14-18 14-18 7-12 7-12 14-30 16-23 7-19 9-14
Commercial spruce 13-17 13-17 6-10 6-10 10-26 11-15 4-15 6-15
Silty water 28-32 28-32 25-31 25-31 21-25 21-25 5-8 5-8
Clear water 14-16 14-16 6-9 6-9 7-14 7-14 2-7 0-5
TABLE 3. Percentages of data remaining after refinement of MSS signatures in train-
ing sets of eight features (ERTS-1 scene 1033-21011 CCT digital data) and
calculated classification limits,
CALCULATED
CLASSIFICATION
----------MSS BANDS-------- ACCURACY
LIMITS
FEATURES 4 5 6 7 MAX MIN
Commercial cottonwood 100 100 100 82 82 82
Deciduous forest 97 100 100 100 97 97
Tall shrub 81 100 100 93 81 75
Muskegs & treeless bogs 96 100 94 93 93 87
Scrub & birch-spruce 100 100 83 80 83 66
Commercial spruce 100 100 85 89 85 76
Silty water 100 100 100 100 100 100
Clear water 100 100 100 93 100 93
TABLE 4. Radiation-intensity median, mean, mode, variance and coefficient of varia-
tion for band 4 signatures of eight features in MSS digital data (ERTS-1
scene 1033-21011).
COEFFICIENT
FEATURES MEDIAN MEAN MODE VARIANCE OF VARIATION
Commercial cottonwood 15.5 15.8 16 .69 4.36
Deciduous forest 16.0 16.1 16 .85 1.76
Tall shrub 17.5 17.2 17 .72 4.19
Treeless bog 27.0 18.7 17 2.42 12.97
Birch-spruce & scrub 15.5 16.2 17 3.27 20.22
Commercial spruce 15.0 14.8 14 .91 6.11
Silty water 30.0 30.2 30 .98 3.24
Clear water 15.0 14.7 15 .70 4.77
TABLE 5. Radiation-intensity median, mean, mode, variance and coefficient of varia-
tion for band 5 signatures of eight features in MSS digital data (ERTS-1
scene 1033-21011).
COEFFICIENT
FEATURES MEDIAN MEAN MODE VARIANCE VARIATION
Commercial cottonwood 9.0 9.4 10 .61 6.47
Deciduous forest 9.0 9.1 9 .82 8.96
Tall shrub 11.5 11.3 11 .76 6.77
Treeless bog 11.0 12.0 12 1.07 8.89
Birch-spruce & scrub 9.5 9.8 10 1.07 10.87
Commercial spruce 8.0 8.3 8 .98 11.79
Silty water 28.0 28.2 29 1.49 5.28
Clear water 7.5 8.1 9 1.06 13.03
TABLE 6. Radiation-intensity median, mean, mode, variance and coefficient of varia-
tion for band 6 signatures of eight features in MSS digital data (ERTS-1
scene 1033-21011).
COEFFICIENT
FEATURES MEDIAN MEAN MODE VARIANCE VARIATION
Commercial cottonwood 25.5 25.6 26 1.96 7.64
Deciduous forest 35.0 34.2 35 3.72 10.87
Tall shrub 31.5 30.6 30 1.91 6.25
Treeless bog 26.0 26.7 28 2.30 8.61
Birch-spruce & scrub 21.5 20.6 19 3.26 15.78
Commercial spruce 18.5 13.4 12 2,53 18.89
Silty water 23.0 22.8 22 .89 3.89
Clear water 10.5 10.3 10-11 2.05 19.98
TABLE 7. Radiation-intensity median, mean, mode, variance and coefficient of varia-
tion for band 7 signatures of eight features in MSS digital data (ERTS-1
scene 1033-21011).
COEFFICIENT
FEATURES MEDIAN MEAN MODE VARIANCE VARIATION
Commercial cottonwood 15.0 15.4 16 .97 6.30
Deciduous forest 22.5 22.1 22 1.43 6.48
Tall shrub 19.0 18.3 18 1.37 7.49
Treeless bog 14.5 14,8 15 1.28 8.65
Birch-spruce & scrub 13.0 11.3 10 2.28 20.16
Commercial spruce 9.5 6.8 7 1.70 24.95
Silty water 6.5 6.4 6 .67 10.46
Clear water 4.5 4.0 4 1.31 32.72
APPENDIX
NINE BI-MONTHLY PROGRESS REPORT
UNIVERSITY OF ALASKA
ERTS PROJECT NO. 110-2
FEBRUARY 6, 1974
PRINCIPAL INVESTIGATOR: Jay D. McKendrick
TITLE OF INVESTIGATION: Identification of Phenological Stages and
Vegetative Types for Land Use Classification
DISCIPLINE: Agriculture/Forestry/Range Resources
SUBDISCIPLINE: Range Survey and Classification
SUMMARY OF SIGNIFICANT RESULTS:
Recent signature identification and refinement techniques indicate that
with automated classification of MSS CCT data commercial stands of cottonwood
and white spruce can be identified with 80% accuracy in the Bonanza Creek
experimental forest. Since that forest is a representative of the vast
interior Alaska forests, this finding has substantial economic importance
to public and private forestry interests in Alaska.
UNIVERSITY OF ALASKA
ALASKA RURAL DEVELOPMENT COUNCIL RESOLUTION - 12/6/73
Mapping Alaskan Vegetation From ERTS
WHEREAS,.ERTS I projects have developed considerable expertise in
vegetative mapping as well as other valuable geographic features and
WHEREAS, this mapping could be done in a more rapid, accurate and
economical manner than any developed so far and
WHEREAS, the information that could be generated by this program would
be of great value in planning the state's development, especially
in rural and remote areas and
WHEREAS, many agencies, state, federal, borough, Native and private
would use the information generated,
LET it be resolved that NASA be requested to give high priority to the
follow-on and expansion of this project and
LET it be further resolved that the federal delegation be requested to
lend their support to the follow-on project and
LET it further be resolved that agencies that would be potential users
of this information be encouraged to express their need for the
information and possible support and cooperation in the project.
ni:il rl i f ", MV
ERTS IMAGE DESC[PTOR FORM
(See instructions on Back)
NDPF USE OMLY1
DATE January 3. 1974 D
N
PRINCIPAL INVESTIGATOR Dr. J. D, McKendrick ID
GSFC UN-641
ORGANIZATION University of Alaska 110-02
PRODUCT ID FREQUENTLY USED DESCRIPTORS*
(INCLUDE BAND AND PRODUCT) _IrE. L- DESCRIPTORS
1422-20212 # & Valley
1426-20441 4 " Matanuska Valley
1426-20444 b Kenai Peninsula
1428-20551 W 6 %
1428-20554 " 0 w
1428-20560
1422-20215 V Vl
1446-20553
1446-20551 t- o Vo
1459-20260 0 6 0'Valley
1461-20364 _ _
1462-20423 0 1
1463-20481 W 0 Urban area (Fbks)
1465-21000 I -
1467-21104 WO
*FOR DESCRIPTORS WH;CH WILL OCCUR FREQUENTLY, WRITE THE DSC~IPTO, TERMrS IN THESE
COLUMN HEADING SPACES NOW AN!D USE A CHEC( (,/) MAR,,( IN TIE APPROPRIATE PRODUCT
ID LHIES. (FOR OTHER DESCRIPTORS, WRI E TKF TERM UNDER THE DESCRIPTOS COLUpMJU).
YA!L 73 E, -T SED GERVEC20
CODE 563
BLDG 23 RCOM E413
NASA GSFC
GREEBE.LT, itD. 20771
301- -82-54CS
GSFC 37-2 (7/72)
-/7-
ERTS IrAGE DSCPOR FORMi
(See Instructions on Bac )
NDPF USE ONLY
DATE January 3, 1974 ID
PRINCIPAL INVESTIGATOR Dr. J. D. McKendrick ID -
GSFC UN-641
ORGANIZATION University of Alaska 110-02
PRODUCT ID FREQUENTLY USED DESCRIPTORS*
(INCLUDE BAND AND PRODUCT) fille (li. DESCRIPTORS
1468-21163 W ___
1477-20260 1 wo
1477-20263 I -
1478-20315 1 0
1482-20544 '
'*FOR DESCRIPTORS3 V'WHICH WILL OCCUR FREQUENTLY, WRITE THE DESCRIPTOR -R-'RfiS IN THESWE
COLUMN HEAD!NG SPACES NOW A.ND USE A CHSCK ( ) AJA( K IN THE FPROPRBATE PROUCT
ID LINES. (FOR O, TER DESCRIPTORS, W' 'I'E THE TERM: U,-R ThE DESCRIPTORS COLUtN-).
CODE 563
BLDG 23 FROOfi E413
NASA GSFC
GlEENBLT, D. 2077u
301-2-E0 j
GSFC 37-2 (7/72)


Identification of phenological stages and vegetative types for land use classification

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