A technique devised using a vector transformation of LANDSAT digital data to indicate when vegetation is undergoing moisture stress is described. A relation established between the remote sensing-based criterion (the Green Index Number) and a ground-based criterion (Crop Moisture Index) is discussed

Thompson, D. R.

Wehmanen, O. A.

English

NASA Technical Reports Server

N78-14540
THE USE OF LANDSAT DIGITAL DATA TO DETECT AND MONITOR
VEGETATION WATER DEFICIENCIES
D. R. Thompson
National Aeronautics and Space Administration
Lyndon B. Johnson Space Center
Houston, Texas
and 0. A. Wehmanen
Lockheed Electronics Company, Inc.*
Systems and Services Division
Houston, Texas
ABSTRACT
In the Large Area Crop Inventory Experiment
a technique was devised using a vector transfor-
mation of Landsat digital data to indicate when
vegetation is undergoing moisture stress. A
relation was established between the remote-
sensing-based criterion (the Green Index Number)
and a ground-based criterion (Crop Moisture Index).
1. INTRODUCTION
Meteorological indexes which provide methods of computing the extent and
severity of general drought have been developed. The most common of these are the
Palmer and Foley drought indexes [1]. The Palmer drought index is the more
Involved and the most satisfactory solution to the problem of combining precipita-
tion and temperature data and producing a predictor variable [2]. During periods
when a major drought is developing and spreading, it provides a means for routinely
assessing the areal distribution of the various degrees of drought severity [3D-
Palmer used a two-layer soil moisture model for estimating soil moisture defi-
ciency on a month-by-month basis [4]. These monthly variations are adequate for
the study of drought; however, they do not reveal the shorter periods of soil mois-
ture fluctuations which are important during strategic phases of plant growth. The
earlier drought study was modified by Palmer to calculate a weekly index of soil
moisture related to crop water requirements [3]. This index uses the same two-
layer models to compute a weekly hydrological balance. It is published in the
United States as the Crop Moisture Index (CMI). Although the CMI provides an indi-
cation of current plant-water relationships, it requires climatological analysis of
a long record in order to derive the constants which define the moisture character-
istics of the climate in the area of interest. This paper describes a procedure
using criteria based on remote sensing to detect and monitor crop moisture defi-
ciencies without analyzing a long record of climatological data.
2. METHODS
The procedure described in this paper was devised in an attempt to quantify
the subjective Judgment of the analyst-interpreter in deciding that a region is or
is not drought affected. The data used are Landsat multlspectral scanner (MSS)
values for Large Area Crop Inventory Experiment (LACIE [5]) sample segments through-
out South Dakota, which were acquired during the 1975 and 1976 crop years (fig. 1),
"Under National Aeronautics and Space Administration (NASA)~~
Contract NAS 9-15200.
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and the CMI values reported weekly during these crop years by the U.S. Department of
Agriculture (USDA) for each crop reporting district (CRD). Using the ideas pre-
sented by Kauth and Thomas [6], we devised a summary number, the Green Index Number
(GIN), which was computed for each acquisition of each segment. Each segment was
classified twice as drought affected or normal — first using only the GIN (remote-
sensing-based criterion) and second using only the CMI (ground-based criterion).
The results of these two classifications then were compared (table 1), and a con-
tingency table of the GIN and CMI classifications (table 2) was prepared.
The GIN is defined as follows: First, the data in the segment acquisition
are summarized by clustering using the Iterative Self-Organizing Clustering System
(ISOCLS) algorithm [7] as implemented on the Earth Resources Interactive Processing
System (ERIPS [8]) on the special purpose processor. (This parallel processor clus-
ters a segment in approximately 30 seconds.) The clustering procedure summarizes the
segment in 20 or fewer cluster means in the four Landsat channels. The count of
pixels belonging to each cluster is also calculated. Each mean vector x1 is then
transformed by
(1)
where
. - £. a vector representing the Landsat-1 version of the Kauth-Thomas trans-
y = ^ = formation of x1 [6]; the subscript number indicates the Landsat channel,
and the supercript is the cluster number.
0.1326 0.6325 0.5857 0.26tl
-.2897 -.5620 .5995 .1907
-.8242 .5329 -.0502 .1850
.2229 .0125 -.5431 .8094
b' = (0.45, -1.50, 10. 61, 2. 22)
Each vector is inspected automatically, and any vector having values unreasonable
for agricultural data is discarded using the following procedure.
A cluster y is accepted as good only if
30 < < 110
-10 <
-10 <
-10 < < 10
(2)
The greenness level m of the soil line then is estimated by the minimum second
channel value y| for acceptable clusters. That is,
m M I N
i is good
(3)
Then the green number g is computed for each cluster by
g1 - y| - m
926
The value of g should be a good measure of the green vegetation present on the
picture elements (pixels) in cluster i. Experience with spectral plots in the
brightness-greenness plane and the corresponding imagery led to the following
assumptions.
• g = 0 indicates bare soil
• g = 5 indicates a trace of vegetation
• g = 15 indicates good cover of vegetation
Operating on these assumptions, we chose the level 15 and defined GIN to be
the percentage of pixels in the entire image within clusters having green numbers
greater than 15- This value was considered somewhat unstable for consecutive-day
data where, for example, a cluster would slip from 15-1 one day to 14.9 the next
day; therefore, cubic weighting was added to smooth this calculation in the follow-
ing manner.
Let a cluster have green number g and contain n pixels. Define the weighting
factor w by
w = 0 if g1 < 11
w = 1 if g1 > 17 (5)
1 | ^ i 1 ll I I ( n- T il ^  I
Otherwise,
L^ 4] [l - ^
The cluster is counted as having w x
 n pixels with green numbers greater than 15.
This curve makes a smooth transition from full counting to not counting as the
green number decreases.
The GIN then is an estimate of the percentage of pixels in a Landsat scene
having green numbers high enough (>15) to indicate full cover of green vegetation.
It is computed using only Landsat data. A sample spectral plot of green numbers
versus brightness [6] is given in figure 2.
Having defined this segment summary number, the results had to be analyzed.
For the test set used, it was impossible to make (1) year-to-year comparisons
because of sparse Landsat acquisitions and important weather differences between
1975 and 1976, (2) area-to-area comparisons because of differing wheat propor-
tions and haze effects, and (3) a direct comparison of GIN's and CMI's because
the GIN contains crop stage effects not taken into account in calculating the
CMI. This comparison also is confused by nonwheat crops with different crop
calendars from those of wheat.
Therefore, the preferred technique for this analysis was a comparison of clas-
sifications. The plot of GIN versus time for a normal, predominantly wheat segment
should follow a curve such as a in figure 3. If an observed point for a segment
fell into the shaded region, the segment was classified as drought affected. The
bounds for the shaded region were defined empirically as shown in figure 3, with t
defined as the approximate spring emergence date in days. For different areas or
years, the shaded area can be moved from side to side to match the greenup curve.
The initial point in South Dakota was usually near day 110 (t = 110). This
classification was compared to a classification based on the CMI for CRD's, wherein
a CRD was classified as drought affected if the CMI fell below -0.5 for 2 consecu-
tive weeks. Both classifications were restricted to similar time frames. Classi-
fication was performed only for data between April 1 and July 10.
3- DATA AND RESULTS
The data used in this study consisted of all LACIE segments in South Dakota
which had at least 5 percent wheat as measured by the LACIE Classification and
Mensuration Subsystem (CAMS [9]) in the 1976 growing season. This definition
yields 17 segments with 34 possible classifications. Of the 34, 12 had either
927
insufficient data during the growing season or data were inaccessible for other
reasons. The final data set contained 22 segment years1 for 13 LACIE segments
(fig. 1, table 1). The contingency table (table 2), which applies the two classi-
fication methods to the 22 good segment years, shows that the classifications based
on the CMI and GIN are related. It was concluded that the GIN is detecting moisture
through crop responses.
An inspection of the five disagreements on the classification results (table 1)
disclosed that on two segments the GIN algorithm was confused by a lake. Three
of the segments were on the edge of their CRD's, and the CMI classifications may not
reflect the actual conditions in these segments. One segment was located on the
eastern edge of the CRD where heavy rains occurred at the weather station located
in the Black Hills in the western part of the CRD, causing a possible incorrect
condition to be reflected by the CMI.
Examples of the segment classification procedure are shown in figures 4 and 5-
The GIN indicates that 1975 was normal for the entire crop season for segment J
(fig. 4). In 1976, the GIN indicated that by May 24 there was moisture stress in
segment J. This indicates that the GIN detected vegetation moisture stress at the
same time as the CMI. Segment L (fig. 5) experienced drought as indicated by both
the GIN and the CMI during the 1975 crop year. In 1976, the GIN indicated moisture
stress on May 26, which was confirmed by the CMI.
4. DISCUSSION
The results reported cover a first attempt at measuring drought conditions
using Landsat data. Preliminary checks indicate that this procedure works in the
U.S. Great Plains. More recently, we have developed a program using raw image data
and avoiding the clustering procedure, enabling the generation of GIN's with any
threshold. This new procedure will be utilized in continuing investigations.
5. CONCLUSIONS
A technique was developed using Landsat digital data from 9-by-ll kilometer
sample segments, which indicates when agricultural vegetation is undergoing mois-
ture stress. A relation between this technique, which utilizes remote sensing, and
a ground-based criterion (the CMI) has been shown. Indications are that this pro-
cedure may be capable of detecting crop moisture deficiencies in areas of the world
where ground information is not available or reliable.
6. REFERENCES
1. World Meteorological Organization: Drought and Agriculture. Technical
Note 138, 1975 (127 pp.).
2. Julian, P. R.; and Fritts, H. C.: On the Possibility of Quantitatively Extend-
ing Climatic Records by Means of Dendroclimatological Analysis. American
Meteorological Society, Proc. of First Statistical Meteorological Conf.
(Hartford, Conn.), May 27-29, 1968 (180 pp.).
3. Palmer, W. C.: Keeping Track of Crop Moisture Conditions Nationwide, The New
Crop Moisture Index. Weatherwise, 21, 1968, pp. 156-161.
4. Palmer, W. C.: Meteorological Drought. U.S. Weather Bureau Paper 45, 1965
(45 pp.).
5. Large Area Crop Inventory Experiment, NASA Press Release, Nov. 6, 1974.
6. Kauth, R. J.; and Thomas, G. S.: The Tasselled Cap — A Graphic Description of
the Spectral-Temporal Development of Agricultural Crops as Seen by Landsat.
Proc. Symp. on Machine Processing of Remotely Sensed Data, Purdue Univ.
(W. Lafayette, Ind.), June 29 -July 2, 1976; IEEE cat. 76, CH1103-1-MPRSD.
A segment year is defined as an observation of 1 segment for 1 year.
928
7. Kan, E. P. P.: The Clustering Program ISOCLS and Its Applications. LEC-0483,
NASA/JSC (Houston), July 1973.
8. Large Area Crop Inventory Experiment (LACIE) User's Guide. Vol. 1, IBM Federal
Systems Division (Houston), Apr. 1, 1975.
9. Large Area Crop Inventory Experiment (LACIE) Classification and Mensuration
Subsystem Requirements. LACIE-C00200, Vol. II (Rev. C), JSC-11330 (Houston),
Jan. 1977.
TABLE 1. RESULTS OP GIN AND
CMI CLASSIFICATIONS
TABLE 2. CONTINGENCY TABLE OP GIN AND
CMI CLASSIFICATION METHODS
D = Drought conditions
W = Normal conditions
- = No data
CMI
Segment
A
B
C
0
E
F
G
H
1
J
K
L
M
1975
GIN
W
-
W
W
W
W
W
W
W
W
W
D
-
CMI
W
W
W
W
W
W
W
W
W
W
W
D
D
1976
GIN
-
W
D
D
D
W
W
D
-
D
D
D
W
CMI
D
D
D
D
D
D
D
W
W
D
D
D
D
Normal
Dry
Normal
10
1
11
Dry
4
7
11
14
8
22
X2 = 7.07 with 1 degree of freedom.
P = 0.0082 = level of significance.
NORTH DAKOTA
B
H SOUTH DAKOTA
M
NEBRASKA
FIGURE 1. SEGMENT LOCATIONS. Map of South Dakota showing locations of
LACIE 9-by-ll kilometer sample segments.
929
actu
m
2
UJ
in
O
50* + + + + + + + + + +
*it
*
*40* cis
t **
35*
*
*30* C13 C14
* X X
25*
*
 C17 C16 C
20dt v v
* A A
1-f > 15 15J < 15 «o
* X cii
10* X
* cu c5 C6c"L **. ^ ** H v v
5
* C2 C/ X X X
* ***• V "2 Cl A .
nT v
* Estimated s
*
-5*
*
*
+
+
+
•f
GIN is the sum of
 +
pixels in clusters C12,
, C13, C14, C15, C16, and
( C17, divided by 229.32. +
k
Green number
for cluster C12
 +
:' V ^r
oil line
+
50 55 60 65 70 75 80 85 90 95 100 105 110 115 120 125 130 135 140 145
BRIGHTNESS
FIGURE 2. SPECTRAL PLOT. Sample spectral plot of cluster statistics.
Planting • (O.t) (0,t+70)
TIME
-^Harvest
FIGURE 3- PLOT. GIN versus time for a normal, predominantly wheat segment.
930
PAGE IS
POOR QUALITY!
Al
m
£
QC
a
X
o.
60
40
O 20
ui
a
UI
o
oc
UJ
o.
O(0.12)
0(0.32)
*X 0.05
LEGEND
X = 1976 GIN
O = 1975 GIN
_ = 1976 CMI
( ) = 1975 CMI
Drought bounds
Q<0.21) (0.1)
O <b
90 120 150 180 210
I I
240
I
May 6 May 24 June 11
DATE
FIGURE GRAPHIC PLOT. GIN versus time with CMI values for segment J.
A)
<r
uimS
60
40
UI
X
I
UI
o
oc
UI
20
X0.02
X(0.04)
0.00 / I
I
I
I
,=3,68 |
x'-Q-621!1
LEGEND
X = 1976 GIN
O = 1975 GIN
=1976 CMI
( ) = 1975 CMI
Drought bounds
o (-1.36)
o (-2-31
COl
90 120 150 180 210
I I
240
I
Apr. 1 May 7 May 26 June 12
DATE
FIGURE 5. GRAPHIC PLOT. GIN versus time with CMI values for segment L.
931


The use of LANDSAT digital data to detect and monitor vegetation water deficiencies

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