1. No category

, 3 1(2001)
Korean Journal of Agricultural and Forest Meteorology, Vol. 3, No. 1, (2001), pp. 5~15
Landsat TM 112
13
, 2, 3
(2000 11 6 )
1
Estimation of Rice-Planted Area using Landsat TM
Imagery in Dangjin-gun area
Suk-Young Hong1, Sang-Kyu Rim1, Kyu-Sung Lee2, In-Sang Jo1 and Kil-Ung Kim3
National Institute of Agricultural Science and Technology (NIAST)
Inha University, Kyungpook National University
(Manuscript received 16 November 2000)
ABSTRACT
For estimating paddy field area with Landsat TM images, two dates, May 31, 1991 (transplanting
stage) and August 19, 1991 (heading stage) were selected by the data analysis of digital numbers considering rice cropping calendar. Four different estimating methods (1) rule-based classification
method, (2) supervised classification(maximum likelihood), (3) unsupervised classification (ISODATA,
No. of class:15), (4) unsupervised classification (ISODATA, No. of class:20) were examined. Paddy field
area was estimated to 7291.19 ha by non-classification method. In comparison with topographical map
(1:25,000), accuracy for paddy field area was 92%. A new image stacked by 10 layers, Landsat TM
band 3, 4, 5, RVI, and wetness in May 31, 1991 and August 19, 1991 was made to estimate paddy field
area by both supervised and unsupervised classification method. Paddy field was classified to 9100.98
ha by supervised classification. Error matrix showed 97.2% overall accuracy for training samples.
Accuracy compared with topographical map was 95%. Unsupervised classifications by ISODATA
using principal axis. Paddy field area by two different classification number of criteria were 6663.60
ha and 5704.56 ha and accuracy compared with topographical map was 87% and 82%. Irrespective
of the estimating methods, paddy fields were discriminated very well by using two-date Landsat TM
images in May 31, 1991 (transplanting stage) and August 19, 1991 (heading stage). Among estimation
methods, rule-based classification method was the easiest to analyze and fast to process.
Key words : Landsat TM imagery, estimation of rice-planted area, rule-based classification, supervised
classification, unsupervised classification
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Corresponding Author : Suk-Young Hong(syhong@rda.go.kr)
6
Korean Journal of Agricultural and Forest Meteorology, Vol. 3, No. 1
.| Qv L8
t$ DK Z}.& '
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3(www.naqs.go.kr). ~, PQ* € "# q# -
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10—“O —` , —… ˜™ .| š› .Ï3. PÆAB; Š 15,534.7 ha&, pixel u
” Š 37Ž ˜.| q# eZ}.& '3.
line __ 373@ 4643.
tœ} Už; Ÿ; # ¡¢£¤ '&
× AB J* 3F‚U «çs}
}¥ e.| Z}. :$ ¦ ¦§B 5 • Zè<$ * éêUs«»(rule-based
?¨¤ '“©O, !¦“O L"# 01
classification) .Ï& ¹ Ã@ tœ} Á”
.& <7* lª«+ .U f¬“O k;
$ ÙsB“O }%& ' ië«»¬(supervised
,­ '3. J; #”®¯`u °.| ±²@ classification) ìië«»¬(unsupervised classifi-
8`²³u ´; ®eµ* ˜(source)@ !,(sink)
cation)* Ã@u ¦§.Ï3.
* ]¶“O .©O ‘ ®eµ* l=@ ³¦
>8.U .| tœ} Už ·¸/ %&
2.1. '3. ¹ºH, q %& ' «»¬‘; «»¼
JAB íî$ } Landsat-5 TM
½* ¾ ¿°(training data) À f¬@
path 116, row 34O ÁX 3ï# ×*
Á”$ ÂI 3³ ) Ã@ HÄÅ3. 4,
Uª F$ ðñò :“O Ñ 6‚U3(Table
()Æ* ">Ç$ È. `³ ~, ÉÊ`³(mixed
1). Landsat 5ó 1984K 3ô 1Ù õ}%æ&, &X
pixel; mixel) «».U Ë3.
705 km, sö5U 16Ù, lª)V 30 m, 7*
Tennakoon j(1992); Landsat TM ÌÍ 1, 3, 4, 5
÷ø ùQ* 3.
Î(EX¬(maximum likelihood)“O 90%
* -XO J «».Ï3. Raou Rao(1987);
2.2. ÐUu Î(7,U Ñ>[ J ”«.C
!, northing@ easting“OÇ¢ #)Uâ­* #X
.Ï3. Panigrahy and Panihar(1992); FªBW
úš(û±ïücï¬) >8.Ï&(image to map
¾(S Landsat TM ÌÍ 5u 7 J \$ '
registration), 3î$ `)(image)$ #)Uâ­*  «»-X ÒS3& .Ï3. Okamoto u
È >8.Ï3(image to image registration). #X
Fukuhara(1996) Landsat TM * _ ÉÊ`³
úš(Xi, Yi, i=1,n)u `)úš(Pi, Li, i=1,n) (mixel)$ n J* Ó$ ( AB¦ .| affine ýþ<(< 1)* >(aÚf) >8.Ï3.
.| J .Ï3. Y(1998); ÐUu ˜U,
тU Landsat TM .| J .|
J* >[… #šA ®Xu c]• ÔÕaÖ3.
› Á”$ ×* 7ؕ .| ×  P =  a b  X +  c
 L  d e  Y  f 
(1)
<$ > #)Uâ­* #Xúšu ýþ 
%& ' J ”«.&, ¹ Ã@ tœ} Á”$
ÙsB“O }%& ' «»U¬@ ¦§. :
½B“O .& '3.
Table 1. Satellite data used
Satellite/Sensor
Path-Row
Date
Landsat/TM
116-34
May 31, 1991
Landsat/TM
116-34
Jun. 2, 1992
Landsat/TM
116-34
Aug. 19, 1991
52' 30''(177424.0 mÚ
Landsat/TM
116-34
Sep. 1, 1996
188584.0 m), Û 36 45' 00''Ú36 52' 30''$ È
Landsat/TM
116-34
Sep. 12, 1994
Ü8 Ùt“O ݄ EÞA, Ê߆, àA,
Landsat/TM
116-34
Sep. 27, 1988
II. ‡" 126
o
45' 00''Ú126
o
o
o
Hong et al.: Estimation of Rice-Planted Area using Landsat TM Imagery in Dangjin-gun area
7
* #Xúšu* gRÿÊ(RMS:root mean square)
C¢ ‘&, 3î SAS N>#O «8«
* $º γ # Ã% γÿ¬$
(ANOVA).Ï3(p, 1994). ¹ Ã@, × AB * ”#C, ¹ gR 0.5 `³(15 m) $ BÊ a »u ‚U Ã.Ï3. Ð
%X .| Î(nearest neighborhood)¬“O S 5ô(1991K)@ ˜US 8ô(1991K) Ñ ‚U$
.Ï3.
ÐU$ )O Æ()* • HÄ
Landsat TM ÌÍ 5, ˜U$ ì „* •
2.3. GPS HÄ RVI, 4 Ñ ‚U* X R
”.| __* Ó@ šâR ” , >Ó
݄ Ê߆ EÞA Ùt* J(29y#)* AB
.U 8­XO HÄæ3. , Á”# •; ‡Ù GPS }.| &Q@ ‡Q
* 5ô@ 8ô Ñ ‚Uª ý` ÔÕaU .|
Ñ #­* #úš ‡‚$ , &Q* tasseled cap transformation .| UÓ(brightness),
Èu e{* #úš ¦§.| gRÈ 8˜.
X(greenness), X(wetness) ”.| Ñ `)
&, }.| ‡Q* È a. trans-
¦§.Ï3(ERDAS, 1997).
location positioning f<“O )(.| í a
ЁS 5ô 31Ù Landsat TM ÌÍ 5* J#
.Ï3. úš û±ïücﬓO ýþ.Ï& Î
Ó* ( +2 šâR) >ӓO a
Ã@ 0Ù; dxfO 7.| * `)$
3 &, ˜US 8ô 19Ù* RVI (!2 šâ
F
.| ‚U… J* «çs}• Z}.C R) a3 ‹", Ñ ‚Uª X* R Uâ“
.Ï3. , GPS .| ’ò J •$ O ( -2 šâR) a3 # Zè $‚
Ý% # * Landsat TM Ó(data file value)*
ä J“O .Ï3. 3î$ %3# Æ() &
Ó$ (.| «8«¬(method in analysis of
µ‹.| × AB #X ‘æ3(Fig. 1).
variance; ANOVA) .| ‚U…(6‚U) «çs
}• ”.Ï3.
2.4. 2.4.1. (rulebased classification)
GPS .| ¨ 29y#* J# AB
feature 6‚U* Landsat TM `)$ F
.|
__ Ó Z}.Ï&, 4 ‡Ù #­* RVI
(ratio vegetation index)u X(wetness) ”.Ï
3(Table 2). J* ‚U… «çs}• R Z}
.U ,
! J# * Landsat TM ÌÍ 1, 2,
3, 4, 5, 6, 7 * Ó, RVI, XO 
Fig. 1. Flow chart for extracting paddy field area with the
spectral characteristics of biseasonal Landsat TM data.
Table 2. Vegetation indicies used in this study
Vegetation Index
Formula
Ratio Vegetation Index
RVI TM band 4TM band 3
Tasseled Cap Transformation
(Kauth and Thomas, 1976;ERDAS, 1997)
Brightness0.3037TM10.2793TM20.4743TM3
0.5585TM40.5082TM50.1863TM7
Greenness-0.2848TM10.2435TM20.5436TM3
0.7243TM40.0840TM50.1800TM7
Wetness0.1509TM10.1973TM20.3279TM3
0.3406TM40.7112TM50.4572TM7
8
Korean Journal of Agricultural and Forest Meteorology, Vol. 3, No. 1
2.4.2. 8ô 19Ù 0.231, 1996K 9ô 1Ù 0.396, 1994K 9
tœ} Á”$ ÙsB“O }%& ' `)
ô 12Ù 0.239, 1988K 9ô 27Ù 0.249Ï3. u
«»¬$ 1991K 5ô 31Ù(ÐU)@ 8ô 19Ù(˜
´ U.aò `) J* «çs}• «, A
U)* Landsat TM ÌÍ(band) 3, 4, 5, RVI, B O .Ï3.
X __ F
.| 7 10 'O ”
ò `) %æ&, «»¼½; J, 2, ,
3.2. #$ %& '()
X‚, (½ ), &, Sl”Z
j“O .Ï3.
JAB ˜$ BÊ ÌÍ ¾.U .|
ië«» ù#* `³ «»¤ «»¼½… «
Landsat TM P ÌÍ* Ó(data file value)
çs} ** «8@ l«8 Ñ B“O @ RVI X(wetness)O HÄÅ J # * ‚
. - «»f¬S Î(EX¬ .Ï&, «
U… «çs} • ÔÕaÖ3(Table 3). ‚ç¾
»¼½ Ã.U .| Ó* «+* •$ U
(* TM ÌÍ 1, 2, 3* J# Ó; ã
z.| ¦+ • ,* ¹-“O H.
³u üO56Íu ´; <
³$ * ç7
¦§} «» ISODATA¬ .| «»¼½ _
u ° 8 „ á Î(S 8ô 19Ù@ 9ô
_ 20u 15O .| /ºµ¢£.Ï3. , «»
1Ù$ , 9Ü:3 j;US 9ô Fƒ.n$ 3‚
0; ý , # 5«0(principal axis) ÒÜ:3. Fª BW¾(* TM ÌÍ 5u 7; «$
.Ï&, šâR 2.0, Î( sö 251, 2
<iC, )O Æ(:open water body)*
>Ó(threshold); 0.95O .Ï3.
ñ= >? 5ô 31Ù@ 6ô 2Ù* Ó ,
9/ HÄ@&, „ ÎU$ ;UO ˆ‘A
III. <
Æ «\ A‘ ­­ Ó ÒÜ:
3. 4 BW¾(* TM ÌÍ 4 <
Æ 7Ø
3.1. ; !"
$ B .# C 0,ñ “O „ ì 8
U.a; #)Uâ­(ground control point) ô 19Ù@ 9ô 1Ù* Ó , ÒÖ3. ,
.| affine ýþ .ÏC, EHI$ }
RVI(ratio vegetation index ; TM4/TM3) ˜ƒ
.& ' Aúš Æ>S û±ïücï¬* FÇt
`US 8ô 19Ù@ 9ô 1Ù$ , ÒÖ& X
­ }.Ï3.
(wetness) ×* 7Ø zU$ ;UO D >
!, 1:50,000 #áX )$ Î
19* #)Uâ­ ¾3  1992K 6ô 2Ù ð
E 9Ü:3. Ã@ c(O E¾ )O 3F
ñ Landsat TM* ‡Ù #­ ”. #X (
#šA ()Æu ¦§B GH ”«% ÐUS 5
`) j.| RMS(root mean square) $º 1
ô 31Ù@ „ á , Ñͺ ˜U 8ô
`³ $ ‘X .Ï3. H4# `)‘; Uâ
19Ù, Ñ ‚U ¾3I3. 4 «$ <i ÐU
“O `) ( `) j.| RMS $º 0.5 `³
* TM 5, „* •J B sñ. ˜U* RVI,
$ ‘X .Ï3. 1992K 6ô 2Ù * RMS
4 € ÌÍ • +\ò ÐUu ˜U* $º 0.692Ï&, 1991K 5ô 31Ù 0.211, 1991K
X JAB aO .UO .Ï3.
Table 3. Spectral characteristics on Landsat TM bands, RVI, and wetness at paddy fields in different dates
Date
TM1
TM2
May 31
112.23 a1) 51.86
Jun. 2
103.98
Aug. 19
77.52
Sep. 1
Sep. 12
Sep. 27
1)
TM3
a
58.89
TM4
TM5
TM6
TM7
RVI
Wetness
a
48.18 d
30.11
d
157.87 b
14.85
d
0.818
d
34.28
a
b 48.40 b 55.20 b
48.17 d
30.53
d
165.15
a
14.75
d
0.872
d
30.89
b
146.70
c
19.39
c
4.080
a
10.31
c
12.20
c
d 33.66
e
28.21
f 114.00 b
64.61
c
69.86
f
31.59
f
32.07
e 128.41
a
70.35
b
129.05 d
18.31
c
4.064
a
72.46
e
35.02 d 35.04 d 101.03
c
68.88
e
125.78
e
22.22
b
2.937
b
5.07 d
80.47
c
43.73
c
75.95
f
25.63
f
26.50
a
2.269
c
4.83 d
c
45.71
c 102.61
Duncan's multiple range test, ***=p<0.001
Hong et al.: Estimation of Rice-Planted Area using Landsat TM Imagery in Dangjin-gun area
9
Fig. 2. Relations between data file values of wetness, TM 5, and RVI (TM4/TM3) in each paddy field taken as polygon features by GPS in May 31 and August 19, 1991.
Fig. 3. Applications of tasseled cap algorithm for change detected area between May 31 and August 19, 1991, in terms of
brightness, greenness, and wetness.
u ´ J* ‚U… «çs}•$ Uz.|
# highlight ò Ç« ý`ò Ç«3. N; JAB ˜$ BÊ ‚Uu ÌÍ ¾. @
O Ç« 5ô 31Ù* • Þ Ç«&
$ GPS .| À; 29 y#* J# $
P(cyan)“O a Ç« 8ô 19Ù* •
Ý. 5ô 31Ù@ 8ô 19Ù* TM 5, RVI, Þ Ç«3. 2 # ; 5ô$ Q &, J # X‘ __ 8­XO HÄæ3(Fig. 2). 1991K
; 8ô Q / HÄ@3. X 8ô ôj9
5ô 31Ù* X 22Ú43 KO 34.27æ
ÞC 2 ÙÇ$ 5ô* • Q Ò/ HÄ@
&, TM 5 19Ú44 KO 30.10æ“"
3. 5ô$ ·O RÜH S㑠«+.
RVI 1 .Ï3. 8ô 19Ù* X 5Ú13 K
# “O 7_ò3. X J
6˜% 5
O 10.62 Ï&, RVI 3.7Ú4.8 KO ô „“O Töò 8ô$ ¦ • ÑÍ
4.08æ3.
º : U 'æ3. , X‚, 5V# ý`
5ô@ 8ô* Landsat TM `)* •J ÔÕaU
V* M :“O HÄ@3. ‚W X# • F
‘ __ UÓ(brightness), X(green-
X тUª J# * ý` , «Y./
ness), X(wetness) ()“O ý` i# :S
HÄZ“©O JAB [\'O ¾3
C(Fig. 3), L/ Hį Ç«; ý` M& .Ï3.
10
Korean Journal of Agricultural and Forest Meteorology, Vol. 3, No. 1
Fig. 4. Paddy field area extracted by biseasonal Landsat TM image.
3.3. *
! 3.3.1. (rule-based classification)
Table 4. Paddy field area in study site and accuracy assessed
by topomap
]* Ã@u ´ J* ‚U… «çs}•$ Uz
Extraction Methods
.| ÐU$ × # Æ()O HÄH&
„* ÎU* RVI Ò; Zè $.
•(Fig. 1)$ ÂI × # .& Fig. 4u
´ JAB ˜.Ï3.
1991K 5ô 31Ù Landsat TM t ^_@ «
1)
Ã@O À JAB #X HÄÅ3. ‚U*
2)
Paddy field Accuracy
area (ha)
(%)
a) Rule-based classification
7291.19
92
b) Supervised classification
9100.98
95
c) Unsupervised classification 11)
6663.60
87
d) Unsupervised classification 22)
5704.56
82
No. of class = 15
No. of class = 20
Landsat TM .| xI? Î(EX¬$
* ië«» Ã@u ¦§ U (› J`$ -S.Ï3. ¹ Ã@ 92#­ J“O, 8#­ J
{‚.# Cî), LO* ¾”Z ab GHI&
Ün :“O HÄH 92%* -X aÏ3(Table
XOX ¾Y./ ͺ@3. LO Ou ´ c
4). ¹ºH, LoÃ@ J Ün # “O Hį #­
ù¢ d$ %# C :; e{ ^e ‹U 30
X ¹ R V* 1`³ S "E kÖ&, m 30 mS "E$ ”….U f‘3. ‘; (ÆO
2u Sˆ J# X V* 01% :“O HÄ@3.
J@ • gS ÉÊ`³(mixel; mixed pixel)O H
u ´; éêUs«»$ * J# * 01; ¦
ÄH «» -X 9 ‚U* Î(E
§B -.& q '“©O, >Ó$ ( |
X¬“O «» "E, ( # XO ¾”ZO H
º «2$* 3_BS Lc , p&q` ‡
Ä@3. sA, Ñ ‚U* J* «çs}•$ Uz
` r$ (.| {&¤ yG '3& 7_3.
Zè< .A JAB ˜$ I? ӑ
* >Ó h.| ÎB* AB ”¤ '3
,­ ' :“O 7_ò3.
3.3.2. (supervised classification)
§} ¦§} «»$ * JAB ˜ J* «çs}•$ * ¾3ò 1991K 5ô 31
éêUs«»$ * × AB ˜f¬$ (
Ù@ 8ô 19Ù Ñ ‚U* Landsat TM ÌÍ 3, 4,
-X L .| i#Ä!(digitizer) .
5, RVI X __ drÌÍO ¾.| F
| Á”# Xã(1992K {))$ J # ­E
s 10* ÌÍ ·Ot `) 7.|
(jkl)“O m& 100„C š‚.| ˜ò ×
«»$ .Ï3.
# $ F
.| J“O -./ ˜%æ#
Î(EX¬$ * ië«» J +\ 7
Hong et al.: Estimation of Rice-Planted Area using Landsat TM Imagery in Dangjin-gun area
11
Table 5. Mean and standard deviation of training samples for supervised classification
Training
samples
Paddy
Forest
Water
59.13
38.71
55.73
City
Bush+
upland
Village
74.43
44.98
63.82
Bands
Band* 1
Band 2
Band 3
Band 4
Band 5
Band 6
Band 7
Band 8
Band 9
Band 10
Mean
Manmade
90.33
Std
7.1
1.7
4.2
11.7
8.8
4.6
20.9
Mean
51.60
70.49
25.03
81.71
110.68
67.41
100.83
Std
8.9
4.8
2.1
9.5
10.4
5.2
12.1
Mean
22.60
61.25
10.05
91.96
92.85
153.98
Std
6.0
4.1
1.2
15.0
13.3
34.7
Mean
0.86
1.81
0.45
1.10
2.56
1.07
1.11
Std
0.1
0.1
0.06
0.2
0.6
0.09
0.2
Mean
42.12
4.07
43.60
0.03
2.72
0.26
0.02
Std
5.2
2.7
2.5
0.3
3.3
1.2
0.1
Mean
28.07
33.75
34.29
55.48
30.64
45.82
55.58
116.60
18.3
Std
1.0
10.0
1.9
12.8
3.8
6.9
15.8
Mean
119.23
73.12
27.75
84.88
95.88
92.00
90.35
Std
4.2
10.14
1.16
13.4
10.3
8.5
7.89
Mean
66.55
55.77
9.67
100.06
78.49
93.48
116.15
Std
3.1
14.0
1.3
20.6
10.0
10.3
28.5
Mean
4.25
2.21
0.81
1.59
3.10
2.26
1.75
Std
0.2
0.2
0.04
0.5
0.5
0.7
0.7
Mean
12.10
7.97
32.08
0.10
0.39
0.26
0.29
Std
2.1
4.3
2.0
0.5
1.1
1.3
1.2
*Band 1=Band 3 of May 31, 1991, Band 2=Band 4 of May 31, 1991,
Band 3=Band 5 of May 31, 1991, Band 4=RVI of May 31, 1991,
Band 5=Wetness of May 31, 1991, Band 6=Band 3 of August 19, 1991,
Band 7=Band 4 of August 19, 1991, Band 8=Band 5 of August 19, 1991,
Band 9=RVI of August 19, 1991, Band 10=Wetness of August 19, 1991.
* «»¼½$ ( Ìͅ Ó@ šâR
@ ½# X &y#O Ìͪ [(Ó* R '
Table 5$ HÄæ3. J; 1991K 5ô 31Ù Land-
% "=; V* ´Ö“H, 1991K 8ô 19Ù* X
sat TM ÌÍ 5(band 3)u X(band 5)$ (band 10) Ó; R ‹/ HÄ@3.
{W 3F «»¼½@ GH ”…%æ&, 1991K
u ´ ¾ò «»¼½ c(O Î(EX¬
8ô 19Ù * TM ÌÍ 4(band 7)u RVI(band 9)
$ * «» x %(Fig. 5) ¹ Ã@$ (
$ , Ò; Ó Hč J ”«$ # ¤
-X L error matrixO HÄæ3(Table 6).
.Ï :“O 7_ò3. 2 PsB“O J* «
Á”# AB ¦§B Ÿ# C& #á@ #½
çs}•@ ¦+ "= aH 5ô 31Ù*
n “O «»-X Ò/ HÄ@3. •9
RVI(band 4)Ó@ X(band 5) Ó$ B ”…ò3.
«»-X J, 2, ; 100%Ï&, Xu#
; 5ô 31Ù* "E J@ ¦+ • #H
91.8%, ”x#* ) ½# 97.4%, v
8ô 19Ù* TM ÌÍ 4(band 8), RVI(band 9), 85.2%, Sl”Z
92.3%O HÄ@3. -X
X(band 10)$ GH ”«ò3. Xu#u v
¦§B 9/ Hį v; `³* Š 15% X
Sl”Z
‘* «çs}•; [(Ó* R
Xu#O «»%æ3. PÆ* «»-X 97.2%
' w ´; "= aS3. 2# @ ”x#* )
Ï3.
12
Korean Journal of Agricultural and Forest Meteorology, Vol. 3, No. 1
Fig. 5. Paddy field area extracted by four different methods.
3.3.3. (unsupervisedclassification)
Ï&, 20O "E 6¼½, 5704.56 ha J“O
Clustering «»0“O ý , # 5
¾.Ï3.
«0(principal axis)“O ISODATA¬ .|
3.3.4. !"#
"#
«»¼½ __ 20¼½, 15¼½“O ¦§} «».
éêUs«», §} ¦§} «»¬“O ˜ò
Ï3(Fig. 5). «»ò ¼½‘ F J k +\.
× # ; Fig. 5u ´&, AB@ #áX)*
¼½ ¾.& H4# ù#* ¼½$ (X BÊ
J# -X Table 4u ´3. Î(EX
.3& 7_% ¼½ Ç|.Ï3. «»¼½ 15
¬$ * ië«» Ã@ × # 9100.98 ha
O "E 3 ¼½, 6663.60 ha J“O ¾.
O , Ÿ/ %æ&, 3î éêUs«», «»¼
Hong et al.: Estimation of Rice-Planted Area using Landsat TM Imagery in Dangjin-gun area
(Unit:Pixel)
Table 6. Error matrix resulting from classifying training set pixels
Paddy
Paddy
Forest
92 (100)
0
(0)
13
Forest
Water
0 (0)
0 (0)
0 (0)
City
Bush+Upland
0 (0)
0
Village
(0)
Man-made
0 (0)
69(100)
0 (0)
0 (0)
0 (0)
0
(0)
0 (0)
Water
0
(0)
0 (0)
260(100)
0 (0)
0 (0)
0
(0)
0 (0)
City
0
(0)
0 (0)
0 (0)
111(91.8)
3 (2.6)
4 (14.8)
3 (5.8)
Bush+Upland
0
(0)
0 (0)
0 (0)
4 (3.3)
114 (97.4)
0
1 (1.9)
Village
0
(0)
0 (0)
0 (0)
1 (0.8)
0 (0)
Man-made
0
(0)
0
0
(0)
5 (4.1)
0 (0)
260 (100)
121 (100)
117 (100)
Total
92 (100)
(0)
69 (100)
(0)
23 (85.2)
0
0 (0)
(0)
48 (92.3)
27 (100)
52 (100)
Overall accuracy = (92 + 69 + 260 + 111 + 114 + 23 + 48)/738 = 97.2%
Table 7. Paddy field area extracted by four different methods in Woogang-myeon
Methods for paddy area extraction
1)
2)
3)
Paddy field area
extracted (ha)
Difference from statistical data of
Woogang-myeon (ha)
Rule-based classification
1967.31( 87.73))
-275.38
Supervised classification
2522.97(112.5)
+280.28
Unsupervised classification 11)
1638.72( 73.1)
-603.97
Unsupervised classification 22)
1865.61( 83.2)
-377.08
No. of class = 15
No. of class = 20
Per cent to total study area
½ 15S ISODATA¬, «»¼½ 20S
AB(2242.69 ha)$ , |/ ò Ã@ éê
ISODATA¬* n“O HÄ@3.
Us«»æ3.
éêUs«»$ * × AB* Lo$u ´
› Á”$ }“O ò J~; +\.A ‹
#áX)* J# Uâ“O 100#­ -S
€H ܵ€O € J* ">, Ou LO {W
Ã@, J# * «»-X ‚ AB ‹Uu ´
# J“O ª5.Ï3. J* 3tB UV ; nO HÄ@3. ~, × AB ‹/ ` . A$ J~ {W n J -XX Ò/ % "=æ3. c#
. : Lr7‚ Á”$ yG¤ :“O 7_I
#½«» XãO ’>ò M“©O zQ
U `3. .#, ÙsB“O N>Áa)$ Hį
{BS -X ¦§  ## CÖ3. u
J* AB; LO, O J~* AB +\.
´; `{ Ã.U .| Á”# F EÞA$
:“O pƒ„ '“©O ië«»$ * ò J
(, 1991K ݄ N>Áa )* c# #½
* AB ‹/ Hį :; J~ k ˜ò Ã
)* JAB Uâ“O × AB ”.& N>
@O 7_ò3.
Áau* ABR 8˜.Ï3(Table 7). EÞA*
× # ; f¬$ >M ÐUu ˜
× AB; ië«»$ * 2522.97 haO ,
U, Ñ ‚U* "E -./ ”«%æ
‹/ %æ&, 3î éêUs«»u «»¼½
3. × AB f¬ F §} ¦§} «»¬
20“O ìië«»¬“O __ 1967.31 hau
; «»r x. }¥(supervisor)$ ÂI «»
1865.61 haO %æ3. «»¼½ 15O ì
Ã@u -X hI '3. * Ó$ U
ië«»$ * × AB 1638.72 haO ,
› éêUs«»(rule-based classification); DE
/ %æ3. ˜ò EÞA* × # ;
…&, Ù >Ó #A Ÿ; # $ (X
Fig. 6@ ´3. , N>Áa )* EÞA* J
àE r V¤ :“O 7_ò3.
14
Korean Journal of Agricultural and Forest Meteorology, Vol. 3, No. 1
Fig. 6. Comparisons of estimating methods for paddy field area in Woogang-myeon, Dangjin-gun, Chungnam Province.
IV. ´; # ˜U$ ì <7* •J a
ä × # “O .| × AB #X J* ‚U… 0,… «çs}• Z}.U ,
.Ï3. × AB; 7291.19 haO %æ&, #
Landsat TM ÌÍ, RVI, X* • «8«
áX 100#­* -X Lo Ã@ 92%O
Ã@, ‚ç¾ ñ * TM ÌÍ 1, 2, 3* J# HÄ@3. 1991K 5ô 31Ù@ 8ô 19Ù Ñ ‚U*
Ó; <
³$ * ç7u ° kÜ
Landsat TM ÌÍ 3, 4, 5, RVI X __
„á Î(S 8ô 19Ù@ 9ô 1Ù$ , 9Ü
drÌÍO ¾.| F
s 10* ÌÍ :3 j;US 9ô Fƒ.n$ 3‚ ÒÜ:3. Fª
`) 7.| Ub* «»¬$ .Ï3. Î(E
BW¾ ñ S TM ÌÍ 5u 7; «$ <i.|,
X¬$ * ië«» Ã@ × AB; 9100.98
)O Æ* ñ= >? 5ô 31Ù@ 6ô 2Ù
haÏ3. Error matrix$ * «»-X 97.2%O
* Ó , 9Ö&, „* ÎU$ ;U
HÄ@&, #áX -X 95%O HÄ@3.
O ˆ‘A <
Æ* «\ A‘ Ó
«»¼½ 15u 20O ISODATA¬$ *
­­ ÒÜ:3. , RVI ˜ƒ`US 8ô
¦§} «»Ã@ × AB __ 6663.60 hau
19Ù@ 9ô 1Ù$ , ÒÖ&, X ×* 7Øz
5704.56 haO %æ&, #áX$ * «»-X
U$ ;UO D >E 9Ü:3. ÐUS 5ô
__ 87%u 82%O HÄ@3. N>Áa UâO
31Ù, ˜US 8ô 19٠тU $ «$ <
.| «»f¬ª ¦§ .| ݄ EÞA$ (.|
i TM ÌÍ 5, <7* •J ͺH RVI, 4
× AB ¦§ % ië«»$ * 2522.97
€ ÌÍ* • +\ò X × AB
haO , ‹/ %æ&, 3î éêUs«»u
aO .|, ÐU$ )&
«»¼½ 20“O ìië«»¬“O __
Hong et al.: Estimation of Rice-Planted Area using Landsat TM Imagery in Dangjin-gun area
1967.31 hau 1865.61 haO %æ3. «»¼½
15O ìië«»$ * × AB 1638.72
haO , / %æ3. , N>Áa )*
EÞA* JAB(2242.69 ha)$ , |/ ò
Ã@ éêUs«»æ3. × # ; f¬
$ >M ÐUu ˜U, Ñ ‚U* "E 3³ R '“H -./ ”«%æ3. * «çs} • éêUs«» DE …
&, q '“", Ÿ; # $ ( àE r
V.3.
. 1994: SAS . .
, , , , , 1994: !" #$%. &'()*.
+,, 1998: -./* 01 234 5678 9, :;<= >7. ?@ABC D*BE .
ERDAS, 1997: Field Guide 4th Edition, ERDAS Inc. p.
158~159.
15
http://www.naqs.go.kr (FG HIJKLM-)
Kauth, R. J. and G. S. Thomas. 1976: The tasseled cap-A
graphic description of the spectral-temporal development of agrucultural crops as seen by Landsat. Proceedings, Symposium on Machine Processing of Remotely
Sensed Data. West Lafayette, IN:Laboratory for Applications of Remote Sensing, pp. 41~51.
Okamoto, K. and M. Fukuhara. 1996: Estimation of paddy
field area using the area ratio of categories in each mixel
of Landsat TM. Int. J. Remote Sensing, 17(9), 1735~
1749.
Panigrahy, S. and J. S. Parihar. 1992: Role of middle infrared bands Landsat thematic mapper in determining the
classification accuracy of rice. Int. J. of Remote Sensing,
13(15), 2943~2949.
Rao, P. P. N. and V. R. Rao. 1987: Rice crop identification
and area estimation using remotely sensed data from
Indian cropping patterns. Int. J. of Remote Sensing, 8,
639~650.
Tennakoon, S. B., V. V. N. Murty, and A. Eiumnoh. 1992:
Estimation of cropped area and grain yield of rice using
remote sensing data. Int. J. of Remote Sensing, 13(3),
427~439.