MSS. TM6 TM5 TM1. ١ - Mesopotamia

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1 (434 ) ( : ) (UTM) (UTM) ETM +.. (NDVI) 1...(0,0820) ١

2 : / ).( ( ) (1986) TM TM. TM (1990) MSS (1998) MSS.. (2001). (GIS). TM (1380).. TM (2001) TM6 TM5 TM1. TM7 TM6 TM5 TM3 (2006) (NDVI). ETM + ١ - Mesopotamia ٢

(SAR EC). 1 COSRI. (0/857 0/885 ).... : 693451 640811.

1-1 ETM + (b 4 -b3) /(b 4 +b3) 1/2 (G R) 1/2 (NIR 2 G 2 R 2 ) 1/2 (G 2 R 2 )

3 (SAR EC). 1 COSRI. (0/857 0/885 ).... : (1 ) (UTM) (UTM). 167/ : ETM ETM + (b 4 -b3) /(b 4 +b3) 1/2 (G R) 1/2 (NIR 2 G 2 R 2 ) 1/2 (G 2 R 2 ) (b 6 ) b 7 b 1 NDVI Salinity Index 1 Salinity Index 2 Salinity Index 3 ١- Combined Spectral Response Index ٣

$\G G 9 2 CG IDRISI AQ b 8! 9 < 1$ KF $,3 b -1, 3 d, _$ :.!G 9 < 1$ 1YG 9 {1Q 12. 2bh.,2 9,$ 4 3 2 3, 9 \, G.? 8 2 '?$ 2 7H F$ 1$ KF $ d G 1YG 6 G 3 C.2 CG ]0 2 4 YG 8 2 ' CH1$ Q ;d" 79 $ 12 ( / 0 12 JG?_G KF $ 3 3 - =21% h 2 8! CY0 2. ) 1 9 A (NDVI) 3 - =21% ]0 2 IH. - 7H F$ 1$ KF $ 9 Soil Index 1 1YG -4 m.

2 7H F$ 1$ KF $ d G 1YG 6 G 3 C.2 CG ]0 2 4 YG 8 2 ' CH1$ Q ;d" 79 $ 12 ( / 0 12 JG?_G KF $ 3 3 - =21% h 2 8! CY0 2. ) 1 9 A (NDVI) 3 - =21% ]0 2 IH. - 7H F$ 1$ KF $ 9 Soil Index 1 1YG -4 m.

$2 w a 12 $ 3 - =21% ) A $ ` G =,21% 1,2 5$, ;", $,,3 $ ) 12 ]0 2 YG 9 3 - =21% ]0 2 1YG Y9 K$. m,p$ 3 $ % 0 9 9 8 5 3 - =21% 1 ` G? ) c 9 \ F$.? =21% ( Q u 7 9 3,3 K CG 1G $ C$ -1 3 CY0 2.$

4 \G G 9 2 CG IDRISI AQ b 8! 9 < 1$ KF $,3 b -1, 3 d, _$ :.!G 9 < 1$ 1YG 9 {1Q 12. 2bh.,2 9,$ , 9 \, G.? 8 2 '? 1$ 3 - =21% ` G 1 9, K$ / 0 12 CY0 2 9 ]0 2 9 {1Q 3 - =21% 12 3 ]0 2 CY0 2 Z[ CG KF $ d G 1YG 9. 2 K$ c. 9 CG 3 1YG : C $ Q8c 8. =h A $ 7H F$ :87 9 ' m.2.? ) 2 m.2 7H F$ 1$ KF $ d G 1YG 6 G 3 C.2 CG ]0 2 4 YG 8 2 ' CH1$ Q ;d" 79 $ 12 ( / 0 12 JG?_G KF $ =21% h 2 8! CY0 2. ) 1 9 A (NDVI) 3 - =21% ]0 2 IH. - 7H F$ 1$ KF $ 9 Soil Index 1 1YG -4 m.2 7H F$ 1$ KF $ 9 NDVI ]0 2 1YG -3 m.2 7H F$ 1$ KF $ 9 Soil Index 3 1YG -6 m.2 7H F$ 1$ KF $ 9 Soil Index 2 1YG -5 m.2 =,21% ]0,2 1,2 ]0,2,,3 7H F$ 1$ 12 KF $ 3 $ ) YG Y9 4 3 $ w a 1,2 = A,Q 9 h $. 3 $ ) KF $ 3 - =21% Y> $ NDVI ]0 2.? ]> $ 5$ 3,&,9 =,21% 7H F$ 1$ 12 KF $ 12 $ 8 3 $ 3 m.2 w a 12 $ 3 - =21% ) A $ ` G =,21% 1,2 5$, ;", $,,3 $ ) 12 ]0 2 YG =21% ]0 2 1YG Y9 K$. m,p$ 3 $ % =21% 1 ` G? ) c 9 \ F$.? =21% ( Q u 7 9 3,3 K CG 1G $ C$ -1 3 CY0 2. ( A / 0 12 JG?_G Ir k H 8 $?91". 2 9!$ 9 =h 4 7H F$ 9 / $? 9 3 7$ ^ _ ` C : 3 9 ; 7 $ b -1 3 c. 9 7H F$ 1$ CY0 2 K$ 9 8,2,', 7$ ^,_ 9 *1G 9.? 9 AQ b ; " KF $ 9 ]0 2 C9 G 2 K$ c. 9 '

5 (SD =14/333) 1.(8 7 ) (SD 3 =21/56 SD 2 =22/72) ( ) Soil Salinity 1 ( ) NDVI -7 ( ) Soil Salinity 3 ( ) Soil Salinity 2-8 ٥

6 . (NDVI) : TM. 2- Al- Khair,F soil salinity detection using satellite remote sensing. Geoinformation science and earth observation, Watershed management, conservation and river basin planning specialization (ph.d. thesis). 3- Alavi panah, S. K., M., De Dapper, and R. Goossenns Characterization of some soil salinity parameters in the playa margin. J. Iran Agriculture Research. Vol.20, 2: Amini, M Geostatistical assessment of soil salinity and alkalinity in selected soils from Rudasht area, M. Sc. thesis of pedology, Isfahan university of technology, College of Agriculture, department of soil science, 119p. 5- Farifteh,J and A. Farshad., 2002, Remote sensing and modeling of topsoil properties, a clue for assessing land degrading. 17 th World-Congress-of soil- Science- Bangkok- Thailand August, Fernandaez- Buces, N., C., Siebe, S., Cram, J. L., Palacio, Mapping soil salinity using a combined spectral response index for bare soil and vegetation: (A case study in the former lake Texcoco, Mexico), J of Aride Environments ٦٥:٦٤٤-٦٦٧. 7- Khajeddin, S. J Use of MSSS satellite data in investigation of vegetation types and saline soils of Jazmourian. Proceeding of 2 th National to Combat Desertification. Forestry and Rangeland Organization. Tehran, Iran. Pp: Mulders, M.A and G.F.Epema, The Thematic mapper :A new tool for soil mapping in arid areas. ITC Jornal No. 1: ٦

7 9- Naigenouri, R Investigation on possibility of detecting salty and gypsy areas kashan plain using TM data, a thesis of presented for M.Sc, Isfahan university of technology, faculty of nature resources, 108p. 10- Saha, S.K., M.Kudrat, and S.K. Bhan, Digital processing of Landsat TM data for watershed mapping in parts of Aligarh District, Uttar pradesh, India. International Jornal of remote sennsing, vol.11: Saxsena R. K., K. S. Verma., Rajeev-Srivastava, Janardan- Yadav., N. K. Patel., R. A. Nasre., A. K. Barthwal., A. A. Shiwalkar and S. L. Londhe Spectral reflectance properties of some dominant soils occurring on different altitudinal zones in Uttarancha Himalayas. Agropedology. 13(2): ٧

8 Comparing soil salinity indexes by using remote sensing data (Case study: Nashabour plane, khorasan razavi state) Soil salinity is one of the dynamic properties of soil and this is the problem of arid and semi arid regions, covering large part of Iran so, due to widespread problem of salinity in Iran and their effect on yield and production its study almost essential. "Neyshabour plane" is one of the known saline region in khorasan state. This study was conducted in this plane covering an area of 750 km 2 situated between latitudes ٦٤٠٨١١ to ٦٩٣٤٥١ (UTM) and longitudes ٣٩٧١١٥٨ to ٤٠١٣٠٤٦ (UTM), by using ETM + satellite image of ٢٠٠٢. Combination image, three soil index and vegetation index images were prepared. Finally their histograms and standard divisions were compared with each other. From studied indexes, first saline index is more suitable than others and had smaller standard division. NDVI had smallest standard division than other indexes (٠ ٠٨٢). ٨

An Approach for Formation of Voltage Control Areas based on Voltage Stability Criterion

16th NATIONAL POWER SYSTEMS CONFERENCE, 15th-17th DECEMBER, 2010 636 An Approach for Formation of Voltage Control Areas d on Voltage Stability Criterion Dushyant Juneja, Student Member, IEEE, Manish Prasad,