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1 GIS 39 Vol.4, No., Summer 0 Iranian emote Sensing & GIS *. GIS. GIS.3 GIS GIS.4 39/4/4 : 390/3/8 :... (MPDI) ALOS ( >0/63 MSE<4 ).. ( > 0/74 MSE<%) ( ).. ALOS : r.darvishzadeh@ut.ac.ir : : *

2 . ( NDVI ) ( LST) 3 Chen et ) ( AVI) : 4 ( VCI) (al., 994 (Kogan, 995a) 5 (Wang et al., 00) ( VTCI) 6 Sandholt et ) (TDVI) 7 (VDI) (al., 00.(Maki et al., 004) (PDI) Ghulam, ).(006. Normalized Difference Vegetation Index. Land Surface Temperature 3. Anomaly Vegetation Index 4. Vegetation Condition Index 5. Vegetation Temperature Condition index 6. Temperature Drought Vegetation Index 7. Vegetation Drought Index 8. eal Time 9. Perpendicular Drought Index ( )..(Ghulam, 006)

3 () /. : ALOS AV : ) (388.. Modified Perpendicular Droght Index. Fraction of Vegetation 3. Artificial Neural Network 4. Linear Unmixing 5. Endmember (PDI) 007. (MPDI). (FV) PDI Ghulam ).(et al., NDVI (Ishyama et al., 997) ). (MPDI (Fv) 3 : 4 Elmore et ) (Carpenter et al., 999) Baret et al., ) (al., 000.(995 5 ETM ALOS ASTE. MPDI

4 .. 0) GPS ( GPS

5 / 83 5/ 85 34/ 7 6/ 67 8/ 6 66/ 67 4/ 7 7/ 36 54/ 7 4/ 7 5/ 85 34/ 7 0/ 83 5/ 85 34/ 7 7/ 50 8/ 67/ 50 / 50 5/ 4 7/ 50 34/ 7 5/ 85 34/ 7 5/ 00 4/ 47 0/ 00 / 67 9/ 3 86/ 67 30/ 00 7/ 07 50/ 00 / 67 6/ 06 36/ 67 5/ 83 3/ 76 4/ 7 / 50 5/ 4 7/ 50 8/ 33 0/ 33 06/ 67 30/ 83 5/ 85 34/ 7 0/ 00 5/ 48 30/ 00 5/ 83 3/ 76 4/ 7 6/ 67 8/ 76 76/ 67 / 67 8/ 6 66/ 67 30/ 00 7/ 07 50/ 00 3/ 33 5/ 6 6/ 67 3/ 33 6/ 06 36/ 67 33/ 33 5/ 6 6/ 67 6/ 67 9/ 3 86/ 67 37/ 50 6/ 37/ 50 5/ 83 6/ 65 44/ 7 8/ 33 6/ 06 36/ 67 6/ 67 5/ 6 6/ 67 5/ 50 6/ 89 47/ 50 3/ 33 5/ 6 6/ 67 9/ 7 7/ 36 54/ 7 7/ 50 5/ 4 7/ 50 / 50 5/ 4 7/ 50 8/ 33 6/ 06 36/ 67 5/ 83 8/ 6 74/ 7 7/ 50 6/ 89 47/ 50 / 50 5/ 4 7/ 50 8/ 33 6/ 06 36/ 67 9/ 7 7/ 36 54/ 7 3/ 33 8/ 6 66/ 67 0/ 83 3/ 76 4/ 7 / 50 6/ 89 47/ 50 4/ 7 6/ 65 44/ 7 4/ 7 5/ 85 34/ 7 9/ 7 3/ 76 4/ 7 3/ 33 5/ 6 6/ 67 6/ 67 5/ 6 6/ 67 / 67 6/ 06 36/ 67 3/ 67 6/ 06 36/ 67 / 67 5/ 6 6/ 67 4/ 50 8/ 67/

6 7 ( 94) /5000 0/ ALOS 7.(3 ) AV : () () L Gain * DN i i i 4 Gain i L i. DN i FLAASH. ENVI4/7 AV. 3. 3/8 009/7/8 56/0 () () 69. eflectance. adiance 3. esample 39 50

7 Atmospherically resistant vegetation index Difference Vegetation Index Global Environment Monitoring lndex Infraed percentage vegetation Index Modified soil Adjusted Vegetation Index Modified soil Adjusted Vegetation Index Normalized Difference Vegetation Index Perpendicular Vegetation Index Soil Adjusted Vegetation Index Soil and Atmospherically esistant Vegetation Index Soil and Atmospherically esistant Vegetation Index atio Vegetation Index Transformed Soil Adjusted Vegetation Index Weighted Difference Vegetation Index Soil Adjusted Vegetation Index Optimized Soil Adjusted Vegetation Index Modified Simple atio AVI DVI GEMI IPVI MSAVI MSAVI NDVI PVI SAVI SAVI SAVI(EVI) S(VI) TSAVI WDVI SAVI OSAVI MS.3 MSAVI B AVI B ( ) B B DVI ( / ) ( / ) GEMI / 5 ( ) / / IPVI ( ) ( ) 8 MSAVI L ( L) L NDVI* WDVI NDVI PVI ( L) SAVI L ( L) SAVI L ( ) SAVI : : : B B B / 5( ) / / B S ( ) TSAVI / ( ) WDVI 0 08 Ed SAVI OSAVI / 6 0/ 6 S S S : Kaufman&Tanre, 99 Tucker, 979 Pinty &Verstraete, 99 Crripen, 990 Qi et al., 994 Qi et al., 994 ouse et al., 974 ichardson& Wiegand, 977 Huete, 988 Kaufman&Tanre, 99 Huete et al., 997 Jordan, 969 Baret & Guyot, 99 Clevers, 989 Major et al., 990 ondeaux et al., 996 Chen, 996 : 39 5

8 n- n.. MSE Geladi and Kowalski, ).(986 n-.. K. 0/ MSE Cross Validation Soil Line (ichardson and Wiegand, 977) TSAVI MSAVI SAVI ( 50 ) ( 606 ).. MATLAB : () () VI VI Max K F V ( ) VImin VImax VImax VI VImin K MSE 0/00 5 0/5.(Baret et al., 995) K 0/5. 0/ Cross Validation 39 5

9 MATLAB :. CGP CGF BFGS SCG CGB.LM OSS. 4 ALOS. :.... Log-sigmoid. Tan-sigmoid.. (- )..(Menhaj, 005) 0.. (Menhaj, 005) Logsig. Tansig...(Menhaj, 005)

10 (3) ed F V ( V,ed V, ) MPDI ( F V ) PDI FV PDIV ( F V ) : : : V V :Fv 0/05 0/5.(ghulam et al., 007) (3 4 ).(Menhaj, 005) Cross Validation.(Geladi and Kowalski, 986) MPDI. MPDI : (3) ( )

11 (OSAVI SAVI MSAVI SAVI).... K t.(p-value> 0/05) MDVI PVI MSAVI TSAVI 0/5 K. MSE 0/00 5.(4 ) / 8 (4) (4).. 3 /8. MPDI 4. Crossvalidation 4 TSAVI WDVI PVI MSAVI MSE TSAVI WDVI PVI MSAVI t..(p-value> 0/05). CrossValidation MSE.4 MSE MSE MSAVI 0/ 64 3/ 04 SAVI 0/ 5 3/ 74 PVI 0/ 63 3/ 08 S 0/ 5 3/ 74 WDVI 0/ 63 3/ 08 SAVI0 0/ 5 3/ 7 TSAVI 0/ 63 3/ MS 0/ 49 3/ 99 NDVI 0/ 60 3/ 4 OSAVI 0/ 48 3/ 65 IPVI 0/ 60 3/ 4 EVI 0/ 39 3/ 97 MSAVI 0/ 59 3/ 5 DVI 0/ 0 5/ 37 SAVI0 0/ 53 3/ 49 GEMI 0/ 0 4/ 84 AVI 0/ 53 3/ 77 MSE(%) 39 55

12 Cross Validation MSE.. t. (P Value 0/ 05) 5. K MSE.4 K 3/70 K MSE 3/70. MSE. Cross Validation.5 MSE () 0/8 0/8 / / /4 /7 / 0/9 /4 /3 /9 / 0/86 0/8 0/79 0/79 0/69 0/63 0/87 0/79 0/78 0/78 0/69 0/65 lm oss cgp cgb cgp cgp lm oss cgp cgb cgp cgp LogSig Tansig B,B,B3,B4 B3,B4 B4 B3 B B B,B,B3,B4 B3,B4 B4 B3 B B 39 56

13 6.MPDI -3-3 MPDI 5.5 MPDI

14 .(NDVI) SAVI S. 008 ) (. GEMI EVI.. (Liang, 003).. : ( ( ). MPDI 6. «000»

15 .. 0/ Baret, F., Clevers, J., & Steven, M.D., 995, The obustness of Canopy Gap Fraction Estimations from ed and Near-infrared eflectances, emote Sensing of Environment, 54, PP Baret, F., Guyot, G., 99, Potentials and Limits of Vegetation Indices for LAI and APA Assessment, emote Sensing of Environment, 35, PP Carpenter, G., Gopal, S., Macomber, S., Martens, S., Woodcock, C., & J., F., 999, A Neural Network Method for Efficient Vegetation Mapping, emote Sensing of Environment, 70, PP Chen, J.M., 996, Evaluation of Vegetation Indices and a Modified Simple atio for Boreal Applications, emote Sensing of Environment,, PP ) ( (... Menhaj, ).(005 (... : /6 0/

16 Chen, W., Xiao, Q., & Sheng, Y., 994, Application of the Anomaly Vegetation Index to Monitoring Heavy Drought in 99, emote Sensing of Environment, 9, PP Clevers, J.G.P.W., 989, The Application of a Weighted Infrared-red Vegetation Index for Estimating Leaf Area Index by Correcting Soil Moisture, emote Sensing of Environment, 9, PP Crippen,.E., 990, Calculating the Vegetation Index Faster, emote Sensing of Environment, 34, PP Elmore, A.J., Mustard, J., Manning, S., & Lobell, D., 000, Quantifying Vegetation Change in Semiarid Environments: Precision and Accuracy of Spectral Mixture Analysis and the Normalized Difference Vegetation Index, emote Sensing of Environment, 73, PP Geladi, P., Kowalski, B.., 986, Partial Leastsquares egression: A Tutorial, Analytica Chimica Acta, 85, PP. -7. Ghulam, A., Qiming, Q., Tashpolat T., Zhao- Liang L., 007, Modified Perpendicular Drought Index (MPDI): A eal-time Drought Monitoring Method, ISPS Journal of Photogrammetry & emote Sensing, 6, PP Ghulam, A., Qin, Q., Zhan, Z., 006, Designing of the Perpendicular Drought Index, Environmental Geology, 0. Huete, H., 988, A Soil-adjusted Vegetation Index (SAVI), emote Sensing of Environment, 5, PP Ishiyama, T., Nakajima, Y., Kajiwara, K. & Tsuchiya, K., 997, Extraction of Vegetation Cover in an Arid Area Based on Satellite Data, Advances in Space esearch, Calibration and Intercalibration of Satellite Sensors and Early esults of adarsat, 9, PP Jordan, C.F., 969, Derivation of Leaf Area Index from Quality of Light on the Forest Floor, Ecology, 50, PP Kaufman, Y.J., Tanre, D., 99, Atmospherically esistant Vegetation Index (AVI) for EOS-MODIS, IEEE Transactions on Geoscience and emote Sensing, 30, PP Kogan, F.N., 995a, Droughts of the late 980s in the United States as Derived from NOAA Polar-orbiting Satellite Data, Bulletin of the American Meteorological Society, 76, PP Liang, S., 003, A Direct Algorithm for Estimating Land Surface Broadband Albedos from MODIS Imagery, IEEE Trans Geosci emote Sensing of Environment, 4, PP Major, D.J., Baret, F., Guyot, G., 990, A ratio Vegetation Index Adjusted for Soil Brightness, International Journal of emote Sensing,, PP Maki, M., Ishiahra, M., & Tamura, M., 004, Estimation of Leaf Water Status to Monitor the isk of Forest Fires by Using emotely Sensed Data, emote Sensing of Environment, 90, PP

17 Menhaj, M.B., 005, Principles of Artificial Neural Network, Amir kabir university, Tehran. Pinty, B., Verstraete, M., 99, GEMI: A Nonlinear Index to Monitor Global Vegetation from Satellites, Vegetation, 0, PP Qi, J., Chehbouni, Al., Huete, A., Kerr, Y., 994, A Modified Soil Adjusted Vegetation Index (MSAVI). emote Sensing of Environment, 48, PP ichardson, A.J., Wiegand, C.L., 977, Distinguishing Vegetation from Soil Background Information, Photogrammetric Engineering and emote Sensing, 43, PP Sandholt, L., asmussen, K., & Andersen, J., 00, A Simple Interpretation of the Surface Temperature/vegetation Index Space for Assessment of Surface Moisture Status, emote Sensing of Environment, 79, PP Tucker, C.J., 979, ed and Photographic Infrared Linear Combinations for Monitoring Vegetation, emote Sensing of Environment, 8, PP Wang, P., Li, X., Gong, J., & Song, C., 00, Vegetaion Temperature Condition Index and its Application for Drought Monitoring, In, International Geoscience and emote Sensing Symposium. PP. 4 43, Sydney, Australia. ondeaux, G., Steven, M., & Baret, F., 996, Optimisation of Soil-adjusted Vegetation Indices, emote Sensing of Environment, 55, PP ouse, J.W., Haas,.H., Schell, J.A., Deering, D.W., Harlan, J.C., 974, Monitoring the Vernal Advancement of etrogradation of Natural Vegetation, NASA/GSFC, Type III, final report, Greenbelt, MD. 39 6