IEG 환경지질연구정보센터
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(2). ;öê ;¯ ¢& Ö®æ ª~¢ *R /DQGVDW (70 'ç ª~æ>~ RÏ ê' *Á·ÿJ ÁB"Ï Á;>. æî¶öö, 305-350 &*7 F9 &;ÿ 30 ÏÎ&v æ~ã"¦, 305-764 &*7 F9 §ÿ 220 1. 2. Application of Landsat ETM Image Indices to Classify the Wildfire Area of Gangneung, Gangweon Province, Korea Jin Young Lee1,*, Dong Yoon Yang1, Ju Yong Kim1, and Gong Soo Chung2 . .RUHD ,QVWLWXWH RI *HRVFLHQFH 0LQHUDO 5HVRXUFHV 'DHMHRQ .RUHD 'HSDUWPHQW RI *HRORJ\ DQG (DUWK (QYLURQPHQWDO 6FLHQFHV &KXQJQDP 1DWLRQDO 8QLYHUVLW\ 'DHMHRQ .RUHD. . $EVWUDFW This study was aimed to examine the Landsat Enhanced Thematic Mapper Plus (ETM+) index, which matches well with the field survey data in the wildfire area of Gangneung, Gangweon Province, Korea. In the wildfire area NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and Tasseled Cap Transformation Index (Brightness, Wetness, Greenness) were compared with field survey data. NDVI and SAVI were very useful in detecting the difference between the wildfire and non-wildfire area, but not so in classify the soil types in the wildfire area. The soil plane based on the Tasseled Cap Transformation showed a better result in classifying the soil types in the wildfire areas than NDVI and SAVI, and corresponded well with field survey data. Using a linear function based on greenness and wetness in the Tasseled Cap Transformation is expected to provide a more efficient and quicker method to classify wildfire areas. ,FZXPSET wildfire area, Landsat ETM+ indices, image classification, Vegetation Index (VI), Soil Adjusted Vegetation Index (SAVI), Tasseled Cap Transformation º £ º ;öê ;¯æ Ö®æ~ bªCj * bæ æRª~¢ Ï'b Landsat Enhanced Thematic Mapper Plus(ETM+) 'çöB Ï > ®º ª~æ>~ 'Ïj ¦Æ~& . æ Ö®æj &çb Landsat TM 'çj Ï~8 * BBB æ>(NDVI)f Æ·j J æ>(SAVI), Tasseled Cap æ~b áj > ®º C8æ>(brightness), ÛJæ>(wetness), ïæ>(greenness)¢ ¢Ò Ö"f jv~& . ªC Ö" æ>f Æ·j J æ>º Ö®Bæ" Ö® B~æ pf æö & ª Â]~&b¾, Ö®B æÚöB bæ ªöº '.~æ pf ©b 2k>î . Ö®BæÚöBº Tasseled Cap æ~öB ¾æ ¾º Æ·ïj Ï H bf &N ¢Ò Ö"f &Ë 7 ª~ Ö"¢ áj > ®î . Tasseled Cap æ~öB æ>f ïæ>¢ z~ F;> Ï~ ³~ ÎN'b Ö®æj ª~& &Ë © b 8&B . ºÚ Ö®æ, *W'çæ>, 'çª~, æ>, Æ·æ>, Tasseled Cap æ~. *Corresponding author: [email protected] Tel: 82-42-868-3066 Fax: 82-42-868-3037.
(3) ;öê;¯¢&Ö®æª~¢*R-BOETBU&5.'çª~æ>~RÏ. * V *W 'çj Ï~ 7º* æj &çb ³~² ªC > ®Ú Ö®æ~ 2kf b Ö®æöB 2N'b B > ®º b ¢ 2k~º FÏ~² ÒÏ> ® . " Ö® Bæö & Landsat TM *W'çj Ï~ Ö®Bæ öæf Ö®Bæ~ b;ê¢ 2 k~V * ·~² & ê¯>î (Huete and Escadafal, 1991; Nagler et. al., 2000; Koutsias and Karteris, 1999). Ö®B öæf &NB º æ>(NDVI; Normalized Difference Vegetation Index) ¢ Ï jv' * O» " >î (Rondeaux, 1996; Nagler et al, 2000; Cloutis, 1996), Ö® Bæ~ b;ê¢ 2k~V *B . Æ· 5 FVbö & & "¢ ®. (Gitelson et al, 2002; Wikars and Schimmel, 2001; Gao et al, 2000; Ternan and Neller, 1999; Rondeaux et al., 1996; Huete and Escadafal, 1991). ÚöBº Ö®æöB Landsat ETM 'çj Ï~ ¢ V~ Ö®bæê ·Wj *~ KT(Kauth-Thomas). . æ~ Ö"f IHS(Intensity-Hue-Saturation)æ~Ö"& jvB : ® (ö;'" ª;^, 2001). Ö®Bæf Ö®b;êö V¢ " Ò& Æ·j ® . $ Ö®~ b& æf ~ Îj®æ pj ¾&æ(bare soil)f ?f æ &¦ª . æöBº Ò $º FV bj
(4) ~º Æ· ª~& ê>î (Rondeaux et al., 1996; Nagler et al, 2001; Cloutis, 1996). æ >(NDVI)º 'çöB ~ ç¢ 2k~º F Ï~² Ï> ® (Eastman and Fulk, 1993). ~ Ï'f Landsat Enhanced Thematic Mapper Plus (ETM+) 'çöB Ö®Bæj öæ~º Ï>º Tasseled Capæ~, NDVI, SAVI¢ ¢ Ò Ö"f jv~ ¢Ò Ö"ö ' ª~æ >& ÚÊ ©&¢ 2k~º ® .. \æ~ Bº æf ¯;ç ;öê ;¯ Ò Ò êÒ, 6vÒ, CvÒ, ÿÒ ö ³~, *Ò ; o ¯ ãö>öj 7b ãê 128 47' 42''~. Fig. 1. Map showing the study area and sampling sites. Solid dots represent sampling sites in wildfire area. Open circles represent sampling sites in non-wildfire area..
(5) . ê'Á·ÿJÁB"ÏÁ;>. 128 52' 30'', *ê 37 47' 30''~37 50' 42''~ æ6ö ³ (Fig. 1). Ö®f 2001j 4úö B~& . æ~ V>zf ¢Vz;z, Æ·f .ïÖâ Æ·b ª~B . Æ· ßWf &¦ª æ ·î ÒÆ 5 Ò·Æ Î¾ï ¸, Æ·~ vþ& »f Æ·, FVb 5 ·ªï Ôf ¿; Æ· ª
(6) (ÿn Ö®bæ ÿÒ , 2000). æ~ f &¦ª~ æ ²¾ Z £b W>Ú ®î, ê& ¸f æf ²¾Z-#> b£~ ¢¦f âæ& ¢¦ ª
(7) ~ ®î (ÿn Ö®bæ ÿÒ, 2000). o. o. o. \ O» Ö® BæöB b;ê¢ ª~V *~ Ö ®æ ;öê ;¯ Ò ¢&öB æRÒ¢ >¯~& . ''~ Òæ6ö & *~{j * ~ GPS(Global Pointing System; Garmin corp.) V V¢ Ï~, 1 : 5,000 æ;ê¢ ^~ ;{ *~¢ Ö;~& . ¢Òº 2000j 8úö æ6ê >ã 30 m~ æj ®º FVb 5 Æ·~ ßWj Ò~& . ö ÏB 'çf Ö® B *ê~ Landsat 7^ ETM+ 'çb, æ ¢& & ¾æ¾º 2000j 6ú 8¢~ ¢V 'ç . 'ç~ ¾Ò 5 ªCj *~ Intersys Ò~ ENVI 3.4 * Îj Ï~& . Ö®Bæ~ ²R; 5 æ;ªCj *~ 1 : 25,000 æ;êf 1 : 5,000 æ;ê¢ Ï~& . ¢Ò& >¯B æ~ Landsat ETM+ 'çf ¢ òj æ6j F~ z²8(DN: Digital Number)j ç7 ~& . ¶ò~ Öê¢ ¸V *~ &Ræj
(8) ~ ' Æ·ê 30 æ6 ç~ z²8j ³~ ï 8j ÒÏ~& . æ>(NDVI)º B~² SR = NIR/R ÒÏ> Vê ~, NDVI = (NIR-R)/(NIR+R) '(R) ' " "'(NIR)'j ÒÏ~ . öB º NDVI¢ Landsat ETM+ 'ç~ Z 4®" 3® j ÒÏ~, NDVI = (band 4 − band 3)/(band 4 + band 3) ö 'Ï~ ~& . SAVIº æ> çö Æ·~ ßWj J~ · WB æ>B æ>(VI)ö Æ·~ 'Ëj ²z ~V *~ Æ·~ ;ê>(L)¢ ÒÏ . ¢>' SAVI~ ;º (1)" ? .. SAVI = (1+L)*(NIR − R)/(NIR+R+L), L = 0.5(Huete, 1988). (1). (1)öB 7º ¶º LB Lf Æ·~ ßW j 6ʺ j ~º, ;ê>(L)& 1ö & rÞ> Æ· Næ~º ' 9Úæ, 0ö & rÞ> Næ~º ' 9Úê (Bausch, 1993). Huete(1988)f ~ þ Ö"öB L = 0.5¢ º ãþ~¢ áîb, ¢ Þ'b 'Ï~ ®. . ¾ Ö® B æf ~ &ê& ~ ìº æV r^ö, öBº L~ 8j 1 'Ï~& . Tasseled Cap æ~f Landsat TM'çöB CVæ >(brightness; (2)), ïæ>(greenness;(3)), ÛJ æ>(wetness; (4))b æ~~º O»bB " Æ·*~ &ê¢ F;æ~ (Crist and Cicone, 1984). Brightness = 0.3037(TM1) + 0.2793(TM2) + 0.4743(TM3) + 0.5585(TM4) + 0.5082(TM5) + 0.1863(TM7) (2) Greenness = (-0.2848(TM1)) + (-0.2435(TM2)) + (-0.5436(TM3)) + 0.7243(TM4) + 0.0840(TM5) + (-0.1800(TM7)) (3) Wetness = 0.1509(TM1) + 0.1973(TM2) + 0.3279(TM3) + 0.3406(TM4) + (-0.7112(TM5)) + (-0.4572(TM7)) (4) æ>º vB~ X-YïöB C>º, C Væ>f ïæ>¢ B~ ïö ¾æÞ ï " CVæ>f æ>¢ B~ ïö ¾æÞ Æ·ï öB ÒÏ>î .. Ö æR`ÒÖ æ~ æRÒ¢ Û Æ·FVb, º~, Æ· 5 z> Â ç¢ 7b æR~ãj Ò~& . *W'ç~ çê¢ J 30 m >ã ÿ¢ ~ãb ¾æ¾º 12B Òæ(KA-1~KA12)öB Æ·ò jf ¢VÒ¢ Û 5B F; b æR~ãj ª~~ ' æ6ö ³~º æ~ Zê z²8j ºÂ~& (Fig. 2). Type If Ö® b& jv' 'Ú .
(9) ;öê;¯¢&Ö®æª~¢*R-BOETBU&5.'çª~æ>~RÏ. Fig. 2. Comparison of mean DN values of Landsat ETM images of each soil type.. jv' ·^~² B~º F;, ®ö ææ pf 20~30 cm vþ~ ¿#" FVb æj b . Type IIº Ö® ~ b& ®Ú ¦² æ ªÖ ¾Zf R[~ FVbræ Ö®ö ~ ¦² æªÖ æ, ¦f ïj ¾æÚº FVÆ· " ¾æ . Type III F;f FVb ~ ìº ZV Æ·b W>îb Æ·[~ vþ& 40~50 cm ;ê . Type IV F;f Ö® B Òf Æ·FVb > cm ;ê ®b¾ FVÆ· ~ ì ZVÆ· "¢ . Type V F;f ZVÆ·" ¶. $º z> Â>, Æ·[ ~ ì . Ö®æ ~ æR b `Ò 5 ª~ Ö Ö®æ~ æRb bî~ «~f Æ·~ ßWö V¢ Ö®æf 5B F;b ª~>îb, ' F; ö V b;& Ò>î . ª~ V&b ÒÏB æRb bî~ «~f Æ·f Landsat ETM+ 'çöBê ÿ¢ V&b ÒÏF > ®î .. . Ö® B æf Ö®B ê º~º " æÎf Ò Æ· æR¢ ®b, æö V¢ ª
(10) ~º '" «~¢ Ò~ · *çj ¾æî . Type I F;f æR¢ b ~º º~ " vâÚ FVb [~ 'Ëb B® ¢Ú¾æ p~ . Type II F;öBº FVb [~ ~¦öB 2*ç¾ 6ç~ ; ~ ¾æ . æRö FVb Ò~æ pæò, ç¦ö æ Îf Ò ö ~ ;Ö~ ç7 ' 'Ëj Aæ pº . F;~ f κ Type I F;ö j~ æò, *çf FVb [ ~¦öB ¾æ . Type III F;öB &R' ;º ^~(rill erosion)" (gully erosion) b FVb [ Ò~æ pº . Ö® Bj * ê~ ~ B £~&¾, Ö® B êö b ç¦~ FVb $º Ò~ [ ìÚr . Type IV F;öBº 6ç" 2*ç ¾æ . Type IV F;f B. ~z¢ê, b~ 'Ëb WB ºòÒ j O~V r^ö J®J ~ κ ·² ¾æ . ¾, ºòÒ[~ ~¦ 2*ç¾ 6ç~ FBF &ËW ®, ºòÒ[j
(11) ç¦Æ· Æ·ÚÒ(soil mass); ÿ &ËW ®º ©b CB . Type V F;f ;¢ J;~V Ú[ . º ~ jòB ç Â>º z> &V>¾, V& zÞ Â>Ú ¾æ¾V r^ö Æ· ~ G Ò~ »Zf ÖÒ ~ &Î' GöB J¢ © .. Fig. 3. Histogram-equalized image of computed NDVI from Landsat 7 ETM+..
(12) . ê'Á·ÿJÁB"ÏÁ;>. Fig. 4. Histogram-equalized image of computed SAVI from Landsat 7 ETM+.. 1'9, 6$9, 5 7DVVHOHG &DS æ~ Ö 'ç¾ÒÖ" áÚê NDVIf SAVIº Fig. 3" Fig. 4ö B~& . Fig. 3" Fig. 4öB BB Ö "º +1öB −1 Ò~ 8~ æz¢ ¾æÚ, ï (+1)öB ï(−1)ræ 6ê' ïb R*>î . ¢>'b NDVIf SAVIöB ~ Kê& ¸ f æf ·(+)~ 8~ ª
(13) ¢ <² >, ~ Kê& Ôf æf r(−)~ 8j <² B . ª~ B ' F;~ NDVI 8f +1öB −1~ º*ö ¾æ ¾º(Fig. 3), ·(+)~ ¦^¢ <º æf Type I, Type II ¾æÚ, Type III, IV, Vº r(−)~ ¦^ ¢ <º æöB ¾æ . $ ª~B ' F;~ SAVI æ>¢ ¾æÞ Ö"(Fig. 4)º Type I F;" Type II, III, IV, V F;" «{~² ª>¾, Type II, III, IV, V F;f ª «{~æ á~ . 'ç¾Ò¢ Û áÚê Tasseled Cap æ~ 'çf Fig. 5ö B~& . $ CVæ>f ïæ>¢ » b ¾æÞ ï(Fig. 6)" CVæ>f æ> ¢ »b ¾æÞ Æ·ï(Fig. 7)j B~&b, ¢ÒöB ª~B ' Typej R~& . ï öB ' Type ¾æÚº *~º Ö® Bæ" jÖ®æ «{~² ª>îb, Type II, Type III, Type IVº F;&ê *~~& (Fig. 6). Æ·ïb B>º 'ç¾Ò Ö"º Ö®æ " jÖ®æ ² ª>, ' Æ· Typef ; jf&ê¢ (Fig. 7). æ>f æ>¢ (n) 'çj Fig. 8ö B~& . æ>f. Fig. 5. R-G-B color composited Tasseled Cap image in study area (R: brightness, G: greenness, B: wetness).. æ>~ 'çf r(−)~ 'j F;Ë(stretch) ~, ïçj 'Ï r Ö®Bæ~ ª>Ú ¾ æÒ . >~& 'f æf Ö®~ B b& ¸ f æ" ¢~~&, >~& ¸f æf Ö®~ B b& 'f æ" ¢~~º Ö"¢ & .. Æ ~ æ Æ·ª~ö ÒÏB æR¢ W~º b bîf æRb bî Òf æÎf ¾m&æ, æ Îf b~ ºÒ, Ò Æ·~ ßWö V¢ >.
(14) ;öê;¯¢&Ö®æª~¢*R-BOETBU&5.'çª~æ>~RÏ. . Fig. 6. Five soil types and nonwildfire area on vegetation plane of brightness and greenness indices. It shows conspicuous difference between wildfire area and nonwildfire area and gradual variation in value of two indices in soil types.. ÒNö N& ®rj ¾æÞ (Nyakatawa et al., 2001). ôf öB æ>ö ¾æ¾º Æ ·~ ßWj æ'~ ® (Gitelson et al, 2002; Ternan and Neller, 1999; Rondeaux et al., 1996; Huete and Escadafal, 1991). ª~B 5B F;f ² æR~ W bî " Òf ?f FVb" Æ ·bî(soil mineral) ªB . Type I, II, IVº æ Rö æÎf " Ò, b~ ºòÒ[ ~ FVb Ö^~² ¾æ¾, Type III" Type Vº Æ·bî(soil mineral) Ö^~² ¾æ . Nagler et al.(2001)ö ~~, Æ· FVb~ æR>Ò ßW f ÂB Æ·(bare soil; Type III, V)~ ßW" êB . $ ÂB Æ·f Landsat TM 'ç~ Z 7ö ³~º 2.1 µm~ 2Ë'öB ßû' > ÒFj (Rondeaux et al., 1996; Koutsias, and Karteris, 2000). V¢B 5 FVb" Â. Æ·~ ßWj ¾æÚº æf ª jv' «{~ . ¾ Type I, II, IV æf Æ·" FV b D ¾æ¾V r^ö O» B>Ú¢ . Type I F;öº Ö®~ b& jv' ~V r^ö º~ ª
(15) . Ò Type IIf Type IV F;f æR Wbî ¦f ï~ Òf ? f FVb W>Ú ® . Ö®BæöB ~ b;ê¢ 2k~V *~ ¢>'b NDVI (Normal Distributed Vegetation Index;;æ>) f ?f æ>¢ ÒÏ z . Rondeaux et al.(1996) f &æ æ>Î" jv~ SAVI(Soil Adjust Vegetation Index)f MSAVI(More Resently Modified SAVI)¢ '; æ>B F;~& . SAVIº ~ b 25% ò æöB NDVI ö j~ ·^ Ö"¢ ¾æÚ, æR¢ b ~º " Æ·~ ßWj J~ Æ·~ Î".
(16) . ê'Á·ÿJÁB"ÏÁ;>. Fig. 7. Five soil types and nonwildfire area onn soil plane of greenness and wetness indices. It shows wide spread in values of two indices in soil types with showing an overall linear trend.. ¢ 6²ÊV r^ö '¾ ~ ìº æ öBê Æ·j
(17) æ>¢ ~º ÒÏF > ® (Gitelson et al, 2002; Wikars et al., 2001). Ö® æf Æ·~ ç¦ " FVb £² B >, æR¢ b~º Wbî <º Ûê, ï ç, Nê ~ ßWö V¢ Landsat ETM+ 'ç~ >Ò8 ² ¾æ (Kutiel et al., 1995; Fox and Bryan, 2000; Ternan and Neller, 1999). V¢B Type II, Type IVf Type III, Vj NDVIf SAVI ~ æ>¢ Û ª~~Vöº ê& ® . Tasseled Cap æ~ 'çöBº Type I" Type V öò jî¢, Type II, Type III, Tpye IV~ ª~& &Ë~& . ïçö ¾æ CVæ>f ïæ >¢ ¾æÞ Fig. 6öB Type I¦V Type Vræ~ Æ·F;~ >Ò8 ª
(18) & NDVIf SAVI" Ò. 7>Ú ¾æ¾æ p, ¢; 'ö R>Ú ' Æ·F;ê ª Nj r > ® . Type I" Type V Ò Type II, Type III, Type IV~ ªf Ö®æöB Landsat ETM'çj Ï~ ¢ V~ Ö®bê ·Wj *~ KTæ~ Ö"f IHS(intensity-hue-saturation)æ~ Ö" jvöB &ËW ¦Ã>î (ö;'" ª;^, 2001). Fig. 7 f ÛJæ>f ïæ>¢ Ï Æ·ïb Type II, Type III, Type IV~ *~& '' ÷z> B F;'b ª
(19) j . º Type II, Type III, Type IVöB N& 6ê' æz&êö ® rj ~ . V¢B Ö®æj ª~~V * Type II, Type III, Type IV F;f æ>f æ>~ ç&&ê¢ Ï~ F;' &ê¢ Ï~º © æ © . Ö®Bæf æ>f .
(20) ;öê;¯¢&Ö®æª~¢*R-BOETBU&5.'çª~æ>~RÏ. . Fig. 8. Histogram-equalized color image of computed function f(a,b) = a + b; a is greenness and b is wetness from Landsat 7 ETM+ Tasseled Cap transformation. Color image map shows linear stretched in wildfire area. The method is based on Crist and Cicone (1984). Table 1. Soil types, their description, erosional features, Landsat ETM DN (digital number) value and in situ investigation sites Soil Types. Description of soil profiles. Type I. Very well developed soil profile consisted of Aoo, Ao, A1, A2, B and C layer; soil surface covered by plant litter and plant after wildfire. Rare occurrence of erosional features; very small trace with pipe erosion and sheet erosion, erosion features below branch and tree roots. High DN value in all bands; exceptional high value in band 5 and 7. Type II. Relatively well developed soil profile consisted of Aoo, Ao, B and C layer; Aoo, Ao layer thicker than 2 cm; soil surface covered by ash and plant leaves. Some erosional features of small rill and gully smaller than 2 cm in width and depth; some sheet erosion below the Aoo, Ao layer. High value in band 1 and 5; high value in band 3 compared to band 7; low value in band 7. Poorly developed soil profile consisted of B1, B2 and C layer; no organic matter at soil surface. Common occurrence of rill and gully erosion greater than 2 cm in width and depth; some sliding features in top-soil layer. High value in band 1 and 5; high value in band 7 ompared to band 3. Poorly developed soil profile consisted of Ao, B and C layer; soil surface consisted of thin ash and burnt plant root. Common occurrence of rill, gully and pipe erosion feature below the burnt plant root; common occurrence of rill and gully greater than 10 cm in width and depth. High value in band 1 and 5; high value in band 1 compared to band 5. Very poorly developed soil profiles consisted of B2 and C layer; some trees at soil surface; common exposure of bedrock and C layer rock-fragments. Common occurrence of rill and gully greater than 10 cm in width and depth; some exposure of bedrock; some slumped features. High value in band 1 and 5; same value in band 2, 3 and 7. Type III. Type IV. Type V. Erosional features. æ>& ;jf &ê¢ ¾æÞ . æ>f æ>¢ 'çf Ö®æ~ " Æ·j J ~, >~& 'f æ Ö®~ B b& ¸f æb ê ', Æ·~ ê& ¸f æj ¾æÞ . f >& æ>f æ>¢ . DN value. Area (%). In situ investigation sites. 12%. KA-3 KA-10. 33%. KA-1 KA-11. 32%. KA-9. 18 %. KA-4 KA-12. 6%. KA-2 KA-5 KA-6 KA-7 KA-8. 'ç(Fig. 8)öB >~& ¸f æf Ö®~ B b & '¾ Æ·~ ÛJê& ¸f æj ¾æÞ . V¢B Ö®bæf ;êö V¢ 6ê'b æz ~º F; &ê rÚ â > ® . ";öB, b º; f(a,b) = (Greenness.
(21) . ê'Á·ÿJÁB"ÏÁ;>. Table 2. DN values of soil types with Landsat ETM image after geometric correction Type. Band 1. Band 2. Band 3. Band 4. Band 5. Band 7. NDVI. Type I. Min Max Mean. 95 112 103. 70 98 84. 75 111 93. 42 77 59. 102 155 128. 104 120 112. -0.28 -0.18 -0.23. Type II. Min Max Mean. 89 95 92. 66 72 69. 71 78 74. 48 49 48. 85 87 86. 68 71 69. -0.24 -0.18 -0.21. Type III. Min Max Mean. 90 90 90. 67 67 67. 74 74 74. 45 45 45. 95 95 95. 85 85 85. -0.24 -0.24 -0.24. Type IV. Min Max Mean. 91 96 93. 56 76 66. 72 82 77. 44 51 47. 88 96 92. 75 78 76. -0.24 -0.23 -0.23. Type V. Min Max Mean. 88 97 91. 66 83 71. 68 85 74. 45 69 52. 80 109 92. 62 81 73. -0.24 -0.10 -0.17. Non-wildfire area. Min Max Mean. 77 84 80. 57 63 59. 47 51 48. 63 74 66. 53 63 57. 28 38 32. 0.1 0.21 0.15. + Wetness)~ ; êÖ";j B~² ~ ÞÒ ~² ÒÏ > ®ê (5)¢ Fê~& . Greenness + Wetness = (-0.1339(TM1)) + (-0.0462(TM2)) + (-0.2157(TM3)) + (0.1649(TM4)) + (-0.6272(TM5)) + (-0.6372(TM6)) (5) (5)¢ Ï Ö"¢ ê Fig. 8öB Ö®æ " jÖ®æ ª>, ' Æ· F;f r~ 8j <º Ö®æöB 6ê'b æz~º ";ö B ª>î . b º;j Û ¾æ Ö" º 0j 7b 0b¦V r(−)OËb .> ~ *þ æ² > 0ö &ròî> ~ *þ ' (Fig. 8). ¾ ~ F;ö V /f «{ ãê¢ J;~V ÚJÚ ç F ;j ^ª~ ¾*Vº Ú[ . r^ö *Òº ~ *þ ç&'b ¸ Ôrj ï&~º Ï'b ÒÏ &Ë~ . º Ö® B ê ¢ê æR ßWj ª~V * ÒÏ>º " Æ·~ Ûê & Ò' ª~ "& F > ®rj ~ . ß ® Æ·~ Ûêº Æ·j ®º bbî~ v þf &NB ©b CB .. Ö V ;öê ;¯ Ò ¢&~ Ö®æ~ æRb. bîö V¢ Ö®æ Æ·j 5B~ F;b ª ~& . ª~B 5B F;~ Ö®æö & ÎN' ªj Ï'b Landsat ETM ¢V 'çj Ï~ NDVIf SAVI Ò Tasseled Cap æ~' çj jv~&b, ''~ F;j ª > ®ºæ ¦Æ~& . ª~B 5B F;j &çb Ö®Bæ~ Landsat ETM+ 'çöB NDVIf SAVIº Ö®Bæ~ æ Rª~ Ï'ö '~æ p~b, Tasseled Cap æ ~'ç~ æ>f æ>¢ »b ~º Æ·ï j Ï r jv' «{ ª~& &Ë~& . ¢ Û b º; f(a,b) = (Greenness + Wetness)~ ; êÖ";j B~² ~ ÞÒ~ ² ÒÏ > ®ê (5)¢ Fê~& . Greenness + Wetness = (-0.1339(TM1)) + (-0.0462(TM2)) + (-0.2157(TM3)) + (0.1649(TM4)) + (-0.6272(TM5)) +(-0.6372(TM6)) (5) Landsat ETM+ 'çf Ö®Bæ~ ;ï' C" 7º* æö & Î"' ª~& &Ë~ V r^ö 'çöB Ö®Bæ~ Æ· ;f Æ · ·çj ª~º ÎN' O»¢ C B . Ö" bÒö & ¦Ã 5 jö & ¢ Û Ö®bæj Ö;~V.
(22) ;öê;¯¢&Ö®æª~¢*R-BOETBU&5.'çª~æ>~RÏ. * ÎN' Ï V&B .. ^ ^ò ÿn Ö®bæ ÿÒ, 2000, ÿn Ö®æ ; Ò* I, 533 p. ö;', ª;^, 2001, ¢ 8~ Landsat 7 ETM+ 'ç j Ï Ö®bæê ·9, &öÏöÒ²æ, 17 (1), 85-97. Cloutis, E.A., 1996. Hyperspectral geological remote sensing: Evaluation of analytical techniques. International Journal of RemoteSensing, 17, 2215-2242. Crist, E.P. and Cicone, R.C., 1984, Comparisons of the dimensionality and features of simulated Landsat-4 MSS and TM data. Remote Sensing of Environment, 14 (1-3), 235-246. Eastman, J.R. and Fulk, M., 1993, Long sequence time series evaluation using standardized principal components. Photogrammetric Engineering & Remote Sensing, 59 (4), 991-996. Fox, D.M., Bryan, R.B., 2000, The relationship of soil loss by interrill erosion to slope gradient. CATENA, 38, 211-222. Gao, X., Huete, A.R., Ni, W. and Miura, T., 2000, OpticalBiophysical Relationships of Vegetation Spectra without Background Contamination. Remote Sensing of Environment, 74 (3), 609-620. Gitelson, A.A., Kaufman, Y.J., Stark, R and Rundquist D., 2002, Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 80 (1),. . 76-87. Huete, A.R. and Escadafal, R., 1991. Assessment of biophysical soil properties through spectral decomposition techniques. Remote Sensing of Environment, 35, 149159. Koutsias, N., Karteris, M., 2000, Burned area mapping using logistic regression modeling of a single post-fire Landsat-5 Thematic Mapper image. International Journal of Remote Sensing, 21, 673-687. Kutiel, P., Lavee, H., Segev, M. and Beyamini, Y., 1995, The effect of fire-induced surface heterogeneity on rainfall-runoff-erosion relationships in an eastern Mediterranean ecosystem. Israel, CATENA, 25. 77-87. Nagler P.L., Daughtry C. S. T., Goward S.N., 2000, Plant litter and soil reflectance. Remote Sensing of Environment, 71, 207-215. Nyakatawa, E. Z., Reddy, K.C., Lemunyon, J.L., 2001, Predicting soil erosion in conservation tillage cotton production systems using the revised universal soil loss equation (RUSLE). Soil and Tillage Research, 57, 213224. Rondeaux, G., Steven, M., and Baret, F., 1996, Optimization of soil-adjusted vegetation indices, Remote Sensing of Environment, 55, 95-107. Ternan, J.L. and Neller, R., 1999, The erodibility of soils beneath wildfire prone grasslands in the humid tropics. Hong Kong, CATENA, 36, 49-64. Wikars, L-O. and Schimmel, J., 2001, Immediate effects of fire-severity on soil invertebrates in cut and uncut pine forests. Forest Ecology and Management, 141 (3), 189200.. 2004j 8ú 13¢ ö 7> 2004j 10ú 20¢ >;ö 7> 2004j 11ú 13¢ ö j.
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