GEO GRAPHICAL RESEARCH

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1 GEO GRAPHICAL RESEARCH Vol. 22, No. 4 J uly, 2003, (,,,, ) :,,,, Landsat TM 6, ENV I, RMS, , 5193 %, %, 4108 %,, : ; ; ; TM ; : P237 ; F : (2003) L UCC [1 ], :, /, / [2 ],, NDV I SAV I MSAV I,, [3,4 ],,,,,,, 5 : (linear) (probabilistic) (geometric2optical) : ; : : ( ) : ( ),,,

2 (stochastic geometric) (fuzzy) [5 ], (linear spectral unmixing),,,,,, USL E [6,7 ] Metternicht, Sacaba [8 ],,, [9,10 ] Neil Stuart Lucas Kenfig, [11 ], [9,11 ], TM,,,, 370m 1850m, %, %, %,,,, 2 211,,, (endmember), [12 ],, [13 ],, ( 1) [13,14 ] : Ri = 6 n F j R E ij + i 6 n F j = 1 j = 1 j = 1, R i i, R E ij j i, F j j, i i,, Landsat 6 ( 6 ) TM Landsat7 ETM + 6 ( ) ,, , Albers, Krasovsky, 105,

3 4 : ( ENV I 315 Constandinos, 2002) Fig11 Sketch map of linear spectral mixing model (Source : ENVI 315 on line help and Constandions, 2002) 25 47, ENV I, (Masking),, 30 30m 2, ENV I315, ENV I RSI ENV I315 MNF (Mini2 mum Noise Fraction) PPI ( Pixel Purity In2 dex), 2 MNF,, 1988,,,,, PPI, PPI ( ),,, 2 Fig12 Process of linear spectral unmixing PPI, PPI MNF N, MNF, PPI PPI MNF,

4 442 22,, RMS (Root Mean Square), 0 1, RMS,,,, TM, MNF, 6 MNF (principal components),, 3, 3, MNF, MNF, 4 : (vegetation) (rock) (bare soil) (shadow), 3 7,, 3 [15 ], PPI (round),, 0 1,, 1 %,,, Fig13 Spectral character of endmembers derived from the images in 1987 and ,, 3 :,, 1, ,,,,

5 4 : , TM, RMS ( 1) 31213, NDV I Tab11 Spectral unmixing result of land cover in the images of 1987 and % % % % % % % % NDV I,, = 99 %, : r V eg- NDV I87 = ; r Veg- T P287 = ; r Veg- NDV I99 = ; r Veg- T P299 = : C V eg87 = 1104 N DV I C Veg87 = T P C V eg99 = N DV I C Veg99 = T P , C V eg, 87 99, N DV I, T P2, 4 / 411,,,, TM 30m,, 46,, , [16 ],,,, ( : hm 2 ) Tab12 Land use status of Guanling County from 1987 to 1999 ( Unit : ha) % % /, ( 3) :

6 (1),,,, 90,, Tab13 Land cover change status of land use type in Guanling County during % % % (2),,,, (3),,, 12,, 3112 % %,,,,,, (4),,,,,,,, (5),,,,,,,, (6),,, (7),,,,,, 12, 5,, :,,,

7 4 : 445, :,,,,, [7,11 ],,,,,, TM, :,,,,,,,,, : [ 1 ] Turner B L, Skole D, Fischer G, et al1 Land2use and land2cover change : science/ research plan1 IGBP Report No135 and HDP Report No , Stockholm and Geneva1 [ 2 ],, 1 1,2001,17 (4) :6 111 [ 3 ], 1 1, 1998, 13 (4) : [ 4 ],,, 1 1,2001,56 (6) : [ 5 ] Ichoku Charles, Karnieli Arnon1 A review of mixture modeling techniques for sub2pixel land cover estimation1 Remote Sensing Review, 1996, 13 : [ 6 ],, 1 GIS ( ) 1, 2000, 20 (1) : [ 7 ],, 1 USL E 1, 2001, 21 (4) : 6 91 [ 8 ] Metternicht G, Fermont A1 Estimating erosion surface features by linear mixture modeling1 Remote Sens1 Environ., 1998, 64 : [ 9 ], 1 1, 1999, 16 (12) : [ 10 ] 1 1, 2001, 20 (2) : [ 11 ] Neil Stuart Lucas, Sanjeevi Shanmugam, Mike Barnsley1 Sub2pixel habitat mapping of a costal dune ecosystem1applied Geography, 2002, (22) : [ 12 ] Roberts D A, Smith M O, Adams J B1 Green vegetation, nonphotosynthetic vegetation, and soils in AVIRIS data1 Re2 mote Sens1Environ.,1993,44 : [ 13 ] Hill J, Hostert P, Tsiourlis G, et al1 Monitoring 20 years of increased grazing impact on the Greek Island of Crete with earth observation satellites1 Journal of Arid Environments, 1998, 39 : [14 ] Adams J B, Smith M O, Johnson P E1 Spectral mixture modeling : a new analysis of rock and soil types at the Viking Lander I Site1 Geophys1 Res.,1986, 91 : [ 15 ], 1 1,2002,51 : [ 16 ] 1 1, 2000, 20 (2) :

8 Applying linear spectral unmixing approach to the research of land cover change in Karst area : A case in Guanling County of Guizhou Province WAN J un, CAI Yun2long (Department of Resources Environment and Geography, Geographic Science Research Center, The Center for Land Study, Peking University ; Laboratory for Earth Surface Processes, The Ministry of Education ; Beijing , China) Abstract : Vegetation and bare soil are t he main land cover types in Karst and non2karst area1bare rock anot her land cover type only appearing in Karst area,is dist ributed extensively1 There are a variety of met hods to detect land cover in Karst area wit h remotely sensed im2 ageries1 But most ordinary remotely sensed approaches can t get the three attributes at the same time1 In fact,they almost can do nothing in detecting the quantitative result of rock deser2 tification1 The linear spectral unmixing approach,based on the linear mixture model can esti2 mate vegetation cover, bare soil and bare rock abundance at the sub2pixel scale1 It s fit for studying land cover in Karst area because of the obtained quantitative unmixing results of vege2 tation cover, bare soil, bare rock and other types1 In the case study of this paper, four end2 members were identified from two TM images of six bands taken on 17th February 1987 and 27th November 1999 respectively1 They represent four land cover types : vegetation, bare soil, rock and shadow1 The abundant dist ribution and RMS ( Root Mean Square) dist ribution of t he four land t ypes were derived by linear spect ral unmixing1 The land cover changes could be learned from the multi2time comparison1 During the period from 1987 to 1999, the proportion of bare soil reduced evidently due to the increase of vegetation cover and the improved cultiva2 tion management1 While, t he proportion of rocky desertification enhanced owing to t he little amount of soil in some place1 Bare soil reduced and rock increased mostly in rainfed cropland, grassland and economic woodland1 Over2cultivating and overgrazing are still t he main driving forces of rocky desertification in Karst area1 So people still need to pay attention to water and water soil conservation in economic woodland because economic woodland mainly came from dry land reforest 1 The linear spectral unmixing approach still needs to be improved, though it has been proved fitting for dealing with mixture pixel and detecting quantitative results of land cover by the case study and many other literature1 If endmembers need to be identified from image, it s hard to detect those elements without extraordinary characters presenting in images of different bands1 For example, urban region is just such an element difficult to detect1 So the approach is not the most suitable method for urban region or rapid urbanized area1 And the im2 pact of shadow is still a problem, just as in other methods1 Key words : linear spectral unmixing approach ; land cover changes ; Karst area ; TM image ; Guanling count y