(2009) Journal of Rem ote Sensing (, 2006) 2. 1 (, 1999), : ( : 2007CB714402) ;

Transcription

1 (2009) Journal of Rem ote Sensing 1, 2, 3, 3, 3, 1 1., ; 2., ; 3., :,,, 3, ( ),1%,, :,,, : TP79 : A 1 20,,,,;, (, 1999),,,,, (, 2006),,,,, : ; : : (: 2007CB714402) ; ( KGCX32SYW 2401) ; (: ) : (1980),,, 3

2 184 Journal of Rem ote Sensing 2009, 13 (2),Mandelbrot 2070 (Raffy, 1988), ( up2scaling), ( down2scaling) ( L iang, 2004), 1 1 Fig. 1 A diagram of retrieving land surface param eters through rem ote sensing satellite at different scales 1, a ( ), p 1 ( a),..., p n ( a) A ( ), p 1,..., p n F, F ( p 1 ( a ),..., p n ( a ) ), F ( p 1,..., p n ), A, : F ( p 1,..., p n ) = F 1 A κa ( p 1 ( a),..., p n ( a) ) da (1) F,,, : F = 1 A κa F ( p 1 ( a),..., p n ( a) ) da (2) F ( p 1,..., p n ) F,,,,,,, (3) scale = F - F ( p 1,..., p n ) (3) 2. 2,,,, ( Taylor series expansion), ( computational geometry) , F, F ( p ( x, y) )p, (Becker & L i, 1995),, : F = 1 A F (p (x, y) ) da = 1 A F (p) + (p (x, y) - p) 5F 5p P ( p ( x, y) - p) 2 52 F 5p 2 (4), :, k = 52 F 5p 2 p 3 p3 p3 da (4) F - F ( p) = 1 kv (5) 2 ; V = 1 A ( p ( x, y) - p) 2 da p 3 A, p, : R ( p) = F ( p) + 1 kv (6) 2 k ;V, p 1 ( x, y), k, V ; p ( x, y) = [ p 1 ( x, y), p 2 ( x, y),, p n ( x, y) ],k, V : F,k = 0,, F = F ( p) ; A p ( x, y), 0, V = 0,

3 : 185, F = F ( p),,, ( Garrigues et al., 2006; Hu & Islam, 1997) ,, p x, y, ( 7) ( Raffy, 1992) : gf F ( p), F ( p) (7), F (p) F, F (p) F,2 2F ( a) ; ( b) (Pelgrum (2000) ) Fig. 2 The m odel and its bounding m odels ( a) Continuous case; ( b) D iscontinuous case (A dap ted from Pelgrum 2000) p ( x, y), gf F ( p) F ( p), gf F ( p), F ( p), 2. 3,,,,F,, p D p 3,,,,,,,,,,,,, (),,,, 3 F ( p) - F ( p) max,,,,,, : R ( p) = 1 2 F ( p) + F ( p) (8) (8), F, F = F (p) = F (p),, F = F (p); p x, y,, F = F (p) = F (p), F = F (p), F, F,, SPOT , 10m,3, NDV I, SPOT5 NDV I 50 50, , , , , 500, 1000, 2000, 5000, 10000m NDV I (9)

4 186 Journal of Rem ote Sensing 2009, 13 (2) 3 ( a); ( b); ( c) Fig. 3 Three different L and surface testing im ages ( a) W ood L and; ( b) Farm L and; ( c) C rop2w ater m ixed L and LA I = - ln ( (1 - NDV I/A ) /B ) C (9) A, B, C, A, B 1,, C 0. 5 (, 1999 ), ( 9 ) ( 1 ), ( 2 ), ( 6 ) ( 8 ) LA I, 1 1 LA I( ) Table 1 The retr ieva l LA I va lue for the whole reg ion a t d ifferen t sca les for wood land, farm land and crop2wa ter m ixed land( row 1000 column 1000) / LA I (NDV I ; ) (NDV I ; ) (NDV I ; ) , LA I SPOT5 10m LA I,, (10) LA I,4,

5 : 187 : RE = 1 N N i =1 x i - y i (10) y i N ; x i LA I ; y i LA I 4 RE Fig. 4 The relative retrieval error of LA I at different aggregation scales 1, LA I, LA I, (7) ( gf LA I, F ( p) LA I )4,,, LA I LA I, 3, 2%, 7%, 7% 3, 45% (Chen, 1999),,,,,2%,, LA I,,,,, LA I 1%, LA I,,,, LA I, LA I, 1%, LA I, 4,,,,,,,,,

6 188 Journal of Rem ote Sensing 2009, 13 (2),,,,,,,,,,,,,,,,, ( Garrigues et al. 2006), ( ),,,,,,,,,, 2,,, 3,, 2%,, 7% ( ),,, 3, 1%, LA I,,,1 /2,,,,,,, REFERENCES B ecker F and L i Z L Surface temperature and em issivity at various scales: D efinition, M easurem ent and Related Problem s. Remote Sensing Review s, 12: Chen J M Spatial scaling of a rem otely sensed surface param eter by contexture. Remote Sensing of Environment, 69: Garrigues S, A llard D, B aret F and W eiss M Influence of landscape spatial heterogeneity on the non2linear estim ation of leaf area index from m oderate spatial resolution rem ote sensing data. Remote Sensing of Environment, 105 (4) : Hu Z and Islam S A fram ew ork for analyzing and designing scale invariant rem ote sensing algorithm s. Geoscience and Remote Sensing, 13: IEEE Transaction on L i X W Review of the p roject of quantitative rem ote sensing of m ajor factors for spatial2temporal heterogeneity on the land surface. A dvances in Ea rth Science, 21 (8) :

7 : 189 L iang S L, Q uantitative Rem ote Sensing of L and Surfaces. N ew Jersey, A John W iley & Sons. INC., Publication Pelgrum H Spatial A ggregation of L and Surface Characteristics. PhD thesis, W ageningen U niversity, N etherlands Raffy A M Rem ote sensing from space: physical aspects and m odelling. Centre N ational D etudes Spatiales. A utosim ilarity, Change of Scale and Rem otely Sensed Param eters. France: Cepadues2Editions Raffy A M Change of scale in m odels of rem ote sensing: a general m ethod for spatialization of m odels. Environment, 40: Remote Sensing of Zhang R H, Sun X M, Su H B, Tang X Z and Zhu Z L Rem ote sensing and scale transferring of levity param eters on earth surface. Remote sensing for land and resources, 3: , 2006, 21 (8) : ,,,, , 3: Comparison and analysis of two general scaling methods for remotely sensed information W U Hua 1, 2, J IANG X iao2guang 3, X I X iao2huan 3, L I Chuan2rong 3, L I Zhao2liang 1 1. Key L abora tory of W a ter Cycle and Rela ted Land Surface P rocesses, Institute of G eographica l Sciences and N a tura l Resources Resea rch, Chinese Academy of Sciences, Beijing , China; 2. G radua te U niversity of Chinese Academy of Sciences, B eijing , China; 3. Academy of Op to2e lectronics, Chinese Academy of Sciences, Beijing , China Abstract: W ith the developm ent of quantitative rem ote sensing, the scaling p roblem s attract m ore and m ore attention. The discrepancy betw een observation scale, m odel scale and land surface p rocess scale m ay lead to different conclusions. N ow, how to effectively scale rem otely sensed inform ation at different scales already becom es one of the m ost im portant research focuses of rem ote sensing. The aim of our research is to compare and analyze tw o general scaling m ethods, the Taylor Series Expansion M odel ( TSM ) and the Computational Geom etry M odel ( CGM ), and app ly them to the scaling of leaf area index (LA I). Firstly, the necessity and im portance of scaling are analyzed. Secondly, based on the research of descrip tion for the sam e object using different scale data, the m echanism of scaling effects is p resented. Then, the tw o general m odels, TSM and CGM, are briefly introduced and their advantages and disadvantages are discussed in detail. Finally, through the retrieval of leaf area index, the tw o m odels are com p rehensively com pared and analyzed in three distinct landscapes. The result show s that the relative scaling error increases w ith the heterogeneity of land surface. The relative scaling error is 2% in the relatively hom ogeneous w oodland; how ever, it arises up to 7% in crop 2w ater m ixed areas. Apparently, the TSM can better characterize the scale effect and obtain m ore accurate surface param eters w hen both sm all scale ( high resolution) data and large scale ( low resolution) data are available. The relative scaling error can be reduced to less than 1% for all these test landscapes w hen TSM is used in scaling. In contrast, CGM can not p roduce rational result and the relative error is still large. It m ay be due to using inapp rop riate w eights or data ranges in the m odel. M ore study about CGM is needed. O n the w hole, it is necessary to select the suitable scaling m odel according to the p ractical app lications. The scaling m akes the rem ote sensing p roducts at different scales com parable and the surface param eter retrieval results m ore accurate. Scaling technique w ill p rovide a pow erful technical support for app lications in resources survey, environm ent and disaster m onitoring, and other relevant fields. Key words: rem ote sensing, scale effect, scaling, NDV I