The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
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3. ESTIMATION METHODS 
Firstly, the canopy height datasets were normalized with a 
square root transformation (SQRTHT); afterwards, all estimated 
SQRTHT values were backtransformed before comparing to 
measured height values (Hudak et al. 2002). 
3.1 Ordinary Least Squares(OLS) regression 
The OLS multiple regression model takes the general form: 
Z = a + j>,(Jr,) + e <T> 
¡=1 
Where, Z is the forest canopy height, Xj is the i explanatory 
variable (SVIs, LAI, forest cover and X and Y location), (f. is 
the linear slope coefficient corresponding to X„ £ is the 
residual error (Kleinbaum et al.1998). 
3.2 Ordinary Kriging(OK) 
Kriging interpolates the sample data to estimate values at 
unsampled locations, based solely on a linear model of 
regionalization. The linear model of regionalization essentially 
is a weighting function required to krig and can be graphically 
represented by a semivariogram. The semivariance /(h) is the 
following equations. 
Where, Z * (p) is the primary variable and X a and fl a are the 
weights and locations of n neighboring samples respectively. 
The kriging 
weights was forced to sum to one: 
(ll) 
a=1 
3.3 Ordinary CoKriging (OCK) 
Cokriging is a multivariate extension of kriging and relies on a 
linear model of co-regionalization that exploits not only the 
autocorrelation in the primary variable, but also the cross 
correlation between the primary variable and a secondary 
variable. Cokriging can be graphically represented by the cross- 
semivariogram, defined as: 
1 N(h) , 
r ^ = 2N(h)^ Zi( ' Ma ^~ Zi( ' Ma+h ^ (12) 
x (zj(M a )-Zj(ju a +h)) 
Where, y..{h) is the cross-semivariance between variables I and 
j, Z- and Z • is the data value of variable i and j at locations 
J 5 Z J J 
jU a and p + h respectively. The OCK estimator of Z * at 
location p takes the form: 
1 N(h) 
rW = Wff^lL( Z ^a)- Z ^a +h )) 2 (8) 
2N(h) 
Where /(h) is semivariance as a function of lag distance h, 
N(h) is the number of pairs of data locations separated by h, 
and Z is the data value at locations jU a and p a +h 
(Goovaerts, 1997). And in this study, the exponential models 
was used to simulate the nugget, sill, range and the shape of the 
sample semivariogram: 
The traditional OCK operates under two nonbias constraints: 
«|=1 OT 2 =l 
/(h) = c 
, ,-3 h. 
1 ~ exp( ) 
a 
(9) 
Where, a is the practical range of the semivariogram, c is sill. 
3.4 Integrated 
Residuals from the OLS regression models were exported and 
imported into ARCGIS software for kriging/cokriging. The 
same rules and procedures were followed for modelling the 
residuals as for modelling the SQRTHT data. 
The OK model estimates a value Z * at each location p and 
takes the general form: 
z*(//) = "|/IWh) 
a=1 
(10) 
4. RESULTS AND DISCUSSION 
In this paper, the needle forest and broadleaf forest were 
classified for estimation of the canopy height. The accuracy of 
classification was validation by our fieldwork.