The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
4.1 Estimation of Plot Maximum Canopy Height 
Broadleaf .634 3.053 4.973 .000 59 
Regression was used to estimate maximum canopy height as a 
function of waveform extent and the 3X3 terrain index. The 
regression models are following equations for each forest type. 
//Needle =-l .379+0.702(w+0.0028g+0.1041) (15) 
//Broadleaf =14.716+0.316(w-0.0127g-3.3861) (16) 
Table 2. Test of OLS Regression Equations 
The mape of forest canopy height based on the OLS model is 
in the Figure 6(a). The predicted canopy height of OLS was 
compared with the measured canopy height in field(Fig.2). 
Forest type 
R 2 
Std. 
F 
Sig. 
Counts. 
Needle 
.840 
2.635 
17.517 
.000 
14 
Broadleaf 
.510 
4.264 
2.772 
.111 
12 
All 
.530 
3.913 
9.762 
.000 
30 
Table 1. Regressions relating Waveform Extent and Terrain 
Index to field measured maximum canopy height 
Field Measured Height Field Measured Height 
Figure 2. Measured canopy height Vs. predicted canopy height 
In order to compare the difference of the results between two 
forest type, we have modeled the results with the linear model. 
For the needle forest, the R 2 coefficient is 69.2%, which greater 
than that of the broadleaf forest(50.62%).The regression model 
results preserved actual vegetation pattern, but underestimated 
taller canopies and overestimated shorter canopies. 
Figure 1. Observed maximum canopy height Vs. estimates of 
the same, for needle and broadleaf forest 
When all forest sites were considered in a single regression, the 
resulting equation explained 53.0% of variance with an Std. of 
3.913. However, individual sites had clear biases. Regression 
equations explained between 51.0% and 84.0% of variance for 
each forest type(Fig.l and Tabl.l). Through comparing 
observed maximum canopy height and estimation of the same, 
the R 2 coefficient of needle forest is 67.5%, which greater than 
that of the broadleaf forest(67.3%). 
4.2 Results of OLS 
In this study, the multiple regression models were developed for 
needle forest and broadleaf forest respectively. The sample size 
of Landsat TM/ETM+ is 60m which similar to the GLAS 
footprint size. The models are following regression equation. 
H needle =39.118-7.0E-007X(GK) -IE-005 Y(GK) +2.65LAI+ 
48.482ARVI-0.001EVI+1.532MSA VI+0.032NDVI-20.311 
SARVI+8.77ISA VI+35.685VII +5.619VI2+0.029VI3-15.311 
VI4+5.701VI5 -3.85FC ( 17) 
Hbroadleaf =-81.368-2E-007X(GK)+5.7E-005Y(GK)+2.875LA1 
+125.038AR VI+0.001EVI-263.005MSA VI+0.083ND VI- 
113.512SARVI+151.085SAVI-II.133VI1+I57.2I4VI2- 
0.205VI3-52.567VI4-22.557VI5+1.258FC (18) 
Where, FC is the forest cover, X(GK) and Y(GK) is the x y 
location in Gaussian Kruge projection. These models explained 
between 55.8%-63.4% of variance at the study area (Table 2). 
Forest type R 2 Std. F Sig. Counts. 
Needle .558 3.323 5.556 .000 82 
4.3 Results of OK/COK 
Firstly, the canopy height data were checked(Fig.3). From the 
following figure, we can find that the data is submit to the 
normal distribution. Hence, we performed the OK and OCK 
model in the ARCGIS software. The result of OK and OCK 
models are illustrated in Figure4. 
Figure 3. normal distribution of canopy height data 
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