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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