The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008
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Figure 4. canopy height of OK and OCK models
From figure4 and figure5, we can find that the result of OK is
similar to that of OCK model. But comparing the predicted
canopy height with the field measurement of the canopy height,
the precision of OCK model is prior to that of the OK. However,
the R 2 coefficient of needle forest is less than that of the
broadleaf forest. Cokriging proved slightly more accurate than
kriging. The spatial models, kriging and cokriging, produced
greater biased results than regression and poorly reproduced
vegetation pattern. This mar be related with the distribution of
lidar sampling points.
Needle Forest + Kriging
Field Measured Height
Broadleaf Forest + Kriging
Field Measured Height
Field Measured Height
Broadleaf Forest + CoKriging
Field Measured Height
Figure 5. Compare of the results of OK and OCK models
4.4 Results of integration model
Due to the precision of OCK is greater than that of OK. Here,
we have only discussed the integration model of
‘OCK+regression’. Residuals from the OLS regression were
imported into cokriging. The result was illustrated in the
figure6(b).
(a) Regression (b) CoKriging+Regression
Figure 6. canopy height of integration model
Through comparing the field measurement of canopy height
with the predicted canopy height, the R 2 coefficient of needle
forest is 64.44%, which greater than that of the broadleaf
forest(60.95%). Obviously, this results are greater than that of
OK/OCK. The the R 2 coefficient of broadleaf forest is also
greater than that of the OLS (50.62%). However, the R~
coefficient of needle forest is less than that of the OLS(69.2%).
This is related with the distribution of lidar sampling points. An
equitable distribution of lidar sampling points proved critical
for efficient lidar Landsat TM/ETM+ integration (Hudak et al.
2002).
0 2 4 6 8 10 12 14 16 18 20 22 24 0 2 4 6 8 10 12 14 16 18 20 22 24
Field Measured Height Field Measured Height
Figure 7. Measured canopy height Vs. predicted canopy height
4.5 Discussion
The results from this study confirm that forest height can be
estimated using GLAS waveform combination with the terrain
index in sloped area. Regression equations explained 51.0% and
84.0% of variance for broadleaf and needle forest respectively,
the result of this work indicate that the terrain index will help to
extract the forest canopy height over a range of slopes.
Integration of GLAS and Landsat TM/ETM+ data using
empirical modeling procedures can be used to improve the
utility of both datasets for forestry applications. In this study,
four integration techniques: OLS,OK,OCK and OCK+OLS
models, were compared. In total, the integrated technique of
ordinary cokriging of the height residuals from an OLS
regression model proved the best method for estimating the
forest canopy height. In future work, to improve the accuracy of
the cnaopy height estimations and test the integration models in
the sloped area once lidar sample data become readily available.
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