The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
the Daba Mountain and to the south is the Yunnan-Guizhou 
Plateau. The areas of river valley and flatland in the reservoir 
area account for 4.3%; hilly areas, 21.7%; and mountainous 
areas, 74%. 
The reservoir area is having a typical subtropical monsoon 
climate, which is rainy, humid, and foggy in fall, warm in 
winter, hot and dry in summer. The average annual temperature 
is about 15°C~19°C. The average annual precipitation is 
1140mm~1450mm and the multi-year average run-off is 
405.6x108 m 3 in the local river. The main vegetation there is 
Evergreen needleleaf forests, Deciduous needleleaf forests, 
Evergreen broadleaf forests, Mixed forests, Brushwood and 
Croplands. Due to agricultural development and human 
activities, the hilly vegetation and natural vegetation will 
gradually be replaced by agricultural crops. The forest cover is 
low in the reservoir area, with that in the eastern Sichuan 
section being only 16-17% and that in the western Hubei 
section being 25-38%. The standing timber structure was 
simple, being mostly pure forests, with masson pine occupying 
70%, mostly young trees. Statistical data in 2001 shows that the 
total population in the reservoir area was 19.621,200, including 
14.389,300 agricultural population and 5,231,900 non- 
agricultural population. 
2.2 Lidar Data and Processing 
ICESat is a spacebome, waveform sampling lidar system that 
using for measuring and monitoring ice sheet and land 
topography as well as cloud, atmospheric, and vegetation 
properties. The Geo-science Laser Altimeter System(GLAS) 
instrument aboard the ICESat satellite launched on 12 January 
2003. GLAS received waveforms record 1064nm wavelength 
laser energy as a function of time reflected from an ellipsoidal 
footprint. It has 70m spot footprints spaced at 175m intervals 
(http://icesat.gsfc.nasa.gov/intro.html). In this study, GLAS 
data from L2A(October to November 19,2003), L3A(October, 
2004) ,L3C(February-March,2005),L3D(October to November, 
2005) and L3F(May-June,2006) were used. 
GLAS have many products (GLA01-GLA16). GLA01 products 
provide the waveforms for each shot. The product GLA14 
provides Global Land Surface and Canopy elevation. GLA14 
doesn’t contain the waveform, but some parameters derived 
from the waveform. Firstly, the GLAS waveforms were 
smoothed using Gaussian filters with width similar to the 
transmitted laser pulse. The noise level before the signal 
beginning and after the signal ending were estimated using a 
histogram method (Sun et al.2008). And then the signal 
beginning and end were identified by a noise threshold. In this 
paper, the threshold was set to the noise plus 4 times the 
standard deviation. The waveform extent is defined as the 
vertical distance between the first and last elevations at which 
the waveform energy exceeds a threshold level (Harding and 
Carabajal, 2005). Since the canopy height is related to the 
ground surface, not the signal ending, the ground peak in the 
waveform was found by comparing a bin’s value with those of 
the two neighboring bins. If the distance between the first peak 
and the signal ending is greater than the half width of the 
transmitted laser pulse, the first significant peak found is the 
ground peak (Sun et al.2008). The canopy height is defined as 
the distance between the signal beginning and the ground peak. 
location to canopy height. Terrain Indices was defined as the 
range of ground surface elevations within n X n sample 
windows applied to DEM at the footprint location (Lefsky, 
2005). The following equation was used to estimation the 
forest canopy height on the sloped area: 
h = b 0 (w - b x g + b 2 l) (Lefsky, 2005) (1) 
Where 
h is the measured canopy height 
w is the waveform extent in meters 
g is the terrain index in meters 
1 is the extent of the leading edge in meters 
b 0 is the coefficient applied to the waveform, when 
corrected for the scaled terrain index 
b x is the coefficient applied to th e terrain index 
b 2 is the coefficient applied to the leading edge 
Then,the equation was fit using Levenberg-Marquardt 
algorithm (Craig Markwarddt). 
2.3 Landsat TM/ETM+ 
In this study, five Landsat (Enhanced) Thematic Mapper (TM/ 
ETM+) scenes (path/row: 125/39,126/39,127/39,127/38 and 
128/39) from 2002 served as the primary data source to 
estimate several spectral vegetation indices(SVIs). Firstly, 
geometric correction and atmospheric correction were 
performed using the Image Geometric Correction and ATCOR 
modules of the ERDAS image processing software respectively. 
These images were then rectified to the Gaussian Kruge 
projection (Spheroid: Krasovsky; Central meridian: 111 0 E; 
Central latitude: 0; False easting: 500000 meters; False nothing: 
0), and were resampled using the nearest neighbour algorithm 
with a pixel size of 30mx30m for all bands. The resultant RMS 
(Root Mean Square) error was found to be less than 0.5 pixels. 
Then some SVIs were calculated which including EVI, NDVI, 
ARVI , MSA VI , SARVI , SAVI and the following vegetation 
indices: 
yjl _ Pbl ~ PbS ~ Pbl 
Pbl + PbS + Pb\ 
(2) 
yp _ Pbl ~ Pbl ~ Pbl 
Pbl + Pbl + Pbl 
(3) 
yjj _ Pbl X PbA 
Pbl 
(4) 
yjj _ Pbl ~ Pbl ~ Pb\ 
Pbl + Pbl + Pb\ 
(5) 
VI5- Pbl 
(6) 
Pbl + 1 
Where p is the reflectance of band b,. Ecologically relevant structural 
attributes such as LAI and forest cover have been estimated from these 
SVIs. 
Prior research has shown that due to Landsat imagery’s 
widespread availability and the grain, extent, and multispectral 
features make it suitable for a variety of environment 
applications at landscape to regional scales. 
The distance between the signal beginning and the ground peak 
was extracted by above methods when the surface is flat. Over 
sloped area, we used the Terrain Indices at the GLAS footprint 
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