The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
32 
named ‘clumping’ (Chen et al., 1997). Many solutions have 
been proposed to overcome this clumping bias. For example, 
two new instruments have been developed to measure the 
between-shoot clumping factor (Q e ): the TRAC developed by 
Chen et al. (1997) and the MVI developed by Kucharik et al. 
(1997). Furthermore, the boundary and illumination conditions, 
data aggregation method, and sampling scheme also influence 
the relative accuracy of LAI measurements. Even though 
hemispherical photography was believed better than LAI-2000, 
it is more suitable for trees instead of prairie regions with low 
canopy vegetation. 
Although LAI can be directly or indirectly measured by several 
ground-based methods, difficulties in deriving it from remotely 
sensed data has led to the development of various approaches 
and methodologies (especially for LAI determination at 
different scales and over diverse types of vegetation canopies) 
(Baret and Guyot, 1991; Haboudane et al., 2004; etc.). 
Estimating LAI from remotely sensed optical data can generally 
be carried out by several methods (Liang, 2003): (1) through 
the empirical relationship between LAI and vegetation indices 
(LAI-VI); (2) through the inversion of a radiative transfer (RT) 
model; (3) the use of look-up tables (LUT), (4) neural networks 
(NN), and (5) a hybrid approach. Using remotely sensed 
imagery, LAI can be derived from an empirical or modeled 
LAI-VI relation. The major limitation of this empirical 
approach is that there is no single LAI-VI equation (with a set 
of coefficients) that can be applied to remote sensing images of 
different surface types. Another limitation of this approach is 
the sensitivity of VI to non-vegetation related factors such as 
soil background properties (e.g., Huete, 1989), atmospheric 
conditions (e.g., Kaufman, 1989), topography (Holben & 
Justice, 1980), bidirectional nature of surfaces (Deering, 1989), 
and the most important, the spatial and temporal dynamics of 
LAI. 
Therefore, even though recent research has attempted to 
improve LAI estimates through a better description and 
sampling of canopy heterogeneity (vertical and horizontal 
heterogeneity, clumping, and canopy closure or gaps), 
quantifying LAI with high accuracy presents numerous 
challenges due to the complex spatial and temporal LAI 
variations. Satellite imagery has provided promising results. 
Therefore, this study will investigate the spatial and temporal 
variations of LAI as well as from the measurement of NDVI. 
2. METHODOLOGY 
2.1 Study Area 
The study area is in St. Denis Wildlife Reserve Area, 40km east 
of Saskatoon, Saskatchewan, Canada. The study area is 
dominated by rolling landscapes in the mixed-grass prairie 
ecodistrict. St. Denis National Wildlife Area has over 200 
temporary and permanent wetlands most of which are fringed 
by tall grass and shrubs. Blocks of native grassland and aspen 
bluffs with willow, serviceberry and chokecherry are distributed 
throughout the Wildlife Area. Almost one-half of the previously 
cultivated land has been seeded to bromegrass and alfalfa for 
nesting cover. The relatively large amount of existing cultivated 
land is used for research on the effects of agricultural practices 
on waterfowl production. 
2.2 Field Data Collection and Satellite Imagery Acquisition 
Field data were collected along one transect with 128 samples 
with 4.5m interval three time during the growing season of 
2007. Variables collected include LAI with LAI-2000 plant 
canopy analyzer, reflectance with ASD handheld 
spectraradiometer, estimated cover, and digital pictures. SPOT 
4 multi-spectral 20m resolution imagery was acquired at 
monthly interval in the summer of 2007, which match with field 
data collection. Five SPOT scenes were from May, June, July, 
August, and September respectively. Images were 
geometrically, radiometrically, and atmospherically corrected. 
Normalized Difference Vegetation Index (NDVI) was 
calculated and LAI values were derived from the satellite 
imagery. 
O £6 Km 
Figure 1. Study area: St. Denis, Saskatchewan, Canada. 
2.3 Data Analysis 
Normalized difference vegetation index (NDVI) was derived 
from ground measured reflectance and SPOT satellite imagery. 
NDVI was calculated based on the ratio of the difference 
between near infrared and the sum of these two bands. 
Wavelet analysis was performed on LAI and NDVI derived 
from ground measurements in July as this is the maximum 
growing season in Canadian prairies. 
3. RESULTS 
3.1 Spatial Variation of LAI and NDVI 
Wavelet analysis indicated that LAI has several levels of 
variations: 4.5m, 12m, and 22.5m. NDVI spatial variation was 
mostly corresponding with LAI. The variations are at 4.5m, 
12m, 18m, and 22.5m. Clearly, the 18m variation from NDVI 
was not found from LAI, indicating that LAI is not the reason 
for the variation on NDVI. It might caused by topography (He 
et al., 2007). Future analysis is necessary. 
3.2 Temporal variation of LAI and NDVI 
Figure 3 demonstrates the temporal change of the study area. 
July is the maximum growing season for Canadian prairies. 
Vegetation greens up in May and senescent in September. The 
SPOT images are in standard false color composite (RGB: Near 
infrared, Red, and Green). Different tone of red indicates 
healthy and dense vegetation, while blue/green is bare ground.