In: Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
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466 
were then chosen: 1) enhanced image classification and 2) thresh 
olding the normalized difference water index (NDWI; McFeeters, 
1996; Ji et al„ 2009). 
Image Classification. Traditional image classification is carried 
out on a pixel-by-pixel basis. Although an increasing number 
of studies show that region-based or object-based classification 
tends to improve results significantly, it was not judged neces 
sary in this particular case for two main reasons. First because 
of the spectral nature of water being a smooth surface with small 
variations (at least in the optical infrared) and secondly because 
these methods usually offer little control over what is defined as 
an object. Conversely, classification approaches such as Maxi 
mum likelihood can produce posterior probability maps that can 
be thereafter thresholded (hardened). The latter approach had the 
advantage to require training data only for the object of interest 
whereas classical classification procedures require all classes to 
have been defined using training data. In this case we opted for 
the posterior probability which can be simplified as the Gaussian 
probability density of the ’’water class”. In simple nominal classi 
fication, a pixel can be classified as pertaining to a particular class 
even if its probability is low, as long as it is higher than for all the 
other classes. By using a high threshold value (i.e. > 90%) to 
attribute a water label to a pixel, we are able to use but a single 
class and avoid having to gather training data for other objects or 
surfaces. 
NDWI threshold. Using the same logic as the normalized differ 
ence vegetation index (NDVI) the normalized difference water 
index (NDWI) was proposed by McFeeters (1996) as a means to 
separate water from other surfaces (Eq. 3). 
NDWI = p9reen ~? NIR (3) 
Pgreen “F pNIR 
where p gr een is the green reflectance (Landsat TM band 2: 0, 52— 
0,60pm) and pnir is the near infrared reflectance (Landsat TM 
band 4: 0,77 — 0,90pm). The NDWI varies between -1 and 
1 and uses zero as the threshold between land (< 0) and water 
(> 0). A number of variations were later proposed for NDWI. 
In their article, Ji et al. (2009) compared a number of these vari 
ations applied to Landsat, ASTER, SPOT and MODIS images. 
They found that the modified NDWI (MNDWI) proposed by Xu 
(2006) performed better (Eq. 4). 
MNDWI = p9reen ~ PSWI . H (4) 
Pgreen “F PSWIR 
where pswiR is the reflectance in short wave infrared (Landsat 
TM band 5: 1, 55 — 1,75p,m). 
2.4 Validation and Statistical Testing 
Two validation data sets were unsed for testing the performance 
of the extraction of the lake contours from the interpolated Land 
sat data which also involved our definition of the “water-land” 
edge. First, the contours from the dry season image of 2006 were 
compared against the contours extracted from a fusionned Ikonos 
image (1 m) five days apart form the Landsat image. Secondly, 
the four lakes of the VPSP (data from the larger lake outside the 
park could not be acquired) were surveyed using a geodetic GPS 
in kinetic mode to be compared with the contour from the Land 
sat image (with a five days difference). Coordinates of the lake 
contour were acquired at an interval of 15 meters with an approx 
imate precision of 10 cm. 
The validation was done by two complementary methods: 1) by 
expressing the difference between the areas as a proportion of 
the validated area ( y4reoi ~' 4 ° b ^ er ' i ' ed x 100); and 2) by overlap 
ping the two contours (interpolated Landsat and validation data) 
and dividing the overlap area (intersection) by the merged areas 
(union) of both contours as illustrated in Figure 3. 
Figure 3: Validation method for testing the accuracy of the lake 
contours extracted from the interpolated Landsat images. 
The statistical testing consists in establishing the strength of the 
relationship between the areas of all six lakes and the AW of the 
same period as the images. Although the response of the wa 
ter level is not spontaneous, the trend should still be statistically 
perceptible. Because the areas of the lakes are not normally dis 
tributed, a regression was not recommended. Spearman’s cor 
relation does not assume a normal distribution of the dependant 
variable and was chosen instead. The correlation was also com 
puted between the area of the lakes themselves as a mean to infer 
a generalized trend. 
3 RESULTS AND DISCUSSION 
3.1 water balance 
The AW was calculated for the period 1983 - 2009 using the 
Thonthwaite method trimonthly (the year 1983 was added in or 
der to feed the Available Water for the beginning of the 1984 
budget). Figure 2 shows the annual budget averaged every five 
years for the period along with the average budget for the whole 
period (white line). Apart from the two first periods (1984-1989 
and 1990-1994) which appear as exceptionally high and excep 
tionally low respectively, the other periods do not show any trend 
towards an increase or a decrease. 
125 
75 \ 
\A\ 
nA 
25 
— — 1984-1988 1 
1990-1994 i 
1995-1999 J 
2000-2004 / 
2005-2009 I 
Average (1984-2009) / 
-25/ A 
; / / / 4 
4 
\ ■ ' 
! mi 
-75 
jf 
-125 
Figure 4: water balance averaged for every five years between 
1984 and 2009 and overall average (white line). 
3.2 Lake Contours Extraction and validation 
The 50 selected Landsat images were geometrically corrected, 
registered to a UTM grid, corrected for atmospheric interferences