63 
IS, Vol. XXXVIII, Part 7B 
In: Wagner W„ Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
[AN 
)ATA 
ite, Brazil, 
e, Brazil 
Hands 
r, soil and vegetation 
ports a miscellany of 
[rounded by semiarid 
quaqntities of organic 
spectral and textural 
v altitude photographs 
s. Classification trials 
oach. As a result five 
iccuracy of over 80%. 
s associée à l'eau, aux 
ne et les populations 
du Pandeiros est un 
tés de macrophytes et 
nous proposons une 
le extraite à partir des 
ihotographies à faible 
graphiques. Des essais 
évaluer l'approche la 
atre classes terrestres 
dioration significative 
ts all this characteristics 
site in Brazil. It is even 
egion of water scarcity 
It provides several 
nutrients, fixing carbon 
its relevance the State 
> a Wildlife Sanctuary. 
:d area does not ensure 
areas still been used to 
e drained and used by 
¡a, 2009). 
tying types of wetlands 
subsidized its proper 
theless, this task is a 
plex ecosystem with a 
The inventory of wetlands demand field surveys, aerial photo 
interpretation and satellite imagery. Melack (2004) points out 
that the use of satellite images is considered the most efficient, 
since it allows a fast data acquisition and cartographic mapping. 
A large range of tools are available to classify this sort of data. 
However, high resolution images require more sophisticated 
approaches. Texture has achieve expressive results in the 
classification of those images. For example, Davis et al (2002) 
obtained an overall accuracy classification of 75% using image 
texture for riparian zones. Thus, as a starting point, we decide 
evaluate texture potential of classify different groups of aquatic 
plants based on Ikonos images. 
1.1 Gray Level Co-occurrence Matrix 
Several methods can be applied to texture analysis. Among 
these, the Gray Level Co-occurrence Matrix (GLCM) seems to 
be the most commonly used (Franklin, 2001) and has been 
recognized as one of the best tools for specific situations of 
classification (Clausi, 2000; Maillard, 2003). GLCM is a second 
order histogram in which each entry reports the join probability 
of finding a set of two grey levels at a certain distance and 
direction from each other over some pre-defined window 
(Maillard, 1999). Haralick et al (1973) was the first to extract 
texture features in order to classify images. 14 textures 
measures were originally described by Haralick. However many 
features are highly correlated which made five of them more 
popular: Contrast, Angular Second Moment, Entropy, Inverse 
Difference Moment and Correlation. 
In this study, we aimed to evaluate the use of GLCM in the 
classification and segmentation of high resolution image of a 
wetland environment. As well as determining the optimal 
parameters of textures, window size and distance to be used in 
the study of IKONOS images for this sort of environment. 
2. METHODOLOGY 
2.1 Study Area 
Figure 1. Location of Pandeiros Wildlife Sanctuary in Minas 
Gerais (MG) - Brazil. 
The Pandeiros Wildlife Sanctuary (PWS) is located near the 
Pandeiros’ River mouth, in the Northern part of the State of 
Minas Gerais (Figure 1). This river is an important affluent of 
Sao Francisco River and is the breading grounds of several 
species of fish. It is also a refuge for numerous rare endemic 
and threatened bird species (Biodiversitas, 2005). The region is 
protected by the State government authorities and is managed 
by the Forest Institute of Minas Gerais (IEF-MG). 
It occupies a total area of 6103 ha. and preserves a unique 
wetland with riparian forests, palm swamps, wet meadows, 
lakes and ponds (Figure 2). Climate presents two distinct 
periods: wet season from October to March, and dry from April 
to September. This variation is characteristic of the Cerrado 
biome where water deficit spans for about half the year. 
2.2 Field Work 
The first of Four field campaigns was conducted in September 
2008 using a boat and an all-terrain vehicle to access difficult 
areas for a general reconnaissance approach. During the second 
one in February 2009 geodetic ground control points were 
collected for the geometric correction and registration of the 
image. A specific work area was also defined and data was 
acquired on the different vegetation physiognomies that could 
be identified on the Ikonos image. The third campaign in May 
2009 was mainly dedicated to acquire low altitude photographs 
using a micro-light aircraft to serve as complementary 
validation data. The Fourth and last one in April 2010 allowed 
acquiring new low altitude photographs and visit a few spots 
where some botanical inventory was still necessary. During the 
last three campaigns, printed copies of the Ikonos image (scale 
1:5000) were used to identify complexes both in the field and 
on the image. This data allowed us to divide vegetation of the 
study area in 9 different classes (Figure 2): Pontederiaceae, 
Nymphaeaceae, Riparian forest, Open Water, Alismataceae, 
Cyperaceae, G - Pasture, H - Flooded Pasture, I - Bare Soil. 
Figure 2. (a) Fusionned, false color Ikonos image of the 
Pandeiros. The image represents an area of 1200x1200 pixels or 
144 ha. Legend: A - Pontederiaceae', B - Nymphaeaceae', C - 
Riparian forest', D - Open Water, E - Alismataceae F - 
Cyperaceae', G - Pasture', H — Flooded Pasture', I - Bare Soil. 
Photographic records of different vegetation typology were 
acquired to constitute a visual inventory of the Pandeiros. The 
dominant plant groups present on the photographs were 
identified by two botanists at the Botanic Taxonomy 
Laboratory of the Universidade Federal de Minas Gerais 
(UFMG). The aerial photographs proved to be useful for the 
inventory and as validation data. Since only a navigation GPS 
was used in the last two campaign, care was taken to note and