Proceedings Article | 17 October 2013
KEYWORDS: Data mining, Agriculture, Vegetation, Image classification, Data modeling, Image segmentation, Image analysis, Visualization, Error analysis, Remote sensing
Forest fragmentation studies have increased since the last 3 decades. Land use and land cover maps (LULC) are
important tools for this analysis, as well as other remote sensing techniques. The object oriented analysis classifies the
image according to patterns as texture, color, shape, and context. However, there are many attributes to be analyzed, and
data mining tools helped us to learn about them and to choose the best ones. In this way, the aim of this paper is to
describe data mining techniques and results of a heterogeneous area, as the municipality of Silva Jardim, Rio de Janeiro,
Brazil. The municipality has forest, urban areas, pastures, water bodies, agriculture and also some shadows as objects to
be represented. Worldview 2 satellite image from 2010 was used and LULC classification was processed using the
values that data mining software has provided according to the J48 method. Afterwards, this classification was
analyzed, and the verification was made by the confusion matrix, being possible to evaluate the accuracy (58,89%). The
best results were in classes “water” and “forest” which have more homogenous reflectance. Because of that, the model
has been adapted, in order to create a model for the most homogeneous classes. As result, 2 new classes were created,
some values and some attributes changed, and others added. In the end, the accuracy was 89,33%. It is important to
highlight this is not a conclusive paper; there are still many steps to develop in highly heterogeneous surfaces.
