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DECISION TREES ON LIDAR TO CLASSIFY LAND USES AND COVERS

The area of Huelva, in the South of Spain, is a well-known case of human pressure on the natural environment. In Huelva, National Parks, like Doñana, and industrial and tourist zones coexist in difficult balance. The Regional Ministry of Andalusia is commissioned to assure the preservation of the natural resources in this part of Spain although its cost can be high in time and money. Remote sensing is a very suitable tool to carry out this task and automatic land use and cover detection can be a key factor to reduce costs. In addition, Light Detection and Ranging (LIDAR) has the advantage of being able to create elevation surfaces that are in 3D, while also having information on LIDAR intensity values. Many measures based on its intensity, density and its capacity for describing third dimension have been used previously with other purposes and outstanding results. In this paper, a new approach to identify land cover at high resolution is proposed selecting the most interesting attributes from a set of LIDAR measures. Our approach is based on data mining principles to take advantage on intelligent techniques (attribute selection and C4.5 algorithm decision tree) to classify quickly and efficiently without the need for manipulating multiespectral images. Seven types of land cover have been classified in a very interesting zone at the mouth of the River Tinto and Odiel with results of accuracy between 71% and 100%. An overall accuracy of 85% has been reached for a resolution of 4 m.