This paper presents an application of neural networks that uses radial basis function net architecture as a tool for simplifying and reducing the cost of ecological mapping. The process speeds up and replaces the classic means of obtaining ecological variables through field studies. The radial basis function networks were applied to estimate field data remotely, using data captured by the Landsat satellite and correlating it with ecological variables in order to substitute for them in the mapping process. The trial was undertaken for an area in south-eastern Spain, whereby, in 43 out of the 45 cases, the ecological variables could be obtained using satellite data. This approach substantially reduces the time and cost of ecological mapping, limiting field studies and automating the generation of the ecological variables.
['Manuel Enrique Ovando Cruz', 'Moisés Espínola', 'Luis Iribarne', 'Rosa Ayala', 'Mercedes Peralta', 'José Antonio Pérez Torres']
Ecological Sectorization Process Improvement through Neural Networks: Synthesis of Vegetation Data from Satellite Images Using RBFs