Modélisation du changement d’échelles en télédétection par une méthode neuronale : application a l’étude de l’évolution de l’occupation hivernale des sols en Bretagne

Abstract

Following land cover changes during winter is one of the main ways to reduce non-point source water pollution in Brittany, in restricting pollutant fluxes towards rivers. The “bare soils/vegetation” ratio monitoring can be carried out daily at a coarse spatial resolution with SPOT VEGETATION (1 km), and also at a higher spatial resolution with SPOT HRVIR (20 m), although with less repetitive and more spatially limited data. Land-cover changes detected at a regional scale with this ratio can be explained by winter vegetation covering changes as well as by the influence of climatic events. Therefore, observed changes have to be validated from a local scale analysis. The aim of this study is to develop a method that enables the assessment of high or low variations detected at a regional scale from SPOT VEGETATION images with data registered at a higher scale, SPOT HRVIR images in this case. In this study, the link between the images of the two sensors is set up from the development of an Artificial Neural Network method based on a Kohonen Self-Organizing Features Map. The originality of this method lies in the utilization of the temporal dimension to solve such a change of scales.

Keywords

• artificial neural network
• remote sensing
• spatial and temporal changes
• winter land cover