Monitoring land use and land cover changes in oceanic and fragmented landscapes with reconstructed MODIS time series

Abstract

Image time series from medium resolution sensors such as NASA EOS/MODIS are frequently used to monitor vegetation phenology at regional and global scales. Facing the limitations of high resolution sensors, that is small coverage areas and low revisit frequencies, data from medium resolution sensors are now assessed to monitor subtle vegetation changes at meso or large scales, even in fragmented landscapes. However, monitoring of subtle changes is difficult to perform with such data without important pre-processing steps. Previous studies showed that time series extracted from original images are often corrupted and hence not exploitable, due to atmospheric and geometric distortions and others artifacts (angle variations, clouds, aerosols for example). In this paper we present an approach to reconstruct high accurate NASA EOS/MODIS time series. Firstly, we propose a method to correct images from atmospheric and geometric distortions. The comparison between different pre-processed NDVI MODIS images and SPOT HRVIR high resolution data points out significant differences, highlighting the necessity of properly pre-processing time serie data. Moreover, on the basis of these first results obtained in using pre-processed series of MODIS images through the smoothing technique developed here to recover the winter vegetation phenology, it is now possible to undertake the identification of subtle changes on land surfaces.