Environment monitoring is crucial for understanding the relationship between climate change and changes in large scale earth structures such as glaciers and forests. For these big structures, monitoring temporal evolution or assessing resilience and adaptation of earth to changes requires the analysis of time series composed of images. When considering remote sensing imagery, analyzing such time series is actually facing dimensionality: observations are huge data both in time and space domains; in addition with intricacy when using coherent acquisition waves (radar imaging for instance). The challenge of remote sensing information science is then developing tools for handling dimensionality of data. The scientific objective of the PHOENIX project is to provide non-stationary multidimensional models for easing information mining and retrieval in long sequences of multisource/distributed image time series issued from recent constellations of satellites. These models will be used to characterize the evolution of earth structures such as glaciers and forests. The technical objective of the PHOENIX project is to provide resilience analysis from information modeling and retrieval in image time series of Alpine glaciers and Amazonian forests. This analysis will be performed through a general framework of random field time series, with two work packages (WP) dedicated to methodological developments. The first package, WP1, will address parsimonious parametric modeling of random field time series by using non-stationary fractionally differenced/integrated parameterizations. The second package, WP2, is dedicated to non-parametric methods for the analysis of random field time series: cumulant analysis and trend/stationary decompositions are some important topics addressed in this WP. The third package, WP3, will focus on the application of WP1 and WP2 methods to 2 kinds of mono/multi-channel earth observation satellite image time series: 1) Synthetic Aperture Radar images which cover large areas and are not impacted by meteorological variability, 2) Spectro-Visible images which can observe specific areas with a higher spatio-spectral resolution. WP3 requires High Performance Computing (HPC). HPC will deserve two types of architectures: a big cluster of CPU (USMB MUST, already operational, efficient for parallel computing on “large databases with small size data”, but limited for loading and processing huge data) and a specific scalable workstation with huge random access memory (ANR support requested) for loading and processing huge size image time series.