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Fast detection of changes in environmental remotely sensed data is a major requirement in the Earth sciences, especially in natural disaster related scenarios. As satellite, transmission, and network technologies continue to improve, the real-time stream processing and delivery of geospatial data from remote sensors requires a systematic approach for change analysis and visualization in a streaming fashion. Although various approaches have been formulated to model the inherent spatial-temporal-spectral complexity of remotely sensed satellite data, there are still challenging peculiarities that demand a precise characterization in the context of environmental change detection. In this paper, we present a formal characterization of fundamental operational aspects for the unambiguous specification of change detection and visualization queries in a streaming fashion. This goal is accomplished by defining spatially-aware temporal operators with a consistent semantics for change analysis tasks, and a practically relevant image stream processing architecture founded on a precise execution model and realized by using scientific workflows particularly targeted at collaborative scientific environments. We illustrate our approach with representative examples in land cover and wildfire detection using live data from environmental remote sensors.