For the years 2001 to 2013 of the ongoing eruption of Shiveluch volcano, a combination of different satellite remote sensing data are used to investigate the dome-collapse events and the resulting pyroclastic deposits. Shiveluch volcano in Kamchatka, Russia, is one of the world's most active dome-building volcanoes, which has produced some of the largest known historical block-and-ash flows (BAFs). Globally, quantitative data for deposits resulting from such large and long-lived dome-forming eruptions, especially like those at Shiveluch, are scarce. We use Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal infrared (TIR), shortwave infrared (SWIR), and visible-near infrared (VNIR) data to analyze the dome-collapse scars and BAF deposits that were formed during eruptions and collapse events in 2001, 2004, 2005, 2007, 2009, 2010, and two events in 2013. These events produced flows with runout distances of as far as 19 km from the dome, and with aerial extents of as much as 22.3 km2. Over the 12 years of this period of investigation, there is no trend in deposit area or runout distances of the flows through time. However, two potentially predictive features are apparent in our data set: 1) the largest dome-collapse events occurred when the dome exceeded a relative height (from dome base to top) of 500 m; 2) collapses were preceded by thermal anomalies in six of the cases in which ASTER data were available, although the areal extent of these precursory thermal areas did not generally match the size of the collapse events as indicated by scar area (volumes are available for three collapse events). Linking the deposit distribution to the area, location, and temperature profiles of the dome-collapse scars provides a basis for determining similar future hazards at Shiveluch and at other dome-forming volcanoes. Because of these factors, we suggest that volcanic hazard analysis and mitigation at volcanoes with similar BAF emplacement behavior may be improved with detailed, synoptic studies, especially when it is possible to access and interpret appropriate remote sensing data in near-real time.