Ecosystem transformations due to climate change, especially those involving the conversion of forests to grasslands, have profound implications for biodiversity, carbon storage, and ecosystem services. Understanding and monitoring such transformations are critical for effective conservation and management efforts, yet the spatial patterns, drivers, and rates of these changes remain poorly characterized across ecoregions. While rapid transformations, often triggered by abrupt disturbances such as wildfires, have received considerable attention, slow transitions are less studied, despite their higher prevalence and potential for early intervention. In this study, leveraging the power of a data cube, we evaluate a suite of remote sensing indicators of vegetation, water, and surface temperature signals together with land cover data, to detect and characterize ecosystem transformations across the EPA Level IV ecoregions in the Southern Rockies. Our findings reveal that non-fire related transformations account for a larger proportion of change compared to post-fire ecosystem transformation, and they predominantly occur along forest edges, areas that are particularly sensitive to environmental and anthropogenic pressures. Unlike abrupt changes, these gradual shifts present detectable early warning signals, enabling proactive management strategies. The methodological framework developed here is transferable to other regions and offers valuable insight for ecological monitoring and intervention planning to enhance ecosystem resilience.