Many ecosystems exhibit striking patterns in the spatial distribution of organisms, for example, patterns of clumping and dispersion in semi-arid vegetation, mussel in inter-tidal beds and even sea-grass and macroalgae. Elucidating local-scale processes that generate these macroscopic patterns is of fundamental ecological importance. In addition, these patterns may provide insights and tools to quantify stability and forecast the future dynamics of ecosystems. We now know that several ecosystems may undergo abrupt and irreversible changes in the density of their dominant communities, potentially resulting in local extinctions. Discerning the vulnerability of ecosystems to such regime shifts has become an important focal area of research in recent times. In this thesis, I investigate methods to detect vulnerability of ecosystems using high-resolution spatial data, which is becoming increasingly cheap and easily available. To do this, I use spatially explicit models of ecosystem regime shifts, inspired by simple models of state transitions in the physics literature. I also demonstrate the theoretical results with vegetation data from semi-arid ecosystems. My main findings are that some key previously proposed metrics of regime shifts when applied to high-resolution spatial data can give misleading signals and are theoretically unfounded. I argue that a clear understanding of how local interactions between organisms scale to their spatial distribution is crucial to correctly inferring ecosystem stability.