Urban green infrastructure plays a crucial role in supporting ecological connectivity, enhancing climate resilience, and promoting human well-being. As cities densify, maintaining functional green networks increasingly depends on understanding the structural continuity of vegetation within complex urban fabrics. Morphological Spatial Pattern Analysis (MSPA) provides a practical framework for quantifying green infrastructure structure; however, its sensitivity to spatial resolution remains insufficiently examined, particularly at metropolitan scales, where high-resolution data are becoming increasingly available. This study examines the impact of spatial resolution on MSPA outputs for mapping and interpreting urban green connectivity in Miami-Dade County, USA. Two scenarios were compared using 10-m canopy data and 2-m high-resolution canopy data processed across 23 tiles. The workflow integrated vegetation preprocessing, MSPA classification, and quantitative and visual comparisons of structural classes to assess scale effects. Results demonstrate that fine-resolution MSPA (2 m) preserves continuous canopy structures and narrow vegetated corridors that the 10-m analysis tends to fragment or omit. High-resolution outputs provide a more realistic representation of neighborhood-scale connectivity, especially in tree-dense areas such as Coral Gables, while also revealing the computational demands of metropolitan-scale MSPA processing. The findings confirm that MSPA results are inherently scale-dependent and that the choice of resolution critically shapes the interpretation of connectivity. This research provides an operational foundation for incorporating high-resolution morphological analyses into urban resilience planning, nature-based solutions, and socio-ecological equity assessments.