An　urban　spatial　cluster　(USC)　describes　one　or　more　geographic　agglomerations　and　the　linkages　among　cities.　USCs　are　conventionally　delineated　based　on　predefined　administrative　boundaries　of　cities,　without　considering　the　dynamic　and　evolving　nature　of　the　spatial　extent　of　USCs.　This　study　uses　Defense　Meteorological　Satellite　Program/Operational　Linescan　System　(DMSP/OLS)　nighttime　light　(NTL)　satellite　images　to　quantitatively　detect　and　characterize　the　evolution　of　USCs.　We　propose　a　dynamic　minimum　spanning　tree　(DMST)　and　a　subgraph　partitioning　method　to　identify　the　evolving　USCs　over　time,　which　considers　both　the　spatial　proximity　of　urban　built-up　areas　and　their　affiliations　with　USCs　at　the　previous　snapshot.　China　is　selected　as　a　case　study　for　its　rapid　urbanization　process　and　the　cluster-based　economic　development　strategy.　Four　DMSTs　are　generated　for　China　using　the　urban　built-up　areas　extracted　from　DMSP/OLS　NTL　satellite　images　collected　in　2000,　2004,　2008,　and　2012.　Each　DMST　is　partitioned　into　various　subtrees　and　the　urban　built-up　areas　connected　by　the　same　subtree　are　identified　as　a　potential　USC.　By　inspecting　the　evolution　of　USCs　over　time,　three　different　types　of　USCs　are　obtained,　including　newly　emerging,　single-core,　and　multicore　clusters.　Using　the　rank-size　distribution,　we　find　that　large-sized　USCs　have　greater　development　than　medium-　and　small-sized　USCs.　A　clear　directionality　and　heterogeneity　are　observed　in　the　expansions　of　the　ten　largest　USCs.　Our　study　provides　further　insight　for　the　understanding　of　urban　system　and　its　spatial　structures,　and　assists　policymakers　in　their　planning　practices　at　national　and　regional　scales.